diff --git a/plalink.ipynb b/plalink.ipynb
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+++ b/plalink.ipynb
@@ -0,0 +1,1251 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2e356e65-9124-4c77-a478-092f89edd586",
+   "metadata": {},
+   "source": [
+    "# PREMİER LİG VERİMLİLİK SKORU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ebce2bbd-02de-4655-945f-f8ecba8e70c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings \n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "55411e4c-9b63-4a2f-b02c-99351c995225",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"premier-player-23-24.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "768ee3b6-73d5-4c90-957e-4e1159a3fa04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player</th>\n",
+       "      <th>Nation</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>MP</th>\n",
+       "      <th>Starts</th>\n",
+       "      <th>Min</th>\n",
+       "      <th>90s</th>\n",
+       "      <th>Gls</th>\n",
+       "      <th>Ast</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Ast_90</th>\n",
+       "      <th>G+A_90</th>\n",
+       "      <th>G-PK_90</th>\n",
+       "      <th>G+A-PK_90</th>\n",
+       "      <th>xG_90</th>\n",
+       "      <th>xAG_90</th>\n",
+       "      <th>xG+xAG_90</th>\n",
+       "      <th>npxG_90</th>\n",
+       "      <th>npxG+xAG_90</th>\n",
+       "      <th>Team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Rodri</td>\n",
+       "      <td>es ESP</td>\n",
+       "      <td>MF</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>34</td>\n",
+       "      <td>2931.0</td>\n",
+       "      <td>32.6</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>Manchester City</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Phil Foden</td>\n",
+       "      <td>eng ENG</td>\n",
+       "      <td>FW,MF</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>33</td>\n",
+       "      <td>2857.0</td>\n",
+       "      <td>31.7</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>Manchester City</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Ederson</td>\n",
+       "      <td>br BRA</td>\n",
+       "      <td>GK</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>33</td>\n",
+       "      <td>2785.0</td>\n",
+       "      <td>30.9</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>Manchester City</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Julián Álvarez</td>\n",
+       "      <td>ar ARG</td>\n",
+       "      <td>MF,FW</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>31</td>\n",
+       "      <td>2647.0</td>\n",
+       "      <td>29.4</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.31</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.44</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>0.66</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>Manchester City</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Kyle Walker</td>\n",
+       "      <td>eng ENG</td>\n",
+       "      <td>DF</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2767.0</td>\n",
+       "      <td>30.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>Manchester City</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 34 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Player   Nation    Pos   Age  MP  Starts     Min   90s   Gls  Ast  \\\n",
+       "0           Rodri   es ESP     MF  27.0  34      34  2931.0  32.6   8.0  9.0   \n",
+       "1      Phil Foden  eng ENG  FW,MF  23.0  35      33  2857.0  31.7  19.0  8.0   \n",
+       "2         Ederson   br BRA     GK  29.0  33      33  2785.0  30.9   0.0  0.0   \n",
+       "3  Julián Álvarez   ar ARG  MF,FW  23.0  36      31  2647.0  29.4  11.0  8.0   \n",
+       "4     Kyle Walker  eng ENG     DF  33.0  32      30  2767.0  30.7   0.0  4.0   \n",
+       "\n",
+       "   ...  Ast_90  G+A_90  G-PK_90  G+A-PK_90  xG_90  xAG_90  xG+xAG_90  npxG_90  \\\n",
+       "0  ...    0.28    0.52     0.25       0.52   0.12    0.12       0.24     0.12   \n",
+       "1  ...    0.25    0.85     0.60       0.85   0.33    0.26       0.59     0.33   \n",
+       "2  ...    0.00    0.00     0.00       0.00   0.00    0.00       0.00     0.00   \n",
+       "3  ...    0.27    0.65     0.31       0.58   0.44    0.22       0.66     0.39   \n",
+       "4  ...    0.13    0.13     0.00       0.13   0.01    0.09       0.10     0.01   \n",
+       "\n",
+       "   npxG+xAG_90             Team  \n",
+       "0         0.24  Manchester City  \n",
+       "1         0.59  Manchester City  \n",
+       "2         0.00  Manchester City  \n",
+       "3         0.61  Manchester City  \n",
+       "4         0.10  Manchester City  \n",
+       "\n",
+       "[5 rows x 34 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b853975d-4e97-46f2-a731-5850d7d64638",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Player         0\n",
+       "Nation         0\n",
+       "Pos            0\n",
+       "Age            0\n",
+       "MP             0\n",
+       "Starts         0\n",
+       "Min            0\n",
+       "90s            0\n",
+       "Gls            0\n",
+       "Ast            0\n",
+       "G+A            0\n",
+       "G-PK           0\n",
+       "PK             0\n",
+       "PKatt          0\n",
+       "CrdY           0\n",
+       "CrdR           0\n",
+       "xG             0\n",
+       "npxG           0\n",
+       "xAG            0\n",
+       "npxG+xAG       0\n",
+       "PrgC           0\n",
+       "PrgP           0\n",
+       "PrgR           0\n",
+       "Gls_90         0\n",
+       "Ast_90         0\n",
+       "G+A_90         0\n",
+       "G-PK_90        0\n",
+       "G+A-PK_90      0\n",
+       "xG_90          0\n",
+       "xAG_90         0\n",
+       "xG+xAG_90      0\n",
+       "npxG_90        0\n",
+       "npxG+xAG_90    0\n",
+       "Team           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "79bda477-b4a0-4390-89dd-035c32a27bb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Gls</th>\n",
+       "      <td>580.0</td>\n",
+       "      <td>2.063793</td>\n",
+       "      <td>3.621238</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ast</th>\n",
+       "      <td>580.0</td>\n",
+       "      <td>1.481034</td>\n",
+       "      <td>2.360729</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>xG</th>\n",
+       "      <td>580.0</td>\n",
+       "      <td>2.076034</td>\n",
+       "      <td>3.402254</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>29.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>npxG</th>\n",
+       "      <td>580.0</td>\n",
+       "      <td>1.929655</td>\n",
+       "      <td>2.977988</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>22.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>xAG</th>\n",
+       "      <td>580.0</td>\n",
+       "      <td>1.493793</td>\n",
+       "      <td>2.115351</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>11.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>npxG+xAG</th>\n",
+       "      <td>580.0</td>\n",
+       "      <td>3.425690</td>\n",
+       "      <td>4.634031</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>27.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          count      mean       std  min  25%   50%  75%   max\n",
+       "Gls       580.0  2.063793  3.621238  0.0  0.0  1.00  2.0  27.0\n",
+       "Ast       580.0  1.481034  2.360729  0.0  0.0  0.00  2.0  13.0\n",
+       "xG        580.0  2.076034  3.402254  0.0  0.1  0.80  2.5  29.2\n",
+       "npxG      580.0  1.929655  2.977988  0.0  0.1  0.75  2.5  22.9\n",
+       "xAG       580.0  1.493793  2.115351  0.0  0.1  0.70  2.0  11.8\n",
+       "npxG+xAG  580.0  3.425690  4.634031  0.0  0.2  1.60  4.5  27.3"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['Gls', 'Ast', 'xG', 'npxG', 'xAG', 'npxG+xAG']].describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "826df292-7cde-4043-a678-8194a600249a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/uUNHi7Vh20Edziv1Wv7jz4eUfbRYt/0hVTYrYwEBIJSRCAcAAAAAIAQ0b95cr7/+up5++mmNGDFCLVu21IMPPqgRI0Z4thk/frycTqcmT56skpIS9e3bV/Pmzav2jcj8IetwkVZ/96tcblPWMIsST4tThC1MBw8XKftosf7zTYbS9+fpkRv7euYMBwCEHhLhAAAAAAAEoeeee67Csp49e2rhwoVV7hMWFqaJEydq4sSJ9RmazxzOK9Hn3++Ty22qbYsm6tejjaIiylIdqZLyCku1esOv2ppxWFPmrtHz9wzwrAcAhJZGdV3Q7NmzNXr0aK9lW7du1ahRo9SrVy9deOGFmjdvntd6t9utWbNmacCAAUpLS9Mtt9yiPXv2NGTYAAAAAACggTmcbn31wz45XW61ahalC9JOq5Dk7tgmTtMnDFJ8TITS9+dp+v9tkNtt+iliAIA/NZpE+FtvvaVZs2Z5LTty5IhuvvlmderUSUuXLtW4ceM0c+ZMLV261LPN7NmztWDBAj311FNauHChDMPQ7bffLrvd3tBFAAAgZNB5DQAA/G3TrkMqLHGqSaRVA3q1kzWs8hRHu5YxmnTzObJZLVq7OVOLP93RwJECABoDvyfCDx48qNtuu00zZ85UYmKi17pFixYpPDxcU6dOVZcuXTRy5EjddNNNmjt3riTJbrfrjTfe0Lhx4zRo0CClpKRoxowZOnjwoFauXOmP4gAAEPTovAYAAP6Wk1uiHXuOSJL6dGutcFvYSbdP6ZSgu0amSZIWfLxdezLz6j1GAEDj4vdE+ObNm9W0aVO9//77SktL81q3fv169e3bV1brb5c29e/fX+np6crJydG2bdtUWFio/v37e9bHxcWpe/fuWrduXYOVAQCAUEDnNQAAaCy+354lU1LHNrE6rWVMtfa5qG97ndO9jZwuUzMXfC+Xy12/QQIAGhW/J8IHDx6s6dOnq3379hXWZWZmqk2bNl7LWrVqJUnav3+/MjMzJUlt27atsM2BAwfqKWIAAEITndcAAKAxOHCoUNlHi2UxDPVKalnt/QzD0F3/r6eaRFr18y9H9dGajPoLEgDQ6DTqWyWXlJQoPDzca1lERIQkqbS0VMXFxZJU6Ta5ubm1Pq5pmioqKqr1/uWKi4sVG2uTw+E4xQHlk+MFo/Lfcfn/qB3qse6oQ9+gHsuYpinDMPwdRo0NHjxYgwcPrnRdZmamkpKSvJY1ROe1L9psu92uqKgoOZ1OORzOGu3rdJZdhl1cXCzTDLwbb4X632Qol5+yU/ZQVNPyB2p7HQq+23FIktS5XZyiI2012rd50yiNvqy75iz7Ue+u2K7fnd1eTaJq9hoAgMDUqBPhkZGRFeYNLS0tlSRFR0crMjJSUtkJbPnz8m2ioqJqfVyHw6GtW7fWev/jpab21NGjR0+6jdvt8tnxglVGRoa/QwgK1GPdUYe+QT1W7MQNdP7qvPZFmx0VFaX4+HjlF+QrO6egRvsa7rJLsdPT0wM6sRTqf5OhXH7KHppCuexSzcofbO11MNi+57D2ZRfKMKRuic1r9RpD+3fUB1/t1q9ZBVry6c+6cVh3H0cJAGiMGnUivE2bNsrKyvJaVv5z69at5XQ6Pcs6dOjgtU1KSkqtj2uz2dS1a9da71+u/IQ4Pj5eNlvVPcwWS5i6detW5+MFo+LiYmVkZKhTp0516twIddRj3VGHvkE9ltm5c6e/Q/A5f3Ve+6LNLo87NiZWpqVmsTSLKytLYmJiwI4ID+W/yVAuP2Wn7KFWdqnm5Q/G9joY/OuL3ZKkTm3jFFPLkdzWMItuHt5DT76xVv/6YpcuPa+TWjWL9mWYAIBGqFEnwvv27asFCxbI5XIpLKzs0uM1a9YoMTFRzZs3V2xsrGJiYrR27VpPIjwvL09btmzRqFGjan1cwzAUHe27RtBms500ES5DPj1eMIqKiqKOfIB6rDvq0DdCvR6D8TJrf3Ve+6LNLv99WK1W2WyV/25M01SJ3aUIW5gslt+2KZ8TPdATSqH+NxnK5afslD0UVbf8wdheB7q8QrvWbCqbUi2pQ7M6vVbf7q11Zpfm+mlXjpZ++rPuHJl26p0AAAHN7zfLPJmRI0eqoKBAkyZN0s6dO7Vs2TLNnz9fY8aMkVR2mdqoUaM0bdo0rVq1Stu2bdO9996rNm3aaMiQIX6OHgCA0NG3b19t2LBBLpfLs+z4zuuUlBRP53W58s7rPn36+CPkasnJLdHqDb9q6Wc7tfzzXVry6c9atW6vfs0qCMgR4AAABLIvftgvp8utFk0jlRAXeeodTsIwDF13SVln/Mdr9yonN3CnOAMAVE+jToQ3b95cr7/+utLT0zVixAi98sorevDBBzVixAjPNuPHj9f/+3//T5MnT9a1116rsLAwzZs3j7ncAABoQMHWee12m9qw7aA+XrtHB3IK5XC6JUkut6msI8X68od9+vz7fSoqOcUNsQEAgE+YpqlV6/dJklI6xvvkNc/s0lw9OjeX0+XW0s+YCgcAgl2jmhrlueeeq7CsZ8+eWrhwYZX7hIWFaeLEiZo4cWJ9hgYAAE6ivPP66aef1ogRI9SyZctKO6+dTqcmT56skpIS9e3bt1F2XrtNU//76YD2ZOZLkjq2jVW3jgmKiwlXQZFD6fvztH3PYR04VKj3Vu/SBb1OV+uE0J1iAACAhrAt44j2ZRcqIjxMXU9vqlJn3V/TMAz9aUiSHvv7Gq1Yk6GrLzpDzWLrNtIcANB4NapEeChwu00dPFykUodL1jBDbVvE+DskAABqLFg7r03T1IatB7UnM1+GIZ3f8zS1bx3rWd80JkK9kloq8bQ4ff79PuUW2vXQK1/q+XsGkAwHAKAeFR67CmvIOR0UbgtTqdN1ij2qJ+2MlkrqEK8de4/qv99k6Nqhtb93CQCgcWvUU6MEm1KHS59u+EWrv/tVazYd0Jc/7Ncn3+5V1uEif4cGAAAk7c3M185fc2VIOje1rVcS/HhNYyJ0cd/2ahYboZzcEj395lqV+GJoGgAAqFSfbq314vjzdOsVZ/r0dQ3D0B8GdpEk/eebDNkdvkmwAwAaHxLhDaSoxKmVa/co+0ixrGGGWidEy2a16HBeie6b+YX2ZOb5O0QAAEJaUYlT67cdlCT16NxcHdvEnXT76Eibhl+QqPiYCKXvz9PMhd9zA00AAOrR6a1iZA3zfRrjvJ6nqUXTSB0tKNUX3+/z+esDABoHEuEN5LvtB5Vf5FB0pFVDzumowX3a6/fndlJCXKQKix168f++89yICwAANLyvNh6Q3eFWs9gI9ejcvFr7xEaH6+Eb+yrMYuirjWVXegEAgMYjMjxM7lN0VFvDLBp2QWdJ0r++2OXp2D7VfgCAwMIc4Q0g60ixfjlYIEPSwN7tFB8bIUmKibJpYO92WrVur3bvz9U/P96mGy7r7t9gAQAIQZt35yjj2Lzg/c9sK4vFqPa+PTo31+hLu+mtD7fo9fd/UlpSS7VqxnzhAAA0BuG2MFkMQ19v3Ke8QnuV24VZJGuYoYwDeZr3r5/ULTFB56e1a8BIAQD1jRHh9czpcuuHnw9Jkrq2j69wB+qoCKvuHNlTkrT005+ZIgUAgAZmmqbmf7hFktSlXVNPh3VNXHlhVyV3bKaiEqdeWfQDU6QAANDI5BXadSS/tMpHcalLndo2lSSt33bwpElzAEBgIhFez/73U6byCh0Kt4UptWuLSrc5N/U0nZvaVm5TWrRyRwNHCABAaPt+xyFtzTisMItR7SlRThRmMTThT70VbrXo+x3Z+vrH/T6OEgAA1Lfkjs0kSfuyC3U0v9TP0QAAfI1EeD37+NhcoUkd4hVhC6tyuz8NSZYkfblxn37Nym+Q2AAAgPTJ+l8lST06Jyg60lbr1zm9VaxGDj5DkjTv/c0qsTt9Eh8AAGgYcU3CdVqLJpKkTbsO+TkaAICvkQivR/sPFWrz7sOSpM7tmp50287tmqpfjzYyTWnxqp8bIjwAACDpd2e109D+HXVWUuVXbtXEVb/rqpbNonToaLGWfEp7DgBAoCkfFb4144iKShx+jgYA4EskwuvRp+v3SZLaNo9Wk2qMMLtmSJIkafV3vyont7heYwMAAGX6dmule67upfCTXLlVXZHhVt16xZmSpPdW79LhvJI6vyYAAGg4rROiFdckXE6XW5+t/8Xf4QAAfIhEeD1xutxa/X3Z/KCJp8VWa58z2jdTj87N5Xab+uTYlCoAACCwnJfaVikdm8nucGnRJ9z7AwCAQGIYhs5oHy9J+vCbDG6ADQBBhER4PXE63XK63GrXsonaJERXe7+h/TtKkj5eu0cuNw0uAACBxjAMjb6smyRpxf8ydPBwkZ8jAgAANdGpbZysYYZ+OZivn3bn+DscAICPkAivJ5ERVr305/P17J3nyWIxqr3feT1PU5Mom7KOFOuHHVn1GCEAAKgvPbu2VNoZLeR0mVrw8XZ/hwMAAGog3BampA5lc4X/5+t0P0cDAPAVEuH1KD42Qk2iTj03+PEibGEa3Ke9JGnF//bUR1gAAKABjL60bFT4p+v36tesfD9HAwAAauLMzs0lSWs2HeCeHwAQJEiEN0JD+5VNj/Lt5kzlFdr9HA0AAKiN5I4JOqd7G7lN6d0VjAoHACCQtIiPUrdOCXK5TQapAUCQIBHeCHVsG6fE0+Lkcpv65sf9/g4HAADU0qhLUyRJX/6wT+n7c/0cDQAAqInLzk+UJP13TYacLrefowEA1BWJ8EZqYO/TJUlffL/Pz5EAAIDaSjytqQb0aidJenfFNj9HAwAAauL8nm3VNCZch/NK9O3mTH+HAwCoIxLhjdTAYyfNP+0+pJzcYj9HAwAAauvaS5JlGNL/fsrU3sw8f4cDAACqyWYN0yXHpi79zzfcNBMAAh2J8EaqVUK0unVKkGlKX/7A9CgAAASq9q1jdW5qW0nSkk9/9nM0AACgJn7fv5MMQ9r48yH9cpCbXwNAICMR3ogN6l02KvzLH371cyQAAKAurh6cJEn6/Pt9yswp9HM0AACgulolRKtvtzaSyuYKBwAELhLhjdh5PU+TYUg79h7VoaNMjwIAQKDq2j5eZyW3ktttatnqnf4OBwAA1MBl53eSJK1at1clpU7/BgMAqDUS4Y2AaZpyutwVHrFNwpXcoZkk6ZtN++VwcpdqAAAC1dUXnSFJ+uTbvTqcV+LnaAAAQHX1Tmqlts2bqLDEqc+/54ptAAhUVn8HAMkwDC36ZEel6yIjyn5F73+xW5edl9iQYQEAAB/q0bm5unVK0NaMw/rX57t08+U9/B0SAACoBovF0KXnddIb/96s/3ydoUv6dZRhGP4OCwBQQ4wIb+RObxUjSco6UqT8IrufowEAALVlGIZnVPhHa9Jp1wEACCAX9e2gcKtFu/fnavueI/4OBwBQCyTCG7nY6HA1jQmXaUrrtx70dzgAAKAO+nRrrU5t41Rc6tIHX6X7OxwAAFBNcU3CNaB3O0nSh9/QhgNAICIRHgDat4qVJK39KdPPkQAAgLowDEN/vChJkvTvL3epmBtuAQAQMMqnK/3qh/3KLSj1czQAgJoiER4AyqdH+X5HlkrsnDADABDIzks7TW1bNFF+kUMr/rfH3+EAAIBqSurQTF3bx8vpcmvlt3v9HQ4AoIZIhAeA+NgINYm0ye5w6/vtWf4OBwAA1EGYxdD/G1w2V/h7q3fK4XT5OSIAAFBdw87rJEn66Jt0udymf4MBANQIifAAYBiGTm9dNip8zaYDfo4GAADU1e/Obq8WTSN1OK9En6z7xd/hAACAahrQ+3TFRNmUdaRYG7ZxHy8ACCQkwgPE6S3LEuHfbjkop8vt52gAAEBd2KwWjfhdV0nSkk9/pm0HACBARNjCdPE5HSRJ//mam2YCQCAhER4gWjSLUlyTcBUWO/TTrkP+DgcAANTRJf06Kj4mQlmHi/TF97/6OxwAAFBNlx6bHuW77Vk6cKjQv8EAAKqNRHiAsBiGzuneRhLTowAAEAwiw626clAXSdKiT35mnlEAAALEaS1idFZyK5mm9N81Gf4OBwBQTSTCA8g5PcoS4d9uzpRpcrIMAECgu/S8ToqJsmlfdoG++XG/v8MBAADVdNmxUeErv92jUgc3vgaAQEAiPID0PKOFIsPDdCi3RLt+zfV3OAAAoI6iI226YkBnSdKiT3bQ0Q0AQIDo072NWjaLUn6RQ19v3OfvcAAA1UAiPIBE2MJ0VkorSdL/fmJ6FAAAgsHwAZ0VFWFVxoE8rdty0N/hAAAASZHhYXKfpIM6zGLo0nM7SZI+POGmmSfbDwDgP1Z/B4Ca6dejrb758YDWbs7UqEu7+TscAABCWmR4mEzTlGEYNd63fL/Y6HANOz9RSz79WQs/2a6+3VvX6vUAAIDvhNvCZDEMfb1xn/IK7ZVuYzEMWQxDO/Ye1fwPN6tVs2jFNQnX+WntGjhaAEB1kAgPMH27t5bFYijjQJ4ycwrVpnkTf4cEAEDICreFyTAMffPjfuUWlFZ7v6YxETqv52men/8wsIve/3K3duw9qh92ZKt3cqv6CBcAANRQXqFdR/KrbuPbt47Rnsx8fbc9W/2O3dcLANA4MTVKgImNDteZnZtLkv73U6afowEAAJKUW1CqI/nVf5yYNI+PjdDv+3eUJC38ZIc/igAAAGrhjPbxkqQ9B/Jk56aZANCokQgPQP3OLOtlZp5wAACCx4gLu8oaZtHm3Tn6adchf4cDAACqoUV8lOJjIuRym9q9P9ff4QAAToJEeADq36OtJGlrek6NLsMGAACNV4v4KF18TgdJjAoHACBQGIbhGRX+8y9HZXKjTABotEiEB6BWCdHqfFpTuU1p3ZaD/g4HAAD4yMjfdVWYxdAPO7K1eXeOv8MBAADV0LFtnGxWiwqKHErfn+fvcAAAVSARHqCYHgUAgODTpnkTDelXNlf42x9tZVQZAAABwGa1eEaFf78jm/YbABopEuEBqv+ZZdOjfL8jWyV2p5+jAQAAvvKnIUmyWcvmCv9+e7a/wwEAANWQ1KGZLIahg4eLtCX9sL/DAQBUgkR4gEo8LU6tmkXJ7nDp++1Z/g4HAAD4SPOmURp2fqIk6e2PtjCqDACAABAVYVXiaXGSpGWf7fRzNACAypAID1CGYejc1NMkSV9vZHoUAACCyf8bfIaiIsK089dcrdlEOw8AQCBI6ZQgSfp2S6b2ZjJXOAA0NiTCA9gFvcoS4d9uOaBSh8vP0QAAAF9pGhOhKwZ2kSS9899tcrkZFQ4AQGMX1yRcnY+NCn9v9S4/RwMAOBGJ8ACW3KGZWsRHqbjUpe+2MT0KAADBZMSgroqJsumXg/n6/Ltf/R0OAACoht7JrSRJq7/7RTm5xX6OBgBwPBLhAcQ0TTldbs/D5TZ1XmrZTTO//GGfnC63HE63n6MEAAC+0CTKppGDz5Akvf3RVq7+AgD4xL59+5ScnFzhsXjxYknS1q1bNWrUKPXq1UsXXnih5s2b5+eIA0vrhGj16NxcTpepf32x29/hAACOY/V3AKg+wzC06JMdXsuKS52SpDWb9uvdFdG6bmiKP0IDAAD14PIBnfWfb9KVfaRYyz/fqWsuTvZ3SACAALd9+3ZFRETok08+kWEYnuWxsbE6cuSIbr75Zl188cV64okn9MMPP+iJJ55QfHy8Ro4c6ceoA8v/G3yGNu/O0UffpGvk77qqaUyEv0MCAIgR4QGvedNIRUda5XSZOnCo0N/hAAAAH4qwhenGy7pLkpas+plLrAEAdbZjxw4lJiaqVatWatmypecRGRmpRYsWKTw8XFOnTlWXLl00cuRI3XTTTZo7d66/ww4oZ6e0Utf28Sqxu7Tss53+DgcAcAyJ8ABnGIY6tI6VJP1yMN/P0QAAAF8b2Ludkjs2U4ndpXc+2ubvcAAAAW779u3q2rVrpevWr1+vvn37ymr97eLx/v37Kz09XTk5OQ0VYsAzDEPXXVJ2FdeH36TraH6pnyMCAEhMjRIU2reO1bY9R7Qvu0ClDpesYfRvAAAQLAzD0G1/OFMTZ32pVev3atgFiep6ery/wwIABKgdO3aoZcuWuu6665SRkaGOHTvqrrvu0oABA5SZmamkpCSv7Vu1Krv54/79+9W8efNaHdM0TRUVFdU5drvdrqioKDmdTjkczmrt43S6jv3vlMPhqPaxar9fmCSpR6c4dWkXp1378rRo5VaN+n3SKfasueLiYq//Qx31URF14o368BYs9WGaptdUXydDIjwIlE+PUlTi1Pfbs3RBWjt/hwQAAE4iMjysRl/YUjomaGDvdvri+32a96+f9Mxd51d7XwAAytntdmVkZCgqKkoPPvigoqOj9f777+v222/Xm2++qZKSEoWHh3vtExFRNr91aWntRzU7HA5t3bq1TrFLUlRUlOLj45Wfn6/swwXV2qd5TFl7mZ+fr+zso9U+Vm33M1wxkqSMjAz162rTrn3SR//bq+RWdsVEhVX7dWoiIyOjXl43UFEfFVEn3qgPb8FQHye2XVUJiET4vn37NHjw4ArLn3rqKV199dXaunWrnn76af3000+Kj4/X6NGjdeutt/ohUv8onx5l254j+nrjfhLhAAC/oL2uvnBbmAzD0Dc/7lduQfUSCx3bxMoaZuin3Tn65scDOj/ttHqOEgAQbMLDw7Vu3TpZrVZP0uDMM8/Url27NG/ePEVGRsput3vtU54Aj46OrvVxbTZbldOx1ER5bLGxsTLDoqq1T2xsnGeflqat2seq7X7N4iIlSYmJierYsaO+3bVOO3/N1daDNt1wqW9vel1cXKyMjAx16tRJUVHVq49gRn1URJ14oz68BUt97NxZ/XsxBEQinLtan1qHNmWJ8PVbD6rE7lRkeED8agEAQYT2uuZyC0p1pAbzhvZKaqn1W7P02vJN6p3cUtGR1T8xBwBAqjyhnZSUpK+++kpt2rRRVlaW17ryn1u3bl3rYxqGUadE+vGvI0lWq1U2WzUvg7eGHbdP9dvN2u9Xdi5enlQadWk3TZ37P61ct0/XDOnmSZT7UlRUlE/qN1hQHxVRJ96oD2+BXh81uVI2ICaT5q7Wp5YQF6mYKJtK7C7976dMf4cDAAhBtNf176zkVmrbookO55Xo7Y/qfok5ACC0bNu2Tb1799b69eu9lv/000/q2rWr+vbtqw0bNsjlcnnWrVmzRomJibWeHzzUnZXcSskdm8nucGnhJzv8HQ4AhLSASIRzV+tTMwxDndqWXbr12fpf/BwNACAU0V7XP2uYRXeN7ClJ+vDrdO3Ye8TPEQEAAklSUpLOOOMMPfHEE1q/fr127dqlZ599Vj/88IPGjh2rkSNHqqCgQJMmTdLOnTu1bNkyzZ8/X2PGjPF36AHLMAyNvrSbJOm/azK0L7t6c5sDAHwvIObPaOi7WvvqjtbFxcWKjbVV6w7T1b0L9cm2O71VtH7anaPvd2Tp18zDSqiHS64aWrDcwdbfqMe6ow59g3osU5ObJAaShm6vQ1WvpFa68OzTtXrDr/rbko168c8DFRYWEGMbAAB+ZrFYNGfOHE2bNk0TJkxQXl6eunfvrjfffFPJyWXzV7/++ut6+umnNWLECLVs2VIPPvigRowY4efIA1vaGS3Vp1trrd96UPM/3KJHbzrH3yEBQEhq9Ilwf9zV2ld3tJak1NSeOnr06Cm3y87OrtbrnWq7lI7NtG3PES1b+aPO7x5brdcMBMFwB9vGgHqsO+rQN6jH6t/VOlD4o72WfNN5bbfbFRUVJafTKYfDWaN9yy8dL9u3ep3addnP6Sybr/T6IV20bkumdu/L1bLPtmvYeR1rEPVvQr1zKpTLT9kpeyiqafmDteM6ISFBzzzzTJXre/bsqYULFzZgRKHh5uHd9d22g1qz6YA2785Rj84MAgCAhtboE+H+uKu1r+5oXf4FKz4+/pQ312jZsmW1XvNU23VPitG2PUe0db9Tt16VEvBf3ILlDrb+Rj3WHXXoG9RjmZrc1TpQ+KO9lnzTeR0VFaX4+HjlF+QrO6dmlys3jy1rZ/ML8pWdfbTe9zPcMZKkw9n7NTg1Vv/+9oj+ufJnNbPlqVlM7b/WhXrnVCiXn7KHplAuu1Sz8gdbxzX8p0ObOF3Sv5P+uyZD897/SdPGD5TFEtjn6wAQaBp9Ilxq+Lta++qO1uVsNtspE+HVvQv1qbY7P+00zXv/J/2aVahfDpUqpWNCteNszAL9DraNBfVYd9Shb4R6PQZ6J2VVGrq9lnzTeV2eoI+NiZVpqVkHTVxsnGfflu7qteV12a/ZsWnPEhMT1aFDR+3MWq+tGUe1YmOJptzSp8Yn1KHeORXK5afslD3Uyi7VvPzB2HEN/7rukmSt3vCLfv7lqL7auE8De5/u75AAIKQ0+kT4tm3bdO2112ru3Lnq06ePZ3n5Xa27deumBQsWyOVyKSys7HLhUL6rdZMom85PO02fbfhVH/9vT9AkwgEAjZu/2mtfdF6Xd0xYrVbZbDVLJJeXpWzf6ie0a7tf+c1GyxM4913XR+Onf6Zte45qxbf7NXLwGdV+reOFeudUKJefslP2UFTd8gdrxzX8p1lcpEYOPkP/999tmv/hFvU/s63CbWH+DgsAQkajv7MSd7WuuaH9O0mSvvhhn4pKqj/vKAAAtUV77R9tmjfR7X9IlSS9899tSt+f6+eIAADAyVw5sIsS4iKVdaRYy1Zz1QEANKRGnwgvv6t1amqqJkyYoBEjRmjjxo2eu1o3b95cr7/+utLT0zVixAi98sorIX9X6+6JCfr/7d13fBzV2f/9z2zXqnfZcpOL3Cs2btiADYHQAjE3BIKpIQSeQCAJLTgB7kAIwfQABmIg5aYbHOAHAQOm2DjuDRv3LtuSrF63zvPHSmvJktywtJL2+36x7O7szO4169GeOdec0j0zDo83wJcr9kQ6HBERiQIqr9uGy2HFNM1Gy844uQdjB2fhDwR57NUV+PyBFrc/dFsRERFpWy6njesuGAzAm59uYu+BY5ufREREjl+7HxoFNKv1sTIMgx+M7cXs977lo0U7OHt8L3XrExGRVqfyuvU57FYMw+CbNXspq/SElw/slcKaLYXs2FfO/85ezIShXZpsmxjnZMKwrm0ZroiIiDRj0ohs5i3ZxapNhcyas4b7fz5edXYRkTbQ7luEy/GZOqY7DruV7XvLWb+9ONLhiIiIyAlUVumhpOLgzeMLMHpgFgCrNhXy7baiRq+XVHgaJc5FREQkcgzD4MYfD8Nus7ByUyELVu2NdEgiIlFBifBOKt7t4PSTQjNQv//1tghHIyIiIq2tW0YcuT2SAFi0dh+V1d7IBiQiIiIt6poex/9MzQXgb++tpapG83uJiLQ2JcI7sfNP6Q3Aom/3UVBSHeFoREREpLWNyM0gNdGFzx9kweq9BALBSIckIiIiLbh4Sl+y02MpLvfwr4++i3Q4IiKdnhLhnVjPLgkM65tGMGjy4cLtkQ5HREREWpnVYjBxeFecdislFR6WbyiIdEgiIiLSArvNyo0/Hg7A//tmO2u3HohwRCIinZsS4Z3cjyb3AeCjRTvU1UpERCQKxLrsjB8Wmixza14ZW/eURjYgERERadHw3HTOPLkHpgmPv7ZC9XYRkVakRHgnN3pgJt0z46mu9fPhN2oVLiIiEg26pMYytG8aAEu/y2d/UVWEIxIREZGW/OxHQ8hMcVNYUsMLc9dGOhwRkU5LifBOzmIxuHhKPwDe+2obHl8gwhGJiIhIWxick0KPrHhMExas3ktxeW2kQxIREen0XA4rQdM8pm3cLju/vnwUFgM+X7abhWv2tlJ0IiLRzRbpAKT1TR6Zzf/95zsKSmqYt3gn59VNoikiIiKdl2EYjBucRU2tn8LSGv7fwu2cMyGH5ARXpEMTERHptBx2KxbDYOHqPMqrvEe9XUKsg2lT+vHWZ5t55q3VDOyVQorKbBGRE0otwqOAzWphWl2r8Lc+20St1x/hiERERKQtWK0WJo3IJt5tp6Lax/++tJhaj84DREREWlt5lZeSCs9R38qrvFz2gwH07ppIRbWXx19dQSB4bC3LRUTk8JQIjxJnntyTjOQYiss9fLhwR6TDERERkTbidFg5dVQ3XA4rW3aX8sDLizVUmoiISDtkt1n4zU9H4XRYWbW5kH9+uD7SIYmIdCpKhEcJu83CZT8YAMDbn2+mulYzUYuIiESLeLeDcyb0IsZpZfXmAzz0yhJ8fiXDRURE2pseWQn86pKRAMyZv4UFq/MiHJGISOehRHgUOf2kbmSnx1FR7WXO/C2RDkdERETaUFZqLH+4bhwOu5XlGwp4+B/L8AeCkQ5LREREDjFpZDY/Pq0vAE++vpKd+8ojHJGISOegRHgUsVotXHXuQADmfrGFguLqCEckIiIibWlInzR+f+3J2G0WFq/bz1NvrdX4oyIiIu3QlecMZHi/NGq9AR58eQmV1Uc/8aaIiDRPifAoM25IF4b1TcPrD/LyB+siHY6IiIi0sRG5Gfzu6pOxWQ0WryvgtS+LqPVqmBQREZH2xGq1cPsVo8lIjmFfURV/emWphjUTEfmelAiPMoZh8LMfDcFiwILVe1m75UCkZULP5gAAPEVJREFUQxIREZE2NnpgJvdcMxaH3cKWfbU8+MpyKtTSTEREpF1JjHMy49qxxDhtrN16gMdfW0lQPblERI6bEuFRKKdrImeP7wXAM2+vwuvTVWUREZFoM3pgJr+/+iRcDoNNu8u4868LOFBaE+mwREREpIGcroncU9eT6+tVeTz3zhpMU8lwEZHjoUR4lLrynEGkJDjJK6zizU83RTocERERiYDcHklce0YGKQlOdudX8NunvmLTrpJIhyUiIiINDM9N57bLRmEY8J9FO3hh7lolw0VEjoMS4VHI5w/idFj52Y+GAvD255vZtKsEfyDY6ObzByMcqYiIiLS2jCQ7/3v9GLpnxlFUVstdzyxg/vLdkQ5LREREGpg8shu3XDISgA8WbOfZOWs0TIqIyDGyRToAaXuGQbgVePeMOHYXVHL/3/7LWeN6YrMevDZyyRm5kQpRRERE2lB6Ugwzb5nMzP9bztL1+Tz26gq27y3nqnMHYbUYkQ5PREREgDNO7kHQNPnrW6v4z6IdVFTWcvoga6TDEhHpMNQiPMqNGZRJjNNKeZWXVZsKIx2OiIiIRIjbZWfGNWPDF8Lf/WILM2Yt1LjhIiIi7cgPxvbk9p+OxmoxWLh2P//3RRGVNb5IhyUi0iEoER7lnA4bYwd3AWDz7lJ27a+IcEQiIiISKRaLwfQfDuSO6aNxOax8u7WIWx6dz3+/3Rfp0ERERDo1l8NK8CjH/Z40Mps//GwcbpeN7fm13DNrCdvyylo5QhGRjk9Dowhd0mIZ2CuF73YUs3jdPhLjHCTGOSMdloiIiETIpBHZ9M5O5JF/LWPrnjIefHkJP5zQi2vPH4zLodNHERGRE81ht2IxDBauzqO8yntU25wzvgcf/3cX+4uruf2pr/j5RUP5wdieGIaGNRMRaY5ahHcypmk2mfTy0FtzhvVNIyPZjT9g8vWqPDzeQBtHLiIiIu1Jdnocj9w8mQtP7QPAR9/s4OaZ81mzRUOpiYiItJbyKi8lFZ6jugFMGZHK6IGZeP1B/vrWav539mKKyjSsmYhIc9Skp5MxDCM8EWZLmpsE02IxmDisCx8v3klFtY+vV+Vx6Zm5jSbPFBERkehit1m47oIhjMxN5+k3V7G/qJp7nvuGs8b15JrzBhMbYz/s9qZpqlWaiIhIK3LYLfz+2jHM/XIr//zoO5Z9l89Nf/mcn541gHMn5mBVnV5EJEyJcAlzOW2cNqob85bsorC0hideX8md00er4BQREYlyowZkcuU5g/h48U7WbSvi4//uZMGqPCYM60LfbknNJrsT45xMGNY1AtGKiIhEF4vF4Men9+WkgRk89cZKNu0q5cV/f8sHC7Zz6Zm5nDaqW5N6fdA0sRzHxerj3U5EpD1QIlwaSYxzcsrwrny5Io9Fa/fx+Gsrue3yUVgtKuhERESiWa3Xz7C+aWSluFm8fj+V1T7mLdnNqs0HOGlABsnxrkiHKCIiEtV6ZiXwyM2T+WTxTv750XfsK6riiddX8sa8TVxyRi6TR2bjsFsBjnk8coCEWAcTh2e3VvgiIq1OiXBpIis1lonDu/LNmr18uXIPALdeNlLDpIiIiAgZKW7OGd+L73aWsH5bEYUlNXy8aCd9uycxuHcqMU6dXoqIiESKxWJw9vhenDqqGx8u3M47X2xhX1EVT76xkr+99y2TR2Zzxpge9OueFB6PXEQkWqimIs3qlhHHb346ikf/bwVfrtxDVa2PO68cjcuhQ0ZERCTaWa0WhvROJadLAis3FbA7v5LNu0vZvreM/j1TGNgrOdIhioiIRLUYp41pU/pxzsQcPly4nQ8WbudAaQ0ffbODj77ZQXZ6HGlJLlISXKQnu9ULXESigrKa0qLxQ7sy41o7D/19Kcu+y+fuZxZwzzVjSUuKiXRoIiIi0g7Extg5ZXg2+cXVrN5USFF5Leu2FbFldymjB2Zyxsk9sNuskQ5TREQkatUnxC88rS9rNhfy6dJdLFq7j7zCSvIKKwGwWQ0ykt2kJsWQlugiNTEGu009wkWk81EiXA5r9MBMHrhhAg+8vJgte8r49RNfcsf00Qzpkxbp0ERERKSdyExxc+bYHuwpqGT15kIqqn0sXLOXzXtKuWRqLlNGd1eFWkREJIKsFoOR/TMY2T+DyhofKzcW8O+vtrJjXzkeb4C9B6rYe6AKAANIjHeGk+KpiS4SYh2R3QERkRNAiXA5ooE5KTx266k88NJiduwr557nFvKTHwzgkqn9msw8LSIiItHJMAy6Z8aTnR7Htr1lrNtWREFxNX99axWvz9vIxaf35cyxPcOTdImIiEhkxMXYmTQim8pqL8XltZRUeCgsqeFAWQ0HSmuorvVTWuGhtMLDlj1lANhtFrqkxlJQWsOAHsnk9kw+pomyg6aJxdDwKyISWUqEy1HJTHHzl5snMeudNXy+bDevfryBRWv38v9dPJz+PVMiHZ6IiIgcB8MwiImJwTiBFVOLxaBvtyRG9EsnaMK7X2zmQGkNs95dyxufbuLHp/fl7HG9cGlSTRERkYgzDIOUhNBY4f0JzfFRXeunqC4pXlRWS3F5LT5/kF35FezKrwhvG++2k5HspktaLN0z4kiKdzZ7TpEQ62Di8Ow22ycRkZaoBiJHLcZp47bLRjEiN50X565l+95ybn/6a86ZkMP0Hw4kNsYe6RBFRESkBS6HFdM0G1VQY2JiGDRoUKt8nt1m4YcTcjhnQi/mLdnFnPmbKSypYfZ763jz002cPb4X507MITVRc4+IiIi0J26XDbcrnu6Z8QAEgyZllR7MusfLN+RTXO6hotpHRXUZW/NCrcbdThuZqW6yUmPJSnHroreItDv6VZJjdvpJ3RnVP4OX3l/H58t28/8Wbmfh6r1Mm9KXs8f3wuXQYSUiItLeOOxWDMPgmzV7Kav0AOD3+ykpLSE5KRmbrfnyu2t6HMP7pX+vzz13Yg4/GNuTL5bv5q3PNrOvqIq3PtvMO/O3MGlENj+a3Ie+3ZOO+zNERESk9VgsBskJLnpmxTNxeDYffbOdgpIaistrOVBaQ35xNYWlNVR7/GzfW872veUApCW56J4Rz+DeqRHeAxGREGUs5bgkxjm57bJRTBndnefmrCavsIrZ763jnflbuHhKP84a3wunxgAVERFpd8oqPZRUhBLhPp+PwqJKTEsMdnvzPbtO1ORYdpuFM8f2ZMqYHixZt49/f7WNdduK+GLFHr5YsYfBvVM5d2IO44ZkYbfpHEJERKQ9s9ssZKa4yUxxM7h3Kv5AkMKSGvYXVbG/uJrSCg8HSms5UFrLyk2F/PfbfUwY1pWJw7rSNT0u0uGLSJRSIly+l+H90vnr7VOYv2w3r3+6iYLial7897e8Pm8jPxjbkx9OyCEzxR3pMEVERKSNNTcUC4DVYjB+aFfGD+3Klt2l/PvrrXy9Mo9124pYt62IeLeDKaO7c+bJPejZJSFC0YuIiMixsFktdEmLpUtaLADVtT72FFSyO7+CwpIatuwpY8ueMv7x4Xfk9kji9JO6M2lENolxzghHLiLRRIlwaZFpmvgDwSOsc7CF12kndefzZbt487PNFBRXM2f+Ft75YgujB2ZyxpgejB6YiUOtxEVERKJCc0OxNGdQrxR6ZsazblsR3+0soaLay7+/2sq/v9rKgJ7J/GBsTyYM66q5SERERDoQt8tObo9kcnsk47RbiXM7+GbNXlZtLmTTrlI27Srlb//+lpMGZHL66G6cPChL+QIRaXVKhEuLDMPgzU83HXad/5naL5wsNwyYOqYHp53UneXf5fPhN9tZvfkAS9fns3R9PrEuGxOGdeXUUd0Y0icNq6XpbNIiIiLSuTQciuVw+vVIpk+3JPYVVbE7v4Kd+yvYsLOEDTtLeHbOGkb1z2DSiK6cPDgLt0tJcRERkY7C7bJx1rienDWuJyUVtXy9Mo/5y3ezZU8ZS9bvZ8n6/bhdNiYO68rpo7szOCcVy3HkCwzDICYmpklvNBGRekqEy/dyuGT5oJxUumXEY7NaWLA6j6KyWuYt2cW8JbtISXAxaUQ244ZkMbBXClarpY0jFxERkfbGYjHITo9jSO9Uxg7pwmdLdzF/+W5251eGK8p2m4XRAzMZP7QLo/pnqEu1iIhIB5Ic7+KCyX24YHIfdudXMH/5br5YsYfCkppwviA9OYbTRnXjtFHd6JYZj+UoE9sxMTEMGjQIgKBpHvV2IhI9lAiXVpUQ6+CSM3K55vzBrK+bEGvhmr0Ul9eGuz3Hux2MGZTJ2MFZjOyfQYxTh6WIiEi0S0lw8T9Tc7l4Sj927a/g69V5LFiVR15hFYvW7mPR2n0YBvTtlsSoARmMHpBJvx7J6nEmIiLSQXTPjOfKcwZxxdkDWbe9iPnLdrNwzV4KS2p467PNvPXZZvp0S6RnVjw9MuOP2CPM7/dTUlJCr+5ZTB7Vo432QkQ6EmUcpdWZpgnAwJwUBuak8LMfDWbFhgL+++1+lm3Ip6Lay+fLdvP5st3YbRaG9kljZP90RvbPIC1eY4SJiIhEM8Mw6NklgZ5dEvjpWQPYsa+cr1flsey7fLbvLWfz7lI27y7ljXmbiIuxM7h3KoNyUhiUk0qfbknYbep1JiIinVdsjKPDt362WAyG9kljaJ80fvHjYSxZv58vlu9h2Xf5bN1TxtY9ZRhAZqqb7hnxZGfENduAzufzUVhcSXLykYdkE5HopES4tLqWhk/pkhbLuRNyOFBag9tlY8n6fPKLq1mxsYAVGwuAdaQmuBjeL41ys4jBvSE5waUKrYiISCfnclgxTbPJGJ+GYZDTNZGcrolcec4gispqWLmxkOUb8lm5qZDKGh+L1+1n8br9ADhsFvr1SGZgrxT6dkuiT7dEMlPcGjtUREQ6DafDhsUwWLg6j/Iq71Ft0yUtlhG5Ga0c2UEuh/Wok/UOu5VThmdzyvBsyio9LFi9l3e/2EJ+cTX7i0K3pd/lk5YUQ/eMOLplxBHndrTBXohIZ6BEuESUxWKQkeLmkjNyiY2xU17lZV9RFfsOVFFYUkNReS2fL9/D58v3AJCV6mZI7zQG905hUO9UuqTGqjIrIiLSyTjsVgzD4Js1eymrPHKrrmF90xjSO5Vqjx+b1cL67UWs315MeZWXdduKWLetKLxurMtG7+xQUrxbegxmjY/cuom/RUREOqryKu9RTU4NoSFM25LDbj3mZD3UNZ6bmIPFgF35FezOr2BPfiVF5bUcKK3hQGkNKzcVkhDrICvVTVqCEyNgtuKeiEhHp0S4tBuGYZAY5yQxzsmAnikEAkH2Hahge14RlbUGZZXeuivAu/h06S4AUhKcDMpJZVBOKgNzUsjpkqCJN0VERDqJskrPUVfqAZLjnfxwQg4XndYX0zTJK6xk3bZiNu8uYeueUnbsq6Cq1s/arQdYu/VAeLsXPi4kOz2OHnVjkPbIiqdHVgJZKW6dV4iIiJwgx5Ksh8YJ+3i3I1z3r6r1kVdQye78CgpLaiiv8lJe5aW+H/rm/dspqfSR2yOZft2TSElwqQGdiABKhEs7ZrVayExxYwlUkZ6ejomFgTkpbNxZwrdbi9i8u5Ti8lBXqQWr9wKhLle5PZIZmJPCoF6p9O+ZTGzM4SfUEBERkc6h4ZAqhmHQLSOebhnxnDWuJwA+f5A9BRVs3VMaGnM0r4xte8vweAPs2FfOjn3ljd7PbrM0SZB3y4gnKzW20VBtzQ3jIiIiIq0j1mUnt0cyuT2S8fgCFISHTamissZHQd1km/WS4p307ZZEv+5J9OqSQHZ6HF3SYnHYNSeZSLRRIlw6DIfdyqj+GZw0IBMAjy/Alt2lrN9exHc7itm4s4TqWj9rthxgzZZQKy/DgB6Z8QzolcKAXikM7JVCRnIMYGiscRERkU7mWIZU6ZEVT9e0GAb3cJDdJZOEOBdfr8xj74FKSso9lFTU4vMHm02QG0B8rIOkOCcZKTGcNCCTrulxZKfHkZYUg9VydEnx402gK/EuIiIS4rRb6Z4ZT/fMeHw+H7vz8nHFJmK32diyp5Rd+RWUVnhY9l0+y77LD29nGJCeFNOo/E5LdJGc4CI9KYaURBcux9GlzDr6ZKUi0USJcOlQWpp4c0DPFPr3SKas0kvPLvH8Z9FODpTWUFnjY+f+Cnbur+Dj/+4EQq3FRvbPYEjvVIb2TaNnVgKWo6ywioiISPt3tEOq+Hw+CouryOlu4eRBWRSV1tAlLRYIJZuranyUVXkpq/RQVhm6r6j24g+Y4W7Yu/IrWPZdQfg9bVYLXdJiyU6PpUtaHBnJMWQku0mvu2/YU+1YxkGvlxjnZMKwrsfwbYiIiEQPl8NC/x5JnD+5HwC1Xj879pazeXcpW/aUsju/grzCSqpr/RSU1FBQUsOqTYXNvpfTbiU2xk5sjI1Ylx23y4bbZSe27t7tspGVGstpJ3Vvy10Uke9BiXDpNAzDICneyVnjelFWGZqAo8bj50BpDYV1E2mUlNdS6w2waO0+Fq3dB0Cc287g3qkM7ZPGkD6pdM+IDyfGTRO1HBcREYlChmEQ53YQ53aQnR4XXm6aJrXeABVVXsqrvfj8QZx2K3sPVLLvQDX+QJDddRN6NSfWZSM92U1GspvURBcHSmvAAJfDRozTistpw+WwHXWrchEREWmZy2EL9xCvZ5omZZVe8gor2VtYyb6iKg6U1lBUVktRWQ35xTX4A0E8vgAeX4Di8sN8ADDr3bWkJDhJjneRkhBqVR5+nhhalnoMLcxFpPXor1A6tRinLdxNCsAfCFJcVkuXtFg+Xbor1Gq82sfib/ez+Nv9QOiqb0ZKqNXWVecOoleXBHU/FhERESCUII9x2ohx2shIcYcn6AQIBE0KS6rZW1hVlxivoqCkmoKSGgpLqqmo9lFV66eqmeFWDuWwW+qS4zacDisuhxWXw0ZKgpOUBBeJ8U6S4kI3l1On9CIiIkervhFdUryTwb1Tm7z+4cJtFJTUUOPxU13rp8YTutV66x8HQs89fgLBUA+yqhofu/MrD/u5SXFOMlPcZKa4yUhxk5ESQ1ZKLJkpbtKT3UfdCE9DsYgcP501S1SxWS1kpLiZNqUfgaBJMGhSXF5LfnE1BSXVFJbU4PEF2J1fye78SpZvKCApzsmQPqkM65vGkD5pdMuIU2JcREREmrBaDLJSY8lKjWUUGU1er/H4KWyQGC8u97BmSyFlld5wBbvW4ydogtcXxOsLDb9yqC9W5DV67nRYSYxzkhTnqLsPVe4T45zh5UnxLhLjHCS4HVitllBCPyZG5zQiIiKHMAwDh92Kwx4qX1timiaxLjsnDcykuLyWkvJaistrKS73hB5XHFxW4wlQWumhtNLDxl0lzb5fbIydxFgHCQ1uiXFOEmIduByheVASYh1MHJ7dWrsu0ukpES5RzWIxQpNiJMUwmFQCQZPishryS2ooKK6mpLyW0koPC1bvZcHqvQAkxzsZ2ieNoX3TGNArhez0OA2fIiIiEqVcDutRT14Z47TRIyuBHlkJ4WUp3zgbjWdumiZeXzDc6qzWGwglyL0BPN4AgUAQp8NKaYWH0goPXn8QjzdAQXE1BcXVR4zBMCDeHapYJ8c7SVxeTXJdq7iDXbpDzxNineEhWjRBp4iISGOGYZAY5yA7Iy7cC70lldVe8ourm9y27imlvMrbqGX53gNVTba3WS3Eue0kxzvZuKuUrFR33cX30HBrNqtyEiJHQ4lwkQasFoP05FC3JHqnctFpfdi6p4y1W4tYu+UAG3YWU1Lh4atVeXy1KtQay2Y16JYRT68uCfTqkkDPLglkpYbew2m3RniPREREpDU57NbjmvSya3ocw/ulN1luGAZOhzXcyvtQDYdiqR+vvKyuhVlphSf8uKzSS1mFJ9z6rKzSQ3mVF9MkPNFnS+OYH4wllLyPdzvolhFHcnwoSV5/nxTnJN7tIM5tJ9Zlx1nXWk1ERCSSYpy2Nhs+xGG3YjEMFq7Oa7YXV3NcDivjhmQxIjeDj77ZTnF5LR5vgMoa38FbtZfKGh9V1T6qPX78gWD4Ivj2vY2HV7MYkJbsJivFTZe00FArXdJiyUqJJcEdmXL5eL9/DfsirU2JcJHDsNusDOkTGhLlsh/0x+sLsHFnCWu3HmDt1gNsyyujutbPjhbG+kyKd5KRHBpvPC0ppq57k5PEOAeJsU4S4hzEuuzEuGw4bJYOW3n0+YMcTeiafFRERDqrskpPo5bdR5IQ6/jen9lwvPKs1Ngjrh8ImlRUeflgwTb2FlZwoKgEhysWr99s3AK97t40obo2ND5q/lG0NrdZDeJiHMTG2Ilz20Pjm9utoZsj1MXcWdfV3GG3YLUYWCwGFqPuvuHjBsusDR9bQ69ZGz22HHxsPbi9wx4aW93pCH1u/XmWhoUREenc7LZjT04DdEmLZURu06HNjkZ5lfe4zwMMwwhNlu20kZYU02TdQCBIVa2PimofwaBJSqKL/Qeq2V9cxf6iary+gz3D1mw50GR7l8Oga1oZXdPjyUoNJcpTEkJDpiW20nwjx/P9a9gXaQudIhEeDAb561//yltvvUV5eTknnXQS9957Lz179ox0aNLBmaaJPxAMP7dYDAbmpDAwJ4VLzsjFNE0KSqrZtb+Cnfsr2LmvnF35FRQUV1PrDYSv2G7aVXrEz7JYDNx1lVmX04bbZQtXbl11lUeHzYLdHposy2m34LBbsdusOO2h5RbDwGKAUV+BNAwMA7xeD7v21xJwFuN01nWzMg/Z10MWmE1eb8wAYly2UCLfaeOjRduxWg6f5L7kjNwjfg8i0rmpzBb5fo5lKJZDWS2hycFSE10EAn4sgUrS0xOx2+1N1g0GTTy+0GRgbqeV7pkJlFR4KKmopaTu/KakvDbcci0YNPEHzHAL9PbGMEITorsctnCLe5ejkBinjbiYg63a49x24twO4mLsxMXYw0n9+gR//VAx0jaO91jXUD7fn8pr6Qy+T3K6PbFaLSTEhoYsa9gzDEK/dyUVHvYXVdXdqhvdl1R4qPWabNtbwba9LfcEczqsxLsd4TyE22kjxmXD7bRjt4UuONusoYvYNqsFq9WCzRq6GG2xhJL5hhFKgBuEchLrtxVR7fGHnocvRh/ymIOPE2IdxMc6DpbVdeV2fT7EojJYToBOkQh/9tlnef3113nooYfIzMzkkUce4frrr+eDDz7A4WifP2TSMRiGwZufbjrsOpeckcuXdZNWdc+Mp3tmfGh8T38wPMZXv+5JlFZ6w92Sy6tC9/WTY0GowllfmWw9Ta8On0hWS6j1VX0Cv2Ey3+2ykVdQSWaqG5ejU/z0NHE0LePVKl6incpske/neIdigZaHY2mOxXKwtXnPrHgmDs/mmzV7iXfb6XHIOKimaeLzB/H4QuOY19+7nDa6psfWTfx5cLnXF8DrCxI0QxOXB4MmgQaPGy4vqfDg9QUwTZOgSd29iWmGzp0aLydcCfd4A/gDQQJBsy5G6sZbDxzTd3aoWJctlCh3hxLl9Unz+Prked1r8XXJ8/r1Ypw2JWaPw/Ec64lxTiYM69qKUUUHldci7ZPLYW00fIhhGKQkhOb4GJST2mT9Wk+oV9f+oir2FlVRUFTN/uJqyuqGTWs434jHW9PWu9PEh9/saPG10EXsUHK8vsGgy2HD5bTictqIcdjq7q04HTZi6pa7HNa6JL4Fv9/L7gIP1thS3G5POLlvsYR6m1F3HgF19/XJ/boivH5Z/WOLpS6VX9cosfnt6l4zDMA8YgNCaV0dPhvl9Xp56aWXuP322zn11FMBePzxx5k0aRLz5s3j3HPPjXCEEo0Mwwh3BU5JcHHB5D4tTl4RDB7sjlxR7eX9r7fh8wfx+YP4AwfvA0GTQMCkd3YiG3YWEwiYdcvqXgvVAjGB5AQXNKgY+v1BPJ5anE5Xo6uoJjRbsai/Kguh4V1C+9R4HdOEao+fqmovVbWhZH4gaFLj8YeT+4eav3wPELrSm5YUQ3pSDOnJdfdJbtKTQxOXJie4OmSLK9M0eX3eJsCs/+/gfd3/L57SD7CFC1uRaKIyW+TEOdahWOD7t3Q7ms+0Wi24rRbcLnujBHpZpYe4mKYtz1tSn7T/6Jvtx7Sf9Z9Zv10waBIIBvEHQr38/IEgHo+PouIS4uITCJoGPn8Qq8WgS1oclTVeKqtDDROqGozRWp9Ar6r1U1XrJ7/4qEMKfS8Wo1HyPDbGXldht4Yr7g0fxzSo2LscVhw2K3a7Bbs1NKyM3WbBbrNisxrtKsEe6k1p4vMHGp3LHryFlnv9QfwNlnkPWcdft47PH2TrnlKqPX7MBt0VD+25CNQNiRMaG7igpIbzJ/XukOeT7YHKa5H261jHJPf7/ZSUlDA4tzsXndqXhavz6JF18IJ2/cXs+uHRfP7QxWpv3e+xxTDomh4XykkETPzBuvsGzzEJX6Q26y9Wmyb7i6rw+oLh32/TPNgTPbQugBlebrWEer17vP7wxWuv7+AFbE/dxOFlHP1wKy0rPAHv8f3YbZa6IdxCSXuno0Fyv2GSv+F93WsxzlCr+fC94+Bze4SG3e0o48J3+ET4hg0bqKqqYty4ceFlCQkJDBo0iKVLl6qQlnbh0CFWDuWoGy8zMc5JamLTMcEauuSM3CO2Uv+fqf0a//CZEAwGsFiscMjvyzG/VzMCQZPXPt4Qbu1V4/FTUxtKiFfXJcara/14faHCrH6Srm15Zc2+n9VikJroqkuWu0mMdxxsadWg9ZXTYcVus9QNG1M33qjVcrCiFE5CE15W3yrNW1/A18Xsq7sKXltX6IYK2foC2E9VjZfCohKcy1biC5jNrNO4kG7J259vCT+2GGCpG9fUajk4xmlo3FNL3bJQ1zOLxQh3PQt1RTvYNc1qNbBZGiyzWrBZDCxNlh98Hnofo1GXtkaf02j5wdgOfZ+DMTTsGtcxKp2HtjqsfxxotJzwY9M0yUxxY9WM7MdNZbZIdIpE0r5eaGxxK/YGtR6fy0rQayM9LTY8LEyXVDenndS9xXMeX11Pv4aJ8orqxo+rGi0L3VdU+8INGsoqQ70BoeqE7Fs9h60uMW4PnRfZraHnDbur14+bDia1tTXE/nd5aAxdy8FEejh5EWzQ6t40GyU16stJXziRHcB3SLK7PVi7tYhhfdPonZ0Y6VA6JJXXIu3f0Q774vP5KCyupGe174jb1eclYhukJA4diuVYHO/F7IZJ/vpcyqGNBcO3QBCb1UJ2ehy1Hj814blOQvX0Go+fWo8/3LDQFwhQW+vBYrURNCEQCDVMDATqyjsOJuihPnlPuNFhwxzD91W/DxXVJ3ZUAItB+EJ6OHnuDP3b2urq7aF7C6YZpLKijEVbN+ByOrBZDRqkU5pchA4PqWsefP3g+QLsO1CJxxcIvV63fvgCSf12Db5np8PKjT8eRp9uSSf0Ozgcw2y4Vx3QJ598ws0338zq1atxuVzh5b/61a+ora3l+eefP6b3W7FiBaZpNjtW4rEKjU1nobLGe2jusZE4t4PK6iNf0Tqa9U7UOm39eS2tYwLBQBCL1YLRxjGdyPeK9Ocd+j229ucdbr1AMHgw2Vh33/BxMNihf5LkEC1dQzE42KXseNPmZotP6heZjV4/EUeW3WYhua6XxPfh8/kwDINRo0adgKg6jvZaZpumicViodbjJ3iMp0U2a+hiXK3Xf0y/X2293eG2DQaDWA7TRbM9xdoa2wWDQez20KSO7T3W77Ndc9se6d++PcV6orc7dN/rtwsNw3JMoWKxGIcd9qy+Ahis763XINF8sDLYuCVdwyFfDla8zWOOrb0IjwNbV/w3GR/WOPi4/tyh/nl9a8OjYRJ6f4fDSozD2vR108Tv92OzHd1QNSqv21d5DcdXZneU3+nj3c5SN9HisWzXUfatrcv14/m8zv6dWCwGLoeNY00dGobRZt+J1WLB2cxv/tFqjTgb5NIbxGngqI8znEhuUPbT9FygpXOE+m0bnk90VLEuO7Ex36+d9rGU1x2+RXhNTWgMo0PHKXM6nZSVNd/a9HAazib/fdW/R7z7yC1a4o5inaNd70St09af1x5j0ued+M+zWixYLfD9T4NFOq7QZDIdo+X8idRey+z67V3O4z8tOt65D9p6u0h8pmJtX9tF4jM7UqwO+/FXpFtSPzGY5bgv/cqJYhjGMY1vrfK6fZXXDd/jeMrsjvLb15bbdYQYO8p2HSHG77Pd8fz9RuK8oS0/73vFabT4RI7DsZTXHT4RXn+F2uv1Nrpa7fF4iIk5/BATzRk5cuQJi01EREQOUpktIiLS/qm8FhGRzqrDD3TapUsXAAoKChotLygoICsrKxIhiYiISDNUZouIiLR/Kq9FRKSz6vCJ8AEDBhAXF8fixYvDy8rLy1m/fj2jR4+OYGQiIiLSkMpsERGR9k/ltYiIdFYdfmgUh8PBFVdcwcyZM0lJSSE7O5tHHnmErKwszjzzzEiHJyIiInVUZouIiLR/Kq9FRKSz6vCJcIBbbrkFv9/PjBkzqK2tZcyYMcyePfuYJkIRERGR1qcyW0REpP1TeS0iIp2RYZqmGekgRERERERERERERERaS4cfI1xERERERERERERE5HCUCBcRERERERERERGRTk2JcBERERERERERERHp1JQIFxEREREREREREZFOTYlwEREREREREREREenUlAgXERERERERERERkU5NiXARERERERERERER6dSUCG8FwWCQp556ikmTJjF8+HCuvfZadu7cGemwOpy8vDz69+/f5PbWW29FOrQO4dlnn2X69OmNln333XdcccUVjBgxgtNOO43Zs2dHKLqOobnv8O67725yTE6ePDlCEbZfpaWl/OEPf2Dy5MmMGjWKyy67jGXLloVf17Eo7UU0l9lH+juNFtu3b2fkyJG88847kQ6lzcydO5dzzjmHoUOHcu655/LRRx9FOqQ24/P5ePzxxznttNMYOXIkl19+OStWrIh0WK0ums8Lm9v3zz//nGnTpjFy5EimTJnCww8/TG1tbYQilKMRzeV1c1RXPiiaf99aonqs6qOHOtL3EU3HhxLhreDZZ5/l9ddf54EHHuCNN97AMAyuv/56vF5vpEPrUDZu3IjT6eTrr79mwYIF4dv5558f6dDavVdeeYWnnnqq0bKSkhKuueYaevXqxZw5c7j55pt58sknmTNnToSibN+a+w4hdFz+4he/aHRMzp07t+0DbOd+/etfs3r1ah577DHefvttBg8ezHXXXcfWrVt1LEq7Es1l9uH+TqOFz+fjt7/9LdXV1ZEOpc38+9//5ne/+x2XXnopH3zwAeeccw6//vWvWblyZaRDaxPPPfccc+bM4YEHHmDu3Ln07t2b66+/nvz8/EiH1mqi+bywuX1ftmwZv/zlLznrrLOYO3cu9913Hx999BH3339/hKKUoxHN5XVzVFcOiebft5aoHhui+mhjRzrvj6rjw5QTyuPxmCNHjjRfffXV8LKysjJz2LBh5gcffBDByDqe5557zrzgggsiHUaHsn//fvO6664zR4wYYZ599tnmFVdcEX5t1qxZ5qRJk0yfzxde9uijj5pnnXVWJEJttw73Hfr9fnPo0KHmvHnzIhhh+7djxw4zNzfXXL58eXhZMBg0zzzzTPOJJ57QsSjtRjSX2Uf6O40Wjz76qDl9+nQzNzfXnDNnTqTDaXXBYNA8/fTTzT//+c+Nll977bXmrFmzIhRV27rgggvMhx56KPy8oqLCzM3NNf/zn/9EMKrWEc3nhYfb99/85jfmNddc02j9uXPnmoMGDTI9Hk9bhypHIZrL65ZEe105mn/fWqJ67EGqjzZ2pO8j2o4PtQg/wTZs2EBVVRXjxo0LL0tISGDQoEEsXbo0gpF1PBs3bqRv376RDqNDWbduHYmJibz33nsMHz680WvLli1jzJgx2Gy28LJx48axfft2ioqK2jrUdutw3+GOHTvweDz06dMnQtF1DMnJybzwwgsMGTIkvMwwDEzTpKysTMeitBvRXGYf6e80GixdupQ33niDhx9+ONKhtJlt27aRl5fXpMXg7NmzueGGGyIUVdtKSkpi/vz57Nmzh0AgwBtvvIHD4WDgwIGRDu2Ei+bzwsPt+7XXXssdd9zRZBu/309lZWVbhSjHIJrL65ZEe105mn/fWqJ67EGqjzZ2pO8j2o4P25FXkWOxf/9+ALp06dJoeUZGBvv27YtESB3Wpk2bSE9P5/LLL2fHjh307NmTm266iUmTJkU6tHZrypQpTJkypdnX9u/fT25ubqNlGRkZAOzdu5fU1NRWj68jONx3uGnTJgzD4O9//ztfffUVFouFU089lVtvvZX4+Pg2jrT9SkhI4NRTT2207KOPPmLXrl2ccsopPP744zoWpV2I5jL7SH+nnV15eTl33HEHM2bMaPLv35nt2LEDgOrqaq677jrWr19Pt27duPHGG1ss+zqbe+65h9tuu42pU6ditVqxWCw8+eST9OjRI9KhnXDRfF54uH0fNGhQo+der5eXX36ZwYMHk5KS0hbhyTGK5vK6JdFeV47m37eWqB57kOqjjR3p+4i240Mtwk+wmpoaABwOR6PlTqcTj8cTiZA6JK/Xy44dO6isrOTWW2/lhRdeYOjQoVx//fUsWrQo0uF1SLW1tc0el4COzaO0efNmLBYL2dnZzJo1izvvvJMvv/ySm266iWAwGOnw2q3ly5fzu9/9jqlTpzJlyhQdi9JuqMw+6NC/087uvvvuY8SIEVE3lmp9a9c777yT8847j5deeomJEydy0003Rc351datW0lISOCZZ57hjTfe4Mc//jF33nknGzZsiHRobUplcYjf7+eOO+5gy5Yt3HvvvZEOR1qg8rox1ZUPT79vTUV7PVb10cYO/T6i7fhQi/ATzOVyAaHCqf4xhP6YYmJiIhVWh+NwOFi6dCk2my38AzVkyBC2bt3K7NmzGT9+fIQj7HhcLleTyWTqf+TdbnckQupwbr75Zq6++moSEhIAyM3NJT09nUsvvZS1a9c26YIm8Omnn/Lb3/6W4cOH89hjjwE6FqX9UJkd0tzfaWc2d+5cli1bxvvvvx/pUNqc3W4H4LrrruOiiy4CYODAgaxfv56XX365059f5eXlcfvtt/PKK68wevRoAIYOHcqWLVt4+umneeaZZyIcYdtRWUw4ibh48WKeeuopnce1YyqvG1Nd+fD0+9ZUNNdjVR9trLnvI9qOD7UIP8Hqu2sVFBQ0Wl5QUEBWVlYkQuqw3G53k6t0ubm55OfnRyiiji0rK6vZ4xIgMzMzEiF1OIZhhAuHevVdquq7bMpB//rXv7j55puZPHkyL774YrjiomNR2guV2S3/nXZmc+bMoaioiNNOO42RI0cycuRIAO69917OPffcCEfXuuqP60O7A/ft25c9e/ZEIqQ2tWbNGnw+H0OHDm20fPjw4eFhY6JFtJfFBQUF/PSnP2XlypW8+OKLUdETpiNTed2U6soti/bft+ZEaz1W9dHGWvo+ou34UCL8BBswYABxcXEsXrw4vKy8vJz169eHW57IkW3YsIGRI0eybNmyRsu//fbbqJ4U5PsYM2YMy5cvJxAIhJctWrSInJycTjcGVmv5zW9+w3XXXddo2dq1awF0XB7i1Vdf5Y9//CM//elPeeKJJxqdqOtYlPYi2svsw/2ddmYzZ87kww8/ZO7cueEbwC233MILL7wQ2eBa2aBBg4iNjWX16tWNlm/atKlTjpF9qPpk2saNGxst37RpEz179oxESBETzWVxWVkZV111FcXFxbz66quNJmCU9inay+tDqa58eNH8+9aSaKzHqj7a2OG+j2g7PpQIP8EcDgdXXHEFM2fO5LPPPmPDhg3cdtttZGVlceaZZ0Y6vA4jNzeXfv36cf/997Ns2TK2bt3KQw89xKpVq/jFL34R6fA6pGnTplFZWck999zDli1beOedd/j73//ODTfcEOnQOozzzjuPhQsX8txzz7Fr1y6+/PJLfve733HeeedFzQzLR2P79u386U9/4swzz+SGG26gqKiIwsJCCgsLqaio0LEo7UY0l9lH+jvtzDIzM+nZs2ejG0BqairZ2dkRjq51uVwufvazn/HMM8/wwQcfsGvXLp577jkWLlzINddcE+nwWt2wYcMYPXo0d955J//973/ZsWMHTzzxBIsWLeLnP/95pMNrU9FcFj/00EPs3r2bRx55hJSUlPBvX2FhYaOkiLQf0VxeN0d15cOL5t+3lkRbPVb10caO9H1E2/GhMcJbwS233ILf72fGjBnU1tYyZswYZs+eHTUtrU4Ei8XCrFmzmDlzJrfeeivl5eUMGjSIl19+mf79+0c6vA4pNTWVv/3tbzz44INcdNFFpKenc8cdd4THCJUjO/3003nyySeZNWsWs2bNIj4+nvPPP59bb7010qG1Kx9//DE+n4958+Yxb968Rq9ddNFF/PnPf9axKO1GtJbZR/N3Kp3TTTfdRExMDI8//jj5+fn06dOHp59+mrFjx0Y6tFZnsVh49tlneeKJJ7j77rspKysjNzeXV155hREjRkQ6vDYVreeFwWCQDz/8EJ/Px1VXXdXk9c8++4xu3bpFIDI5kmgtr5ujuvLhRevv2+FEWz1W9dHGjub7iKbjwzBN04x0ECIiIiIiIiIiIiIirUVDo4iIiIiIiIiIiIhIp6ZEuIiIiIiIiIiIiIh0akqEi4iIiIiIiIiIiEinpkS4iIiIiIiIiIiIiHRqSoSLiIiIiIiIiIiISKemRLiIiIiIiIiIiIiIdGpKhIuIiIiIiIiIiIhIp2aLdAAiEj3WrFnDa6+9xpIlSygsLMTlcjFgwAAuvvhizj//fAzDiHSIIiIincaiRYu4+uqr6dOnDx9++GGL6wUCAd555x3ee+89tm3bRllZGWlpaYwbN46f/exn9O3btw2jFhER6XwiWRc2TZOpU6eSl5fHm2++yfDhw9tlnCJtQYlwEWkTr7zyCn/5y18YPXo0v/zlL+nWrRtlZWV88skn3HHHHaxYsYL77rsv0mGKiIh0GnPmzCE3N5dNmzaxZMkSTj755CbrVFZWcuONN7J69WouueQSrrnmGuLj49m+fTv/+te/mDZtGi+++GKz24qIiMiRRbouvGjRIvbv30+fPn14/fXXW0yERzpOkbZgmKZpRjoIEencFi9ezFVXXcVll13Gvffe2+T1l156iYcffpjXXnuNUaNGRSBCERGRzqWiooJTTjmFGTNm8Morr9C/f38ee+yxJuvdfffdvPfee/zzn/9sUgZ7PB4uv/xyKisr+fjjj9sqdBERkU7j+9SF9+zZw9SpU/nHP/7B2LFjjzuG3/zmN+zbt48pU6bw9NNP8/XXX5OQkHDC4hTpSDRGuEgUmzJlCk899RQPP/wwEyZMYNiwYVx33XVs374dgLvuuovp06fz9ttvc/rppzNy5EiuvPJK1q9fD4S6Ul988cWMGzeO4uLi8Pvec889DBs2jC1btgDwzDPPkJWVxZ133tlsHFdeeSVTp06ltra2lfdYRESkY/vss8/o378/Tz/9dHjZ9u3bGTFiRKNy9oMPPsDr9TJ58mQuuOACPvnkk0ZlNUBeXh7vvvsuV155ZbOVWqfTyW233cawYcOoqKhovZ0SERFpYx25LlxZWcmUKVM4++yz8Xq9QGj4k2uvvZbx48dz4MCB8Lrl5eXMmzeP0047jfPPPx+v18u7777b5D1VZ5dooUS4SJT7xz/+wbZt23jooYd44IEH+Pbbb7nrrrvCr3/33Xc8/vjj/PKXv+SRRx6htLSU6dOnk5+fj9Vq5eGHH6a6upqHH34YgC+++IK3336b22+/nb59+1JeXs7SpUs544wzcLlczcZgs9l49tlnmTBhQpvss4iISEc1depULrzwQp5//nm2bt1KIBDgrrvuIiUlhd///vfh9ebMmcOECRPIzMzkwgsvJBgM8vbbbzd6r88++wzTNDn//PNb/LxTTjmFRx55hPj4+FbbJxERkUhoj3Vh0zTx+/34/X6CwSAAwWCw0bK4uDgefPBBduzYwaxZswB49dVXWbhwIQ8++CBpaWnh93///ffx+Xz86Ec/IjMzkwkTJvDGG280ikF1dokmSoSLRLmEhASeffZZTj31VC644AKmT5/OqlWrKCkpAUJdq5944gmmTZvGGWecwd/+9jd8Ph+vvPIKAH369OFXv/oVc+fOZd68ecyYMYNJkyZxxRVXAKHuXMFgkJycnEaf27CAr78FAoE23XcREZGO6J577iElJYX77ruP2bNns3btWv7yl78QFxcHwObNm1m7di3Tpk0DIDMzk4kTJ/Lmm2/ScFTEXbt2AdCrV69G79+wwl1/02iKIiLS2bTHuvC7777L4MGDGTx4MGeeeSYAV199dXjZM888A8D48eO5/PLLeeGFF/jqq6+YOXMml156KVOmTGn0WXPmzGHixIlkZmYCMG3aNLZu3cqSJUvC66jOLtFEiXCRKDd06FCsVmv4eVZWFgA1NTUAdO3alTFjxoRfz8jIYOTIkSxfvjy87JprrmHUqFHccsst+P1+HnroofBs0vVXsQ+1aNGicGFef7v66qtP9O6JiIh0OgkJCTzwwAMsWbKExx9/nOuvv57Ro0eHX3/77beJjY3l5JNPpry8nPLycs4++2x2797NggULwuu1lNy+4447mpTRzXWjFhER6cjaY1349NNP5+233+btt9/mueeeA+D+++8PL7vkkkvC7/Pb3/6WLl26cMMNN5CRkcHdd9/d6HM2btzIunXrOOuss8LnA2PHjiU+Pp7XX389vJ7q7BJNbJEOQEQiKyYmptFziyV0fay+MMzIyGiyTWpqKuvWrWu0zQUXXMCKFSsYMmQI6enp4de6du0KhK4yNzRs2LBGXbSbm5BDREREmjd+/Hi6dOkSnvyqns/n47333qOqqoqJEyc22e71119n0qRJAGRnZwOhscL79esXXudXv/oVV111FQCFhYXceOONrbkrIiIiEdEe68LJyckkJyc32i4nJ4ehQ4c2icXtdnPWWWfx4osvMm7cuCb7U/8ZM2bMYMaMGY1eq587JCUlRXV2iSpqES4ih1VaWtpk2YEDB0hNTW30/Mknn2TgwIF8/fXXvP/+++HXUlJSGDVqFJ9++mmjblRxcXEMHTo0fIuNjW3V/RAREelMnnnmGQ4cOECfPn2YMWNGeLKs+fPnU1xczH333cc//vGPRrfzzjuP+fPnk5+fD4TGGwf4z3/+0+i9u3fvHi6fc3Nz23bHRERE2on2XhfesmULf//73xk4cCBvvvkmy5YtC7/m9Xp5//33mTp1apPzgYcffhifzxdOcqvOLtFEiXAROaxdu3aFZ7wGyM/PZ9WqVYwfPz68rP7K8EsvvcRZZ53FAw88QEFBQfj1W265hT179vDnP/+52W5XZWVljdYXERGRlq1du5YXX3yRX/ziFzz66KNs27aNp59+GgiNBZqRkcGll17K2LFjG92uuuoqAoEAb731FgA9e/bkwgsv5MUXX2TFihXNftbmzZvbbL9ERETak/ZcF/b7/dx5551kZ2fz2muvMWTIEO6++26qq6sB+PzzzykpKeGyyy5rcj5w4YUX0rdv30Zzh6jOLtFCQ6OIyGGZpslNN93ErbfeitVq5a9//SsJCQlMnz4dgLlz5/Lpp58yc+ZMUlJSuOeeezj33HP5/e9/z/PPPw+Eum///ve/509/+hOrVq1i2rRp5OTkUFNTw+LFi5kzZw4ej4crr7wykrsqIiLS7nm9Xu666y5ycnL4+c9/jsPh4Morr2T27NlMmjSJr7/+munTp4e7dzc0bNgw+vTpw1tvvcWNN96I1Wrl3nvvpaioiOnTp/OjH/2IU089lZSUFPLy8vj444+ZP38+vXv3ZtiwYRHYWxERkciJdF24W7dubNy4sdnYnn/+edatW8e//vUvYmJi+OMf/8i0adOYOXMmf/jDH5gzZw4pKSmNkvYNXXjhhcycOZMFCxYwadIk1dklaigRLiKH1bVrV6655hr+9Kc/UVNTw4QJE3juuedISkoiPz+fBx98kMmTJ3P++ecDkJmZya9//Wvuv/9+5syZw7Rp0wC4/PLLGTNmDK+99hovv/wy+/fvx2q1kpOTwxVXXMGll14anslaREREmvfEE0+wbds2XnvtNRwOBxBqxfXJJ5+EK+bnnXdei9tfeOGFPProo8yfP58zzjgDt9vNiy++yEcffcTcuXP54x//SGlpKYmJiQwdOpS//OUv/PCHP8Rut7fJ/omIiLQX7bUuvGHDBp577jl+8pOfhCfLHjBgAFdffTWzZ89m5MiRLFy4kJ/85CfYbM2n/S644AIee+yxRnOHqM4u0cAwW5ouXkSi3l133cWSJUv4/PPPIx2KiIiIiIiISJtQXVikc9IY4SIiIiIiIiIiIiLSqSkRLiIiIiIiIiIiIiKdmoZGEREREREREREREZFOTS3CRURERERERERERKRTUyJcRERERERERERERDo1JcJFREREREREREREpFNTIlxEREREREREREREOjUlwkVERERERERERESkU1MiXEREREREREREREQ6NSXCRURERERERERERKRTUyJcRERERERERERERDo1JcJFREREREREREREpFP7/wFkCNEul5DDMwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1800x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, axs = plt.subplots(2, 3, figsize=(18, 10))\n",
+    "metrics = ['Gls', 'Ast', 'xG', 'npxG', 'xAG', 'npxG+xAG']\n",
+    "\n",
+    "for i, metric in enumerate(metrics):\n",
+    "    sns.histplot(df[metric], kde=True, ax=axs[i//3, i%3])\n",
+    "    axs[i//3, i%3].set_title(f'Dağılım of {metric}')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "a765fbc9-6ee9-490b-96f6-a30e9defa353",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "team_performance = df.groupby('Team').agg({\n",
+    "    'Gls': 'sum',\n",
+    "    'Ast': 'sum',\n",
+    "    'xG': 'sum',\n",
+    "    'npxG': 'sum',\n",
+    "    'xAG': 'sum'\n",
+    "}).reset_index()\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "team_performance.plot(kind='barh', x='Team', y=['Gls', 'Ast'], ax=axs[0], legend=True)\n",
+    "team_performance.plot(kind='barh', x='Team', y=['xG', 'npxG'], ax=axs[1], legend=True)\n",
+    "\n",
+    "axs[0].set_title('Takımların Gol ve Asist Sayıları')\n",
+    "axs[1].set_title('Takımların xG ve npxG değerleri')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "99450cbe-e94b-4e11-9b8e-88b7be2d8823",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "alpha = 2.0  # Gol\n",
+    "beta = 1.5   # Asist\n",
+    "gamma = 1.2  # xG\n",
+    "delta = 1.0  # xAG\n",
+    "epsilon = 0.5 # Oynama Süresi\n",
+    "zeta = 0.7   # npxG\n",
+    "eta = 0.3    # PrgC\n",
+    "theta = 1.0  # G+A\n",
+    "\n",
+    "df['Efficiency_Score'] = (alpha * df['Gls'] +\n",
+    "                          beta * df['Ast'] +\n",
+    "                          gamma * df['xG'] +\n",
+    "                          delta * df['xAG'] +\n",
+    "                          epsilon * df['90s'] +\n",
+    "                          zeta * df['npxG'] +\n",
+    "                          eta * df['PrgC'] +\n",
+    "                          theta * df['G+A'])\n",
+    "\n",
+    "top_20_players = df[['Player', 'Pos', 'Efficiency_Score']].sort_values(by='Efficiency_Score', ascending=False).head(20)\n",
+    "\n",
+    "plt.figure(figsize=(14, 10))\n",
+    "sns.set(style=\"whitegrid\")\n",
+    "\n",
+    "bar_plot = sns.barplot(x='Efficiency_Score', y='Player', data=top_20_players, edgecolor='black')\n",
+    "\n",
+    "plt.xlabel('Verimlilik Skoru')\n",
+    "plt.ylabel('Futbolcu')\n",
+    "plt.title('Verimlilik Skoru En Yüksek 20 Futbolcu')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "5af0a482-ae3d-4cfb-9589-82a579b376c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "seasonal_performance = df.groupby('Pos')['Efficiency_Score'].mean()\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.lineplot(x=seasonal_performance.index, y=seasonal_performance.values)\n",
+    "plt.title('Pozisyonların Ortlama Verimlilik Puanları')\n",
+    "plt.xlabel('Pozisyon')\n",
+    "plt.ylabel('Verimlilik Skoru')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "07f2b658-3104-40bd-9a46-9b0aa33b213d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gol Kralı:\n",
+      "Erling Haaland 27.0\n",
+      "\n",
+      "------------------------------\n",
+      "\n",
+      "Asist Kralı:\n",
+      "Ollie Watkins 13.0\n",
+      "\n",
+      "------------------------------\n",
+      "\n",
+      "Gol + Asist Kralı:\n",
+      "Cole Palmer 33.0\n",
+      "\n",
+      "------------------------------\n",
+      "\n",
+      "Pas Kralı:\n",
+      "Rodri 376.0\n",
+      "\n",
+      "------------------------------\n",
+      "\n",
+      "xG Kralı:\n",
+      "Erling Haaland 29.2\n",
+      "\n",
+      "------------------------------\n",
+      "\n",
+      "xAG Kralı:\n",
+      "Bruno Fernandes 11.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Gol kralı\n",
+    "top_scorer = df[['Player', 'Gls']].sort_values(by='Gls', ascending=False).head(1)\n",
+    "\n",
+    "# Asist kralı\n",
+    "top_assist = df[['Player', 'Ast']].sort_values(by='Ast', ascending=False).head(1)\n",
+    "\n",
+    "# Gol + Asist kralı\n",
+    "top_goal_assist = df[['Player', 'G+A']].sort_values(by='G+A', ascending=False).head(1)\n",
+    "\n",
+    "# Pas kralı\n",
+    "top_pass = df[['Player', 'PrgP']].sort_values(by='PrgP', ascending=False).head(1)\n",
+    "\n",
+    "# xG kralı\n",
+    "top_xg = df[['Player', 'xG']].sort_values(by='xG', ascending=False).head(1)\n",
+    "\n",
+    "# xAG kralı\n",
+    "top_xag = df[['Player', 'xAG']].sort_values(by='xAG', ascending=False).head(1)\n",
+    "\n",
+    "print(\"Gol Kralı:\")\n",
+    "print(top_scorer.to_string(index=False, header=False))\n",
+    "print(\"\\n\" + \"-\"*30 + \"\\n\")\n",
+    "\n",
+    "print(\"Asist Kralı:\")\n",
+    "print(top_assist.to_string(index=False, header=False))\n",
+    "print(\"\\n\" + \"-\"*30 + \"\\n\")\n",
+    "\n",
+    "print(\"Gol + Asist Kralı:\")\n",
+    "print(top_goal_assist.to_string(index=False, header=False))\n",
+    "print(\"\\n\" + \"-\"*30 + \"\\n\")\n",
+    "\n",
+    "print(\"Pas Kralı:\")\n",
+    "print(top_pass.to_string(index=False, header=False))\n",
+    "print(\"\\n\" + \"-\"*30 + \"\\n\")\n",
+    "\n",
+    "print(\"xG Kralı:\")\n",
+    "print(top_xg.to_string(index=False, header=False))\n",
+    "print(\"\\n\" + \"-\"*30 + \"\\n\")\n",
+    "\n",
+    "print(\"xAG Kralı:\")\n",
+    "print(top_xag.to_string(index=False, header=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "6ddcc43b-dbef-4f18-93b5-2707c3406758",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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m5qaOHTtq+vTpypcvn6pVq6b9+/crJCRE77zzTpbV6erqqq1bt2r//v0qUaKE9u7dqxkzZkjSQ5fUWUKBAgXUoUMHzZw5U87OzvL29lZERIS+/vprDRo0yBTSOTk56eTJk/rrr7/k4OCgzz77TPnz51fjxo11/fp1bd++Pd1+Ux988IGGDx+u5ORkjR49Wj4+PqZlkVWrVtWmTZv0yiuvyMXFRV9++WWaQAoAAAAAAORM2TqAat68ue7evau5c+fq888/V+HChdWyZUv179/f1KZjx44aNmyYWrVqpT179qhNmzbasmWLWrZsqS1btig4OFiFCxfWzJkzdeXKFZUoUUJ9+/ZVz549s6zO/v37Ky4uTr169ZIklStXThMmTNDQoUN1+PDhh+5VZQnDhw9XoUKFNHXqVMXFxalMmTIaOXKkOnToYGrTvXt3LViwQKdOndLnn3+ujz/+WF988YWmTZsmR0dHNWjQQEFBQWn6bdmypXr27KnExET5+flp+PDhpkBr4MCBunHjht555x0ZDAa1b99e/v7+VpkFBgAAAAAAsg+7FNIBmCE8PFzBwcE6ceKEVeuIiorSqVOnVHDTJlUpWdKqtQAAAABZ7eDFi2o4b54OHDigqlWrWrscZEJ8fLyOHz8uT09P9oBCjhEVFSVJ8vLyemjbbL0HFAAAAAAAAGwfARQAAAAAAAAsigAKZgkICLD68jsAAAAAAGCbCKAAAAAAAABgUQRQAAAAAAAAsCgHaxcAPIqYuDhrlwAAAABkOf6cC+BpRQAFm2NvZ6ee4eHWLgMAAACwCCeDQS4uLtYuAwCyFAEUbE6x4sUVEREhg8Fg7VKAbMtoNCo2Nlbu7u6MFSADjBPAPIyVJ8/FxUWlS5e2dhkAkKUIoGBzHBwc5OXlJScnJ2uXAmRb8fHxcnR0lKenJ2MFyADjBDAPYwUAkBXYhBwAAAAAAAAWRQAFAAAAAAAAiyKAAgAAAAAAgEWxBxRsTlJSkiIjI9kEE3iA1A1jb9++zVgBMsA4wdOMTawBANkNARRszpXLl9WqdWtrlwEAAJBtORkMOh4dTQgFAMg2CKBgc5JTUjQvIEAVXFysXQoAAEC2ExMXp57h4YqLiyOAAgBkGwRQsEkVXFxUpWRJa5cBAAAAAADMwCbkAAAAAAAAsCgCKAAAAAAAAFgUARQAAAAAAAAsigAKAAAAAAAAFpXtA6g+ffqoQ4cO6Y6/8cYb8vDw0L59+9Ic37x5szw8PHTp0qXHvndiYqIWLVr0wDYeHh4KDw+/7zk/Pz+FhoY+dh2ZERoaKj8/v/ue27t3rzw8PHT+/Hmz+jp//rw8PDy0d+9eSVJ8fLyWLVv2WPX9t08AAAAAAPD0y/YBVJ06dXTs2DHdvn3bdOzmzZs6fPiwXF1d9dNPP6Vpv3//fj3//PMqUaLEY997w4YNmjhx4mP3Y6tcXV0VEREhHx8fSdIXX3yhhQsXWrkqAAAAAABga7J9AFW7dm0lJiYqKirKdGz37t165pln1L59e+3atStN+19//VW+vr5Zcu+UlJQs6cdW5cqVS0WLFlWePHkk8T4AAAAAAMCjyfYBVOpspt9++810bNeuXapTp47q1aun6OhoXblyRZL0999/KyYmRnXr1pUkJSQkKCQkRPXq1ZOPj486dOigiIgIUz93795VSEiIGjRooEqVKqlZs2Zavny5JCk8PFzBwcGSlGVLxn788Ud17NhRPj4+8vLyUrt27bR7927T+cDAQE2fPl0jRoyQj4+PatWqpdmzZ+vUqVPq3LmzvL291apVKx0+fPixa5GkoKAgDR06VJMmTVLt2rVVuXJl9e7dW1evXpWUdrlcaGioZs2apQsXLqRZxhcWFqbmzZvL29tbzZs31+LFi5WcnGy6R0xMjLp27aoqVaqoadOm+uWXX7KkdgAAAAAAYDscrF2AOWrXrq3IyEjT54iICL3//vuqVKmSChYsqF27dqlt27Y6cOCAcuXKpZdeekmSFBwcrP/9738KCQlRiRIltH37dvXq1UuzZs1Sw4YN9fXXX2vz5s2aNm2aihcvru3bt2v06NEqX768/P39dfPmTU2YMEERERFydnZ+rGc4cuSI+vTpo6FDhyokJET//POPpk2bpiFDhmjHjh2mWUYLFixQ7969tW7dOq1bt04zZszQ6tWrFRQUpGeffVYfffSRRo8eneG+U5n13Xff6dVXX9XSpUt18eJFDRkyRNOmTdOECRPStOvevbvi4+O1adMmrVq1SoULF9aKFSs0depUjRw5UpUrV9axY8c0btw4Xb58WR988IFu3rypt956S1WqVNHKlSt15coVjRgxIkvqBgAAwIMZjUbFx8dnST///i+A+2OsICdKSUmRnZ2dWW1tJoCaMGGCUlJSdPLkSV26dEm+vr6yt7dX7dq1TQHUr7/+Kh8fHzk5OenMmTPasGGDVq1aJS8vL0lSt27dFB0drYULF6phw4Y6e/asnJyc5ObmpqJFi6pLly56/vnn9dxzz8nR0VEFChSQJBUtWvSB9Y0aNUrjxo1Ld/zf/+PJlSuXPvroI3Xu3Nl0rGvXrurevbv+/PNPubq6SpIqVKig3r17S7oX+sycOVP+/v5q3LixJCkgICBdOPQ48ufPr7Fjxyp37twqW7asWrdurZ07d6Zrly9fPjk5OZmW5UnS7Nmz9e6776ply5aSJDc3N926dUtjxozRgAEDtHHjRhmNRk2aNEkFChRQ+fLl9eGHH6pPnz5ZVj8AAADuLzY2Vo6OjlnaH4CHY6wgp0mdUPMwNhNAXb9+XadOnVJERIReeOEFUwhSt25dffrpp5LubUCe+g1wx44dk3Qv5Pm3xMREPfPMM5Kkzp07a+vWrapfv74qVaokX19fNW/eXEWKFMlUff3799crr7yS7nhgYKDp556ennJ2dtb8+fN1+vRpxcbG6vjx45LuLQVM9dxzz5l+bjAYJN0LdlLlzZtXCQkJGdbi4OCQZgncv6Uez507t+lYmTJl0nwuUKCAEhMTM+w/1bVr13Tp0iXNmDFDs2bNSnOPO3fu6Pz584qJiZG7u7spyJNk2tAcAAAAluXu7i5PT8/H7sdoNCo2Nlbu7u6mP58CSI+xgpzo999/N7utTQRQxYoVU7ly5RQZGamIiAjTHk/SvQBq+PDhOnLkiI4dO2Za4pW6YfayZcuUL1++NP3Z29/b+srd3V0//PCD9u3bp59//lnbtm3TnDlzNHHiRL322mtm11ekSBGVKVMm3XEHh/97vb/++qu6d++uBg0aqHr16mrRooWMRmO62UD/DoP+W685nJ2ddfPmzfueu379uiSZAjjJ/KTyv1LDrODgYNWpUyfd+dQZXf/duPzf7wQAAACWYzAY5OTklG37A55WjBXkJOYuv5NsYBPyVKn7QB04cCBNAFWiRAmVK1dO33zzjfLly6eKFStKksqXLy9JunLlisqUKWP6ER4errCwMEnSkiVL9MMPP8jX11cffPCB1q9fr9q1a2vTpk2SMvciH2bhwoWqWbOmZs2apbfeeku+vr76448/JGXtt8t5eXnp1q1bptlV/7Z//36VL1/+kdP4f7+PIkWKqEiRIjp79mya93v06FFNnz5d0r1ZX6dPn9a1a9dM1/372wwBAAAAAEDOYFMBVGowVLVq1TTn6tatq40bN6pOnTqm2ULly5dXo0aNNGrUKG3btk3nzp3TwoULNXfuXNOStj///FNjx47Vtm3bdOHCBf300086duyYaZlYamp95MgR3b59+7Hqd3V11YkTJ7R//36dP39eYWFhmjFjhiQ9cEldZnl5ecnX11fvv/++6bmOHTum0NBQffvtt+rbt+8j9+3k5KQbN27o9OnTSkpKUo8ePfTVV1/pq6++0tmzZ7V161aNGTNGefLkUZ48edSiRQsVKVJEgwcPVnR0tPbt25el+1cBAAAAAADbYDProWrWrKmEhATVq1cv3bKxunXratGiRfL19U1zfNq0aZo2bZpGjRqlGzduyM3NTePGjVPbtm0lSX379lVSUpLGjRunuLg4FS1aVJ06ddK7774rSapVq5YqV66sjh07KiQkRM2bN3/k+vv376+4uDj16tVLklSuXDlNmDBBQ4cO1eHDh1W2bNlH7vu/Zs+erc8++0yTJ0/WH3/8obx58+qFF17QrFmz1KBBg0fu95VXXtG3336rVq1aaenSperevbvy5s2rr776SpMmTVKRIkUUEBCggQMHSroXWC1ZskRjx47VG2+8IWdnZw0YMEBBQUFZ9agAAAAAAMAG2KVk5fovwMKioqJ06tQpFdy0SVVKlrR2OQAAANnOwYsX1XDePB04cCDdyoFHER8fr+PHj8vT05N9bYAHYKwgJ0rdZsfLy+uhbW1mCR4AAAAAAABsEwEUAAAAAAAALIoACgAAAAAAABZFAAUAAAAAAACLIoACAAAAAACARTlYuwDgUcTExVm7BAAAgGyJPycBALIjAijYHHs7O/UMD7d2GQAAANmWk8EgFxcXa5cBAIAJARRsTrHixRURESGDwWDtUoBsy2g0KjY2Vu7u7owVIAOMEzzNXFxcVLp0aWuXAQCACQEUbI6Dg4O8vLzk5ORk7VKAbCs+Pl6Ojo7y9PRkrAAZYJwAAAA8OWxCDgAAAAAAAIsigAIAAAAAAIBFEUABAAAAAADAotgDCjYnKSlJkZGRbBgLPEDq5sq3b99mrAAZYJzAFrG5OADAVhFAweZcuXxZrVq3tnYZAAAAT5yTwaDj0dGEUAAAm0MABZuTnJKieQEBquDiYu1SAAAAnpiYuDj1DA9XXFwcARQAwOYQQMEmVXBxUZWSJa1dBgAAAAAAMAObkAMAAAAAAMCiCKAAAAAAAABgUQRQAAAAAAAAsCgCKAAAAAAAAFiU1QMoPz8/eXh46Msvv7zv+ZEjR8rDw0OhoaFPrKbAwEAFBQVZ9B4XL17Uxo0bs7zfB9UeGhoqPz8/s/sKDw+Xh4eH6XNW1PzfPgEAAAAAwNPP6gGUJOXOnVubN29OdzwpKUk//PCD7OzsrFCVZQ0bNky7du2ydhkP5O/vr4iICNNnW6gZAAAAAABkP9kigKpdu7YOHTqkP/74I83xX375RU5OTnJ1dbVSZTmbo6OjihYtau0yAAAAAACAjcsWAZS3t7dKliyZbhbUpk2b1Lx583QzoMLCwtSmTRt5e3urSpUqCgwM1NGjR03n/fz8NG/ePPXr108+Pj6qWbOmJkyYoKSkJFObI0eOqFu3bvLx8VGdOnU0cuRIxcfHm87/888/+vDDD1W9enVVq1ZNQUFBac6fPHlS77zzjnx8fFS3bl0NHjxYV69eNZ2PjY3V22+/rWrVqsnHx0dvv/22Tpw4IeneMrl9+/Zp9erVpiVxCQkJCgkJUb169eTj46MOHTqkmX0UHh4uPz8/ffzxx6pevbp69er1OK/crPf07+Vyj1KzJG3ZskWvvvqqvL291aVLF128ePGx6wYAAAAAALbFwdoFpGrevLk2b96sbt26SboXbmzdulWLFi3Sd999Z2q3ZcsWjRo1SuPHj1eNGjUUFxen8ePHa/jw4VqzZo2pXWhoqIYOHarBgwcrIiJC48eP14svvqg2bdro/PnzCgwMlJ+fn1asWKFbt24pODhYI0eO1JQpUyRJP/zwg959912Fh4frf//7nwYOHChXV1cNGDBAly9fVqdOndSiRQsFBQXJaDQqNDRUHTt21Pr16+Xk5KRBgwbJw8NDYWFhSkpK0qRJk9S3b19t2bJFoaGh6tWrl0qUKKGRI0dKkoKDg/W///1PISEhKlGihLZv365evXpp1qxZatiwoSTpwoULunz5slavXq3bt29nyXt/0Hv6b7vM1vzbb7+pX79+6tOnj1q2bKn9+/dr3LhxWVI3AABATmU0GtP8w+iTuN+//wvg/hgryIlSUlLM3jYpWwVQCxcu1B9//CFXV1f9/PPPKlSokF588cU07QoWLKjx48ebApJSpUqpffv2GjVqVJp29erVU9euXSVJ7u7uWrVqlX777Te1adNG3377rZydnfXJJ58od+7ckqTx48dr3759puu9vLw0aNAgSVLp0qXl6+urI0eOSJKWL1+uYsWKmYIYSZo+fbpq1aqlzZs3KyAgQGfPnpWvr6+effZZOTg4aMKECTp16pSSk5NVsGBB5c6dW46OjipcuLDOnDmjDRs2aNWqVfLy8pIkdevWTdHR0Vq4cKEpgJKk3r17y83NLQve+MPf0789Ss1Lly5V1apV1a9fP0nSc889p5iYGC1ZsiTL6gcAAMhpYmNj5ejoaJX7Ang4xgpymjx58pjVLtsEUJUqVZKbm5tpFtSmTZvUsmXLdO1q1KihwoULa/bs2Tpz5oxOnz6t48ePKzk5OU27smXLpvlcoEABJSYmSpJOnDihihUrmsKn1H5r1Khh+vzcc8+lud7Z2VkXLlyQJB07dkwnT56Uj49PmjZ37tzRyZMnJUkDBw7UhAkTtHz5ctWqVUv16tVT8+bNZW+fftXjsWPHJMkUBKVKTEzUM888k+aYu7t7uuv/zcHBId27SJWcnCwHh7S/5A96Tw9iTs0xMTHy9fVNc97Hx4cACgAA4DG4u7vL09Pzid3PaDQqNjZW7u7uMhgMT+y+gK1hrCAn+v33381um20CKOn/luF16tRJ27Zt08qVK9O12bhxoz744AO1bNlS3t7eateunWJiYjR27Ng07e6XwKWkpEi6F9I8bIpYrly5MjyXnJysWrVqpZt1Jd0LcCSpc+fOatasmXbu3Kk9e/bo008/VWhoqNasWSMXF5f71rVs2TLly5cvzbn/BlYP+9cuZ2dn/f333/c9d/36dTk7O6c59qD39CDm1vzfvv4d+gEAACDzDAaDnJyccsx9AVvDWEFOYu7yOymbbEKeqnnz5jp06JBWrVolNze3dLNzJGnOnDlq166dJk2apM6dO6tGjRo6d+6cJPOCE0kqV66cjh07prt375qObdmyRfXr1zdrvW758uV18uRJubq6qkyZMipTpoycnZ01YcIExcTEKC4uTmPHjlViYqICAgIUEhKidevW6erVq2mW+f27P0m6cuWKqb8yZcooPDxcYWFhZj1TKi8vL0VFRenOnTtpjqekpGj//v2m5XKPy5yaPT099dtvv6W5LioqKkvuDwAAAAAAbEe2CqA8PT1VpkwZffrpp2rRosV927i6uuq3337T0aNHdfbsWS1atEhLly6VdG/jcnN06tRJf/31l0aNGqWTJ09q//79mjJlinx9fc2aKtmpUyfdvHlTgwYN0vHjxxUdHa3Bgwfr8OHDKl++vAoWLKgdO3boo48+0vHjx3Xu3Dl9/fXXyp07typVqiRJypcvny5cuKBLly6pfPnyatSokUaNGqVt27bp3LlzWrhwoebOnZvp/Z7atm0rOzs79enTRwcOHNCFCxf022+/afDgwfrjjz9Mm7w/iszW3L17d0VHR2vSpEk6ffq01q1bp2XLlj3y/QEAAAAAgG3KVgGUdG8W1K1bt+Tv73/f8yNGjJCLi4u6dOmi9u3ba/v27Zo8ebIk6dChQ2bdo3jx4vriiy90+vRpvfbaa3r//ffVoEGD+y6pux83NzctXbpURqNRnTp1UpcuXWRnZ6fFixerSJEicnBw0Pz582Vvb6+33npLLVq00C+//KJ58+apdOnSkqSOHTsqJiZGrVq10t27dzVt2jQ1bdpUo0aNkr+/v8LCwjRu3Di1bdvWrJpSFSxYUCtWrFDhwoX1/vvvq2nTpurXr5+Sk5O1YsWKx9rAPLM1e3p6av78+dq7d69atWqlRYsWqVevXo98fwAAAAAAYJvsUsxdtwZkA1FRUTp16pQKbtqkKiVLWrscAACAJ+bgxYtqOG+eDhw4oKpVqz6x+8bHx+v48ePy9PRkXxvgARgryIlSt9kxZ7ufbDcDCgAAAAAAAE8XAigAAAAAAABYFAEUAAAAAAAALIoACgAAAAAAABZFAAUAAAAAAACLcrB2AcCjiImLs3YJAAAATxR//gEA2DICKNgcezs79QwPt3YZAAAAT5yTwSAXFxdrlwEAQKYRQMHmFCteXBERETIYDNYuBci2jEajYmNj5e7uzlgBMsA4gS1ycXFR6dKlrV0GAACZRgAFm+Pg4CAvLy85OTlZuxQg24qPj5ejo6M8PT0ZK0AGGCcAAABPDpuQAwAAAAAAwKIIoAAAAAAAAGBRBFAAAAAAAACwKPaAgs1JSkpSZGQkG8YCD5C6ufLt27cZK0AGGCfITthcHADwtCOAgs25cvmyWrVube0yAAAAsoyTwaDj0dGEUACApxYBFGxOckqK5gUEqIKLi7VLAQAAeGwxcXHqGR6uuLg4AigAwFOLAAo2qYKLi6qULGntMgAAAAAAgBnYhBwAAAAAAAAWRQAFAAAAAAAAiyKAAgAAAAAAgEURQAEAAAAAAMCicnQAlZSUpMWLFysgIEA+Pj6qWbOmunXrpj179lj0vikpKVq9erX+/PNPs6/x8/NTaGhohuc9PDzS/HjxxRdVs2ZNvfvuu4qJiTH7PqGhofLz8zO7PQAAAAAAwMPk2AAqISFBb775phYvXqzAwECtXr1aixcvVrly5dS9e3etWbPGYvf+9ddfFRQUJKPRmKX9fvjhh4qIiFBERIS2b9+uBQsW6Pr16+revbvi4+Oz9F4AAAAAAADmcniUi6KiohQZGam///473Tk7Ozv16dPnsQuztJkzZyo6OlobN25UiRIlTMeHDx+u+Ph4TZgwQU2aNFG+fPmy/N4pKSlZ3qckFShQQEWLFjV9Ll68uIYNG6Y33nhDe/bsUePGjS1yXwAAAAAAgAfJdAC1ePFiffLJJxmGKLYQQCUmJmrlypVq165dmvAp1YABA/T666/L0dFR0r3lbb169dK6detMM6c+//xz7d69WwaDQZKUnJyshg0bqkePHuratasWLlyo5cuX69KlSypWrJjatm2r3r17a9++ferataskqXHjxpo4caICAgIUFhamr776SqdOnZK9vb28vLwUFBSkihUrPtazOjjc+yXOkyePJOl///ufpk2bpgMHDuiff/6Rq6urunTpojfffPO+15vT/ueff1ZoaKiOHz8uZ2dntW7dWu+//75y5cqlhIQEzZgxQ+vWrdOtW7dUvnx59e/fX3Xr1n2s5wIAAAAAALYj0wHUl19+qcaNG2v8+PEqWLCgBUqyvHPnzun69euqUqXKfc8XK1ZMxYoVS3NsxYoVmj9/vu7evavSpUtr5syZ+uGHH9S6dWtJ0u7du3Xt2jW1bNlSP/74o+bMmaPp06frueee08GDB/XBBx/o2WefVfPmzRUaGqp+/fpp5cqVqlChgrZs2aJRo0Zp/PjxqlGjhuLi4jR+/HgNHz78kZcCpqSk6OzZswoJCVHx4sXl4+Mjo9Gobt26qVatWvr666/l4OCgsLAwTZgwQS+99JI8PT3T9GFO+0OHDqlHjx5688039fHHH+uPP/7QkCFDZG9vr4EDByo4OFj/+9//FBISohIlSmj79u3q1auXZs2apYYNGz7SswEAADyNjEZjttw2IXXbiKzePgJ42jBWkBOlpKTIzs7OrLaZDqBu3LihN954w2bDJ+neM0iSs7Oz2de0bt1aXl5eps9+fn5at26dKYBavXq1/Pz8VLhwYZ09e1Z58+bVs88+q5IlS6pkyZIqVqyYSpYsqTx58pjuW7hwYTk6OqpgwYIaP3682rRpI0kqVaqU2rdvr1GjRmXquUaNGqVx48ZJujfLKykpSRUrVtSsWbOUP39+Xbt2TV27dlWnTp2UP39+SVLfvn01d+5cnThx4r4B1MPaL1myRN7e3goKCpIklS1bVuPGjdOVK1d05swZbdiwQatWrTK9u27duik6OloLFy4kgAIAAPiX2NhY0wz87Cg2NtbaJQA2gbGCnCZ1xdXDZDqAqlu3riIjI+Xr65vporKLwoULS5KuX79u9jVlypRJ87lt27bq1auXLl+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+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "team_avg_efficiency = df.groupby('Team')['Efficiency_Score'].mean().reset_index()\n",
+    "team_avg_efficiency = team_avg_efficiency.sort_values(by='Efficiency_Score', ascending=True)\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.barh(team_avg_efficiency['Team'], team_avg_efficiency['Efficiency_Score'], color='skyblue', edgecolor='black')\n",
+    "plt.xlabel('Verimlilik Skoru')\n",
+    "plt.ylabel('Takım')\n",
+    "plt.title('Takımların Ortalama Verimlilik Skorları')\n",
+    "plt.show()\n",
+    "\n",
+    "team_total_goals = df.groupby('Team')['Gls'].sum().reset_index()\n",
+    "team_total_goals = team_total_goals.sort_values(by='Gls', ascending=True)\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.barh(team_total_goals['Team'], team_total_goals['Gls'], color='lightcoral', edgecolor='black')\n",
+    "plt.xlabel('Gol Sayısı')\n",
+    "plt.ylabel('Takım')\n",
+    "plt.title('Takımların Toplam Golleri')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "aa4c7e9f-7529-4d98-a7e6-26f680787ff9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "team_avg_xg = df.groupby('Team')['xG'].mean().reset_index()\n",
+    "team_avg_xg = team_avg_xg.sort_values(by='xG', ascending=True)\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.barh(team_avg_xg['Team'], team_avg_xg['xG'], color='deepskyblue', edgecolor='black')\n",
+    "plt.xlabel('Average xG')\n",
+    "plt.ylabel('Team')\n",
+    "plt.title('Average xG by Team')\n",
+    "plt.show()\n",
+    "\n",
+    "team_avg_xag = df.groupby('Team')['xAG'].mean().reset_index()\n",
+    "team_avg_xag = team_avg_xag.sort_values(by='xAG', ascending=True)\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.barh(team_avg_xag['Team'], team_avg_xag['xAG'], color='gold', edgecolor='black')\n",
+    "plt.xlabel('Average xAG')\n",
+    "plt.ylabel('Team')\n",
+    "plt.title('Average xAG by Team')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "a0d9ad30-8c25-440a-810d-a587c0830607",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ortalama Kare Hatası (MSE): 20.21\n",
+      "R^2 Skoru: 0.99\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.ensemble import GradientBoostingRegressor\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "features = df[['xG', 'xAG', 'G+A', '90s', 'PrgC']]  \n",
+    "target = df['Efficiency_Score']  # Hedef değişken \n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
+    "\n",
+    "model = GradientBoostingRegressor(n_estimators=100, random_state=42)\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = model.predict(X_test)\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "print(f\"Ortalama Kare Hatası (MSE): {mse:.2f}\")\n",
+    "print(f\"R^2 Skoru: {r2:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "43e9f65b-3743-4a0c-a12d-5460d970e717",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  0.27167065,  11.25438299,   5.19515194,  76.98331281,\n",
+       "        28.27769877,  18.83606479,  30.18969797,  17.93670578,\n",
+       "        10.34256734,   3.51951747,   8.00112037,  24.67057495,\n",
+       "        11.60123049,  54.15883647,   7.15207708,   0.20619029,\n",
+       "       149.93287667,  49.12007177,  54.86384055,   1.98419543,\n",
+       "         3.26803462,  17.92124059,  15.27311402,  16.91250392,\n",
+       "        15.10962646,  27.49884449,  35.49620821,  90.09942744,\n",
+       "        32.48171533,  14.74617075,  72.19400211,  26.60723279,\n",
+       "       120.7553211 ,  25.97220433,  34.82862431,  19.14132813,\n",
+       "         0.69429082,  63.97123592,   8.28012857,   0.38153038,\n",
+       "         0.21252324,  46.58478834,   0.53005484,  35.72075687,\n",
+       "         0.83878733,   4.46609222, 140.41315134,   2.481998  ,\n",
+       "         2.80764651,   0.86763958, 102.70153731,   3.46679617,\n",
+       "        13.77097584,  25.97734333,  16.26319282,   4.40447235,\n",
+       "         2.46133426,   0.75820938,   4.59253657,  21.94042497,\n",
+       "       170.08510593,  41.56327906,  56.42002619,   3.51951747,\n",
+       "         0.21252324,  24.75936014,  16.17767182,  10.8600833 ,\n",
+       "        74.80085305,   0.52528368,   0.20619029,  78.31643031,\n",
+       "         6.82104968,  27.99190387,   0.21252324,  39.64384941,\n",
+       "        54.42793057,  20.18714657,  20.50449928,  28.81517594,\n",
+       "        93.1500072 ,  53.26343855,   3.85094018,  27.28618022,\n",
+       "         0.83878733,   0.83245437,   0.21252324,  44.24697442,\n",
+       "         6.46043748,  67.45314781,  80.50150834,   0.79196614,\n",
+       "        11.41925137,  42.01645095,  91.14936257,  70.22493806,\n",
+       "        54.36592905,  17.37296638,   0.21252324,  41.64084406,\n",
+       "         3.68852461,  57.88681981,  35.05340856,  17.5229738 ,\n",
+       "        12.91687231,  26.45392128,  25.83366338,  96.58601756,\n",
+       "         3.41924907,   4.2732384 ,   0.21252324,   1.47207723,\n",
+       "        50.62446206,  15.36097402,  10.60371   ,  95.79727236])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "060d6eb5-7a2f-4209-819f-1f1a139784a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player</th>\n",
+       "      <th>Nation</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>MP</th>\n",
+       "      <th>Starts</th>\n",
+       "      <th>Min</th>\n",
+       "      <th>90s</th>\n",
+       "      <th>Gls</th>\n",
+       "      <th>Ast</th>\n",
+       "      <th>...</th>\n",
+       "      <th>G+A_90</th>\n",
+       "      <th>G-PK_90</th>\n",
+       "      <th>G+A-PK_90</th>\n",
+       "      <th>xG_90</th>\n",
+       "      <th>xAG_90</th>\n",
+       "      <th>xG+xAG_90</th>\n",
+       "      <th>npxG_90</th>\n",
+       "      <th>npxG+xAG_90</th>\n",
+       "      <th>Team</th>\n",
+       "      <th>Efficiency_Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Rodri</td>\n",
+       "      <td>es ESP</td>\n",
+       "      <td>MF</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>34</td>\n",
+       "      <td>2931.0</td>\n",
+       "      <td>32.6</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>Manchester City</td>\n",
+       "      <td>97.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Phil Foden</td>\n",
+       "      <td>eng ENG</td>\n",
+       "      <td>FW,MF</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>33</td>\n",
+       "      <td>2857.0</td>\n",
+       "      <td>31.7</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>Manchester City</td>\n",
+       "      <td>148.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Ederson</td>\n",
+       "      <td>br BRA</td>\n",
+       "      <td>GK</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>33</td>\n",
+       "      <td>2785.0</td>\n",
+       "      <td>30.9</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>Manchester City</td>\n",
+       "      <td>15.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Julián Álvarez</td>\n",
+       "      <td>ar ARG</td>\n",
+       "      <td>MF,FW</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>31</td>\n",
+       "      <td>2647.0</td>\n",
+       "      <td>29.4</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.31</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.44</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>0.66</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>Manchester City</td>\n",
+       "      <td>116.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Kyle Walker</td>\n",
+       "      <td>eng ENG</td>\n",
+       "      <td>DF</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2767.0</td>\n",
+       "      <td>30.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>Manchester City</td>\n",
+       "      <td>50.91</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 35 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Player   Nation    Pos   Age  MP  Starts     Min   90s   Gls  Ast  \\\n",
+       "0           Rodri   es ESP     MF  27.0  34      34  2931.0  32.6   8.0  9.0   \n",
+       "1      Phil Foden  eng ENG  FW,MF  23.0  35      33  2857.0  31.7  19.0  8.0   \n",
+       "2         Ederson   br BRA     GK  29.0  33      33  2785.0  30.9   0.0  0.0   \n",
+       "3  Julián Álvarez   ar ARG  MF,FW  23.0  36      31  2647.0  29.4  11.0  8.0   \n",
+       "4     Kyle Walker  eng ENG     DF  33.0  32      30  2767.0  30.7   0.0  4.0   \n",
+       "\n",
+       "   ...  G+A_90  G-PK_90  G+A-PK_90  xG_90  xAG_90  xG+xAG_90  npxG_90  \\\n",
+       "0  ...    0.52     0.25       0.52   0.12    0.12       0.24     0.12   \n",
+       "1  ...    0.85     0.60       0.85   0.33    0.26       0.59     0.33   \n",
+       "2  ...    0.00     0.00       0.00   0.00    0.00       0.00     0.00   \n",
+       "3  ...    0.65     0.31       0.58   0.44    0.22       0.66     0.39   \n",
+       "4  ...    0.13     0.00       0.13   0.01    0.09       0.10     0.01   \n",
+       "\n",
+       "   npxG+xAG_90             Team  Efficiency_Score  \n",
+       "0         0.24  Manchester City             97.29  \n",
+       "1         0.59  Manchester City            148.72  \n",
+       "2         0.00  Manchester City             15.55  \n",
+       "3         0.61  Manchester City            116.95  \n",
+       "4         0.10  Manchester City             50.91  \n",
+       "\n",
+       "[5 rows x 35 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "f0202d66-bb5a-422e-ae4c-7592fc46c1a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              Player  Actual_Score  Predicted_Score\n",
+      "575       Sam Curtis          0.15         0.271671\n",
+      "277  Nathaniel Clyne         11.03        11.254383\n",
+      "104      Ian Maatsen          5.47         5.195152\n",
+      "177  Marcus Rashford         82.34        76.983313\n",
+      "456       Ryan Yates         27.69        28.277699\n",
+      "..               ...           ...              ...\n",
+      "542        Jack Cork          1.35         1.472077\n",
+      "299       Bobby Reid         51.94        50.624462\n",
+      "165    Manor Solomon         12.76        15.360974\n",
+      "272   Dean Henderson          9.00        10.603710\n",
+      "211     Moussa Diaby         98.20        95.797272\n",
+      "\n",
+      "[116 rows x 3 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Test setindeki futbolcuların verileri\n",
+    "test_data = X_test.copy()\n",
+    "test_data['Actual_Score'] = y_test\n",
+    "test_data['Predicted_Score'] = y_pred\n",
+    "\n",
+    "# Futbolcu bilgilerini içeren Df'e eklemek (örneğin futbolcuların adları)\n",
+    "test_data = pd.concat([test_data, df.loc[X_test.index, ['Player']]], axis=1)\n",
+    "\n",
+    "print(test_data[['Player', 'Actual_Score', 'Predicted_Score']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "c5e3f655-81e7-4881-9c51-13851dae2803",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>xG</th>\n",
+       "      <th>xAG</th>\n",
+       "      <th>G+A</th>\n",
+       "      <th>90s</th>\n",
+       "      <th>PrgC</th>\n",
+       "      <th>Actual_Score</th>\n",
+       "      <th>Predicted_Score</th>\n",
+       "      <th>Player</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>29.2</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>28.4</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>173.57</td>\n",
+       "      <td>140.413151</td>\n",
+       "      <td>Erling Haaland</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     xG  xAG   G+A   90s  PrgC  Actual_Score  Predicted_Score          Player\n",
+       "6  29.2  4.3  32.0  28.4  35.0        173.57       140.413151  Erling Haaland"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data[(test_data[\"Player\"] == \"Erling Haaland\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "996c2032-e799-4c0a-ab53-f272d92e049d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "En Yüksek Gerçek Verimlilik Skorlarına Sahip 10 Oyuncu:\n",
+      "            Player  Actual_Score  Predicted_Score\n",
+      "       Cole Palmer        183.81       170.085106\n",
+      "    Erling Haaland        173.57       140.413151\n",
+      "     Ollie Watkins        165.17       149.932877\n",
+      "   Nicolas Jackson        130.69       120.755321\n",
+      "       Jeremy Doku        112.60        93.150007\n",
+      "    Mohammed Kudus        101.09       102.701537\n",
+      "       Pascal Groß         99.09        96.586018\n",
+      "      Moussa Diaby         98.20        95.797272\n",
+      "Gabriel Martinelli         96.32        90.099427\n",
+      "    Carlton Morris         91.73        91.149363\n"
+     ]
+    }
+   ],
+   "source": [
+    "top_10_actual_scores = test_data[['Player', 'Actual_Score',\"Predicted_Score\"]].sort_values(by='Actual_Score', ascending=False).head(10)\n",
+    "\n",
+    "print(\"En Yüksek Gerçek Verimlilik Skorlarına Sahip 10 Oyuncu:\")\n",
+    "print(top_10_actual_scores.to_string(index=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "1b58d18f-7147-40fa-a0a9-0731b8b6eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(y_test, y_pred, color='blue', edgecolor='k', alpha=0.7)\n",
+    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], color='red', linestyle='--')\n",
+    "plt.xlabel('Gerçek Değerler')\n",
+    "plt.ylabel('Tahmin Edilen Değerler')\n",
+    "plt.title('Gerçek vs. Tahmin Edilen Değerler')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7adf4840-8f77-4f3c-bd88-657bc85b9fbe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/premier-player-23-24.csv b/premier-player-23-24.csv
new file mode 100644
index 0000000..44dcb0f
--- /dev/null
+++ b/premier-player-23-24.csv
@@ -0,0 +1,581 @@
+Player,Nation,Pos,Age,MP,Starts,Min,90s,Gls,Ast,G+A,G-PK,PK,PKatt,CrdY,CrdR,xG,npxG,xAG,npxG+xAG,PrgC,PrgP,PrgR,Gls_90,Ast_90,G+A_90,G-PK_90,G+A-PK_90,xG_90,xAG_90,xG+xAG_90,npxG_90,npxG+xAG_90,Team
+Rodri,es ESP,MF,27.0,34,34,2931.0,32.6,8.0,9.0,17.0,8.0,0.0,0.0,8.0,1.0,4.1,4.1,3.9,8.0,76.0,376.0,55.0,0.25,0.28,0.52,0.25,0.52,0.12,0.12,0.24,0.12,0.24,Manchester City
+Phil Foden,eng ENG,"FW,MF",23.0,35,33,2857.0,31.7,19.0,8.0,27.0,19.0,0.0,0.0,2.0,0.0,10.3,10.3,8.4,18.7,93.0,168.0,269.0,0.6,0.25,0.85,0.6,0.85,0.33,0.26,0.59,0.33,0.59,Manchester City
+Ederson,br BRA,GK,29.0,33,33,2785.0,30.9,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.1,0.1,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Manchester City
+Julián Álvarez,ar ARG,"MF,FW",23.0,36,31,2647.0,29.4,11.0,8.0,19.0,9.0,2.0,2.0,2.0,0.0,13.0,11.5,6.4,17.9,64.0,103.0,180.0,0.37,0.27,0.65,0.31,0.58,0.44,0.22,0.66,0.39,0.61,Manchester City
+Kyle Walker,eng ENG,DF,33.0,32,30,2767.0,30.7,0.0,4.0,4.0,0.0,0.0,0.0,2.0,0.0,0.4,0.4,2.6,3.0,74.0,157.0,172.0,0.0,0.13,0.13,0.0,0.13,0.01,0.09,0.1,0.01,0.1,Manchester City
+Bernardo Silva,pt POR,"MF,FW",28.0,33,29,2578.0,28.6,6.0,9.0,15.0,6.0,0.0,0.0,8.0,0.0,3.7,3.7,7.6,11.3,140.0,177.0,260.0,0.21,0.31,0.52,0.21,0.52,0.13,0.27,0.4,0.13,0.4,Manchester City
+Erling Haaland,no NOR,FW,23.0,31,29,2552.0,28.4,27.0,5.0,32.0,20.0,7.0,8.0,1.0,0.0,29.2,22.9,4.3,27.2,35.0,26.0,126.0,0.95,0.18,1.13,0.71,0.88,1.03,0.15,1.18,0.81,0.96,Manchester City
+Rúben Dias,pt POR,DF,26.0,30,28,2559.0,28.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.4,1.4,0.3,1.7,34.0,173.0,16.0,0.0,0.0,0.0,0.0,0.0,0.05,0.01,0.06,0.05,0.06,Manchester City
+Manuel Akanji,ch SUI,"DF,MF",28.0,30,28,2511.0,27.9,2.0,0.0,2.0,2.0,0.0,0.0,4.0,1.0,1.9,1.9,0.5,2.4,46.0,148.0,36.0,0.07,0.0,0.07,0.07,0.07,0.07,0.02,0.09,0.07,0.09,Manchester City
+Joško Gvardiol,hr CRO,DF,21.0,28,26,2328.0,25.9,4.0,1.0,5.0,4.0,0.0,0.0,3.0,0.0,3.1,3.1,1.4,4.5,63.0,136.0,115.0,0.15,0.04,0.19,0.15,0.19,0.12,0.06,0.18,0.12,0.18,Manchester City
+Nathan Aké,nl NED,DF,28.0,29,24,2042.0,22.7,2.0,2.0,4.0,2.0,0.0,0.0,0.0,0.0,2.5,2.5,1.2,3.7,24.0,116.0,18.0,0.09,0.09,0.18,0.09,0.18,0.11,0.05,0.16,0.11,0.16,Manchester City
+Jeremy Doku,be BEL,"FW,MF",21.0,29,18,1595.0,17.7,3.0,8.0,11.0,3.0,0.0,0.0,3.0,0.0,2.5,2.5,4.6,7.1,218.0,57.0,295.0,0.17,0.45,0.62,0.17,0.62,0.14,0.26,0.4,0.14,0.4,Manchester City
+Mateo Kovačić,hr CRO,MF,29.0,30,16,1551.0,17.2,1.0,0.0,1.0,1.0,0.0,0.0,4.0,0.0,1.3,1.3,1.6,2.9,37.0,100.0,38.0,0.06,0.0,0.06,0.06,0.06,0.07,0.09,0.17,0.07,0.17,Manchester City
+Kevin De Bruyne,be BEL,MF,32.0,18,15,1221.0,13.6,4.0,10.0,14.0,4.0,0.0,0.0,2.0,0.0,2.4,2.4,11.5,14.0,47.0,111.0,133.0,0.29,0.74,1.03,0.29,1.03,0.18,0.85,1.03,0.18,1.03,Manchester City
+John Stones,eng ENG,"DF,MF",29.0,16,12,1064.0,11.8,1.0,0.0,1.0,1.0,0.0,0.0,2.0,0.0,1.0,1.0,0.2,1.3,16.0,57.0,24.0,0.08,0.0,0.08,0.08,0.08,0.09,0.02,0.11,0.09,0.11,Manchester City
+Jack Grealish,eng ENG,"FW,MF",27.0,20,10,1009.0,11.2,3.0,1.0,4.0,3.0,0.0,0.0,6.0,0.0,2.1,2.1,2.5,4.6,87.0,50.0,146.0,0.27,0.09,0.36,0.27,0.36,0.19,0.22,0.41,0.19,0.41,Manchester City
+Rico Lewis,eng ENG,"DF,MF",18.0,16,8,809.0,9.0,2.0,0.0,2.0,2.0,0.0,0.0,1.0,0.0,0.6,0.6,1.1,1.8,21.0,33.0,46.0,0.22,0.0,0.22,0.22,0.22,0.07,0.12,0.19,0.07,0.19,Manchester City
+Matheus Nunes,pt POR,MF,24.0,17,7,661.0,7.3,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.8,0.8,1.1,2.0,28.0,34.0,58.0,0.0,0.27,0.27,0.0,0.27,0.11,0.15,0.27,0.11,0.27,Manchester City
+Stefan Ortega,de GER,GK,30.0,9,5,635.0,7.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Manchester City
+Oscar Bobb,no NOR,"FW,MF",20.0,14,2,302.0,3.4,1.0,1.0,2.0,1.0,0.0,0.0,0.0,0.0,1.4,1.4,1.2,2.6,13.0,25.0,38.0,0.3,0.3,0.6,0.3,0.6,0.41,0.37,0.78,0.41,0.78,Manchester City
+Kalvin Phillips,eng ENG,MF,27.0,4,0,93.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.0,0.1,7.0,7.0,3.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.07,0.07,0.07,Manchester City
+Sergio Gómez,es ESP,"FW,MF",22.0,6,0,53.0,0.6,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.2,0.4,4.0,2.0,4.0,0.0,1.7,1.7,0.0,1.7,0.41,0.31,0.73,0.41,0.73,Manchester City
+Aymeric Laporte,es ESP,DF,29.0,1,0,12.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.27,0.0,0.27,0.27,0.27,Manchester City
+Cole Palmer,eng ENG,MF,21.0,1,0,11.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Manchester City
+James Mcatee,eng ENG,MF,20.0,1,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Manchester City
+Virgil van Dijk,nl NED,DF,32.0,36,36,3177.0,35.3,2.0,2.0,4.0,2.0,0.0,0.0,3.0,1.0,2.8,2.8,1.6,4.4,32.0,200.0,7.0,0.06,0.06,0.11,0.06,0.11,0.08,0.04,0.12,0.08,0.12,Liverpool
+Luis Díaz,co COL,FW,26.0,37,32,2629.0,29.2,8.0,5.0,13.0,8.0,0.0,0.0,3.0,0.0,11.9,11.9,5.7,17.5,137.0,113.0,302.0,0.27,0.17,0.45,0.27,0.45,0.41,0.19,0.6,0.41,0.6,Liverpool
+Alexis Mac Allister,ar ARG,MF,24.0,33,31,2599.0,28.9,5.0,5.0,10.0,4.0,1.0,1.0,7.0,1.0,3.7,2.9,3.6,6.4,44.0,209.0,48.0,0.17,0.17,0.35,0.14,0.31,0.13,0.12,0.25,0.1,0.22,Liverpool
+Mohamed Salah,eg EGY,FW,31.0,32,28,2534.0,28.2,18.0,10.0,28.0,13.0,5.0,7.0,2.0,0.0,21.2,15.5,11.8,27.3,98.0,147.0,341.0,0.64,0.36,0.99,0.46,0.82,0.75,0.42,1.17,0.55,0.97,Liverpool
+Alisson,br BRA,GK,30.0,28,28,2520.0,28.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Liverpool
+Trent Alexander-Arnold,eng ENG,DF,24.0,28,25,2155.0,23.9,3.0,4.0,7.0,3.0,0.0,0.0,6.0,0.0,2.6,2.3,7.2,9.5,45.0,205.0,97.0,0.13,0.17,0.29,0.13,0.29,0.11,0.3,0.41,0.1,0.4,Liverpool
+Dominik Szoboszlai,hu HUN,MF,22.0,33,25,2104.0,23.4,3.0,2.0,5.0,3.0,0.0,0.0,1.0,0.0,3.7,3.7,5.8,9.5,75.0,152.0,160.0,0.13,0.09,0.21,0.13,0.21,0.16,0.25,0.4,0.16,0.4,Liverpool
+Darwin Núñez,uy URU,FW,24.0,36,22,2047.0,22.7,11.0,8.0,19.0,11.0,0.0,1.0,9.0,0.0,16.3,15.5,6.0,21.5,58.0,54.0,206.0,0.48,0.35,0.84,0.48,0.84,0.72,0.27,0.98,0.68,0.95,Liverpool
+Wataru Endo,jp JPN,MF,30.0,29,20,1722.0,19.1,1.0,0.0,1.0,1.0,0.0,0.0,10.0,0.0,0.5,0.5,1.7,2.3,10.0,101.0,21.0,0.05,0.0,0.05,0.05,0.05,0.03,0.09,0.12,0.03,0.12,Liverpool
+Andrew Robertson,sct SCO,DF,29.0,23,18,1693.0,18.8,3.0,2.0,5.0,3.0,0.0,0.0,2.0,0.0,1.9,1.9,5.4,7.3,63.0,140.0,130.0,0.16,0.11,0.27,0.16,0.27,0.1,0.29,0.39,0.1,0.39,Liverpool
+Joe Gomez,eng ENG,DF,26.0,32,17,1786.0,19.8,0.0,1.0,1.0,0.0,0.0,0.0,5.0,0.0,0.8,0.8,1.3,2.1,44.0,100.0,105.0,0.0,0.05,0.05,0.0,0.05,0.04,0.07,0.1,0.04,0.1,Liverpool
+Cody Gakpo,nl NED,"FW,MF",24.0,35,17,1649.0,18.3,8.0,5.0,13.0,8.0,0.0,0.0,1.0,0.0,9.0,9.0,2.9,11.8,43.0,83.0,130.0,0.44,0.27,0.71,0.44,0.71,0.49,0.16,0.64,0.49,0.64,Liverpool
+Ibrahima Konaté,fr FRA,DF,24.0,22,17,1574.0,17.5,0.0,0.0,0.0,0.0,0.0,0.0,5.0,1.0,0.7,0.7,0.4,1.1,24.0,118.0,3.0,0.0,0.0,0.0,0.0,0.0,0.04,0.02,0.06,0.04,0.06,Liverpool
+Curtis Jones,eng ENG,MF,22.0,23,14,1167.0,13.0,1.0,1.0,2.0,1.0,0.0,0.0,3.0,1.0,2.6,2.6,0.7,3.3,31.0,74.0,88.0,0.08,0.08,0.15,0.08,0.15,0.2,0.05,0.25,0.2,0.25,Liverpool
+Diogo Jota,pt POR,FW,26.0,21,14,1145.0,12.7,10.0,3.0,13.0,10.0,0.0,0.0,3.0,1.0,5.3,5.3,3.3,8.6,54.0,34.0,111.0,0.79,0.24,1.02,0.79,1.02,0.42,0.26,0.68,0.42,0.68,Liverpool
+Jarell Quansah,eng ENG,DF,20.0,17,13,1190.0,13.2,2.0,0.0,2.0,2.0,0.0,0.0,1.0,0.0,1.0,1.0,0.1,1.1,13.0,67.0,1.0,0.15,0.0,0.15,0.15,0.15,0.08,0.01,0.08,0.08,0.08,Liverpool
+Ryan Gravenberch,nl NED,MF,21.0,26,12,1121.0,12.5,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,2.0,0.9,2.9,39.0,78.0,71.0,0.08,0.0,0.08,0.08,0.08,0.16,0.07,0.23,0.16,0.23,Liverpool
+Harvey Elliott,eng ENG,"MF,FW",20.0,34,11,1352.0,15.0,3.0,6.0,9.0,3.0,0.0,0.0,2.0,0.0,2.7,2.7,3.5,6.2,44.0,128.0,122.0,0.2,0.4,0.6,0.2,0.6,0.18,0.23,0.42,0.18,0.42,Liverpool
+Caoimhín Kelleher,ie IRL,GK,24.0,10,10,900.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Liverpool
+Conor Bradley,nir NIR,DF,20.0,11,10,753.0,8.4,1.0,3.0,4.0,1.0,0.0,0.0,2.0,0.0,0.9,0.9,1.5,2.3,35.0,29.0,64.0,0.12,0.36,0.48,0.12,0.48,0.1,0.17,0.28,0.1,0.28,Liverpool
+Joël Matip,cm CMR,DF,31.0,10,9,781.0,8.7,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.5,0.5,0.1,0.6,8.0,34.0,1.0,0.0,0.0,0.0,0.0,0.0,0.06,0.01,0.06,0.06,0.06,Liverpool
+Kostas Tsimikas,gr GRE,DF,27.0,13,8,679.0,7.5,0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,1.0,1.1,24.0,31.0,73.0,0.0,0.4,0.4,0.0,0.4,0.01,0.14,0.15,0.01,0.15,Liverpool
+Bobby Clark,eng ENG,MF,18.0,5,1,89.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.0,0.1,3.0,3.0,7.0,0.0,0.0,0.0,0.0,0.0,0.14,0.0,0.14,0.14,0.14,Liverpool
+Stefan Bajcetic,es ESP,MF,18.0,1,0,26.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Liverpool
+Ben Doak,sct SCO,FW,17.0,1,0,14.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Liverpool
+Jayden Danns,eng ENG,FW,17.0,2,0,9.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Liverpool
+Owen Beck,wls WAL,DF,20.0,1,0,8.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Liverpool
+Thiago Alcántara,es ESP,MF,32.0,1,0,6.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Liverpool
+James McConnell,eng ENG,MF,18.0,3,0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Liverpool
+Kaide Gordon,eng ENG,FW,18.0,1,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Liverpool
+William Saliba,fr FRA,DF,22.0,38,38,3420.0,38.0,2.0,1.0,3.0,2.0,0.0,0.0,4.0,0.0,1.6,1.6,0.2,1.8,16.0,157.0,6.0,0.05,0.03,0.08,0.05,0.08,0.04,0.01,0.05,0.04,0.05,Arsenal
+Declan Rice,eng ENG,MF,24.0,38,37,3225.0,35.8,7.0,8.0,15.0,7.0,0.0,0.0,5.0,0.0,3.3,3.3,5.3,8.6,75.0,278.0,64.0,0.2,0.22,0.42,0.2,0.42,0.09,0.15,0.24,0.09,0.24,Arsenal
+Martin Ødegaard,no NOR,MF,24.0,35,35,3091.0,34.3,8.0,10.0,18.0,6.0,2.0,2.0,2.0,0.0,7.4,5.8,9.6,15.5,91.0,344.0,202.0,0.23,0.29,0.52,0.17,0.47,0.22,0.28,0.5,0.17,0.45,Arsenal
+Ben White,eng ENG,DF,25.0,37,35,2988.0,33.2,4.0,4.0,8.0,4.0,0.0,0.0,8.0,0.0,1.2,1.2,3.8,5.0,44.0,192.0,162.0,0.12,0.12,0.24,0.12,0.24,0.03,0.11,0.15,0.03,0.15,Arsenal
+Bukayo Saka,eng ENG,FW,21.0,35,35,2919.0,32.4,16.0,9.0,25.0,10.0,6.0,6.0,4.0,0.0,15.5,10.8,10.5,21.2,155.0,126.0,508.0,0.49,0.28,0.77,0.31,0.59,0.48,0.32,0.8,0.33,0.65,Arsenal
+Gabriel Magalhães,br BRA,DF,25.0,36,34,3044.0,33.8,4.0,0.0,4.0,4.0,0.0,0.0,4.0,0.0,4.3,4.3,0.7,5.0,13.0,117.0,8.0,0.12,0.0,0.12,0.12,0.12,0.13,0.02,0.15,0.13,0.15,Arsenal
+David Raya,es ESP,GK,27.0,32,32,2880.0,32.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Arsenal
+Kai Havertz,de GER,"MF,FW",24.0,37,30,2634.0,29.3,13.0,7.0,20.0,12.0,1.0,1.0,11.0,0.0,12.3,11.6,4.4,15.9,55.0,99.0,191.0,0.44,0.24,0.68,0.41,0.65,0.42,0.15,0.57,0.39,0.54,Arsenal
+Gabriel Martinelli,br BRA,FW,22.0,35,24,2019.0,22.4,6.0,4.0,10.0,6.0,0.0,0.0,1.0,0.0,6.8,6.8,6.1,12.9,127.0,65.0,345.0,0.27,0.18,0.45,0.27,0.45,0.31,0.27,0.58,0.31,0.58,Arsenal
+Oleksandr Zinchenko,ua UKR,DF,26.0,27,20,1722.0,19.1,1.0,2.0,3.0,1.0,0.0,0.0,2.0,0.0,0.6,0.6,2.5,3.1,40.0,229.0,58.0,0.05,0.1,0.16,0.05,0.16,0.03,0.13,0.16,0.03,0.16,Arsenal
+Leandro Trossard,be BEL,"FW,MF",28.0,34,18,1649.0,18.3,12.0,1.0,13.0,12.0,0.0,0.0,2.0,0.0,7.9,7.9,2.8,10.7,66.0,76.0,155.0,0.65,0.05,0.71,0.65,0.71,0.43,0.15,0.58,0.43,0.58,Arsenal
+Gabriel Jesus,br BRA,FW,26.0,27,17,1478.0,16.4,4.0,5.0,9.0,4.0,0.0,0.0,6.0,0.0,6.3,6.3,3.8,10.1,38.0,42.0,155.0,0.24,0.3,0.55,0.24,0.55,0.39,0.23,0.62,0.39,0.62,Arsenal
+Jakub Kiwior,pl POL,DF,23.0,20,11,946.0,10.5,1.0,3.0,4.0,1.0,0.0,0.0,1.0,0.0,0.8,0.8,0.9,1.7,4.0,55.0,16.0,0.1,0.29,0.38,0.1,0.38,0.08,0.09,0.16,0.08,0.16,Arsenal
+Takehiro Tomiyasu,jp JPN,DF,24.0,22,10,1149.0,12.8,2.0,1.0,3.0,2.0,0.0,0.0,2.0,1.0,1.5,1.5,0.4,1.9,17.0,72.0,37.0,0.16,0.08,0.23,0.16,0.23,0.12,0.03,0.15,0.12,0.15,Arsenal
+Eddie Nketiah,eng ENG,FW,24.0,27,10,1082.0,12.0,5.0,2.0,7.0,5.0,0.0,0.0,3.0,0.0,4.5,4.5,0.8,5.4,22.0,20.0,77.0,0.42,0.17,0.58,0.42,0.58,0.38,0.07,0.45,0.38,0.45,Arsenal
+Jorginho,it ITA,MF,31.0,24,10,921.0,10.2,0.0,2.0,2.0,0.0,0.0,0.0,1.0,0.0,0.1,0.1,1.4,1.5,8.0,101.0,8.0,0.0,0.2,0.2,0.0,0.2,0.01,0.13,0.14,0.01,0.14,Arsenal
+Thomas Partey,gh GHA,"MF,DF",30.0,14,9,788.0,8.8,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.4,0.4,0.3,0.7,15.0,72.0,10.0,0.0,0.0,0.0,0.0,0.0,0.05,0.03,0.08,0.05,0.08,Arsenal
+Aaron Ramsdale,eng ENG,GK,25.0,6,6,540.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Arsenal
+Emile Smith Rowe,eng ENG,MF,23.0,13,3,353.0,3.9,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,0.2,2.2,11.0,23.0,18.0,0.0,0.25,0.25,0.0,0.25,0.5,0.06,0.56,0.5,0.56,Arsenal
+Fabio Vieira,pt POR,"MF,FW",23.0,11,2,297.0,3.3,1.0,2.0,3.0,0.0,1.0,1.0,0.0,1.0,1.3,0.5,1.6,2.1,12.0,15.0,30.0,0.3,0.61,0.91,0.0,0.61,0.38,0.49,0.87,0.14,0.64,Arsenal
+Reiss Nelson,eng ENG,FW,23.0,15,1,268.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.2,0.2,0.6,0.8,9.0,5.0,26.0,0.0,0.0,0.0,0.0,0.0,0.07,0.2,0.27,0.07,0.27,Arsenal
+Jurriën Timber,nl NED,DF,22.0,2,1,71.0,0.8,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Arsenal
+Cédric Soares,pt POR,DF,31.0,3,0,62.0,0.7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Arsenal
+Mohamed Elneny,eg EGY,MF,31.0,3,0,28.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.0,0.1,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.26,0.0,0.26,0.26,0.26,Arsenal
+Ethan Nwaneri,eng ENG,FW,16.0,1,0,14.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.15,0.15,0.0,0.15,Arsenal
+Conor Gallagher,eng ENG,MF,23.0,37,37,3128.0,34.8,5.0,7.0,12.0,5.0,0.0,0.0,9.0,1.0,4.2,4.2,5.0,9.2,62.0,172.0,122.0,0.14,0.2,0.35,0.14,0.35,0.12,0.15,0.27,0.12,0.27,Chelsea
+Moisés Caicedo,ec ECU,MF,21.0,35,33,2866.0,31.8,1.0,3.0,4.0,1.0,0.0,0.0,11.0,0.0,0.4,0.4,2.6,3.0,27.0,173.0,27.0,0.03,0.09,0.13,0.03,0.13,0.01,0.08,0.09,0.01,0.09,Chelsea
+Nicolas Jackson,sn SEN,FW,22.0,35,31,2799.0,31.1,14.0,5.0,19.0,14.0,0.0,0.0,10.0,0.0,18.6,18.6,4.3,22.9,70.0,67.0,200.0,0.45,0.16,0.61,0.45,0.61,0.6,0.14,0.74,0.6,0.74,Chelsea
+Cole Palmer,eng ENG,"FW,MF",21.0,33,29,2607.0,29.0,22.0,11.0,33.0,13.0,9.0,9.0,7.0,0.0,18.2,11.1,11.1,22.2,117.0,197.0,195.0,0.76,0.38,1.14,0.45,0.83,0.63,0.38,1.01,0.38,0.77,Chelsea
+Axel Disasi,fr FRA,DF,25.0,31,29,2579.0,28.7,2.0,0.0,2.0,2.0,0.0,0.0,6.0,0.0,2.4,2.4,0.3,2.7,30.0,112.0,21.0,0.07,0.0,0.07,0.07,0.07,0.08,0.01,0.09,0.08,0.09,Chelsea
+Thiago Silva,br BRA,DF,38.0,31,28,2527.0,28.1,3.0,1.0,4.0,3.0,0.0,0.0,4.0,0.0,0.8,0.8,1.0,1.8,2.0,102.0,5.0,0.11,0.04,0.14,0.11,0.14,0.03,0.04,0.06,0.03,0.06,Chelsea
+Enzo Fernández,ar ARG,MF,22.0,28,26,2209.0,24.5,3.0,2.0,5.0,2.0,1.0,2.0,7.0,0.0,6.2,4.6,3.4,8.0,52.0,210.0,54.0,0.12,0.08,0.2,0.08,0.16,0.25,0.14,0.39,0.19,0.33,Chelsea
+Đorđe Petrović,rs SRB,GK,23.0,23,22,1987.0,22.1,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Chelsea
+Raheem Sterling,eng ENG,"FW,MF",28.0,31,22,1978.0,22.0,8.0,4.0,12.0,8.0,0.0,0.0,7.0,0.0,6.5,6.5,4.5,11.0,120.0,78.0,242.0,0.36,0.18,0.55,0.36,0.55,0.29,0.2,0.5,0.29,0.5,Chelsea
+Levi Colwill,eng ENG,DF,20.0,23,20,1797.0,20.0,1.0,1.0,2.0,1.0,0.0,0.0,2.0,0.0,1.3,1.3,1.2,2.5,23.0,73.0,27.0,0.05,0.05,0.1,0.05,0.1,0.06,0.06,0.12,0.06,0.12,Chelsea
+Marc Cucurella,es ESP,DF,25.0,21,20,1782.0,19.8,0.0,2.0,2.0,0.0,0.0,0.0,10.0,0.0,1.1,1.1,1.1,2.2,31.0,50.0,73.0,0.0,0.1,0.1,0.0,0.1,0.06,0.05,0.11,0.06,0.11,Chelsea
+Malo Gusto,fr FRA,DF,20.0,27,19,1751.0,19.5,0.0,6.0,6.0,0.0,0.0,0.0,7.0,1.0,0.9,0.9,4.3,5.2,78.0,86.0,135.0,0.0,0.31,0.31,0.0,0.31,0.05,0.22,0.27,0.05,0.27,Chelsea
+Mykhailo Mudryk,ua UKR,FW,22.0,31,18,1576.0,17.5,5.0,2.0,7.0,5.0,0.0,0.0,5.0,0.0,4.4,4.4,4.1,8.5,92.0,45.0,139.0,0.29,0.11,0.4,0.29,0.4,0.25,0.23,0.48,0.25,0.48,Chelsea
+Robert Sánchez,es ESP,GK,25.0,16,16,1433.0,15.9,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Chelsea
+Benoît Badiashile,fr FRA,DF,22.0,18,15,1338.0,14.9,0.0,1.0,1.0,0.0,0.0,0.0,3.0,0.0,0.3,0.3,1.5,1.8,3.0,46.0,3.0,0.0,0.07,0.07,0.0,0.07,0.02,0.1,0.12,0.02,0.12,Chelsea
+Noni Madueke,eng ENG,FW,21.0,23,13,1053.0,11.7,5.0,2.0,7.0,4.0,1.0,1.0,2.0,0.0,2.5,1.7,2.6,4.3,76.0,52.0,136.0,0.43,0.17,0.6,0.34,0.51,0.21,0.23,0.44,0.14,0.37,Chelsea
+Trevoh Chalobah,eng ENG,DF,24.0,13,10,952.0,10.6,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.2,0.2,0.1,0.3,5.0,31.0,9.0,0.09,0.0,0.09,0.09,0.09,0.02,0.01,0.02,0.02,0.02,Chelsea
+Ben Chilwell,eng ENG,DF,26.0,13,9,756.0,8.4,0.0,1.0,1.0,0.0,0.0,0.0,5.0,0.0,1.2,1.2,2.2,3.4,22.0,25.0,59.0,0.0,0.12,0.12,0.0,0.12,0.14,0.26,0.41,0.14,0.41,Chelsea
+Armando Broja,al ALB,FW,21.0,13,6,454.0,5.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.4,2.4,0.2,2.5,11.0,6.0,37.0,0.2,0.0,0.2,0.2,0.2,0.47,0.03,0.5,0.47,0.5,Chelsea
+Reece James,eng ENG,DF,23.0,10,5,419.0,4.7,0.0,2.0,2.0,0.0,0.0,0.0,2.0,2.0,0.3,0.3,1.7,2.1,19.0,21.0,35.0,0.0,0.43,0.43,0.0,0.43,0.07,0.37,0.44,0.07,0.44,Chelsea
+Lesley Ugochukwu,fr FRA,MF,19.0,12,3,301.0,3.3,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.1,0.1,8.0,17.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03,0.03,0.0,0.03,Chelsea
+Christopher Nkunku,fr FRA,"FW,MF",25.0,11,2,443.0,4.9,3.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,2.3,2.3,0.2,2.5,10.0,19.0,35.0,0.61,0.0,0.61,0.61,0.61,0.47,0.05,0.52,0.47,0.52,Chelsea
+Carney Chukwuemeka,eng ENG,"MF,FW",19.0,9,2,227.0,2.5,1.0,1.0,2.0,1.0,0.0,0.0,2.0,0.0,0.2,0.2,0.3,0.5,4.0,12.0,19.0,0.4,0.4,0.79,0.4,0.79,0.06,0.13,0.19,0.06,0.19,Chelsea
+Alfie Gilchrist,eng ENG,DF,19.0,11,2,207.0,2.3,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.3,0.3,0.0,0.3,1.0,7.0,4.0,0.43,0.0,0.43,0.43,0.43,0.14,0.0,0.14,0.14,0.14,Chelsea
+Ian Maatsen,nl NED,"FW,MF",21.0,12,1,209.0,2.3,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.8,0.8,0.1,0.9,9.0,8.0,15.0,0.0,0.0,0.0,0.0,0.0,0.35,0.04,0.39,0.35,0.39,Chelsea
+Cesare Casadei,it ITA,"FW,MF",20.0,11,0,79.0,0.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,1.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.12,0.12,0.0,0.12,Chelsea
+Roméo Lavia,be BEL,MF,19.0,1,0,33.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Chelsea
+Deivid Washington,br BRA,"DF,FW",18.0,2,0,13.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.0,0.5,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,3.23,0.0,3.23,3.23,3.23,Chelsea
+Mason Burstow,eng ENG,FW,19.0,2,0,9.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Chelsea
+Joshua Acheampong,eng ENG,DF,17.0,1,0,6.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Chelsea
+Alex Matos,eng ENG,MF,18.0,1,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Chelsea
+Jimi Tauriainen,eng ENG,FW,19.0,1,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Chelsea
+Bruno Guimarães,br BRA,MF,25.0,37,37,3263.0,36.3,7.0,8.0,15.0,7.0,0.0,0.0,9.0,0.0,4.8,4.8,6.4,11.2,65.0,283.0,73.0,0.19,0.22,0.41,0.19,0.41,0.13,0.18,0.31,0.13,0.31,Newcastle United
+Fabian Schär,ch SUI,DF,31.0,36,35,3054.0,33.9,4.0,1.0,5.0,4.0,0.0,0.0,5.0,0.0,2.5,2.5,0.7,3.3,28.0,133.0,20.0,0.12,0.03,0.15,0.12,0.15,0.07,0.02,0.1,0.07,0.1,Newcastle United
+Anthony Gordon,eng ENG,FW,22.0,35,34,2890.0,32.1,11.0,10.0,21.0,10.0,1.0,1.0,12.0,1.0,10.2,9.4,8.0,17.4,138.0,101.0,232.0,0.34,0.31,0.65,0.31,0.62,0.32,0.25,0.57,0.29,0.54,Newcastle United
+Dan Burn,eng ENG,DF,31.0,33,32,2726.0,30.3,2.0,2.0,4.0,2.0,0.0,0.0,4.0,0.0,3.1,3.1,1.1,4.2,14.0,75.0,25.0,0.07,0.07,0.13,0.07,0.13,0.1,0.04,0.14,0.1,0.14,Newcastle United
+Sean Longstaff,eng ENG,MF,25.0,35,30,2747.0,30.5,6.0,2.0,8.0,6.0,0.0,0.0,7.0,0.0,6.6,6.6,2.3,8.8,24.0,145.0,116.0,0.2,0.07,0.26,0.2,0.26,0.22,0.07,0.29,0.22,0.29,Newcastle United
+Alexander Isak,se SWE,FW,23.0,30,27,2255.0,25.1,21.0,2.0,23.0,16.0,5.0,6.0,1.0,0.0,20.3,15.6,3.7,19.3,68.0,71.0,129.0,0.84,0.08,0.92,0.64,0.72,0.81,0.15,0.96,0.62,0.77,Newcastle United
+Kieran Trippier,eng ENG,DF,32.0,28,26,2236.0,24.8,1.0,10.0,11.0,1.0,0.0,0.0,5.0,0.0,0.7,0.7,7.5,8.2,34.0,157.0,128.0,0.04,0.4,0.44,0.04,0.44,0.03,0.3,0.33,0.03,0.33,Newcastle United
+Miguel Almirón,py PAR,FW,29.0,33,23,1937.0,21.5,3.0,1.0,4.0,3.0,0.0,0.0,2.0,0.0,4.5,4.5,2.6,7.2,78.0,78.0,162.0,0.14,0.05,0.19,0.14,0.19,0.21,0.12,0.33,0.21,0.33,Newcastle United
+Martin Dúbravka,sk SVK,GK,34.0,23,22,1985.0,22.1,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.1,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Newcastle United
+Sven Botman,nl NED,DF,23.0,17,15,1377.0,15.3,2.0,2.0,4.0,2.0,0.0,0.0,2.0,0.0,0.6,0.6,1.0,1.6,0.0,35.0,5.0,0.13,0.13,0.26,0.13,0.26,0.04,0.07,0.11,0.04,0.11,Newcastle United
+Nick Pope,eng ENG,GK,31.0,15,15,1345.0,14.9,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Newcastle United
+Joelinton,br BRA,"MF,FW",26.0,20,15,1279.0,14.2,2.0,1.0,3.0,2.0,0.0,0.0,6.0,0.0,3.1,3.1,2.8,5.9,29.0,56.0,60.0,0.14,0.07,0.21,0.14,0.21,0.22,0.2,0.41,0.22,0.41,Newcastle United
+Lewis Miley,eng ENG,MF,17.0,17,14,1202.0,13.4,1.0,3.0,4.0,1.0,0.0,0.0,1.0,0.0,1.2,1.2,1.7,2.9,14.0,53.0,54.0,0.07,0.22,0.3,0.07,0.3,0.09,0.13,0.22,0.09,0.22,Newcastle United
+Jacob Murphy,eng ENG,"FW,DF",28.0,21,14,1189.0,13.2,3.0,7.0,10.0,3.0,0.0,0.0,1.0,0.0,2.1,2.1,5.0,7.1,33.0,52.0,113.0,0.23,0.53,0.76,0.23,0.76,0.16,0.37,0.54,0.16,0.54,Newcastle United
+Jamaal Lascelles,eng ENG,DF,29.0,16,13,1080.0,12.0,1.0,0.0,1.0,1.0,0.0,0.0,4.0,0.0,0.7,0.7,0.1,0.8,8.0,31.0,3.0,0.08,0.0,0.08,0.08,0.08,0.06,0.01,0.07,0.06,0.07,Newcastle United
+Valentino Livramento,eng ENG,DF,20.0,26,12,1314.0,14.6,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.4,0.4,0.8,1.2,44.0,61.0,76.0,0.07,0.0,0.07,0.07,0.07,0.03,0.05,0.08,0.03,0.08,Newcastle United
+Elliot Anderson,sct SCO,"MF,FW",20.0,21,10,1027.0,11.4,0.0,2.0,2.0,0.0,0.0,0.0,3.0,0.0,1.5,1.5,1.9,3.5,37.0,57.0,70.0,0.0,0.18,0.18,0.0,0.18,0.13,0.17,0.3,0.13,0.3,Newcastle United
+Callum Wilson,eng ENG,FW,31.0,20,9,991.0,11.0,9.0,1.0,10.0,7.0,2.0,2.0,3.0,0.0,8.8,7.2,0.6,7.8,11.0,9.0,77.0,0.82,0.09,0.91,0.64,0.73,0.8,0.05,0.85,0.65,0.71,Newcastle United
+Emil Krafth,se SWE,DF,28.0,17,8,913.0,10.1,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.2,0.2,1.0,1.2,12.0,29.0,33.0,0.0,0.0,0.0,0.0,0.0,0.02,0.1,0.12,0.02,0.12,Newcastle United
+Lewis Hall,eng ENG,DF,18.0,18,8,781.0,8.7,1.0,0.0,1.0,1.0,0.0,0.0,2.0,0.0,0.3,0.3,0.3,0.6,19.0,46.0,27.0,0.12,0.0,0.12,0.12,0.12,0.04,0.03,0.07,0.04,0.07,Newcastle United
+Harvey Barnes,eng ENG,"FW,MF",25.0,21,7,802.0,8.9,5.0,3.0,8.0,5.0,0.0,0.0,2.0,0.0,3.6,3.6,3.2,6.8,45.0,34.0,79.0,0.56,0.34,0.9,0.56,0.9,0.4,0.36,0.76,0.4,0.76,Newcastle United
+Sandro Tonali,it ITA,MF,23.0,8,5,438.0,4.9,1.0,0.0,1.0,1.0,0.0,0.0,2.0,0.0,0.7,0.7,0.6,1.3,11.0,19.0,21.0,0.21,0.0,0.21,0.21,0.21,0.13,0.13,0.26,0.13,0.26,Newcastle United
+Joe Willock,eng ENG,MF,23.0,9,5,419.0,4.7,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.8,0.8,0.6,1.4,16.0,17.0,26.0,0.21,0.0,0.21,0.21,0.21,0.17,0.12,0.3,0.17,0.3,Newcastle United
+Loris Karius,de GER,GK,30.0,1,1,90.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Newcastle United
+Matt Targett,eng ENG,DF,27.0,3,1,78.0,0.9,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03,0.03,0.0,0.03,Newcastle United
+Matt Ritchie,sct SCO,"FW,MF",33.0,13,0,128.0,1.4,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,5.0,8.0,13.0,0.7,0.0,0.7,0.7,0.7,0.68,0.0,0.68,0.68,0.68,Newcastle United
+Ben Parkinson,eng ENG,FW,18.0,1,0,25.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Newcastle United
+Paul Dummett,wls WAL,DF,31.0,5,0,20.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11,0.0,0.11,0.11,0.11,Newcastle United
+Alex Murphy,ie IRL,"DF,FW",19.0,2,0,13.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Newcastle United
+Joe White,eng ENG,MF,20.0,4,0,13.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Newcastle United
+Amadou Diallo,eng ENG,MF,20.0,1,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Newcastle United
+Michael Ndiweni,eng ENG,FW,19.0,1,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Newcastle United
+Guglielmo Vicario,it ITA,GK,26.0,38,38,3420.0,38.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Tottenham Hotspur
+Pedro Porro,es ESP,DF,23.0,35,35,3089.0,34.3,3.0,7.0,10.0,3.0,0.0,0.0,3.0,0.0,2.9,2.9,7.2,10.1,57.0,194.0,158.0,0.09,0.2,0.29,0.09,0.29,0.09,0.21,0.3,0.09,0.3,Tottenham Hotspur
+Son Heung-min,kr KOR,FW,31.0,35,34,2934.0,32.6,17.0,10.0,27.0,15.0,2.0,2.0,1.0,0.0,12.0,10.4,11.8,22.2,119.0,139.0,325.0,0.52,0.31,0.83,0.46,0.77,0.37,0.36,0.73,0.32,0.68,Tottenham Hotspur
+Cristian Romero,ar ARG,DF,25.0,33,33,2790.0,31.0,5.0,0.0,5.0,5.0,0.0,0.0,7.0,1.0,3.5,3.5,0.4,3.9,32.0,179.0,8.0,0.16,0.0,0.16,0.16,0.16,0.11,0.01,0.13,0.11,0.13,Tottenham Hotspur
+Dejan Kulusevski,se SWE,"FW,MF",23.0,36,31,2762.0,30.7,8.0,3.0,11.0,8.0,0.0,0.0,7.0,0.0,4.7,4.7,7.7,12.4,151.0,144.0,388.0,0.26,0.1,0.36,0.26,0.36,0.15,0.25,0.4,0.15,0.4,Tottenham Hotspur
+Destiny Udogie,it ITA,DF,20.0,28,28,2391.0,26.6,2.0,3.0,5.0,2.0,0.0,0.0,7.0,1.0,2.4,2.4,2.2,4.6,83.0,155.0,138.0,0.08,0.11,0.19,0.08,0.19,0.09,0.08,0.17,0.09,0.17,Tottenham Hotspur
+Micky van de Ven,nl NED,DF,22.0,27,27,2339.0,26.0,3.0,0.0,3.0,3.0,0.0,0.0,5.0,0.0,0.6,0.6,0.0,0.6,19.0,108.0,9.0,0.12,0.0,0.12,0.12,0.12,0.02,0.0,0.02,0.02,0.02,Tottenham Hotspur
+Pape Matar Sarr,sn SEN,MF,20.0,34,27,2069.0,23.0,3.0,3.0,6.0,3.0,0.0,0.0,9.0,0.0,3.4,3.4,2.2,5.7,43.0,150.0,90.0,0.13,0.13,0.26,0.13,0.26,0.15,0.1,0.25,0.15,0.25,Tottenham Hotspur
+James Maddison,eng ENG,MF,26.0,28,26,2135.0,23.7,4.0,9.0,13.0,4.0,0.0,0.0,5.0,0.0,6.4,6.4,5.9,12.4,58.0,224.0,156.0,0.17,0.38,0.55,0.17,0.55,0.27,0.25,0.52,0.27,0.52,Tottenham Hotspur
+Yves Bissouma,ml MLI,MF,26.0,28,26,2068.0,23.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,2.0,1.4,1.4,0.4,1.8,43.0,169.0,13.0,0.0,0.0,0.0,0.0,0.0,0.06,0.02,0.08,0.06,0.08,Tottenham Hotspur
+Brennan Johnson,wls WAL,FW,22.0,32,23,2085.0,23.2,5.0,10.0,15.0,5.0,0.0,0.0,3.0,0.0,10.3,10.3,8.1,18.3,97.0,72.0,293.0,0.22,0.43,0.65,0.22,0.65,0.44,0.35,0.79,0.44,0.79,Tottenham Hotspur
+Richarlison,br BRA,FW,26.0,28,18,1491.0,16.6,11.0,4.0,15.0,11.0,0.0,0.0,3.0,0.0,9.6,9.6,2.1,11.7,26.0,38.0,106.0,0.66,0.24,0.91,0.66,0.91,0.58,0.13,0.7,0.58,0.7,Tottenham Hotspur
+Rodrigo Bentancur,uy URU,MF,26.0,23,13,1005.0,11.2,1.0,1.0,2.0,1.0,0.0,0.0,7.0,0.0,1.9,1.9,0.5,2.4,16.0,72.0,31.0,0.09,0.09,0.18,0.09,0.18,0.17,0.04,0.22,0.17,0.22,Tottenham Hotspur
+Ben Davies,wls WAL,DF,30.0,17,12,1090.0,12.1,1.0,0.0,1.0,1.0,0.0,0.0,2.0,0.0,2.3,2.3,0.4,2.7,13.0,71.0,20.0,0.08,0.0,0.08,0.08,0.08,0.19,0.03,0.23,0.19,0.23,Tottenham Hotspur
+Emerson,br BRA,DF,24.0,22,11,1161.0,12.9,1.0,0.0,1.0,1.0,0.0,0.0,4.0,0.0,0.2,0.2,0.4,0.6,21.0,57.0,35.0,0.08,0.0,0.08,0.08,0.08,0.02,0.03,0.05,0.02,0.05,Tottenham Hotspur
+Timo Werner,de GER,FW,27.0,13,10,809.0,9.0,2.0,3.0,5.0,2.0,0.0,0.0,1.0,0.0,3.7,3.7,2.7,6.4,79.0,41.0,124.0,0.22,0.33,0.56,0.22,0.56,0.41,0.3,0.72,0.41,0.72,Tottenham Hotspur
+Pierre Højbjerg,dk DEN,MF,27.0,36,8,1311.0,14.6,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.7,0.7,1.2,1.9,17.0,134.0,29.0,0.0,0.0,0.0,0.0,0.0,0.05,0.08,0.13,0.05,0.13,Tottenham Hotspur
+Oliver Skipp,eng ENG,"MF,DF",22.0,21,5,707.0,7.9,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.7,0.7,0.6,1.3,8.0,37.0,25.0,0.0,0.0,0.0,0.0,0.0,0.09,0.07,0.16,0.09,0.16,Tottenham Hotspur
+Giovani Lo Celso,ar ARG,MF,27.0,22,4,506.0,5.6,2.0,2.0,4.0,2.0,0.0,0.0,2.0,0.0,0.6,0.6,1.9,2.5,25.0,68.0,29.0,0.36,0.36,0.71,0.36,0.71,0.1,0.34,0.45,0.1,0.45,Tottenham Hotspur
+Radu Drăgușin,ro ROU,DF,21.0,9,4,429.0,4.8,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.1,0.1,0.0,0.1,5.0,8.0,1.0,0.0,0.0,0.0,0.0,0.0,0.03,0.0,0.03,0.03,0.03,Tottenham Hotspur
+Bryan Gil,es ESP,"FW,MF",22.0,11,2,209.0,2.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8,0.8,0.2,1.0,11.0,16.0,49.0,0.0,0.0,0.0,0.0,0.0,0.32,0.09,0.41,0.32,0.41,Tottenham Hotspur
+Manor Solomon,il ISR,FW,24.0,5,2,199.0,2.2,0.0,2.0,2.0,0.0,0.0,0.0,1.0,0.0,0.4,0.4,0.8,1.2,17.0,11.0,44.0,0.0,0.9,0.9,0.0,0.9,0.19,0.38,0.56,0.19,0.56,Tottenham Hotspur
+Eric Dier,eng ENG,DF,29.0,4,1,201.0,2.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Tottenham Hotspur
+Ivan Perišić,hr CRO,FW,34.0,5,0,103.0,1.1,0.0,1.0,1.0,0.0,0.0,0.0,2.0,0.0,0.1,0.1,0.9,1.0,15.0,9.0,17.0,0.0,0.87,0.87,0.0,0.87,0.1,0.75,0.85,0.1,0.85,Tottenham Hotspur
+Davinson Sánchez,co COL,DF,27.0,1,0,77.0,0.9,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.1,0.1,0.0,0.1,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.14,0.0,0.14,0.14,0.14,Tottenham Hotspur
+Alejo Véliz,ar ARG,FW,19.0,8,0,50.0,0.6,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.2,0.2,0.0,0.2,0.0,1.0,3.0,1.8,0.0,1.8,1.8,1.8,0.45,0.0,0.45,0.45,0.45,Tottenham Hotspur
+Dane Scarlett,eng ENG,FW,19.0,4,0,22.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Tottenham Hotspur
+Mikey Moore,eng ENG,MF,15.0,2,0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Tottenham Hotspur
+Jamie Donley,eng ENG,FW,18.0,3,0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Tottenham Hotspur
+André Onana,cm CMR,GK,27.0,38,38,3420.0,38.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.1,0.1,1.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Manchester United
+Diogo Dalot,pt POR,DF,24.0,36,35,3174.0,35.3,2.0,3.0,5.0,2.0,0.0,0.0,6.0,1.0,1.7,1.7,2.9,4.6,52.0,124.0,111.0,0.06,0.09,0.14,0.06,0.14,0.05,0.08,0.13,0.05,0.13,Manchester United
+Bruno Fernandes,pt POR,"MF,FW",28.0,35,35,3118.0,34.6,10.0,8.0,18.0,6.0,4.0,5.0,9.0,0.0,10.0,6.1,11.8,17.8,86.0,297.0,182.0,0.29,0.23,0.52,0.17,0.4,0.29,0.34,0.63,0.18,0.51,Manchester United
+Alejandro Garnacho,ar ARG,FW,19.0,36,30,2565.0,28.5,7.0,4.0,11.0,7.0,0.0,0.0,4.0,0.0,8.4,8.3,5.1,13.4,178.0,62.0,281.0,0.25,0.14,0.39,0.25,0.39,0.29,0.18,0.47,0.29,0.47,Manchester United
+Marcus Rashford,eng ENG,FW,25.0,33,26,2271.0,25.2,7.0,2.0,9.0,5.0,2.0,2.0,2.0,0.0,7.4,5.8,2.3,8.1,95.0,65.0,184.0,0.28,0.08,0.36,0.2,0.28,0.29,0.09,0.38,0.23,0.32,Manchester United
+Rasmus Højlund,dk DEN,FW,20.0,30,25,2158.0,24.0,10.0,2.0,12.0,10.0,0.0,0.0,2.0,0.0,7.6,7.6,1.6,9.2,36.0,33.0,111.0,0.42,0.08,0.5,0.42,0.5,0.32,0.07,0.38,0.32,0.38,Manchester United
+Casemiro,br BRA,"MF,DF",31.0,25,24,1982.0,22.0,1.0,2.0,3.0,1.0,0.0,0.0,7.0,0.0,3.5,3.5,1.0,4.5,12.0,112.0,36.0,0.05,0.09,0.14,0.05,0.14,0.16,0.05,0.2,0.16,0.2,Manchester United
+Kobbie Mainoo,eng ENG,MF,18.0,24,24,1924.0,21.4,3.0,1.0,4.0,3.0,0.0,0.0,3.0,0.0,0.9,0.9,0.7,1.6,25.0,64.0,36.0,0.14,0.05,0.19,0.14,0.19,0.04,0.03,0.07,0.04,0.07,Manchester United
+Aaron Wan-Bissaka,eng ENG,DF,25.0,22,20,1780.0,19.8,0.0,2.0,2.0,0.0,0.0,0.0,4.0,0.0,0.1,0.1,1.5,1.6,30.0,77.0,54.0,0.0,0.1,0.1,0.0,0.1,0.01,0.08,0.08,0.01,0.08,Manchester United
+Scott McTominay,sct SCO,"MF,FW",26.0,32,18,1901.0,21.1,7.0,1.0,8.0,7.0,0.0,0.0,2.0,0.0,5.7,5.7,0.8,6.6,29.0,64.0,47.0,0.33,0.05,0.38,0.33,0.38,0.27,0.04,0.31,0.27,0.31,Manchester United
+Harry Maguire,eng ENG,DF,30.0,22,18,1653.0,18.4,2.0,2.0,4.0,2.0,0.0,0.0,4.0,0.0,2.0,2.0,1.2,3.3,20.0,73.0,2.0,0.11,0.11,0.22,0.11,0.22,0.11,0.07,0.18,0.11,0.18,Manchester United
+Raphaël Varane,fr FRA,DF,30.0,22,16,1375.0,15.3,1.0,0.0,1.0,1.0,0.0,0.0,2.0,0.0,1.3,1.3,0.1,1.3,16.0,59.0,2.0,0.07,0.0,0.07,0.07,0.07,0.08,0.0,0.09,0.08,0.09,Manchester United
+Jonny Evans,nir NIR,DF,35.0,23,15,1392.0,15.5,0.0,1.0,1.0,0.0,0.0,0.0,2.0,0.0,0.1,0.1,1.2,1.2,7.0,37.0,1.0,0.0,0.06,0.06,0.0,0.06,0.0,0.08,0.08,0.0,0.08,Manchester United
+Antony,br BRA,FW,23.0,29,15,1324.0,14.7,1.0,1.0,2.0,1.0,0.0,0.0,5.0,0.0,3.4,3.4,2.8,6.1,53.0,64.0,149.0,0.07,0.07,0.14,0.07,0.14,0.23,0.19,0.42,0.23,0.42,Manchester United
+Victor Lindelöf,se SWE,DF,29.0,19,14,1328.0,14.8,1.0,1.0,2.0,1.0,0.0,0.0,2.0,0.0,0.8,0.8,0.3,1.1,17.0,40.0,17.0,0.07,0.07,0.14,0.07,0.14,0.05,0.02,0.07,0.05,0.07,Manchester United
+Christian Eriksen,dk DEN,MF,31.0,22,12,1141.0,12.7,1.0,2.0,3.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,2.3,3.3,20.0,68.0,23.0,0.08,0.16,0.24,0.08,0.24,0.08,0.18,0.26,0.08,0.26,Manchester United
+Luke Shaw,eng ENG,DF,28.0,12,12,959.0,10.7,0.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,1.0,1.0,10.0,45.0,30.0,0.0,0.0,0.0,0.0,0.0,0.0,0.09,0.1,0.0,0.1,Manchester United
+Sofyan Amrabat,ma MAR,"MF,DF",26.0,21,10,938.0,10.4,0.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,0.1,0.1,0.3,0.4,6.0,66.0,10.0,0.0,0.0,0.0,0.0,0.0,0.01,0.03,0.04,0.01,0.04,Manchester United
+Lisandro Martínez,ar ARG,DF,25.0,11,8,646.0,7.2,0.0,1.0,1.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.1,0.1,8.0,28.0,1.0,0.0,0.14,0.14,0.0,0.14,0.0,0.01,0.01,0.0,0.01,Manchester United
+Mason Mount,eng ENG,"MF,FW",24.0,14,5,516.0,5.7,1.0,0.0,1.0,1.0,0.0,0.0,2.0,0.0,0.4,0.4,0.3,0.7,9.0,21.0,34.0,0.17,0.0,0.17,0.17,0.17,0.07,0.06,0.13,0.07,0.13,Manchester United
+Anthony Martial,fr FRA,FW,27.0,13,5,448.0,5.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.2,1.2,0.2,1.5,6.0,12.0,36.0,0.2,0.0,0.2,0.2,0.2,0.25,0.05,0.29,0.25,0.29,Manchester United
+Sergio Reguilón,es ESP,DF,26.0,9,4,412.0,4.6,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.2,0.2,0.7,0.9,11.0,27.0,38.0,0.0,0.0,0.0,0.0,0.0,0.04,0.16,0.2,0.04,0.2,Manchester United
+Amad Diallo,ci CIV,"FW,MF",21.0,9,3,390.0,4.3,1.0,1.0,2.0,1.0,0.0,0.0,1.0,0.0,0.5,0.5,1.1,1.6,11.0,10.0,31.0,0.23,0.23,0.46,0.23,0.46,0.12,0.24,0.36,0.12,0.36,Manchester United
+Willy Kambwala,fr FRA,DF,18.0,8,3,330.0,3.7,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,8.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Manchester United
+Facundo Pellistri,uy URU,"FW,MF",21.0,9,1,172.0,1.9,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.7,0.7,0.1,0.8,6.0,6.0,31.0,0.0,0.52,0.52,0.0,0.52,0.38,0.05,0.43,0.38,0.43,Manchester United
+Hannibal Mejbri,tn TUN,"MF,FW",20.0,5,1,133.0,1.5,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.4,0.4,2.0,6.0,10.0,0.68,0.0,0.68,0.68,0.68,0.02,0.25,0.27,0.02,0.27,Manchester United
+Omari Forson,eng ENG,FW,19.0,4,1,70.0,0.8,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,1.0,2.0,2.0,0.0,1.29,1.29,0.0,1.29,0.04,0.23,0.27,0.04,0.27,Manchester United
+Jadon Sancho,eng ENG,"FW,MF",23.0,3,0,79.0,0.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,10.0,4.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.06,0.06,0.0,0.06,Manchester United
+Ethan Wheatley,eng ENG,FW,17.0,3,0,16.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Manchester United
+Donny van de Beek,nl NED,DF,26.0,1,0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.2,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.09,4.44,5.54,1.09,5.54,Manchester United
+Daniel Gore,eng ENG,MF,18.0,1,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Manchester United
+Ollie Watkins,eng ENG,FW,27.0,37,37,3217.0,35.7,19.0,13.0,32.0,19.0,0.0,0.0,4.0,0.0,16.8,16.8,7.3,24.2,62.0,55.0,214.0,0.53,0.36,0.9,0.53,0.9,0.47,0.21,0.68,0.47,0.68,Aston Villa
+Ezri Konsa,eng ENG,DF,25.0,35,35,3069.0,34.1,1.0,0.0,1.0,1.0,0.0,0.0,5.0,0.0,2.5,2.5,0.5,3.0,37.0,136.0,33.0,0.03,0.0,0.03,0.03,0.03,0.07,0.02,0.09,0.07,0.09,Aston Villa
+John McGinn,sct SCO,"MF,FW",28.0,35,35,2999.0,33.3,6.0,4.0,10.0,6.0,0.0,0.0,9.0,1.0,5.0,5.0,4.6,9.6,59.0,189.0,84.0,0.18,0.12,0.3,0.18,0.3,0.15,0.14,0.29,0.15,0.29,Aston Villa
+Douglas Luiz,br BRA,MF,25.0,35,35,2993.0,33.3,9.0,5.0,14.0,5.0,4.0,4.0,12.0,0.0,6.9,3.8,5.4,9.2,60.0,168.0,50.0,0.27,0.15,0.42,0.15,0.3,0.21,0.16,0.37,0.11,0.28,Aston Villa
+Emiliano Martínez,ar ARG,GK,30.0,34,34,3015.0,33.5,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Aston Villa
+Pau Torres,es ESP,DF,26.0,29,27,2464.0,27.4,2.0,0.0,2.0,2.0,0.0,0.0,1.0,0.0,1.0,1.0,0.4,1.4,42.0,128.0,16.0,0.07,0.0,0.07,0.07,0.07,0.04,0.02,0.05,0.04,0.05,Aston Villa
+Lucas Digne,fr FRA,DF,30.0,33,27,2410.0,26.8,1.0,3.0,4.0,1.0,0.0,0.0,7.0,0.0,1.1,1.1,4.1,5.2,42.0,77.0,200.0,0.04,0.11,0.15,0.04,0.15,0.04,0.15,0.19,0.04,0.19,Aston Villa
+Moussa Diaby,fr FRA,"FW,MF",24.0,38,25,2174.0,24.2,6.0,8.0,14.0,6.0,0.0,0.0,1.0,0.0,7.0,7.0,6.6,13.6,94.0,72.0,183.0,0.25,0.33,0.58,0.25,0.58,0.29,0.27,0.56,0.29,0.56,Aston Villa
+Matty Cash,pl POL,DF,25.0,29,23,2142.0,23.8,2.0,2.0,4.0,2.0,0.0,0.0,8.0,0.0,4.2,4.2,2.3,6.5,62.0,88.0,101.0,0.08,0.08,0.17,0.08,0.17,0.18,0.1,0.27,0.18,0.27,Aston Villa
+Leon Bailey,jm JAM,"MF,FW",25.0,35,22,2068.0,23.0,10.0,9.0,19.0,10.0,0.0,0.0,5.0,0.0,6.3,6.3,9.2,15.5,102.0,82.0,226.0,0.44,0.39,0.83,0.44,0.83,0.27,0.4,0.67,0.27,0.67,Aston Villa
+Diego Carlos,br BRA,DF,30.0,27,20,1816.0,20.2,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,1.9,1.9,0.1,2.0,4.0,59.0,1.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.1,0.1,0.1,Aston Villa
+Boubacar Kamara,fr FRA,MF,23.0,20,20,1652.0,18.4,0.0,1.0,1.0,0.0,0.0,0.0,7.0,1.0,0.1,0.1,1.3,1.4,19.0,83.0,10.0,0.0,0.05,0.05,0.0,0.05,0.01,0.07,0.08,0.01,0.08,Aston Villa
+Youri Tielemans,be BEL,"MF,FW",26.0,32,17,1622.0,18.0,2.0,6.0,8.0,2.0,0.0,0.0,3.0,0.0,1.3,1.3,2.6,3.9,23.0,128.0,46.0,0.11,0.33,0.44,0.11,0.44,0.07,0.14,0.21,0.07,0.21,Aston Villa
+Clément Lenglet,fr FRA,DF,28.0,14,14,1153.0,12.8,0.0,1.0,1.0,0.0,0.0,0.0,3.0,0.0,0.9,0.9,0.8,1.6,26.0,51.0,1.0,0.0,0.08,0.08,0.0,0.08,0.07,0.06,0.13,0.07,0.13,Aston Villa
+Álex Moreno,es ESP,DF,30.0,21,11,1031.0,11.5,2.0,0.0,2.0,2.0,0.0,0.0,2.0,0.0,1.1,1.1,1.1,2.3,32.0,24.0,83.0,0.17,0.0,0.17,0.17,0.17,0.1,0.1,0.2,0.1,0.2,Aston Villa
+Nicolò Zaniolo,it ITA,"FW,MF",24.0,25,9,839.0,9.3,2.0,0.0,2.0,2.0,0.0,0.0,7.0,0.0,2.6,2.6,1.0,3.6,35.0,30.0,60.0,0.21,0.0,0.21,0.21,0.21,0.28,0.11,0.39,0.28,0.39,Aston Villa
+Jacob Ramsey,eng ENG,"FW,MF",22.0,16,8,853.0,9.5,1.0,1.0,2.0,1.0,0.0,0.0,2.0,0.0,2.2,2.2,1.0,3.2,33.0,28.0,69.0,0.11,0.11,0.21,0.11,0.21,0.23,0.11,0.34,0.23,0.34,Aston Villa
+Morgan Rogers,eng ENG,"MF,FW",21.0,11,8,638.0,7.1,3.0,1.0,4.0,3.0,0.0,0.0,3.0,0.0,1.4,1.4,0.6,2.0,22.0,28.0,30.0,0.42,0.14,0.56,0.42,0.56,0.19,0.09,0.28,0.19,0.28,Aston Villa
+Robin Olsen,se SWE,GK,33.0,5,4,405.0,4.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Aston Villa
+Jhon Durán,co COL,"FW,MF",19.0,23,3,475.0,5.3,5.0,0.0,5.0,5.0,0.0,0.0,5.0,0.0,2.0,2.0,0.3,2.3,9.0,9.0,25.0,0.95,0.0,0.95,0.95,0.95,0.38,0.06,0.44,0.38,0.44,Aston Villa
+Tim Iroegbunam,eng ENG,MF,20.0,9,1,164.0,1.8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Aston Villa
+Calum Chambers,eng ENG,"MF,DF",28.0,5,1,152.0,1.7,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.1,0.1,0.0,5.0,1.0,0.0,0.59,0.59,0.0,0.59,0.0,0.06,0.06,0.0,0.06,Aston Villa
+Leander Dendoncker,be BEL,MF,28.0,8,1,119.0,1.3,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.7,0.7,0.0,0.7,0.0,3.0,2.0,0.76,0.0,0.76,0.76,0.76,0.56,0.0,0.56,0.56,0.56,Aston Villa
+Tyrone Mings,eng ENG,DF,30.0,1,1,30.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.07,0.09,0.02,0.09,Aston Villa
+Omari Kellyman,eng ENG,FW,17.0,2,0,37.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.0,0.1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.17,0.0,0.17,0.17,0.17,Aston Villa
+Philippe Coutinho,br BRA,"MF,FW",31.0,2,0,25.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.0,0.1,5.0,5.0,2.0,0.0,0.0,0.0,0.0,0.0,0.52,0.0,0.52,0.52,0.52,Aston Villa
+Bertrand Traoré,bf BFA,"DF,FW",27.0,2,0,11.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Aston Villa
+Cameron Archer,eng ENG,MF,22.0,1,0,6.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Aston Villa
+Kaine Kesler-Hayden,eng ENG,"MF,DF",20.0,3,0,6.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Aston Villa
+Finley Munroe,eng ENG,MF,18.0,1,0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Aston Villa
+Jaden Philogene Bidace,eng ENG,FW,21.0,1,0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Aston Villa
+Vladimír Coufal,cz CZE,DF,30.0,36,36,3132.0,34.8,0.0,7.0,7.0,0.0,0.0,0.0,7.0,1.0,0.3,0.3,4.0,4.3,68.0,119.0,126.0,0.0,0.2,0.2,0.0,0.2,0.01,0.11,0.12,0.01,0.12,West Ham United
+Emerson Palmieri,it ITA,DF,28.0,36,35,3137.0,34.9,1.0,2.0,3.0,1.0,0.0,0.0,10.0,0.0,1.5,1.5,2.4,3.9,59.0,125.0,124.0,0.03,0.06,0.09,0.03,0.09,0.04,0.07,0.11,0.04,0.11,West Ham United
+Jarrod Bowen,eng ENG,FW,26.0,34,34,3019.0,33.5,16.0,6.0,22.0,16.0,0.0,0.0,2.0,0.0,11.6,11.6,5.5,17.2,90.0,52.0,186.0,0.48,0.18,0.66,0.48,0.66,0.35,0.16,0.51,0.35,0.51,West Ham United
+James Ward-Prowse,eng ENG,MF,28.0,37,34,3001.0,33.3,7.0,7.0,14.0,5.0,2.0,2.0,4.0,0.0,6.0,4.4,7.8,12.3,18.0,144.0,63.0,0.21,0.21,0.42,0.15,0.36,0.18,0.23,0.41,0.13,0.37,West Ham United
+Tomáš Souček,cz CZE,MF,28.0,37,34,2870.0,31.9,7.0,2.0,9.0,7.0,0.0,0.0,7.0,0.0,6.7,6.7,1.7,8.4,7.0,86.0,39.0,0.22,0.06,0.28,0.22,0.28,0.21,0.05,0.26,0.21,0.26,West Ham United
+Kurt Zouma,fr FRA,DF,28.0,33,32,2838.0,31.5,3.0,0.0,3.0,3.0,0.0,0.0,3.0,0.0,1.8,1.8,0.2,2.0,7.0,53.0,1.0,0.1,0.0,0.1,0.1,0.1,0.06,0.01,0.06,0.06,0.06,West Ham United
+Alphonse Areola,fr FRA,GK,30.0,31,31,2699.0,30.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,West Ham United
+Lucas Paquetá,br BRA,"FW,MF",25.0,31,31,2622.0,29.1,4.0,6.0,10.0,2.0,2.0,2.0,10.0,0.0,5.4,3.8,5.4,9.2,34.0,187.0,116.0,0.14,0.21,0.34,0.07,0.27,0.18,0.18,0.37,0.13,0.32,West Ham United
+Edson Álvarez,mx MEX,MF,25.0,31,28,2377.0,26.4,1.0,1.0,2.0,1.0,0.0,0.0,11.0,0.0,1.3,1.3,0.6,1.9,20.0,91.0,26.0,0.04,0.04,0.08,0.04,0.08,0.05,0.02,0.07,0.05,0.07,West Ham United
+Mohammed Kudus,gh GHA,"FW,MF",22.0,33,27,2484.0,27.6,8.0,6.0,14.0,8.0,0.0,0.0,6.0,0.0,5.1,5.1,3.5,8.6,117.0,80.0,216.0,0.29,0.22,0.51,0.29,0.51,0.19,0.13,0.31,0.19,0.31,West Ham United
+Nayef Aguerd,ma MAR,DF,27.0,21,21,1857.0,20.6,1.0,0.0,1.0,1.0,0.0,0.0,3.0,1.0,1.5,1.5,0.3,1.8,10.0,68.0,3.0,0.05,0.0,0.05,0.05,0.05,0.07,0.01,0.09,0.07,0.09,West Ham United
+Michail Antonio,jm JAM,FW,33.0,26,21,1695.0,18.8,6.0,2.0,8.0,6.0,0.0,0.0,6.0,0.0,5.8,5.8,0.9,6.7,48.0,22.0,104.0,0.32,0.11,0.42,0.32,0.42,0.31,0.05,0.35,0.31,0.35,West Ham United
+Konstantinos Mavropanos,gr GRE,DF,25.0,19,16,1500.0,16.7,1.0,0.0,1.0,1.0,0.0,0.0,2.0,0.0,1.2,1.2,0.2,1.4,10.0,38.0,2.0,0.06,0.0,0.06,0.06,0.06,0.07,0.01,0.08,0.07,0.08,West Ham United
+Łukasz Fabiański,pl POL,GK,38.0,10,7,721.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,West Ham United
+Angelo Ogbonna,it ITA,DF,35.0,11,7,635.0,7.1,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,West Ham United
+Saïd Benrahma,dz ALG,"FW,MF",27.0,13,5,620.0,6.9,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.8,1.0,1.4,2.5,28.0,28.0,70.0,0.0,0.15,0.15,0.0,0.15,0.27,0.21,0.47,0.15,0.36,West Ham United
+Ben Johnson,eng ENG,"DF,FW",23.0,14,4,544.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.2,0.2,0.0,0.2,17.0,22.0,33.0,0.0,0.0,0.0,0.0,0.0,0.03,0.0,0.03,0.03,0.03,West Ham United
+Aaron Cresswell,eng ENG,"DF,FW",33.0,11,4,436.0,4.8,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.4,0.4,4.0,26.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.09,0.09,0.0,0.09,West Ham United
+Pablo Fornals,es ESP,"MF,FW",27.0,15,4,426.0,4.7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.3,0.6,4.0,29.0,27.0,0.0,0.0,0.0,0.0,0.0,0.07,0.07,0.13,0.07,0.13,West Ham United
+Danny Ings,eng ENG,FW,31.0,20,3,404.0,4.5,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.3,1.3,0.3,1.5,7.0,8.0,29.0,0.22,0.0,0.22,0.22,0.22,0.28,0.06,0.34,0.28,0.34,West Ham United
+Kalvin Phillips,eng ENG,MF,27.0,8,3,309.0,3.4,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,0.1,0.1,0.0,0.1,3.0,19.0,4.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.02,0.02,0.02,West Ham United
+Maxwel Cornet,ci CIV,"MF,FW",26.0,7,1,112.0,1.2,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.4,0.4,0.2,0.6,3.0,2.0,8.0,0.8,0.0,0.8,0.8,0.8,0.32,0.13,0.46,0.32,0.46,West Ham United
+Divin Mubama,eng ENG,FW,18.0,5,0,80.0,0.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.0,0.1,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.17,0.0,0.17,0.17,0.17,West Ham United
+George Earthy,eng ENG,FW,18.0,3,0,34.0,0.4,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.3,0.3,0.0,0.3,0.0,1.0,3.0,2.65,0.0,2.65,2.65,2.65,0.82,0.0,0.82,0.82,0.82,West Ham United
+Thilo Kehrer,de GER,"DF,MF",26.0,4,0,22.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,West Ham United
+Kaelan Casey,eng ENG,DF,18.0,1,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,West Ham United
+Joachim Andersen,dk DEN,DF,27.0,38,38,3415.0,37.9,2.0,3.0,5.0,2.0,0.0,0.0,7.0,0.0,1.7,1.7,2.8,4.5,21.0,147.0,10.0,0.05,0.08,0.13,0.05,0.13,0.04,0.07,0.12,0.04,0.12,Crystal Palace
+Tyrick Mitchell,eng ENG,"DF,MF",23.0,37,37,3204.0,35.6,2.0,3.0,5.0,2.0,0.0,0.0,6.0,0.0,1.6,1.6,2.8,4.4,32.0,94.0,159.0,0.06,0.08,0.14,0.06,0.14,0.04,0.08,0.12,0.04,0.12,Crystal Palace
+Jordan Ayew,gh GHA,"FW,MF",31.0,35,30,2543.0,28.3,4.0,7.0,11.0,4.0,0.0,0.0,9.0,1.0,3.9,3.9,4.2,8.1,74.0,63.0,173.0,0.14,0.25,0.39,0.14,0.39,0.14,0.15,0.29,0.14,0.29,Crystal Palace
+Jefferson Lerma,co COL,"MF,DF",28.0,28,27,2400.0,26.7,1.0,1.0,2.0,1.0,0.0,0.0,8.0,0.0,2.7,2.7,2.2,4.8,14.0,106.0,51.0,0.04,0.04,0.07,0.04,0.07,0.1,0.08,0.18,0.1,0.18,Crystal Palace
+Jean-Philippe Mateta,fr FRA,FW,26.0,35,25,2282.0,25.4,16.0,5.0,21.0,14.0,2.0,2.0,3.0,0.0,10.9,9.4,3.1,12.5,29.0,25.0,135.0,0.63,0.2,0.83,0.55,0.75,0.43,0.12,0.56,0.37,0.49,Crystal Palace
+Eberechi Eze,eng ENG,"MF,FW",25.0,27,24,2055.0,22.8,11.0,4.0,15.0,10.0,1.0,1.0,3.0,0.0,9.1,8.3,5.2,13.5,68.0,93.0,139.0,0.48,0.18,0.66,0.44,0.61,0.4,0.23,0.62,0.36,0.59,Crystal Palace
+Chris Richards,us USA,"DF,MF",23.0,26,23,2094.0,23.3,1.0,1.0,2.0,1.0,0.0,0.0,3.0,0.0,0.6,0.6,0.4,1.0,1.0,43.0,1.0,0.04,0.04,0.09,0.04,0.09,0.03,0.02,0.04,0.03,0.04,Crystal Palace
+Marc Guéhi,eng ENG,DF,23.0,25,23,2021.0,22.5,0.0,1.0,1.0,0.0,0.0,0.0,2.0,0.0,0.6,0.6,0.6,1.2,15.0,67.0,7.0,0.0,0.04,0.04,0.0,0.04,0.03,0.03,0.05,0.03,0.05,Crystal Palace
+Joel Ward,eng ENG,DF,33.0,26,23,1980.0,22.0,0.0,1.0,1.0,0.0,0.0,0.0,4.0,0.0,0.9,0.9,0.2,1.1,16.0,66.0,14.0,0.0,0.05,0.05,0.0,0.05,0.04,0.01,0.05,0.04,0.05,Crystal Palace
+Will Hughes,eng ENG,MF,28.0,30,23,1891.0,21.0,0.0,1.0,1.0,0.0,0.0,0.0,6.0,0.0,0.3,0.3,1.1,1.5,14.0,76.0,17.0,0.0,0.05,0.05,0.0,0.05,0.02,0.05,0.07,0.02,0.07,Crystal Palace
+Sam Johnstone,eng ENG,GK,30.0,20,20,1796.0,20.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Crystal Palace
+Dean Henderson,eng ENG,GK,26.0,18,18,1620.0,18.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Crystal Palace
+Odsonne Édouard,fr FRA,"FW,MF",25.0,30,17,1555.0,17.3,7.0,0.0,7.0,7.0,0.0,0.0,4.0,0.0,6.2,6.2,1.2,7.4,33.0,36.0,100.0,0.41,0.0,0.41,0.41,0.41,0.36,0.07,0.43,0.36,0.43,Crystal Palace
+Jeffrey Schlupp,gh GHA,"FW,MF",30.0,29,17,1352.0,15.0,2.0,2.0,4.0,2.0,0.0,0.0,1.0,0.0,1.8,1.8,2.3,4.1,20.0,33.0,52.0,0.13,0.13,0.27,0.13,0.27,0.12,0.15,0.27,0.12,0.27,Crystal Palace
+Daniel Muñoz,co COL,"DF,MF",27.0,16,16,1439.0,16.0,0.0,4.0,4.0,0.0,0.0,0.0,4.0,0.0,1.2,1.2,2.4,3.7,25.0,38.0,80.0,0.0,0.25,0.25,0.0,0.25,0.08,0.15,0.23,0.08,0.23,Crystal Palace
+Adam Wharton,eng ENG,MF,19.0,16,15,1297.0,14.4,0.0,3.0,3.0,0.0,0.0,0.0,2.0,0.0,0.3,0.3,2.4,2.8,14.0,79.0,10.0,0.0,0.21,0.21,0.0,0.21,0.02,0.17,0.19,0.02,0.19,Crystal Palace
+Nathaniel Clyne,eng ENG,DF,32.0,19,14,1338.0,14.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.2,0.4,10.0,33.0,18.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.03,0.01,0.03,Crystal Palace
+Michael Olise,fr FRA,"FW,MF",21.0,19,14,1275.0,14.2,10.0,6.0,16.0,9.0,1.0,1.0,0.0,0.0,5.5,4.7,5.8,10.5,68.0,78.0,134.0,0.71,0.42,1.13,0.64,1.06,0.39,0.41,0.8,0.33,0.74,Crystal Palace
+Cheick Doucouré,ml MLI,MF,23.0,11,11,919.0,10.2,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.3,0.3,0.7,1.0,9.0,49.0,5.0,0.0,0.0,0.0,0.0,0.0,0.03,0.07,0.1,0.03,0.1,Crystal Palace
+Jaïro Riedewald,nl NED,"MF,FW",26.0,9,2,235.0,2.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,10.0,3.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.01,0.01,0.01,Crystal Palace
+Matheus França,br BRA,"MF,FW",19.0,10,1,232.0,2.6,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.4,0.4,0.9,1.2,12.0,8.0,12.0,0.0,0.39,0.39,0.0,0.39,0.14,0.34,0.48,0.14,0.48,Crystal Palace
+Naouirou Ahamada,fr FRA,"MF,FW",21.0,20,0,349.0,3.9,0.0,0.0,0.0,0.0,0.0,0.0,4.0,1.0,0.5,0.5,0.0,0.5,19.0,25.0,20.0,0.0,0.0,0.0,0.0,0.0,0.13,0.01,0.13,0.13,0.13,Crystal Palace
+David Ozoh,eng ENG,"MF,DF",18.0,9,0,156.0,1.7,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,4.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Crystal Palace
+Jesurun Rak Sakyi,eng ENG,"MF,FW",20.0,6,0,136.0,1.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.2,0.6,7.0,2.0,18.0,0.0,0.0,0.0,0.0,0.0,0.28,0.1,0.38,0.28,0.38,Crystal Palace
+James Tomkins,eng ENG,DF,34.0,4,0,10.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Crystal Palace
+Remi Matthews,eng ENG,GK,29.0,1,0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Crystal Palace
+Bernd Leno,de GER,GK,31.0,38,38,3420.0,38.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Fulham
+Antonee Robinson,us USA,DF,25.0,37,37,3265.0,36.3,0.0,6.0,6.0,0.0,0.0,0.0,6.0,0.0,0.7,0.7,4.2,4.8,99.0,129.0,194.0,0.0,0.17,0.17,0.0,0.17,0.02,0.11,0.13,0.02,0.13,Fulham
+Andreas Pereira,br BRA,MF,27.0,37,34,2613.0,29.0,3.0,7.0,10.0,3.0,0.0,0.0,6.0,0.0,4.6,4.6,7.1,11.7,56.0,138.0,125.0,0.1,0.24,0.34,0.1,0.34,0.16,0.24,0.4,0.16,0.4,Fulham
+João Palhinha,pt POR,MF,28.0,33,31,2699.0,30.0,4.0,1.0,5.0,4.0,0.0,0.0,13.0,0.0,3.3,3.3,0.8,4.1,8.0,97.0,14.0,0.13,0.03,0.17,0.13,0.17,0.11,0.03,0.14,0.11,0.14,Fulham
+Timothy Castagne,be BEL,DF,27.0,34,29,2634.0,29.3,1.0,3.0,4.0,1.0,0.0,0.0,3.0,0.0,1.6,1.6,1.9,3.5,48.0,77.0,113.0,0.03,0.1,0.14,0.03,0.14,0.06,0.07,0.12,0.06,0.12,Fulham
+Calvin Bassey,ng NGA,DF,23.0,29,25,2303.0,25.6,1.0,0.0,1.0,1.0,0.0,0.0,6.0,1.0,0.3,0.3,0.0,0.4,15.0,62.0,4.0,0.04,0.0,0.04,0.04,0.04,0.01,0.0,0.01,0.01,0.01,Fulham
+Alex Iwobi,ng NGA,"FW,MF",27.0,30,25,2192.0,24.4,5.0,2.0,7.0,5.0,0.0,0.0,2.0,0.0,5.3,5.3,4.7,10.0,109.0,147.0,175.0,0.21,0.08,0.29,0.21,0.29,0.22,0.19,0.41,0.22,0.41,Fulham
+Willian,br BRA,FW,34.0,31,24,2053.0,22.8,4.0,2.0,6.0,2.0,2.0,2.0,1.0,0.0,5.1,3.6,3.9,7.5,92.0,153.0,181.0,0.18,0.09,0.26,0.09,0.18,0.22,0.17,0.4,0.16,0.33,Fulham
+Tosin Adarabioyo,eng ENG,DF,25.0,20,18,1617.0,18.0,2.0,0.0,2.0,2.0,0.0,0.0,2.0,0.0,0.7,0.7,0.1,0.8,10.0,62.0,5.0,0.11,0.0,0.11,0.11,0.11,0.04,0.01,0.05,0.04,0.05,Fulham
+Rodrigo Muniz,br BRA,FW,22.0,26,18,1593.0,17.7,9.0,1.0,10.0,9.0,0.0,0.0,1.0,0.0,8.7,8.7,2.0,10.7,8.0,19.0,76.0,0.51,0.06,0.56,0.51,0.56,0.49,0.12,0.61,0.49,0.61,Fulham
+Raúl Jiménez,mx MEX,FW,32.0,24,18,1395.0,15.5,7.0,0.0,7.0,7.0,0.0,0.0,3.0,1.0,5.2,5.2,1.5,6.6,11.0,24.0,87.0,0.45,0.0,0.45,0.45,0.45,0.33,0.1,0.43,0.33,0.43,Fulham
+Tim Ream,us USA,DF,35.0,18,17,1503.0,16.7,1.0,0.0,1.0,1.0,0.0,0.0,4.0,1.0,1.0,1.0,0.0,1.0,11.0,65.0,3.0,0.06,0.0,0.06,0.06,0.06,0.06,0.0,0.06,0.06,0.06,Fulham
+Bobby Reid,jm JAM,FW,30.0,33,17,1423.0,15.8,6.0,2.0,8.0,6.0,0.0,0.0,4.0,0.0,4.6,4.6,1.8,6.4,35.0,47.0,95.0,0.38,0.13,0.51,0.38,0.51,0.28,0.11,0.4,0.28,0.4,Fulham
+Harry Wilson,wls WAL,FW,26.0,35,16,1620.0,18.0,4.0,6.0,10.0,4.0,0.0,0.0,6.0,0.0,3.4,3.4,3.5,6.8,41.0,60.0,130.0,0.22,0.33,0.56,0.22,0.56,0.19,0.19,0.38,0.19,0.38,Fulham
+Issa Diop,fr FRA,DF,26.0,18,16,1424.0,15.8,0.0,0.0,0.0,0.0,0.0,0.0,4.0,1.0,0.3,0.3,0.0,0.3,11.0,66.0,1.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.02,0.02,0.02,Fulham
+Harrison Reed,eng ENG,MF,28.0,27,15,1325.0,14.7,0.0,2.0,2.0,0.0,0.0,0.0,6.0,0.0,0.3,0.3,1.1,1.4,10.0,56.0,40.0,0.0,0.14,0.14,0.0,0.14,0.02,0.07,0.09,0.02,0.09,Fulham
+Tom Cairney,sct SCO,MF,32.0,34,14,1488.0,16.5,1.0,4.0,5.0,1.0,0.0,0.0,3.0,0.0,1.1,1.1,2.3,3.4,35.0,145.0,72.0,0.06,0.24,0.3,0.06,0.3,0.07,0.14,0.2,0.07,0.2,Fulham
+Saša Lukić,rs SRB,MF,26.0,24,13,1122.0,12.5,1.0,0.0,1.0,1.0,0.0,0.0,3.0,0.0,1.6,1.6,0.8,2.4,12.0,43.0,27.0,0.08,0.0,0.08,0.08,0.08,0.13,0.06,0.19,0.13,0.19,Fulham
+Kenny Tete,nl NED,DF,27.0,14,10,867.0,9.6,1.0,0.0,1.0,1.0,0.0,0.0,2.0,0.0,0.9,0.9,0.4,1.2,20.0,54.0,38.0,0.1,0.0,0.1,0.1,0.1,0.09,0.04,0.13,0.09,0.13,Fulham
+Carlos Vinícius,br BRA,FW,28.0,13,2,315.0,3.5,2.0,0.0,2.0,2.0,0.0,0.0,2.0,0.0,1.6,1.6,0.8,2.4,3.0,3.0,13.0,0.57,0.0,0.57,0.57,0.57,0.46,0.23,0.69,0.46,0.69,Fulham
+Adama Traoré,es ESP,"FW,MF",27.0,17,1,377.0,4.2,2.0,3.0,5.0,2.0,0.0,0.0,2.0,0.0,1.5,1.5,0.7,2.2,29.0,16.0,48.0,0.48,0.72,1.19,0.48,1.19,0.36,0.17,0.53,0.36,0.53,Fulham
+Armando Broja,al ALB,FW,21.0,8,0,88.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,2.0,2.0,7.0,0.0,1.02,1.02,0.0,1.02,0.0,0.41,0.41,0.0,0.41,Fulham
+Luke Harris,wls WAL,MF,18.0,1,0,75.0,0.8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.0,0.3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.33,0.0,0.33,0.33,0.33,Fulham
+Fodé Ballo-Touré,sn SEN,"DF,MF",26.0,6,0,71.0,0.8,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Fulham
+Aleksandar Mitrović,rs SRB,FW,28.0,1,0,33.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.0,0.2,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.44,0.0,0.44,0.44,0.44,Fulham
+Jordan Pickford,eng ENG,GK,29.0,38,38,3420.0,38.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.1,0.1,1.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Everton
+James Tarkowski,eng ENG,DF,30.0,38,38,3419.0,38.0,1.0,1.0,2.0,1.0,0.0,0.0,11.0,0.0,2.6,2.6,2.5,5.1,15.0,130.0,20.0,0.03,0.03,0.05,0.03,0.05,0.07,0.06,0.13,0.07,0.13,Everton
+Jarrad Branthwaite,eng ENG,DF,21.0,35,35,3116.0,34.6,3.0,0.0,3.0,3.0,0.0,0.0,8.0,0.0,1.7,1.7,1.0,2.8,2.0,74.0,6.0,0.09,0.0,0.09,0.09,0.09,0.05,0.03,0.08,0.05,0.08,Everton
+James Garner,eng ENG,MF,22.0,37,34,3004.0,33.4,1.0,2.0,3.0,1.0,0.0,0.0,7.0,0.0,2.7,2.7,4.6,7.3,36.0,100.0,76.0,0.03,0.06,0.09,0.03,0.09,0.08,0.14,0.22,0.08,0.22,Everton
+Dwight McNeil,eng ENG,"MF,FW",23.0,35,33,2892.0,32.1,3.0,6.0,9.0,3.0,0.0,0.0,2.0,0.0,3.4,3.4,8.8,12.2,80.0,114.0,147.0,0.09,0.19,0.28,0.09,0.28,0.11,0.27,0.38,0.11,0.38,Everton
+Abdoulaye Doucouré,ml MLI,"FW,MF",30.0,32,32,2629.0,29.2,7.0,1.0,8.0,7.0,0.0,0.0,7.0,0.0,8.8,8.8,2.9,11.6,55.0,97.0,140.0,0.24,0.03,0.27,0.24,0.27,0.3,0.1,0.4,0.3,0.4,Everton
+Vitaliy Mykolenko,ua UKR,DF,24.0,28,28,2469.0,27.4,2.0,0.0,2.0,2.0,0.0,0.0,1.0,0.0,1.5,1.5,0.9,2.4,14.0,55.0,68.0,0.07,0.0,0.07,0.07,0.07,0.06,0.03,0.09,0.06,0.09,Everton
+Ashley Young,eng ENG,"DF,MF",38.0,31,27,2280.0,25.3,0.0,0.0,0.0,0.0,0.0,0.0,7.0,1.0,0.6,0.6,1.5,2.1,40.0,82.0,61.0,0.0,0.0,0.0,0.0,0.0,0.02,0.06,0.08,0.02,0.08,Everton
+Dominic Calvert-Lewin,eng ENG,FW,26.0,32,26,2171.0,24.1,7.0,2.0,9.0,5.0,2.0,2.0,2.0,0.0,12.9,11.4,3.1,14.5,36.0,31.0,122.0,0.29,0.08,0.37,0.21,0.29,0.54,0.13,0.66,0.47,0.6,Everton
+Jack Harrison,eng ENG,"MF,FW",26.0,29,25,2214.0,24.6,3.0,3.0,6.0,3.0,0.0,0.0,1.0,0.0,3.6,3.6,3.4,7.0,65.0,62.0,135.0,0.12,0.12,0.24,0.12,0.24,0.14,0.14,0.28,0.14,0.28,Everton
+Idrissa Gana Gueye,sn SEN,MF,33.0,25,24,1884.0,20.9,4.0,0.0,4.0,4.0,0.0,0.0,8.0,0.0,1.3,1.3,1.1,2.4,29.0,83.0,41.0,0.19,0.0,0.19,0.19,0.19,0.06,0.05,0.12,0.06,0.12,Everton
+Amadou Onana,be BEL,MF,21.0,30,23,2091.0,23.2,2.0,0.0,2.0,2.0,0.0,0.0,5.0,0.0,2.0,2.0,2.7,4.7,21.0,107.0,39.0,0.09,0.0,0.09,0.09,0.09,0.09,0.11,0.2,0.09,0.2,Everton
+Ben Godfrey,eng ENG,DF,25.0,15,13,1120.0,12.4,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,1.7,1.7,0.3,2.0,11.0,43.0,26.0,0.0,0.0,0.0,0.0,0.0,0.14,0.02,0.16,0.14,0.16,Everton
+Nathan Patterson,sct SCO,DF,21.0,20,9,1005.0,11.2,0.0,2.0,2.0,0.0,0.0,0.0,4.0,0.0,0.3,0.3,1.7,2.0,17.0,38.0,39.0,0.0,0.18,0.18,0.0,0.18,0.03,0.15,0.18,0.03,0.18,Everton
+Beto,gw GNB,FW,25.0,30,9,951.0,10.6,3.0,0.0,3.0,3.0,0.0,1.0,2.0,0.0,6.8,6.0,0.9,6.9,14.0,14.0,58.0,0.28,0.0,0.28,0.28,0.28,0.64,0.09,0.73,0.57,0.66,Everton
+Séamus Coleman,ie IRL,DF,34.0,12,7,665.0,7.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,5.0,22.0,16.0,0.0,0.0,0.0,0.0,0.0,0.0,0.14,0.14,0.0,0.14,Everton
+Arnaut Danjuma,nl NED,"MF,FW",26.0,14,5,595.0,6.6,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.6,2.6,1.1,3.7,24.0,9.0,44.0,0.15,0.0,0.15,0.15,0.15,0.4,0.17,0.57,0.4,0.57,Everton
+Michael Keane,eng ENG,DF,30.0,9,4,439.0,4.9,1.0,0.0,1.0,1.0,0.0,0.0,2.0,0.0,0.7,0.7,0.0,0.7,4.0,14.0,3.0,0.21,0.0,0.21,0.21,0.21,0.14,0.0,0.14,0.14,0.14,Everton
+André Gomes,pt POR,"MF,FW",30.0,12,3,508.0,5.6,1.0,0.0,1.0,1.0,0.0,0.0,4.0,0.0,0.3,0.3,0.4,0.7,8.0,36.0,29.0,0.18,0.0,0.18,0.18,0.18,0.05,0.07,0.13,0.05,0.13,Everton
+Alex Iwobi,ng NGA,MF,27.0,2,2,140.0,1.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.2,0.5,6.0,4.0,8.0,0.0,0.0,0.0,0.0,0.0,0.21,0.14,0.35,0.21,0.35,Everton
+Lewis Dobbin,eng ENG,MF,20.0,12,1,232.0,2.6,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.3,0.3,0.5,0.8,6.0,3.0,20.0,0.39,0.0,0.39,0.39,0.39,0.11,0.19,0.3,0.11,0.3,Everton
+Youssef Chermiti,pt POR,FW,19.0,18,1,208.0,2.3,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.7,0.7,0.8,1.5,5.0,5.0,15.0,0.0,0.0,0.0,0.0,0.0,0.31,0.34,0.65,0.31,0.65,Everton
+Neal Maupay,fr FRA,FW,26.0,2,1,111.0,1.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.4,1.4,0.1,1.5,1.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,1.16,0.05,1.22,1.16,1.22,Everton
+Thomas Cannon,ie IRL,FW,20.0,1,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Everton
+Tyler Onyango,eng ENG,MF,20.0,1,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Everton
+Lewis Warrington,eng ENG,FW,20.0,1,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Everton
+Pascal Groß,de GER,"MF,DF",32.0,36,34,3114.0,34.6,4.0,10.0,14.0,3.0,1.0,1.0,6.0,0.0,6.5,5.7,8.1,13.8,83.0,302.0,180.0,0.12,0.29,0.4,0.09,0.38,0.19,0.24,0.42,0.16,0.4,Brighton
+Lewis Dunk,eng ENG,DF,31.0,33,33,2869.0,31.9,3.0,1.0,4.0,3.0,0.0,0.0,7.0,1.0,3.1,3.1,0.9,4.0,33.0,152.0,13.0,0.09,0.03,0.13,0.09,0.13,0.1,0.03,0.12,0.1,0.12,Brighton
+Jan Paul van Hecke,nl NED,DF,23.0,28,26,2369.0,26.3,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,1.2,1.2,0.9,2.1,25.0,147.0,7.0,0.0,0.0,0.0,0.0,0.0,0.05,0.03,0.08,0.05,0.08,Brighton
+Simon Adingra,ci CIV,FW,21.0,31,25,2222.0,24.7,6.0,1.0,7.0,6.0,0.0,0.0,3.0,0.0,4.3,4.3,3.7,8.0,111.0,50.0,278.0,0.24,0.04,0.28,0.24,0.28,0.17,0.15,0.32,0.17,0.32,Brighton
+Billy Gilmour,sct SCO,MF,22.0,30,24,2120.0,23.6,0.0,1.0,1.0,0.0,0.0,0.0,8.0,1.0,1.0,1.0,2.0,3.0,39.0,193.0,33.0,0.0,0.04,0.04,0.0,0.04,0.04,0.09,0.13,0.04,0.13,Brighton
+Bart Verbruggen,nl NED,GK,20.0,21,21,1890.0,21.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brighton
+Danny Welbeck,eng ENG,"FW,MF",32.0,29,21,1693.0,18.8,5.0,1.0,6.0,5.0,0.0,0.0,4.0,0.0,5.3,5.3,1.7,7.0,32.0,44.0,100.0,0.27,0.05,0.32,0.27,0.32,0.28,0.09,0.37,0.28,0.37,Brighton
+João Pedro,br BRA,"FW,MF",21.0,31,19,2045.0,22.7,9.0,3.0,12.0,5.0,4.0,5.0,2.0,0.0,11.9,7.9,3.8,11.7,83.0,92.0,205.0,0.4,0.13,0.53,0.22,0.35,0.53,0.17,0.69,0.35,0.51,Brighton
+Igor,br BRA,DF,25.0,24,17,1637.0,18.2,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.3,0.3,0.7,1.0,30.0,107.0,12.0,0.0,0.0,0.0,0.0,0.0,0.02,0.04,0.05,0.02,0.05,Brighton
+Joël Veltman,nl NED,DF,31.0,27,17,1588.0,17.6,1.0,1.0,2.0,1.0,0.0,0.0,4.0,0.0,0.4,0.4,0.5,0.9,24.0,85.0,54.0,0.06,0.06,0.11,0.06,0.11,0.02,0.03,0.05,0.02,0.05,Brighton
+Jason Steele,eng ENG,GK,32.0,17,17,1530.0,17.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brighton
+Facundo Buonanotte,ar ARG,"FW,MF",18.0,27,17,1364.0,15.2,3.0,1.0,4.0,3.0,0.0,0.0,7.0,0.0,2.4,2.4,1.8,4.2,23.0,42.0,69.0,0.2,0.07,0.26,0.2,0.26,0.16,0.12,0.28,0.16,0.28,Brighton
+Kaoru Mitoma,jp JPN,FW,26.0,19,15,1486.0,16.5,3.0,4.0,7.0,3.0,0.0,0.0,4.0,0.0,3.5,3.5,2.7,6.2,121.0,64.0,239.0,0.18,0.24,0.42,0.18,0.42,0.21,0.16,0.37,0.21,0.37,Brighton
+Evan Ferguson,ie IRL,FW,18.0,27,15,1367.0,15.2,6.0,0.0,6.0,6.0,0.0,0.0,1.0,0.0,4.6,4.6,0.5,5.1,13.0,15.0,62.0,0.4,0.0,0.4,0.4,0.4,0.3,0.04,0.34,0.3,0.34,Brighton
+Carlos Baleba,cm CMR,MF,19.0,27,15,1325.0,14.7,0.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,0.8,0.8,0.3,1.1,19.0,50.0,9.0,0.0,0.0,0.0,0.0,0.0,0.06,0.02,0.08,0.06,0.08,Brighton
+Pervis Estupiñán,ec ECU,DF,25.0,19,15,1243.0,13.8,2.0,3.0,5.0,2.0,0.0,0.0,4.0,0.0,1.0,1.0,3.5,4.4,47.0,83.0,88.0,0.14,0.22,0.36,0.14,0.36,0.07,0.25,0.32,0.07,0.32,Brighton
+Adam Webster,eng ENG,DF,28.0,15,13,1144.0,12.7,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.4,0.4,0.1,0.6,16.0,80.0,2.0,0.0,0.0,0.0,0.0,0.0,0.03,0.01,0.05,0.03,0.05,Brighton
+Adam Lallana,eng ENG,"MF,FW",35.0,25,13,850.0,9.4,0.0,1.0,1.0,0.0,0.0,0.0,2.0,0.0,0.8,0.8,1.7,2.5,9.0,39.0,55.0,0.0,0.11,0.11,0.0,0.11,0.09,0.18,0.27,0.09,0.27,Brighton
+James Milner,eng ENG,"DF,MF",37.0,15,11,773.0,8.6,0.0,2.0,2.0,0.0,0.0,0.0,3.0,0.0,0.3,0.3,1.8,2.0,11.0,69.0,73.0,0.0,0.23,0.23,0.0,0.23,0.03,0.21,0.24,0.03,0.24,Brighton
+Tariq Lamptey,gh GHA,"DF,FW",22.0,19,10,914.0,10.2,0.0,3.0,3.0,0.0,0.0,0.0,3.0,0.0,0.4,0.4,1.2,1.7,39.0,17.0,64.0,0.0,0.3,0.3,0.0,0.3,0.04,0.12,0.16,0.04,0.16,Brighton
+Jack Hinshelwood,eng ENG,DF,18.0,12,8,869.0,9.7,3.0,0.0,3.0,3.0,0.0,0.0,1.0,0.0,1.9,1.9,0.7,2.5,16.0,19.0,38.0,0.31,0.0,0.31,0.31,0.31,0.19,0.07,0.26,0.19,0.26,Brighton
+Solly March,eng ENG,"FW,DF",29.0,7,7,554.0,6.2,3.0,1.0,4.0,3.0,0.0,0.0,1.0,0.0,2.0,2.0,1.0,2.9,14.0,24.0,44.0,0.49,0.16,0.65,0.49,0.65,0.32,0.16,0.47,0.32,0.47,Brighton
+Jakub Moder,pl POL,"MF,DF",24.0,17,6,618.0,6.9,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.6,0.6,0.8,1.3,10.0,23.0,26.0,0.0,0.0,0.0,0.0,0.0,0.08,0.11,0.2,0.08,0.2,Brighton
+Mahmoud Dahoud,de GER,MF,27.0,9,6,452.0,5.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.2,0.2,0.9,1.1,4.0,11.0,6.0,0.0,0.2,0.2,0.0,0.2,0.04,0.18,0.22,0.04,0.22,Brighton
+Julio Enciso,py PAR,"MF,FW",19.0,12,5,475.0,5.3,0.0,2.0,2.0,0.0,0.0,0.0,2.0,0.0,1.4,1.4,1.7,3.2,28.0,21.0,29.0,0.0,0.38,0.38,0.0,0.38,0.27,0.32,0.59,0.27,0.59,Brighton
+Ansu Fati,es ESP,"MF,FW",20.0,19,3,521.0,5.8,2.0,0.0,2.0,2.0,0.0,0.0,2.0,0.0,3.4,3.4,0.8,4.2,16.0,28.0,50.0,0.35,0.0,0.35,0.35,0.35,0.59,0.14,0.73,0.59,0.73,Brighton
+Valentín Barco,ar ARG,"DF,FW",19.0,6,3,307.0,3.4,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.2,0.2,0.2,0.4,8.0,14.0,20.0,0.0,0.0,0.0,0.0,0.0,0.05,0.07,0.13,0.05,0.13,Brighton
+Odeluga Offiah,eng ENG,DF,20.0,4,1,128.0,1.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,4.0,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brighton
+Mark O'Mahony,ie IRL,FW,18.0,3,1,63.0,0.7,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.1,0.1,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.08,0.0,0.08,0.08,0.08,Brighton
+Benicio Boaitey,eng ENG,"FW,DF",19.0,5,0,40.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.1,0.1,1.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.05,0.13,0.19,0.05,0.19,Brighton
+Illia Zabarnyi,ua UKR,DF,20.0,37,37,3330.0,37.0,1.0,0.0,1.0,1.0,0.0,0.0,5.0,0.0,1.1,1.1,0.4,1.5,24.0,109.0,1.0,0.03,0.0,0.03,0.03,0.03,0.03,0.01,0.04,0.03,0.04,Bournemouth
+Dominic Solanke,eng ENG,FW,25.0,38,37,3325.0,36.9,19.0,3.0,22.0,17.0,2.0,3.0,3.0,0.0,19.6,17.2,3.0,20.3,52.0,60.0,184.0,0.51,0.08,0.6,0.46,0.54,0.53,0.08,0.61,0.47,0.55,Bournemouth
+Ryan Christie,sct SCO,MF,28.0,37,35,2907.0,32.3,0.0,5.0,5.0,0.0,0.0,0.0,6.0,0.0,2.3,2.3,5.4,7.7,60.0,167.0,78.0,0.0,0.15,0.15,0.0,0.15,0.07,0.17,0.24,0.07,0.24,Bournemouth
+Neto,br BRA,GK,34.0,32,32,2880.0,32.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Bournemouth
+Lewis Cook,eng ENG,MF,26.0,33,32,2786.0,31.0,0.0,3.0,3.0,0.0,0.0,0.0,5.0,1.0,0.4,0.4,4.3,4.6,22.0,142.0,15.0,0.0,0.1,0.1,0.0,0.1,0.01,0.14,0.15,0.01,0.15,Bournemouth
+Marcos Senesi,ar ARG,DF,26.0,31,26,2242.0,24.9,4.0,5.0,9.0,4.0,0.0,0.0,13.0,0.0,2.0,2.0,1.7,3.7,31.0,127.0,4.0,0.16,0.2,0.36,0.16,0.36,0.08,0.07,0.15,0.08,0.15,Bournemouth
+Justin Kluivert,nl NED,"MF,FW",24.0,32,26,1916.0,21.3,7.0,1.0,8.0,7.0,0.0,0.0,5.0,0.0,6.2,6.2,1.9,8.1,65.0,50.0,114.0,0.33,0.05,0.38,0.33,0.38,0.29,0.09,0.38,0.29,0.38,Bournemouth
+Adam Smith,eng ENG,DF,32.0,28,25,2150.0,23.9,0.0,2.0,2.0,0.0,0.0,0.0,6.0,0.0,0.1,0.1,1.3,1.4,24.0,89.0,28.0,0.0,0.08,0.08,0.0,0.08,0.0,0.05,0.06,0.0,0.06,Bournemouth
+Marcus Tavernier,eng ENG,FW,24.0,30,25,2118.0,23.5,3.0,4.0,7.0,3.0,0.0,0.0,3.0,0.0,3.9,3.9,5.2,9.1,79.0,84.0,171.0,0.13,0.17,0.3,0.13,0.3,0.17,0.22,0.39,0.17,0.39,Bournemouth
+Antoine Semenyo,gh GHA,FW,23.0,33,25,2105.0,23.4,8.0,2.0,10.0,8.0,0.0,0.0,6.0,0.0,4.6,4.6,3.2,7.8,89.0,64.0,188.0,0.34,0.09,0.43,0.34,0.43,0.2,0.14,0.34,0.2,0.34,Bournemouth
+Milos Kerkez,hu HUN,DF,19.0,28,22,1970.0,21.9,0.0,1.0,1.0,0.0,0.0,0.0,4.0,1.0,1.2,1.2,0.8,2.0,46.0,44.0,93.0,0.0,0.05,0.05,0.0,0.05,0.06,0.04,0.09,0.06,0.09,Bournemouth
+Lloyd Kelly,eng ENG,DF,24.0,23,17,1559.0,17.3,0.0,1.0,1.0,0.0,0.0,0.0,2.0,0.0,0.3,0.3,1.2,1.6,15.0,56.0,28.0,0.0,0.06,0.06,0.0,0.06,0.02,0.07,0.09,0.02,0.09,Bournemouth
+Philip Billing,dk DEN,MF,27.0,29,13,1398.0,15.5,2.0,2.0,4.0,2.0,0.0,0.0,3.0,1.0,2.5,2.5,3.2,5.7,22.0,53.0,63.0,0.13,0.13,0.26,0.13,0.26,0.16,0.21,0.36,0.16,0.36,Bournemouth
+Max Aarons,eng ENG,DF,23.0,20,13,1237.0,13.7,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.8,0.9,22.0,43.0,26.0,0.0,0.07,0.07,0.0,0.07,0.0,0.06,0.06,0.0,0.06,Bournemouth
+Dango Ouattara,bf BFA,"FW,DF",21.0,30,12,1229.0,13.7,1.0,2.0,3.0,1.0,0.0,0.0,3.0,0.0,2.6,2.6,1.5,4.1,47.0,35.0,90.0,0.07,0.15,0.22,0.07,0.22,0.19,0.11,0.3,0.19,0.3,Bournemouth
+Alex Scott,eng ENG,MF,19.0,23,11,1014.0,11.3,1.0,1.0,2.0,1.0,0.0,0.0,3.0,0.0,0.7,0.7,1.7,2.4,24.0,50.0,35.0,0.09,0.09,0.18,0.09,0.18,0.06,0.15,0.22,0.06,0.22,Bournemouth
+Luis Sinisterra,co COL,"FW,MF",24.0,20,7,699.0,7.8,2.0,2.0,4.0,2.0,0.0,0.0,0.0,0.0,2.5,2.5,2.4,5.0,51.0,38.0,85.0,0.26,0.26,0.52,0.26,0.52,0.33,0.31,0.64,0.33,0.64,Bournemouth
+Chris Mepham,wls WAL,DF,25.0,10,6,617.0,6.9,0.0,1.0,1.0,0.0,0.0,0.0,2.0,0.0,0.1,0.1,0.4,0.5,4.0,25.0,1.0,0.0,0.15,0.15,0.0,0.15,0.01,0.06,0.07,0.01,0.07,Bournemouth
+Mark Travers,ie IRL,GK,24.0,4,4,360.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Bournemouth
+Joe Rothwell,eng ENG,MF,28.0,11,3,386.0,4.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.2,0.3,6.0,20.0,3.0,0.0,0.0,0.0,0.0,0.0,0.02,0.06,0.08,0.02,0.08,Bournemouth
+Enes Ünal,tr TUR,"FW,MF",26.0,16,2,328.0,3.6,2.0,2.0,4.0,2.0,0.0,0.0,1.0,0.0,2.9,2.9,1.0,3.9,4.0,7.0,28.0,0.55,0.55,1.1,0.55,1.1,0.8,0.28,1.07,0.8,1.07,Bournemouth
+David Brooks,wls WAL,"FW,MF",26.0,13,2,282.0,3.1,1.0,1.0,2.0,1.0,0.0,0.0,1.0,0.0,2.2,2.2,1.0,3.2,8.0,21.0,27.0,0.32,0.32,0.64,0.32,0.64,0.7,0.33,1.03,0.7,1.03,Bournemouth
+Ionuț Radu,ro ROU,GK,26.0,2,2,180.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Bournemouth
+Jaidon Anthony,eng ENG,FW,23.0,3,2,152.0,1.7,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.1,0.1,0.0,0.1,2.0,8.0,9.0,0.0,0.0,0.0,0.0,0.0,0.04,0.0,0.04,0.04,0.04,Bournemouth
+Tyler Adams,us USA,MF,24.0,3,1,121.0,1.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,4.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.06,0.06,0.0,0.06,Bournemouth
+James Hill,eng ENG,DF,21.0,5,1,76.0,0.8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Bournemouth
+Kieffer Moore,wls WAL,"FW,MF",30.0,8,0,108.0,1.2,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.5,1.5,0.2,1.7,2.0,4.0,15.0,0.83,0.0,0.83,0.83,0.83,1.23,0.2,1.44,1.23,1.44,Bournemouth
+Hamed Junior Traorè,ci CIV,MF,23.0,3,0,47.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.0,0.2,2.0,3.0,4.0,0.0,0.0,0.0,0.0,0.0,0.34,0.07,0.41,0.34,0.41,Bournemouth
+Romain Faivre,fr FRA,"MF,FW",25.0,5,0,41.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.2,0.2,0.0,0.2,4.0,5.0,4.0,0.0,0.0,0.0,0.0,0.0,0.34,0.0,0.34,0.34,0.34,Bournemouth
+Dominic Sadi,eng ENG,FW,19.0,1,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Bournemouth
+Max Kilman,eng ENG,DF,26.0,38,38,3420.0,38.0,2.0,0.0,2.0,2.0,0.0,0.0,7.0,0.0,1.8,1.8,1.4,3.2,22.0,117.0,5.0,0.05,0.0,0.05,0.05,0.05,0.05,0.04,0.08,0.05,0.08,Wolverhampton
+Nélson Semedo,pt POR,"DF,MF",29.0,36,36,3084.0,34.3,0.0,1.0,1.0,0.0,0.0,0.0,10.0,1.0,1.3,1.3,1.7,3.0,103.0,138.0,150.0,0.0,0.03,0.03,0.0,0.03,0.04,0.05,0.09,0.04,0.09,Wolverhampton
+José Sá,pt POR,GK,30.0,35,35,3037.0,33.7,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.03,0.03,0.0,0.03,0.0,0.0,0.0,0.0,0.0,Wolverhampton
+Mario Lemina,ga GAB,MF,29.0,35,34,2968.0,33.0,4.0,1.0,5.0,4.0,0.0,0.0,12.0,1.0,4.6,4.6,1.5,6.2,32.0,76.0,49.0,0.12,0.03,0.15,0.12,0.15,0.14,0.05,0.19,0.14,0.19,Wolverhampton
+João Gomes,br BRA,MF,22.0,34,32,2649.0,29.4,2.0,1.0,3.0,2.0,0.0,0.0,11.0,0.0,1.7,1.7,1.1,2.8,28.0,101.0,44.0,0.07,0.03,0.1,0.07,0.1,0.06,0.04,0.1,0.06,0.1,Wolverhampton
+Toti Gomes,pt POR,DF,24.0,35,31,2772.0,30.8,1.0,3.0,4.0,1.0,0.0,0.0,7.0,0.0,1.6,1.6,1.2,2.8,22.0,79.0,11.0,0.03,0.1,0.13,0.03,0.13,0.05,0.04,0.09,0.05,0.09,Wolverhampton
+Matheus Cunha,br BRA,"FW,MF",24.0,32,29,2440.0,27.1,12.0,7.0,19.0,11.0,1.0,1.0,9.0,0.0,9.5,8.7,3.2,11.9,107.0,87.0,139.0,0.44,0.26,0.7,0.41,0.66,0.35,0.12,0.47,0.32,0.44,Wolverhampton
+Rayan Aït-Nouri,dz ALG,"DF,MF",22.0,33,29,2329.0,25.9,2.0,1.0,3.0,2.0,0.0,0.0,7.0,0.0,3.3,3.3,2.4,5.6,67.0,99.0,99.0,0.08,0.04,0.12,0.08,0.12,0.13,0.09,0.22,0.13,0.22,Wolverhampton
+Craig Dawson,eng ENG,DF,33.0,25,25,2209.0,24.5,1.0,1.0,2.0,1.0,0.0,0.0,7.0,0.0,1.5,1.5,1.4,2.9,1.0,49.0,4.0,0.04,0.04,0.08,0.04,0.08,0.06,0.06,0.12,0.06,0.12,Wolverhampton
+Hwang Hee-chan,kr KOR,"FW,MF",27.0,29,25,2119.0,23.5,12.0,3.0,15.0,11.0,1.0,1.0,6.0,0.0,7.7,6.9,3.0,9.9,37.0,37.0,120.0,0.51,0.13,0.64,0.47,0.59,0.33,0.13,0.45,0.29,0.42,Wolverhampton
+Pablo Sarabia,es ESP,"MF,FW",31.0,30,20,1745.0,19.4,4.0,7.0,11.0,2.0,2.0,2.0,5.0,0.0,5.5,3.9,5.9,9.8,41.0,109.0,137.0,0.21,0.36,0.57,0.1,0.46,0.28,0.3,0.59,0.2,0.5,Wolverhampton
+Pedro Neto,pt POR,"FW,MF",23.0,20,18,1516.0,16.8,2.0,9.0,11.0,2.0,0.0,0.0,4.0,0.0,3.5,3.5,4.4,7.9,98.0,54.0,149.0,0.12,0.53,0.65,0.12,0.65,0.21,0.26,0.47,0.21,0.47,Wolverhampton
+Tommy Doyle,eng ENG,MF,21.0,26,11,1222.0,13.6,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.4,0.4,0.4,0.9,21.0,81.0,17.0,0.0,0.0,0.0,0.0,0.0,0.03,0.03,0.06,0.03,0.06,Wolverhampton
+Jean-Ricner Bellegarde,fr FRA,"MF,FW",25.0,22,10,958.0,10.6,2.0,1.0,3.0,2.0,0.0,0.0,0.0,1.0,1.6,1.6,1.0,2.5,30.0,39.0,49.0,0.19,0.09,0.28,0.19,0.28,0.15,0.09,0.24,0.15,0.24,Wolverhampton
+Matt Doherty,ie IRL,"DF,MF",31.0,30,9,1150.0,12.8,1.0,0.0,1.0,1.0,0.0,0.0,2.0,0.0,0.8,0.8,1.3,2.2,12.0,31.0,64.0,0.08,0.0,0.08,0.08,0.08,0.07,0.1,0.17,0.07,0.17,Wolverhampton
+Santiago Bueno,uy URU,DF,24.0,12,9,822.0,9.1,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.1,0.1,3.0,11.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,Wolverhampton
+Boubacar Traoré,ml MLI,MF,21.0,24,7,819.0,9.1,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.3,0.3,0.4,0.7,9.0,29.0,15.0,0.0,0.0,0.0,0.0,0.0,0.03,0.04,0.07,0.03,0.07,Wolverhampton
+Hugo Bueno,es ESP,"DF,MF",20.0,22,7,741.0,8.2,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.3,0.3,0.8,1.0,22.0,8.0,40.0,0.0,0.0,0.0,0.0,0.0,0.03,0.09,0.13,0.03,0.13,Wolverhampton
+Daniel Bentley,eng ENG,GK,30.0,5,3,383.0,4.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Wolverhampton
+Fábio Silva,pt POR,FW,21.0,8,3,264.0,2.9,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,1.0,1.0,0.2,1.2,5.0,10.0,13.0,0.0,0.0,0.0,0.0,0.0,0.35,0.07,0.42,0.35,0.42,Wolverhampton
+Matheus Nunes,pt POR,MF,24.0,2,2,179.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.1,0.1,0.1,0.2,7.0,8.0,9.0,0.0,0.0,0.0,0.0,0.0,0.04,0.05,0.09,0.04,0.09,Wolverhampton
+Leon Chiwome,eng ENG,FW,17.0,3,2,174.0,1.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.0,0.1,1.0,2.0,7.0,0.0,0.0,0.0,0.0,0.0,0.05,0.0,0.05,0.05,0.05,Wolverhampton
+Nathan Fraser,ie IRL,FW,18.0,7,1,186.0,2.1,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.3,0.3,0.1,0.4,1.0,2.0,21.0,0.0,0.0,0.0,0.0,0.0,0.16,0.04,0.19,0.16,0.19,Wolverhampton
+Sasa Kalajdzic,at AUT,FW,26.0,11,1,166.0,1.8,2.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,1.1,1.1,1.0,2.0,1.0,5.0,9.0,1.08,0.0,1.08,1.08,1.08,0.57,0.52,1.09,0.57,1.09,Wolverhampton
+Tawanda Chirewa,zw ZIM,"MF,FW",19.0,8,1,145.0,1.6,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.2,0.2,0.0,0.2,1.0,7.0,8.0,0.0,0.0,0.0,0.0,0.0,0.1,0.02,0.12,0.1,0.12,Wolverhampton
+Jonny Castro,es ESP,MF,29.0,1,0,5.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Wolverhampton
+Enso Gonzalez,py PAR,"MF,DF",18.0,1,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Wolverhampton
+Mark Flekken,nl NED,GK,30.0,37,37,3285.0,36.5,0.0,1.0,1.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.5,0.5,0.0,5.0,0.0,0.0,0.03,0.03,0.0,0.03,0.0,0.01,0.01,0.0,0.01,Brentford
+Vitaly Janelt,de GER,"MF,DF",25.0,38,37,3054.0,33.9,1.0,3.0,4.0,1.0,0.0,0.0,8.0,0.0,1.8,1.8,4.8,6.5,25.0,123.0,81.0,0.03,0.09,0.12,0.03,0.12,0.05,0.14,0.19,0.05,0.19,Brentford
+Christian Nørgaard,dk DEN,MF,29.0,31,30,2505.0,27.8,2.0,1.0,3.0,2.0,0.0,0.0,8.0,0.0,3.5,3.5,1.9,5.4,15.0,161.0,42.0,0.07,0.04,0.11,0.07,0.11,0.12,0.07,0.19,0.12,0.19,Brentford
+Nathan Collins,ie IRL,DF,22.0,32,29,2652.0,29.5,1.0,1.0,2.0,1.0,0.0,0.0,3.0,0.0,1.7,1.7,2.2,3.9,11.0,97.0,18.0,0.03,0.03,0.07,0.03,0.07,0.06,0.07,0.13,0.06,0.13,Brentford
+Yoane Wissa,cd COD,FW,26.0,34,29,2493.0,27.7,12.0,3.0,15.0,12.0,0.0,0.0,7.0,0.0,10.6,10.6,3.9,14.5,45.0,48.0,150.0,0.43,0.11,0.54,0.43,0.54,0.38,0.14,0.52,0.38,0.52,Brentford
+Ethan Pinnock,jm JAM,DF,30.0,29,28,2521.0,28.0,2.0,0.0,2.0,2.0,0.0,0.0,1.0,0.0,2.0,2.0,1.0,3.0,11.0,61.0,16.0,0.07,0.0,0.07,0.07,0.07,0.07,0.04,0.11,0.07,0.11,Brentford
+Mathias Jensen,dk DEN,MF,27.0,32,27,2204.0,24.5,3.0,3.0,6.0,3.0,0.0,0.0,5.0,0.0,0.7,0.7,4.0,4.7,19.0,123.0,66.0,0.12,0.12,0.25,0.12,0.25,0.03,0.16,0.19,0.03,0.19,Brentford
+Mads Roerslev,dk DEN,DF,24.0,34,22,1985.0,22.1,1.0,2.0,3.0,1.0,0.0,0.0,1.0,0.0,0.9,0.9,1.8,2.7,18.0,43.0,90.0,0.05,0.09,0.14,0.05,0.14,0.04,0.08,0.12,0.04,0.12,Brentford
+Bryan Mbeumo,cm CMR,FW,23.0,25,22,1960.0,21.8,9.0,6.0,15.0,6.0,3.0,3.0,2.0,0.0,9.8,7.4,4.6,12.1,74.0,88.0,195.0,0.41,0.28,0.69,0.28,0.55,0.45,0.21,0.66,0.34,0.55,Brentford
+Kristoffer Ajer,no NOR,DF,25.0,28,21,1832.0,20.4,2.0,1.0,3.0,2.0,0.0,0.0,5.0,0.0,1.3,1.3,2.3,3.6,22.0,61.0,31.0,0.1,0.05,0.15,0.1,0.15,0.06,0.11,0.18,0.06,0.18,Brentford
+Ivan Toney,eng ENG,FW,27.0,17,16,1450.0,16.1,4.0,2.0,6.0,4.0,0.0,0.0,5.0,0.0,6.0,6.0,1.5,7.4,23.0,40.0,98.0,0.25,0.12,0.37,0.25,0.37,0.37,0.09,0.46,0.37,0.46,Brentford
+Keane Lewis-Potter,eng ENG,"FW,DF",22.0,30,15,1441.0,16.0,3.0,1.0,4.0,3.0,0.0,0.0,0.0,0.0,6.3,6.3,1.7,8.0,56.0,32.0,135.0,0.19,0.06,0.25,0.19,0.25,0.39,0.11,0.5,0.39,0.5,Brentford
+Ben Mee,eng ENG,DF,33.0,16,15,1272.0,14.1,2.0,0.0,2.0,2.0,0.0,0.0,1.0,1.0,1.5,1.5,0.3,1.8,11.0,49.0,9.0,0.14,0.0,0.14,0.14,0.14,0.1,0.02,0.13,0.1,0.13,Brentford
+Sergio Reguilón,es ESP,DF,26.0,16,14,1118.0,12.4,0.0,4.0,4.0,0.0,0.0,0.0,4.0,1.0,0.8,0.8,2.8,3.6,34.0,38.0,40.0,0.0,0.32,0.32,0.0,0.32,0.06,0.23,0.29,0.06,0.29,Brentford
+Neal Maupay,fr FRA,FW,26.0,29,13,1321.0,14.7,6.0,3.0,9.0,6.0,0.0,0.0,8.0,0.0,5.7,5.7,2.3,8.1,21.0,37.0,91.0,0.41,0.2,0.61,0.41,0.61,0.39,0.16,0.55,0.39,0.55,Brentford
+Mathias Jørgensen,dk DEN,DF,33.0,14,12,1087.0,12.1,1.0,0.0,1.0,1.0,0.0,0.0,4.0,0.0,0.7,0.7,0.9,1.7,5.0,50.0,4.0,0.08,0.0,0.08,0.08,0.08,0.06,0.08,0.14,0.06,0.14,Brentford
+Frank Onyeka,ng NGA,MF,25.0,26,11,1161.0,12.9,1.0,2.0,3.0,1.0,0.0,0.0,8.0,0.0,2.2,2.2,1.8,4.0,17.0,33.0,39.0,0.08,0.16,0.23,0.08,0.23,0.17,0.14,0.31,0.17,0.31,Brentford
+Aaron Hickey,sct SCO,DF,21.0,9,9,713.0,7.9,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.2,0.2,0.1,0.3,9.0,21.0,13.0,0.0,0.0,0.0,0.0,0.0,0.03,0.01,0.04,0.03,0.04,Brentford
+Mikkel Damsgaard,dk DEN,MF,23.0,23,7,842.0,9.4,0.0,2.0,2.0,0.0,0.0,0.0,2.0,0.0,0.9,0.9,1.4,2.4,16.0,61.0,32.0,0.0,0.21,0.21,0.0,0.21,0.1,0.16,0.25,0.1,0.25,Brentford
+Saman Ghoddos,ir IRN,DF,29.0,19,7,739.0,8.2,1.0,0.0,1.0,1.0,0.0,0.0,5.0,0.0,0.3,0.3,1.1,1.4,22.0,36.0,31.0,0.12,0.0,0.12,0.12,0.12,0.04,0.13,0.17,0.04,0.17,Brentford
+Yehor Yarmoliuk,ua UKR,MF,19.0,27,6,688.0,7.6,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,1.0,1.0,0.1,1.1,13.0,35.0,36.0,0.0,0.0,0.0,0.0,0.0,0.13,0.01,0.14,0.13,0.14,Brentford
+Rico Henry,eng ENG,DF,26.0,5,5,401.0,4.5,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.1,0.1,1.0,1.1,13.0,20.0,12.0,0.0,0.22,0.22,0.0,0.22,0.02,0.22,0.24,0.02,0.24,Brentford
+Kevin Schade,de GER,"FW,MF",21.0,11,3,333.0,3.7,2.0,1.0,3.0,2.0,0.0,0.0,0.0,0.0,1.2,1.2,0.7,1.9,8.0,9.0,23.0,0.54,0.27,0.81,0.54,0.81,0.32,0.19,0.52,0.32,0.52,Brentford
+Shandon Baptiste,gd GRN,MF,25.0,10,2,229.0,2.5,1.0,1.0,2.0,1.0,0.0,0.0,1.0,0.0,0.9,0.9,0.2,1.1,3.0,5.0,1.0,0.39,0.39,0.79,0.39,0.79,0.35,0.08,0.43,0.35,0.43,Brentford
+Thomas Strakosha,al ALB,GK,28.0,2,1,135.0,1.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brentford
+Michael Olakigbe,eng ENG,"DF,FW",19.0,8,0,59.0,0.7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.0,0.1,10.0,4.0,17.0,0.0,0.0,0.0,0.0,0.0,0.18,0.0,0.18,0.18,0.18,Brentford
+Josh Dasilva,eng ENG,"DF,FW",24.0,3,0,24.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,2.0,2.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.72,0.72,0.0,0.72,Brentford
+Myles Peart-Harris,eng ENG,"DF,MF",20.0,3,0,13.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.12,0.12,0.0,0.12,Brentford
+Morgan Gibbs-White,eng ENG,"MF,FW",23.0,37,35,3156.0,35.1,5.0,10.0,15.0,4.0,1.0,1.0,9.0,0.0,6.9,6.1,8.4,14.6,80.0,170.0,181.0,0.14,0.29,0.43,0.11,0.4,0.2,0.24,0.44,0.17,0.42,Nottingham Forest
+Murillo,br BRA,DF,21.0,32,32,2789.0,31.0,0.0,2.0,2.0,0.0,0.0,0.0,5.0,0.0,1.8,1.8,1.2,3.0,22.0,89.0,7.0,0.0,0.06,0.06,0.0,0.06,0.06,0.04,0.1,0.06,0.1,Nottingham Forest
+Anthony Elanga,se SWE,"FW,MF",21.0,36,25,2433.0,27.0,5.0,9.0,14.0,5.0,0.0,0.0,1.0,0.0,6.6,6.6,6.7,13.3,98.0,49.0,196.0,0.18,0.33,0.52,0.18,0.52,0.24,0.25,0.49,0.24,0.49,Nottingham Forest
+Ryan Yates,eng ENG,MF,25.0,35,21,1992.0,22.1,1.0,1.0,2.0,1.0,0.0,0.0,6.0,0.0,2.6,2.6,0.8,3.5,18.0,73.0,37.0,0.05,0.05,0.09,0.05,0.09,0.12,0.04,0.16,0.12,0.16,Nottingham Forest
+Callum Hudson-Odoi,eng ENG,"FW,MF",22.0,29,20,1854.0,20.6,8.0,1.0,9.0,8.0,0.0,0.0,0.0,0.0,2.7,2.7,2.8,5.5,111.0,58.0,171.0,0.39,0.05,0.44,0.39,0.44,0.13,0.14,0.27,0.13,0.27,Nottingham Forest
+Chris Wood,nz NZL,FW,31.0,31,20,1812.0,20.1,14.0,1.0,15.0,14.0,0.0,0.0,0.0,0.0,11.9,11.9,2.1,14.0,16.0,30.0,76.0,0.7,0.05,0.75,0.7,0.75,0.59,0.1,0.7,0.59,0.7,Nottingham Forest
+Danilo,br BRA,MF,22.0,29,20,1792.0,19.9,2.0,2.0,4.0,2.0,0.0,0.0,4.0,0.0,1.8,1.8,1.5,3.4,22.0,83.0,28.0,0.1,0.1,0.2,0.1,0.2,0.09,0.08,0.17,0.09,0.17,Nottingham Forest
+Ola Aina,ng NGA,DF,26.0,22,20,1692.0,18.8,1.0,1.0,2.0,1.0,0.0,0.0,3.0,0.0,0.3,0.3,0.9,1.2,53.0,68.0,38.0,0.05,0.05,0.11,0.05,0.11,0.01,0.05,0.06,0.01,0.06,Nottingham Forest
+Orel Mangala,be BEL,MF,25.0,20,19,1563.0,17.4,1.0,0.0,1.0,1.0,0.0,0.0,6.0,0.0,0.8,0.8,0.2,1.1,13.0,45.0,14.0,0.06,0.0,0.06,0.06,0.06,0.05,0.01,0.06,0.05,0.06,Nottingham Forest
+Nicolás Domínguez,ar ARG,"MF,FW",25.0,26,19,1495.0,16.6,2.0,2.0,4.0,2.0,0.0,0.0,4.0,0.0,1.3,1.3,1.0,2.3,14.0,91.0,43.0,0.12,0.12,0.24,0.12,0.24,0.08,0.06,0.14,0.08,0.14,Nottingham Forest
+Neco Williams,wls WAL,DF,22.0,26,18,1637.0,18.2,0.0,1.0,1.0,0.0,0.0,0.0,4.0,0.0,0.8,0.8,1.6,2.5,48.0,47.0,86.0,0.0,0.05,0.05,0.0,0.05,0.04,0.09,0.13,0.04,0.13,Nottingham Forest
+Willy Boly,ci CIV,DF,32.0,20,18,1438.0,16.0,2.0,1.0,3.0,2.0,0.0,0.0,3.0,1.0,1.5,1.5,1.3,2.8,4.0,32.0,3.0,0.13,0.06,0.19,0.13,0.19,0.09,0.08,0.18,0.09,0.18,Nottingham Forest
+Matt Turner,us USA,GK,29.0,17,17,1530.0,17.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Nottingham Forest
+Matz Sels,be BEL,GK,31.0,16,16,1440.0,16.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.1,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Nottingham Forest
+Moussa Niakhate,sn SEN,DF,27.0,21,15,1460.0,16.2,1.0,0.0,1.0,1.0,0.0,0.0,5.0,1.0,1.1,1.1,0.1,1.2,13.0,39.0,6.0,0.06,0.0,0.06,0.06,0.06,0.07,0.01,0.08,0.07,0.08,Nottingham Forest
+Harry Toffolo,eng ENG,DF,27.0,23,15,1459.0,16.2,1.0,3.0,4.0,1.0,0.0,0.0,3.0,0.0,1.6,1.6,1.5,3.2,10.0,42.0,34.0,0.06,0.19,0.25,0.06,0.25,0.1,0.1,0.2,0.1,0.2,Nottingham Forest
+Ibrahim Sangaré,ci CIV,MF,25.0,17,13,1035.0,11.5,0.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,0.6,0.6,0.4,1.0,5.0,44.0,7.0,0.0,0.0,0.0,0.0,0.0,0.06,0.03,0.09,0.06,0.09,Nottingham Forest
+Taiwo Awoniyi,ng NGA,FW,25.0,20,12,1042.0,11.6,6.0,3.0,9.0,6.0,0.0,0.0,2.0,0.0,4.7,4.7,1.7,6.4,8.0,14.0,45.0,0.52,0.26,0.78,0.52,0.78,0.41,0.15,0.56,0.41,0.56,Nottingham Forest
+Serge Aurier,ci CIV,"DF,MF",30.0,12,10,833.0,9.3,0.0,2.0,2.0,0.0,0.0,0.0,2.0,0.0,0.2,0.2,0.5,0.6,10.0,47.0,29.0,0.0,0.22,0.22,0.0,0.22,0.02,0.05,0.07,0.02,0.07,Nottingham Forest
+Gonzalo Montiel,ar ARG,DF,26.0,14,8,801.0,8.9,0.0,2.0,2.0,0.0,0.0,0.0,5.0,0.0,0.1,0.1,1.4,1.5,10.0,28.0,32.0,0.0,0.22,0.22,0.0,0.22,0.01,0.15,0.17,0.01,0.17,Nottingham Forest
+Andrew Omobamidele,ie IRL,DF,21.0,11,8,783.0,8.7,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.1,0.1,0.1,0.2,1.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.01,0.03,0.02,0.03,Nottingham Forest
+Divock Origi,be BEL,FW,28.0,20,6,606.0,6.7,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.4,1.4,2.1,3.5,19.0,14.0,41.0,0.0,0.15,0.15,0.0,0.15,0.21,0.31,0.51,0.21,0.51,Nottingham Forest
+Nuno Tavares,pt POR,DF,23.0,8,5,457.0,5.1,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.2,0.2,0.5,0.7,14.0,24.0,12.0,0.0,0.0,0.0,0.0,0.0,0.03,0.1,0.13,0.03,0.13,Nottingham Forest
+Odisseas Vlachodimos,gr GRE,GK,29.0,5,5,450.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,Nottingham Forest
+Joe Worrall,eng ENG,DF,26.0,7,5,439.0,4.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.1,0.1,0.2,0.3,0.0,8.0,1.0,0.0,0.0,0.0,0.0,0.0,0.02,0.04,0.06,0.02,0.06,Nottingham Forest
+Scott McKenna,sct SCO,DF,26.0,5,5,416.0,4.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.2,0.4,1.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05,0.04,0.08,0.05,0.08,Nottingham Forest
+Felipe,br BRA,DF,34.0,7,4,375.0,4.2,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.1,0.1,0.1,0.2,1.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03,0.01,0.04,0.03,0.04,Nottingham Forest
+Brennan Johnson,wls WAL,"FW,MF",22.0,3,3,237.0,2.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.2,0.5,8.0,1.0,14.0,0.0,0.0,0.0,0.0,0.0,0.12,0.08,0.21,0.12,0.21,Nottingham Forest
+Gio Reyna,us USA,"FW,MF",20.0,9,2,235.0,2.6,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.5,0.8,3.0,12.0,14.0,0.0,0.38,0.38,0.0,0.38,0.11,0.18,0.29,0.11,0.29,Nottingham Forest
+Cheikhou Kouyaté,sn SEN,MF,33.0,12,2,212.0,2.4,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.3,0.3,0.0,0.3,0.0,5.0,5.0,0.0,0.0,0.0,0.0,0.0,0.12,0.02,0.14,0.12,0.14,Nottingham Forest
+Rodrigo Ribeiro,pt POR,"MF,FW",18.0,4,0,17.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Nottingham Forest
+Andrey Santos,br BRA,MF,19.0,1,0,8.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.64,0.0,0.64,0.64,0.64,Nottingham Forest
+Brandon Aguilera,cr CRC,FW,20.0,1,0,5.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Nottingham Forest
+Thomas Kaminski,be BEL,GK,30.0,38,38,3405.0,37.8,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Luton Town
+Alfie Doughty,eng ENG,DF,23.0,37,34,2925.0,32.5,2.0,8.0,10.0,2.0,0.0,0.0,5.0,0.0,1.3,1.3,6.3,7.6,94.0,106.0,164.0,0.06,0.25,0.31,0.06,0.31,0.04,0.19,0.23,0.04,0.23,Luton Town
+Carlton Morris,eng ENG,"FW,MF",27.0,38,32,2862.0,31.8,11.0,4.0,15.0,6.0,5.0,5.0,4.0,0.0,11.4,7.5,2.8,10.2,37.0,56.0,120.0,0.35,0.13,0.47,0.19,0.31,0.36,0.09,0.45,0.23,0.32,Luton Town
+Ross Barkley,eng ENG,MF,29.0,32,30,2618.0,29.1,5.0,4.0,9.0,5.0,0.0,0.0,6.0,0.0,4.1,4.1,3.7,7.9,48.0,163.0,39.0,0.17,0.14,0.31,0.17,0.31,0.14,0.13,0.27,0.14,0.27,Luton Town
+Teden Mengi,eng ENG,DF,21.0,30,28,2469.0,27.4,1.0,0.0,1.0,1.0,0.0,0.0,3.0,0.0,0.6,0.6,0.3,0.9,15.0,54.0,1.0,0.04,0.0,0.04,0.04,0.04,0.02,0.01,0.03,0.02,0.03,Luton Town
+Gabriel Osho,ng NGA,DF,24.0,21,21,1846.0,20.5,2.0,0.0,2.0,2.0,0.0,0.0,3.0,0.0,1.1,1.1,0.1,1.2,10.0,37.0,4.0,0.1,0.0,0.1,0.1,0.1,0.05,0.01,0.06,0.05,0.06,Luton Town
+Issa Kaboré,bf BFA,DF,22.0,24,21,1729.0,19.2,0.0,2.0,2.0,0.0,0.0,0.0,7.0,0.0,0.2,0.2,2.1,2.3,60.0,54.0,85.0,0.0,0.1,0.1,0.0,0.1,0.01,0.11,0.12,0.01,0.12,Luton Town
+Amari'i Bell,jm JAM,DF,29.0,21,21,1720.0,19.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.5,0.7,32.0,76.0,10.0,0.0,0.0,0.0,0.0,0.0,0.01,0.02,0.04,0.01,0.04,Luton Town
+Chiedozie Ogbene,ie IRL,"MF,DF",26.0,30,20,1995.0,22.2,4.0,1.0,5.0,4.0,0.0,0.0,1.0,0.0,4.2,4.2,3.1,7.4,127.0,53.0,150.0,0.18,0.05,0.23,0.18,0.23,0.19,0.14,0.33,0.19,0.33,Luton Town
+Tahith Chong,nl NED,"MF,FW",23.0,33,20,1982.0,22.0,4.0,0.0,4.0,4.0,0.0,0.0,4.0,0.0,2.7,2.7,0.5,3.2,57.0,72.0,101.0,0.18,0.0,0.18,0.18,0.18,0.12,0.02,0.15,0.12,0.15,Luton Town
+Reece Burke,eng ENG,DF,26.0,22,20,1660.0,18.4,0.0,4.0,4.0,0.0,0.0,0.0,5.0,0.0,1.3,1.3,1.4,2.7,11.0,67.0,14.0,0.0,0.22,0.22,0.0,0.22,0.07,0.07,0.15,0.07,0.15,Luton Town
+Elijah Adebayo,eng ENG,FW,25.0,27,16,1419.0,15.8,10.0,0.0,10.0,10.0,0.0,0.0,1.0,0.0,5.9,5.9,0.7,6.6,16.0,23.0,110.0,0.63,0.0,0.63,0.63,0.63,0.37,0.04,0.42,0.37,0.42,Luton Town
+Albert Sambi Lokonga,be BEL,MF,23.0,17,16,1303.0,14.5,1.0,3.0,4.0,1.0,0.0,0.0,4.0,0.0,0.6,0.6,1.4,2.0,29.0,72.0,13.0,0.07,0.21,0.28,0.07,0.28,0.04,0.1,0.14,0.04,0.14,Luton Town
+Jordan Clark,eng ENG,MF,29.0,23,14,1304.0,14.5,1.0,1.0,2.0,1.0,0.0,0.0,4.0,0.0,1.0,1.0,1.3,2.2,21.0,51.0,39.0,0.07,0.07,0.14,0.07,0.14,0.07,0.09,0.16,0.07,0.16,Luton Town
+Andros Townsend,eng ENG,"MF,FW",32.0,27,14,1210.0,13.4,1.0,3.0,4.0,1.0,0.0,0.0,1.0,0.0,1.2,1.2,1.3,2.5,47.0,35.0,71.0,0.07,0.22,0.3,0.07,0.3,0.09,0.1,0.19,0.09,0.19,Luton Town
+Tom Lockyer,wls WAL,DF,28.0,14,14,1169.0,13.0,1.0,1.0,2.0,1.0,0.0,0.0,4.0,0.0,1.1,1.1,0.6,1.7,6.0,37.0,2.0,0.08,0.08,0.15,0.08,0.15,0.08,0.05,0.13,0.08,0.13,Luton Town
+Marvelous Nakamba,zw ZIM,MF,29.0,13,13,1130.0,12.6,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.2,0.2,0.4,0.6,1.0,36.0,5.0,0.0,0.0,0.0,0.0,0.0,0.01,0.03,0.05,0.01,0.05,Luton Town
+Jacob Brown,sct SCO,"MF,FW",25.0,19,12,1019.0,11.3,2.0,0.0,2.0,2.0,0.0,0.0,4.0,0.0,2.8,2.8,0.7,3.4,25.0,19.0,53.0,0.18,0.0,0.18,0.18,0.18,0.25,0.06,0.3,0.25,0.3,Luton Town
+Pelly Ruddock Mpanzu,cd COD,MF,30.0,27,11,1101.0,12.2,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.6,0.6,0.4,1.1,10.0,34.0,15.0,0.0,0.0,0.0,0.0,0.0,0.05,0.04,0.09,0.05,0.09,Luton Town
+Daiki Hashioka,jp JPN,DF,24.0,10,6,595.0,6.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.7,0.8,3.0,21.0,16.0,0.0,0.0,0.0,0.0,0.0,0.02,0.1,0.12,0.02,0.12,Luton Town
+Ryan John Giles,eng ENG,"DF,MF",23.0,11,5,503.0,5.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.2,0.3,9.0,17.0,30.0,0.0,0.0,0.0,0.0,0.0,0.02,0.04,0.06,0.02,0.06,Luton Town
+Fred Onyedinma,eng ENG,"DF,MF",26.0,8,4,407.0,4.5,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.2,0.2,0.1,0.3,9.0,6.0,21.0,0.0,0.0,0.0,0.0,0.0,0.04,0.02,0.06,0.04,0.06,Luton Town
+Mads Juel Andersen,dk DEN,DF,25.0,8,4,400.0,4.4,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.3,0.3,0.0,0.3,0.0,7.0,1.0,0.23,0.0,0.23,0.23,0.23,0.06,0.0,0.06,0.06,0.06,Luton Town
+Cauley Woodrow,eng ENG,"FW,MF",28.0,24,2,427.0,4.7,1.0,1.0,2.0,1.0,0.0,0.0,2.0,0.0,1.4,1.4,1.2,2.6,7.0,6.0,22.0,0.21,0.21,0.42,0.21,0.42,0.3,0.25,0.55,0.3,0.55,Luton Town
+Luke Berry,eng ENG,"MF,DF",31.0,17,2,323.0,3.6,2.0,1.0,3.0,2.0,0.0,0.0,1.0,0.0,1.0,1.0,0.3,1.3,1.0,8.0,7.0,0.56,0.28,0.84,0.56,0.84,0.28,0.07,0.35,0.28,0.35,Luton Town
+Joseph Johnson,eng ENG,DF,17.0,2,0,77.0,0.9,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Luton Town
+James Shea,eng ENG,GK,32.0,1,0,15.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Luton Town
+Zack Nelson,eng ENG,"MF,DF",18.0,2,0,7.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Luton Town
+Sander Berge,no NOR,MF,25.0,37,34,3005.0,33.4,1.0,2.0,3.0,1.0,0.0,0.0,10.0,1.0,1.9,1.9,2.4,4.3,64.0,164.0,29.0,0.03,0.06,0.09,0.03,0.09,0.06,0.07,0.13,0.06,0.13,Burnley
+Dara O'Shea,ie IRL,DF,24.0,33,32,2892.0,32.1,3.0,4.0,7.0,3.0,0.0,0.0,5.0,1.0,0.7,0.7,2.0,2.7,32.0,122.0,10.0,0.09,0.12,0.22,0.09,0.22,0.02,0.06,0.08,0.02,0.08,Burnley
+James Trafford,eng ENG,GK,20.0,28,28,2520.0,28.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Burnley
+Zeki Amdouni,ch SUI,FW,22.0,34,27,1953.0,21.7,5.0,1.0,6.0,4.0,1.0,1.0,2.0,0.0,5.8,5.0,1.4,6.5,63.0,49.0,93.0,0.23,0.05,0.28,0.18,0.23,0.27,0.07,0.33,0.23,0.3,Burnley
+Vitinho,br BRA,"DF,MF",24.0,32,26,2314.0,25.7,0.0,2.0,2.0,0.0,0.0,0.0,1.0,0.0,1.6,1.6,1.3,2.9,59.0,66.0,110.0,0.0,0.08,0.08,0.0,0.08,0.06,0.05,0.11,0.06,0.11,Burnley
+Charlie Taylor,eng ENG,DF,29.0,28,25,2331.0,25.9,1.0,1.0,2.0,1.0,0.0,0.0,8.0,0.0,0.2,0.2,1.2,1.5,36.0,82.0,81.0,0.04,0.04,0.08,0.04,0.08,0.01,0.05,0.06,0.01,0.06,Burnley
+Wilson Odobert,fr FRA,"MF,FW",18.0,29,25,2098.0,23.3,3.0,2.0,5.0,3.0,0.0,0.0,3.0,0.0,2.8,2.8,3.3,6.1,102.0,58.0,123.0,0.13,0.09,0.21,0.13,0.21,0.12,0.14,0.26,0.12,0.26,Burnley
+Josh Brownhill,eng ENG,MF,27.0,33,23,2252.0,25.0,4.0,2.0,6.0,4.0,0.0,0.0,4.0,1.0,1.9,1.9,2.1,4.0,19.0,90.0,36.0,0.16,0.08,0.24,0.16,0.24,0.08,0.08,0.16,0.08,0.16,Burnley
+Josh Cullen,ie IRL,MF,27.0,25,23,2057.0,22.9,2.0,3.0,5.0,2.0,0.0,0.0,9.0,0.0,0.6,0.6,1.8,2.4,22.0,123.0,22.0,0.09,0.13,0.22,0.09,0.22,0.03,0.08,0.11,0.03,0.11,Burnley
+Lyle Foster,za RSA,"FW,MF",22.0,24,22,1899.0,21.1,5.0,3.0,8.0,5.0,0.0,0.0,0.0,1.0,4.5,4.5,1.9,6.4,55.0,37.0,104.0,0.24,0.14,0.38,0.24,0.38,0.21,0.09,0.3,0.21,0.3,Burnley
+Jacob Bruun Larsen,dk DEN,"MF,FW",24.0,32,16,1249.0,13.9,6.0,0.0,6.0,5.0,1.0,1.0,1.0,0.0,5.0,4.2,1.5,5.7,61.0,38.0,146.0,0.43,0.0,0.43,0.36,0.36,0.36,0.1,0.46,0.3,0.41,Burnley
+Maxime Estève,fr FRA,DF,21.0,16,15,1309.0,14.5,0.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,0.3,0.3,0.0,0.3,15.0,40.0,3.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.02,0.02,0.02,Burnley
+Lorenz Assignon,tg TOG,DF,23.0,15,15,1234.0,13.7,1.0,2.0,3.0,1.0,0.0,0.0,5.0,1.0,0.4,0.4,2.6,3.0,38.0,34.0,57.0,0.07,0.15,0.22,0.07,0.22,0.03,0.19,0.22,0.03,0.22,Burnley
+Jordan Beyer,de GER,DF,23.0,15,15,1234.0,13.7,0.0,1.0,1.0,0.0,0.0,0.0,6.0,0.0,0.5,0.5,0.3,0.8,9.0,25.0,1.0,0.0,0.07,0.07,0.0,0.07,0.04,0.02,0.06,0.04,0.06,Burnley
+Luca Koleosho,it ITA,"MF,FW",18.0,15,13,974.0,10.8,1.0,1.0,2.0,1.0,0.0,0.0,0.0,0.0,2.0,2.0,1.3,3.3,53.0,19.0,82.0,0.09,0.09,0.18,0.09,0.18,0.18,0.12,0.3,0.18,0.3,Burnley
+Jóhann Berg Guðmundsson,is ISL,"MF,FW",32.0,26,12,1192.0,13.2,1.0,3.0,4.0,1.0,0.0,0.0,2.0,0.0,1.9,1.9,2.7,4.5,13.0,59.0,69.0,0.08,0.23,0.3,0.08,0.3,0.14,0.2,0.34,0.14,0.34,Burnley
+Ameen Al-Dakhil,be BEL,DF,21.0,13,12,1056.0,11.7,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.3,1.3,0.1,1.4,7.0,35.0,9.0,0.09,0.0,0.09,0.09,0.09,0.11,0.01,0.12,0.11,0.12,Burnley
+Arijanet Muric,xk KVX,GK,24.0,10,10,900.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.02,0.0,0.02,Burnley
+David Datro Fofana,ci CIV,FW,20.0,15,10,879.0,9.8,4.0,1.0,5.0,4.0,0.0,0.0,1.0,0.0,4.5,4.5,1.8,6.4,21.0,15.0,39.0,0.41,0.1,0.51,0.41,0.51,0.46,0.19,0.65,0.46,0.65,Burnley
+Connor Roberts,wls WAL,DF,27.0,14,8,823.0,9.1,0.0,1.0,1.0,0.0,0.0,0.0,5.0,1.0,0.0,0.0,0.2,0.2,6.0,38.0,13.0,0.0,0.11,0.11,0.0,0.11,0.0,0.02,0.02,0.0,0.02,Burnley
+Jay Rodriguez,eng ENG,FW,34.0,21,7,822.0,9.1,2.0,1.0,3.0,1.0,1.0,1.0,0.0,0.0,3.5,2.7,0.2,2.9,2.0,17.0,28.0,0.22,0.11,0.33,0.11,0.22,0.38,0.02,0.4,0.3,0.32,Burnley
+Hannes Delcroix,be BEL,DF,24.0,12,5,589.0,6.5,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.4,0.4,0.2,0.6,2.0,18.0,6.0,0.0,0.0,0.0,0.0,0.0,0.05,0.03,0.09,0.05,0.09,Burnley
+Aaron Ramsey,eng ENG,"MF,FW",20.0,14,5,527.0,5.9,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.3,0.3,0.4,0.7,8.0,8.0,17.0,0.0,0.0,0.0,0.0,0.0,0.06,0.07,0.13,0.06,0.13,Burnley
+Hjalmar Ekdal,se SWE,DF,24.0,8,5,437.0,4.9,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.1,0.1,0.1,0.1,5.0,11.0,1.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.03,0.01,0.03,Burnley
+Mike Trésor,be BEL,"FW,MF",24.0,16,3,420.0,4.7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.4,0.9,15.0,10.0,31.0,0.0,0.0,0.0,0.0,0.0,0.1,0.08,0.18,0.1,0.18,Burnley
+Anass Zaroury,ma MAR,"FW,MF",22.0,6,1,152.0,1.7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.2,0.2,0.0,0.2,2.0,2.0,12.0,0.0,0.0,0.0,0.0,0.0,0.1,0.02,0.12,0.1,0.12,Burnley
+Benson Manuel,ao ANG,"MF,FW",26.0,8,1,116.0,1.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.0,0.1,5.0,2.0,14.0,0.0,0.0,0.0,0.0,0.0,0.05,0.03,0.08,0.05,0.08,Burnley
+Nathan Redmond,eng ENG,"FW,MF",29.0,12,0,142.0,1.6,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.3,0.3,7.0,8.0,22.0,0.0,0.0,0.0,0.0,0.0,0.0,0.19,0.19,0.0,0.19,Burnley
+Jack Cork,eng ENG,MF,34.0,4,0,27.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Burnley
+Michael Obafemi,ie IRL,"FW,MF",23.0,2,0,20.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.0,0.1,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.4,0.0,0.4,0.4,0.4,Burnley
+Han-Noah Massengo,fr FRA,"MF,DF",22.0,3,0,16.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Burnley
+Gustavo Hamer,nl NED,MF,26.0,36,34,2910.0,32.3,4.0,6.0,10.0,4.0,0.0,0.0,9.0,0.0,3.0,3.0,6.1,9.1,36.0,135.0,86.0,0.12,0.19,0.31,0.12,0.31,0.09,0.19,0.28,0.09,0.28,Sheffield United
+Jayden Bogle,eng ENG,DF,23.0,34,32,2788.0,31.0,3.0,0.0,3.0,3.0,0.0,0.0,9.0,0.0,2.3,2.3,1.0,3.3,38.0,60.0,92.0,0.1,0.0,0.1,0.1,0.1,0.07,0.03,0.11,0.07,0.11,Sheffield United
+Jack Robinson,eng ENG,DF,29.0,34,31,2885.0,32.1,1.0,1.0,2.0,1.0,0.0,0.0,10.0,0.0,1.2,1.2,1.2,2.4,8.0,63.0,14.0,0.03,0.03,0.06,0.03,0.06,0.04,0.04,0.08,0.04,0.08,Sheffield United
+Vinicius Souza,br BRA,MF,24.0,36,29,2668.0,29.6,1.0,0.0,1.0,1.0,0.0,0.0,3.0,0.0,0.6,0.6,0.2,0.8,7.0,66.0,21.0,0.03,0.0,0.03,0.03,0.03,0.02,0.01,0.03,0.02,0.03,Sheffield United
+Anel Ahmedhodžić,ba BIH,DF,24.0,31,29,2649.0,29.4,2.0,0.0,2.0,2.0,0.0,0.0,11.0,1.0,3.1,3.1,0.8,4.0,15.0,43.0,21.0,0.07,0.0,0.07,0.07,0.07,0.11,0.03,0.14,0.11,0.14,Sheffield United
+Wes Foderingham,eng ENG,GK,32.0,30,29,2649.0,29.4,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.1,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Sheffield United
+Auston Trusty,us USA,DF,24.0,32,28,2572.0,28.6,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.4,0.4,1.1,1.5,10.0,40.0,15.0,0.0,0.0,0.0,0.0,0.0,0.01,0.04,0.05,0.01,0.05,Sheffield United
+Cameron Archer,eng ENG,"FW,MF",22.0,29,21,1832.0,20.4,4.0,1.0,5.0,4.0,0.0,0.0,1.0,0.0,5.5,5.5,2.8,8.3,33.0,15.0,113.0,0.2,0.05,0.25,0.2,0.25,0.27,0.14,0.41,0.27,0.41,Sheffield United
+James Mcatee,eng ENG,"MF,FW",20.0,30,20,1822.0,20.2,3.0,3.0,6.0,2.0,1.0,1.0,2.0,0.0,3.1,2.3,2.9,5.2,55.0,51.0,110.0,0.15,0.15,0.3,0.1,0.25,0.15,0.15,0.3,0.11,0.26,Sheffield United
+Oliver Norwood,nir NIR,MF,32.0,27,16,1440.0,16.0,1.0,0.0,1.0,0.0,1.0,1.0,8.0,0.0,1.1,0.3,0.6,0.9,3.0,73.0,10.0,0.06,0.0,0.06,0.0,0.0,0.07,0.04,0.11,0.02,0.06,Sheffield United
+Oliver McBurnie,sct SCO,FW,27.0,21,15,1280.0,14.2,6.0,3.0,9.0,4.0,2.0,2.0,7.0,2.0,6.9,5.3,2.0,7.3,6.0,30.0,60.0,0.42,0.21,0.63,0.28,0.49,0.48,0.14,0.62,0.37,0.51,Sheffield United
+Ben Osborn,eng ENG,"DF,MF",28.0,24,14,1367.0,15.2,0.0,2.0,2.0,0.0,0.0,0.0,3.0,0.0,0.2,0.2,0.9,1.1,16.0,55.0,44.0,0.0,0.13,0.13,0.0,0.13,0.01,0.06,0.07,0.01,0.07,Sheffield United
+Ben Brereton,cl CHI,"FW,MF",24.0,14,14,1105.0,12.3,6.0,1.0,7.0,5.0,1.0,1.0,3.0,0.0,6.1,5.3,1.5,6.7,23.0,37.0,78.0,0.49,0.08,0.57,0.41,0.49,0.49,0.12,0.61,0.43,0.55,Sheffield United
+Andre Brooks,eng ENG,"MF,DF",19.0,20,12,938.0,10.4,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.4,0.4,0.1,0.5,12.0,18.0,22.0,0.0,0.0,0.0,0.0,0.0,0.04,0.01,0.05,0.04,0.05,Sheffield United
+George Baldock,gr GRE,DF,30.0,13,11,969.0,10.8,0.0,1.0,1.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.8,0.8,9.0,31.0,24.0,0.0,0.09,0.09,0.0,0.09,0.0,0.07,0.08,0.0,0.08,Sheffield United
+Oliver Arblaster,eng ENG,MF,19.0,12,11,943.0,10.5,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.7,0.8,13.0,43.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.07,0.0,0.07,Sheffield United
+Luke Thomas,eng ENG,DF,22.0,12,11,917.0,10.2,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.5,0.5,0.1,0.6,8.0,33.0,22.0,0.0,0.0,0.0,0.0,0.0,0.05,0.0,0.06,0.05,0.06,Sheffield United
+William Osula,dk DEN,FW,19.0,21,9,786.0,8.7,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,1.6,1.6,0.1,1.7,7.0,2.0,41.0,0.0,0.0,0.0,0.0,0.0,0.19,0.01,0.2,0.19,0.2,Sheffield United
+Ivo Grbić,hr CRO,GK,27.0,9,9,771.0,8.6,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Sheffield United
+Mason Holgate,eng ENG,DF,26.0,10,9,710.0,7.9,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,0.6,0.6,0.4,1.1,0.0,13.0,0.0,0.0,0.0,0.0,0.0,0.0,0.08,0.05,0.13,0.08,0.13,Sheffield United
+Yasser Larouci,dz ALG,DF,22.0,11,6,531.0,5.9,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.2,0.2,0.3,0.5,18.0,14.0,12.0,0.0,0.0,0.0,0.0,0.0,0.04,0.05,0.09,0.04,0.09,Sheffield United
+John Egan,ie IRL,DF,30.0,6,6,482.0,5.4,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.2,0.2,0.0,0.2,0.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.03,0.0,0.03,0.03,0.03,Sheffield United
+Max Lowe,eng ENG,"DF,MF",26.0,10,4,503.0,5.6,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.6,0.6,0.7,1.3,5.0,12.0,11.0,0.0,0.0,0.0,0.0,0.0,0.1,0.13,0.23,0.1,0.23,Sheffield United
+Anis Ben Slimane,tn TUN,MF,22.0,15,4,419.0,4.7,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.8,0.8,0.3,1.1,3.0,12.0,21.0,0.0,0.0,0.0,0.0,0.0,0.17,0.06,0.23,0.17,0.23,Sheffield United
+Bénie Adama Traore,ci CIV,"FW,MF",20.0,8,3,387.0,4.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.5,0.8,7.0,9.0,14.0,0.0,0.0,0.0,0.0,0.0,0.06,0.13,0.19,0.06,0.19,Sheffield United
+Rhian Brewster,eng ENG,"FW,MF",23.0,13,3,346.0,3.8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.4,0.4,0.1,0.5,1.0,3.0,21.0,0.0,0.0,0.0,0.0,0.0,0.09,0.03,0.12,0.09,0.12,Sheffield United
+Chris Basham,eng ENG,"DF,MF",35.0,8,3,321.0,3.6,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.1,0.1,0.3,0.4,1.0,7.0,6.0,0.0,0.0,0.0,0.0,0.0,0.04,0.07,0.1,0.04,0.1,Sheffield United
+Tom Davies,eng ENG,MF,25.0,9,2,221.0,2.5,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.4,0.4,0.1,0.4,3.0,8.0,3.0,0.0,0.0,0.0,0.0,0.0,0.14,0.03,0.17,0.14,0.17,Sheffield United
+Rhys Norrington-Davies,wls WAL,DF,24.0,5,2,110.0,1.2,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,3.0,2.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.01,Sheffield United
+John Fleck,sct SCO,"MF,DF",31.0,4,1,92.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.4,0.4,2.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.44,0.44,0.0,0.44,Sheffield United
+Sam Curtis,ie IRL,DF,17.0,1,0,28.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Sheffield United
+Daniel Jebbison,eng ENG,FW,19.0,1,0,21.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Sheffield United
+Antwoine Hackford,eng ENG,FW,19.0,2,0,13.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Sheffield United
+Sydie Peck,eng ENG,MF,18.0,1,0,10.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Sheffield United
+Ryan One,sct SCO,FW,17.0,1,0,8.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Sheffield United