PGA Monte Carlo Point System.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import csv\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_list = ['https://www.pgatour.com/stats/stat.112.2018.html', #par 3 birdie or better\n",
    "       'https://www.pgatour.com/stats/stat.447.2018.html', #par 4 eagles\n",
    "       'https://www.pgatour.com/stats/stat.113.2018.html', #par 4 birdies or better\n",
    "       'https://www.pgatour.com/stats/stat.448.2018.html', #par 5 eagles\n",
    "       'https://www.pgatour.com/stats/stat.114.2018.html', #par 5 birdies or better\n",
    "       'https://www.pgatour.com/stats/stat.02419.2018.html', #bogey average\n",
    "       'https://www.pgatour.com/stats/stat.02415.2018.html'] #birdie to bogey ratio (to get DB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = ['par3_bird_2018.csv', 'par4_eag_2018.csv', 'par4_bird_2018.csv',\n",
    "             'par5_eag_2018.csv', 'par5_bird_2018.csv', 'bogey_2018.csv',\n",
    "             'doublebogey_2018.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_date(url_list, filenames):\n",
    "    sleep = 30\n",
    "    for idx, url in enumerate(url_list):\n",
    "        if idx != 0:\n",
    "            print(f\"sleeping {sleep} seconds\")\n",
    "            time.sleep(sleep)\n",
    "        print(f\"fetching {url}\")\n",
    "        f = requests.get(url, verify=False).text #gets the html text\n",
    "        html = BeautifulSoup(f.replace('\\n',''), 'html.parser') #parses the html\n",
    "        csv_lines = [] #initiate empty csv list\n",
    "        table = html.find('table', class_='table-styled') #get the headers\n",
    "        headings = [t.text for t in table.find('thead').find_all('th')]\n",
    "        csv_lines.append(headings)\n",
    "        for tr in table.find('tbody').find_all('tr'): #get all the data\n",
    "            info = [td.text.replace(u'\\xa0', u' ').strip() for td in tr.find_all('td')]\n",
    "            csv_lines.append(info)\n",
    "        cur_filename = filenames[idx]\n",
    "        print(f\"writing to {cur_filename}\")\n",
    "        with open(cur_filename, \"w\") as output: #save the data to file\n",
    "            writer = csv.writer(output, delimiter=',', lineterminator='\\n')\n",
    "            writer.writerows(csv_lines)\n",
    "        #Sleep a bit longer so the pattern isn't so obvious.\n",
    "        #Don't want to get our IP blocked.\n",
    "        sleep += 5\n",
    "\n",
    "    print(\"finished fetching data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A list of url's with the relevant 2018 statistics\n",
    "url_list = ['https://www.pgatour.com/stats/stat.112.html', #par 3 birdie or better\n",
    "       'https://www.pgatour.com/stats/stat.447.html', #par 4 eagles\n",
    "       'https://www.pgatour.com/stats/stat.113.html', #par 4 birdies or better\n",
    "       'https://www.pgatour.com/stats/stat.448.html', #par 5 eagles\n",
    "       'https://www.pgatour.com/stats/stat.114.html', #par 5 birdies or better\n",
    "       'https://www.pgatour.com/stats/stat.02419.html', #bogey average\n",
    "       'https://www.pgatour.com/stats/stat.02415.html'] #birdie to bogey ratio (to get DB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = ['par3_bird_2019.csv', 'par4_eag_2019.csv', 'par4_bird_2019.csv', \n",
    "             'par5_eag_2019.csv', 'par5_bird_2019.csv', 'bogey_2019.csv',\n",
    "             'doublebogey_2019.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fetching https://www.pgatour.com/stats/stat.112.html\n",
      "writing to par3_bird_2019.csv\n",
      "sleeping 35 seconds\n",
      "fetching https://www.pgatour.com/stats/stat.447.html\n",
      "writing to par4_eag_2019.csv\n",
      "sleeping 40 seconds\n",
      "fetching https://www.pgatour.com/stats/stat.113.html\n",
      "writing to par4_bird_2019.csv\n",
      "sleeping 45 seconds\n",
      "fetching https://www.pgatour.com/stats/stat.448.html\n",
      "writing to par5_eag_2019.csv\n",
      "sleeping 50 seconds\n",
      "fetching https://www.pgatour.com/stats/stat.114.html\n",
      "writing to par5_bird_2019.csv\n",
      "sleeping 55 seconds\n",
      "fetching https://www.pgatour.com/stats/stat.02419.html\n",
      "writing to bogey_2019.csv\n",
      "sleeping 60 seconds\n",
      "fetching https://www.pgatour.com/stats/stat.02415.html\n",
      "writing to doublebogey_2019.csv\n",
      "sleeping 65 seconds\n"
     ]
    }
   ],
   "source": [
    "get_date(url_list, filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bog = pd.read_csv('bogey_2018.csv', header=0, index_col='PLAYER NAME', usecols=['PLAYER NAME', 'TOTAL BOGEYS', 'ROUNDS PLAYED'])\n",
    "df_bog = df_bog.rename(index=str, columns={'TOTAL BOGEYS':'B', 'ROUNDS PLAYED':'Rounds'})\n",
    "\n",
    "df_db = pd.read_csv('doublebogey_2018.csv', header=0, index_col='PLAYER NAME', usecols=['PLAYER NAME', 'TOTAL BOGEYS AND WORSE'])\n",
    "df_db = df_db.rename(index=str, columns={'TOTAL BOGEYS AND WORSE':'DBoW'})\n",
    "\n",
    "df_bir3 = pd.read_csv('par3_bird_2018.csv', header=0, index_col='PLAYER NAME', usecols=['PLAYER NAME', 'PAR 3 BIRDIES OR BETTER', 'PAR 3 HOLES'])\n",
    "df_bir3 = df_bir3.rename(index=str, columns={'PAR 3 BIRDIES OR BETTER':'P3b', 'PAR 3 HOLES':'P3H'})\n",
    "\n",
    "df_eag4 = pd.read_csv('par4_eag_2018.csv', header=0, index_col='PLAYER NAME', usecols=['PLAYER NAME', 'TOTAL']) \n",
    "df_eag4 = df_eag4.rename(index=str, columns={'TOTAL':'P4e'})\n",
    "\n",
    "df_bir4 = pd.read_csv('par4_bird_2018.csv', header=0, index_col='PLAYER NAME', usecols=['PLAYER NAME', 'PAR 4 BIRDIES OR BETTER', 'PAR 4 HOLES'])\n",
    "if df_bir4['PAR 4 HOLES'].dtype == 'O': #need this if the number includes commas and quotes\n",
    "    df_bir4['PAR 4 HOLES'] = df_bir4['PAR 4 HOLES'].str.replace(',', '').astype(float)\n",
    "df_bir4 = df_bir4.rename(index=str, columns={'PAR 4 BIRDIES OR BETTER':'P4be', 'PAR 4 HOLES':'P4H'})\n",
    "\n",
    "df_eag5 = pd.read_csv('par5_eag_2018.csv', header=0, index_col='PLAYER NAME', usecols=['PLAYER NAME', 'TOTAL'])\n",
    "df_eag5 = df_eag5.rename(index=str, columns={'TOTAL': 'P5e'})\n",
    "\n",
    "df_bir5 = pd.read_csv('par5_bird_2018.csv', header=0, index_col='PLAYER NAME', usecols=['PLAYER NAME', 'PAR 5 BIRDIES OR BETTER', 'PAR 5 HOLES'])\n",
    "df_bir5 = df_bir5.rename(index=str, columns={'PAR 5 BIRDIES OR BETTER':'P5be', 'PAR 5 HOLES':'P5H'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Joining to create overall dataframe\n",
    "#Now I join them all\n",
    "df = df_bog.join(df_db).join(df_bir3).join(df_eag4).join(df_bir4).join(df_eag5).join(df_bir5)\n",
    "df = df.fillna(0) #this is just for eagle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now subtract eagles from birdies or better to get just birdies\n",
    "df['P4b'] = df['P4be'].subtract(df['P4e'])\n",
    "df = df.drop('P4be', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['P5b'] = df['P5be'].subtract(df['P5e'])\n",
    "df = df.drop('P5be', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now subtract bogeys from bogeys or worse to get double bogeys (or worse)\n",
    "df['DB'] = df['DBoW'].subtract(df['B'])\n",
    "df = df.drop('DBoW', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Saving dataframe\n",
    "df.to_pickle('stats_2018.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>B</th>\n",
       "      <th>Rounds</th>\n",
       "      <th>P3b</th>\n",
       "      <th>P3H</th>\n",
       "      <th>P4e</th>\n",
       "      <th>P4H</th>\n",
       "      <th>P5e</th>\n",
       "      <th>P5H</th>\n",
       "      <th>P4b</th>\n",
       "      <th>P5b</th>\n",
       "      <th>DB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLAYER NAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Henrik Stenson</th>\n",
       "      <td>110</td>\n",
       "      <td>56</td>\n",
       "      <td>19</td>\n",
       "      <td>222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>618.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>168</td>\n",
       "      <td>108.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rickie Fowler</th>\n",
       "      <td>147</td>\n",
       "      <td>74</td>\n",
       "      <td>45</td>\n",
       "      <td>288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>832.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>212</td>\n",
       "      <td>154.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Webb Simpson</th>\n",
       "      <td>186</td>\n",
       "      <td>92</td>\n",
       "      <td>37</td>\n",
       "      <td>368</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1036.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>252</td>\n",
       "      <td>191.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dustin Johnson</th>\n",
       "      <td>151</td>\n",
       "      <td>74</td>\n",
       "      <td>55</td>\n",
       "      <td>291</td>\n",
       "      <td>5.0</td>\n",
       "      <td>817.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>224</td>\n",
       "      <td>179.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooks Koepka</th>\n",
       "      <td>131</td>\n",
       "      <td>62</td>\n",
       "      <td>36</td>\n",
       "      <td>240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>712.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>164</td>\n",
       "      <td>141.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  B  Rounds  P3b  P3H  P4e     P4H   P5e  P5H    P4b    P5b  \\\n",
       "PLAYER NAME                                                                   \n",
       "Henrik Stenson  110      56   19  222  0.0   618.0   5.0  168  108.0   77.0   \n",
       "Rickie Fowler   147      74   45  288  0.0   832.0   7.0  212  154.0   97.0   \n",
       "Webb Simpson    186      92   37  368  3.0  1036.0  15.0  252  191.0  102.0   \n",
       "Dustin Johnson  151      74   55  291  5.0   817.0  10.0  224  179.0  114.0   \n",
       "Brooks Koepka   131      62   36  240  0.0   712.0   8.0  164  141.0   83.0   \n",
       "\n",
       "                DB  \n",
       "PLAYER NAME         \n",
       "Henrik Stenson  13  \n",
       "Rickie Fowler   22  \n",
       "Webb Simpson    20  \n",
       "Dustin Johnson  17  \n",
       "Brooks Koepka   28  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "\"\"\"\n",
    "B = Bogeys\n",
    "R = Rounds\n",
    "P3b = Par 3 birdies\n",
    "P3H = Par 3 holes\n",
    "P4e = Par 4 eagles\n",
    "P4H = Par 4 holes\n",
    "P5e = Par 5 eagles\n",
    "P5H = Par 5 holes\n",
    "P4b = Par 4 birdies\n",
    "P5b = Par 5 birides\n",
    "DB = Double bogies or worse\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "now = datetime.datetime.now()\n",
    "date = now.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bog = pd.read_csv('bogey_2019.csv', header=0, index_col='PLAYER NAME', usecols=['PLAYER NAME', 'TOTAL BOGEYS', 'ROUNDS PLAYED'])\n",
    "df_bog = df_bog.rename(index=str, columns={'TOTAL BOGEYS':'B', 'ROUNDS PLAYED':'Rounds'})\n",
    "\n",
    "df_db = pd.read_csv('doublebogey_2019.csv', header=0, index_col='PLAYER NAME', usecols=['PLAYER NAME', 'TOTAL BOGEYS AND WORSE'])\n",
    "df_db = df_db.rename(index=str, columns={'TOTAL BOGEYS AND WORSE':'DBoW'})\n",
    "\n",
    "df_bir3 = pd.read_csv('par3_bird_2019.csv', header=0, index_col='PLAYER NAME', usecols=['PLAYER NAME', 'PAR 3 BIRDIES OR BETTER', 'PAR 3 HOLES'])\n",
    "df_bir3 = df_bir3.rename(index=str, columns={'PAR 3 BIRDIES OR BETTER':'P3b', 'PAR 3 HOLES':'P3H'})\n",
    "\n",
    "df_eag4 = pd.read_csv('par4_eag_2019.csv', header=0, index_col='PLAYER NAME', usecols=['PLAYER NAME', 'TOTAL']) \n",
    "df_eag4 = df_eag4.rename(index=str, columns={'TOTAL':'P4e'})\n",
    "\n",
    "df_bir4 = pd.read_csv('par4_bird_2019.csv', header=0, index_col='PLAYER NAME', usecols=['PLAYER NAME', 'PAR 4 BIRDIES OR BETTER', 'PAR 4 HOLES'])\n",
    "if df_bir4['PAR 4 HOLES'].dtype == 'O': #need this if the number includes commas and quotes\n",
    "    df_bir4['PAR 4 HOLES'] = df_bir4['PAR 4 HOLES'].str.replace(',', '').astype(float)\n",
    "df_bir4 = df_bir4.rename(index=str, columns={'PAR 4 BIRDIES OR BETTER':'P4be', 'PAR 4 HOLES':'P4H'})\n",
    "\n",
    "df_eag5 = pd.read_csv('par5_eag_2019.csv', header=0, index_col='PLAYER NAME', usecols=['PLAYER NAME', 'TOTAL'])\n",
    "df_eag5 = df_eag5.rename(index=str, columns={'TOTAL': 'P5e'})\n",
    "\n",
    "df_bir5 = pd.read_csv('par5_bird_2019.csv', header=0, index_col='PLAYER NAME', usecols=['PLAYER NAME', 'PAR 5 BIRDIES OR BETTER', 'PAR 5 HOLES'])\n",
    "#df_bir5['%'] = df_bir5['%']/100\n",
    "df_bir5 = df_bir5.rename(index=str, columns={'PAR 5 BIRDIES OR BETTER':'P5be', 'PAR 5 HOLES':'P5H'})\n",
    "\n",
    "#%% Joining to create overall dataframe\n",
    "#Now I join them all\n",
    "df = df_bog.join(df_db).join(df_bir3).join(df_eag4).join(df_bir4).join(df_eag5).join(df_bir5)\n",
    "df = df.fillna(0) #this is just for eagle\n",
    "\n",
    "#Now subtract eagles from birdies or better to get just birdies\n",
    "df['P4b'] = df['P4be'].subtract(df['P4e'])\n",
    "df = df.drop('P4be', axis=1)\n",
    "\n",
    "df['P5b'] = df['P5be'].subtract(df['P5e'])\n",
    "df = df.drop('P5be', axis=1)\n",
    "\n",
    "#Now subtract bogeys from bogeys or worse to get double bogeys (or worse)\n",
    "df['DB'] = df['DBoW'].subtract(df['B'])\n",
    "df = df.drop('DBoW', axis=1)\n",
    "\n",
    "#%% Saving dataframe\n",
    "df.to_pickle('stats_'+date+'.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2018 = pd.read_pickle(\"stats_2018.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2019 = pd.read_pickle(f\"stats_{date}.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stats = df_2018.join(df_2019, how='outer', lsuffix='_2018', rsuffix='_2019')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stats = df_stats.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#need to assign weght to newest year\n",
    "weight=2 #this just doubles the importance of the newest stats\n",
    "df_stats['B'] = df_stats['B_2018'] + df_stats['B_2019']*weight\n",
    "df_stats['DB'] = df_stats['DB_2018'] + df_stats['DB_2019']*weight\n",
    "df_stats['Rounds'] = df_stats['Rounds_2018'] + df_stats['Rounds_2019']*weight\n",
    "df_stats['P3b'] = df_stats['P3b_2018'] + df_stats['P3b_2019']*weight\n",
    "df_stats['P3H'] = df_stats['P3H_2018'] + df_stats['P3H_2019']*weight\n",
    "df_stats['P4e'] = df_stats['P4e_2018'] + df_stats['P4e_2019']*weight\n",
    "df_stats['P4H'] = df_stats['P4H_2018'] + df_stats['P4H_2019']*weight\n",
    "df_stats['P4b'] = df_stats['P4b_2018'] + df_stats['P4b_2019']*weight\n",
    "df_stats['P5e'] = df_stats['P5e_2018'] + df_stats['P5e_2019']*weight\n",
    "df_stats['P5H'] = df_stats['P5H_2018'] + df_stats['P5H_2019']*weight\n",
    "df_stats['P5b'] = df_stats['P5b_2018'] + df_stats['P5b_2019']*weight\n",
    "df_stats['Real_Rounds']=df_stats['Rounds_2018'] + df_stats['Rounds_2019']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "par = 72\n",
    "par3 = 4\n",
    "par4 = 10\n",
    "par5 = 4\n",
    "if par3 + par4 + par5 != 18:\n",
    "    raise NameError(\"Holes don't add up to 18. Something is off.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Set the draftkings scoring system\n",
    "pts_b = 3\n",
    "pts_e = 8\n",
    "pts_p = 0.5\n",
    "pts_B = -0.5\n",
    "pts_DB = -1\n",
    "\n",
    "pts_streak = 3\n",
    "pts_BF = 3\n",
    "pts_4U70 = 5\n",
    "\n",
    "def pts_place_tmp(plc):\n",
    "    if plc>50:\n",
    "        return 0\n",
    "    if plc>40:\n",
    "        return 1\n",
    "    if plc>30:\n",
    "        return 2\n",
    "    if plc>25:\n",
    "        return 3\n",
    "    if plc>20:\n",
    "        return 4\n",
    "    if plc>15:\n",
    "        return 5\n",
    "    if plc>10:\n",
    "        return 6\n",
    "    if plc==10:\n",
    "        return 7\n",
    "    if plc==9:\n",
    "        return 8\n",
    "    if plc==8:\n",
    "        return 9\n",
    "    if plc==7:\n",
    "        return 10\n",
    "    if plc==6:\n",
    "        return 12\n",
    "    if plc==5:\n",
    "        return 14\n",
    "    if plc==4:\n",
    "        return 16\n",
    "    if plc==3:\n",
    "        return 18\n",
    "    if plc==2:\n",
    "        return 20\n",
    "    if plc==1:\n",
    "        return 30\n",
    "\n",
    "pts_place = np.vectorize(pts_place_tmp) #This makes it element wise for np.arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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>B_2018</th>\n",
       "      <th>Rounds_2018</th>\n",
       "      <th>P3b_2018</th>\n",
       "      <th>P3H_2018</th>\n",
       "      <th>P4e_2018</th>\n",
       "      <th>P4H_2018</th>\n",
       "      <th>P5e_2018</th>\n",
       "      <th>P5H_2018</th>\n",
       "      <th>P4b_2018</th>\n",
       "      <th>P5b_2018</th>\n",
       "      <th>...</th>\n",
       "      <th>P5H</th>\n",
       "      <th>P5b</th>\n",
       "      <th>Real_Rounds</th>\n",
       "      <th>B/R</th>\n",
       "      <th>DB/R</th>\n",
       "      <th>3b/H</th>\n",
       "      <th>4e/H</th>\n",
       "      <th>4b/H</th>\n",
       "      <th>5e/H</th>\n",
       "      <th>5b/H</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLAYER NAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [B_2018, Rounds_2018, P3b_2018, P3H_2018, P4e_2018, P4H_2018, P5e_2018, P5H_2018, P4b_2018, P5b_2018, DB_2018, B_2019, Rounds_2019, P3b_2019, P3H_2019, P4e_2019, P4H_2019, P5e_2019, P5H_2019, P4b_2019, P5b_2019, DB_2019, B, DB, Rounds, P3b, P3H, P4e, P4H, P4b, P5e, P5H, P5b, Real_Rounds, B/R, DB/R, 3b/H, 4e/H, 4b/H, 5e/H, 5b/H]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 41 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Need to enter draft kings logic here.\n",
    "df_stats[df_stats['B']== 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import groupby\n",
    "\n",
    "def len_iter(items):\n",
    "    return sum(1 for _ in items)\n",
    "    \n",
    "def consecutive_one(data):\n",
    "    return max(len_iter(run) for val, run in groupby(data) if val)\n",
    "\n",
    "# #now time for the monte carlo analysis\n",
    "trials=100000\n",
    "results = np.empty(trials) \n",
    "final_results = np.zeros(19)   \n",
    "for b in range(3,13):\n",
    "    for k in range(trials):\n",
    "        vec = np.append(np.zeros(18-b), np.ones(b))\n",
    "        np.random.shuffle(vec)\n",
    "        results[k] = consecutive_one(vec)\n",
    "    final_results[b] = np.mean(results>=3)\n",
    "final_results[13:]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.     , 0.     , 0.     , 0.01991, 0.07403, 0.17166, 0.31579,\n",
       "       0.48848, 0.67159, 0.82888, 0.93419, 0.98423, 0.99866, 1.     ,\n",
       "       1.     , 1.     , 1.     , 1.     , 1.     ])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "streak_prob = {\n",
    "    0:0,\n",
    "    1:0,\n",
    "    2:0,\n",
    "    3:0.01991, \n",
    "    4:0.07403, \n",
    "    5:0.17166, \n",
    "    6:0.31579,\n",
    "    7:0.48848, \n",
    "    8:0.67159, \n",
    "    9:0.82888, \n",
    "    10:0.93419, \n",
    "    11:0.98423, \n",
    "    12:0.99866, \n",
    "    13:1,\n",
    "    14:1, \n",
    "    15:1, \n",
    "    16:1,\n",
    "    17:1, \n",
    "    18:1     \n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "sims = 10000\n",
    "#%% Calculate poisson rates\n",
    "df_stats['B/R'] = df_stats['B'].div(df_stats['Rounds']) \n",
    "df_stats['DB/R'] = df_stats['DB'].div(df_stats['Rounds']) \n",
    "df_stats['3b/H'] = df_stats['P3b'].div(df_stats['P3H'])\n",
    "df_stats['4e/H'] = df_stats['P4e'].div(df_stats['P4H'])\n",
    "df_stats['4b/H'] = df_stats['P4b'].div(df_stats['P4H']) \n",
    "df_stats['5e/H'] = df_stats['P5e'].div(df_stats['P5H'])\n",
    "df_stats['5b/H'] = df_stats['P5b'].div(df_stats['P5H'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>B_2018</th>\n",
       "      <th>Rounds_2018</th>\n",
       "      <th>P3b_2018</th>\n",
       "      <th>P3H_2018</th>\n",
       "      <th>P4e_2018</th>\n",
       "      <th>P4H_2018</th>\n",
       "      <th>P5e_2018</th>\n",
       "      <th>P5H_2018</th>\n",
       "      <th>P4b_2018</th>\n",
       "      <th>P5b_2018</th>\n",
       "      <th>...</th>\n",
       "      <th>P5H</th>\n",
       "      <th>P5b</th>\n",
       "      <th>Real_Rounds</th>\n",
       "      <th>B/R</th>\n",
       "      <th>DB/R</th>\n",
       "      <th>3b/H</th>\n",
       "      <th>4e/H</th>\n",
       "      <th>4b/H</th>\n",
       "      <th>5e/H</th>\n",
       "      <th>5b/H</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLAYER NAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aaron Baddeley</th>\n",
       "      <td>205.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>843.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>...</td>\n",
       "      <td>628.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>2.420213</td>\n",
       "      <td>0.271277</td>\n",
       "      <td>0.104084</td>\n",
       "      <td>0.001502</td>\n",
       "      <td>0.175263</td>\n",
       "      <td>0.022293</td>\n",
       "      <td>0.393312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aaron Wise</th>\n",
       "      <td>234.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>959.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>...</td>\n",
       "      <td>616.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>2.649485</td>\n",
       "      <td>0.355670</td>\n",
       "      <td>0.153946</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>0.197813</td>\n",
       "      <td>0.014610</td>\n",
       "      <td>0.469156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abraham Ancer</th>\n",
       "      <td>231.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1044.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>...</td>\n",
       "      <td>754.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>2.437500</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>0.134096</td>\n",
       "      <td>0.000768</td>\n",
       "      <td>0.183564</td>\n",
       "      <td>0.019894</td>\n",
       "      <td>0.419098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Hadwin</th>\n",
       "      <td>230.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>966.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>...</td>\n",
       "      <td>700.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>2.663594</td>\n",
       "      <td>0.276498</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.001284</td>\n",
       "      <td>0.172517</td>\n",
       "      <td>0.027143</td>\n",
       "      <td>0.432857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Long</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>360.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>0.410714</td>\n",
       "      <td>0.102222</td>\n",
       "      <td>0.003317</td>\n",
       "      <td>0.154229</td>\n",
       "      <td>0.038889</td>\n",
       "      <td>0.322222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Schenk</th>\n",
       "      <td>217.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>...</td>\n",
       "      <td>786.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>2.495833</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.136972</td>\n",
       "      <td>0.001561</td>\n",
       "      <td>0.158018</td>\n",
       "      <td>0.026718</td>\n",
       "      <td>0.413486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Scott</th>\n",
       "      <td>208.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>...</td>\n",
       "      <td>537.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>2.597561</td>\n",
       "      <td>0.280488</td>\n",
       "      <td>0.154780</td>\n",
       "      <td>0.002847</td>\n",
       "      <td>0.168565</td>\n",
       "      <td>0.027933</td>\n",
       "      <td>0.489758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Svensson</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>340.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>2.823529</td>\n",
       "      <td>0.274510</td>\n",
       "      <td>0.144928</td>\n",
       "      <td>0.001848</td>\n",
       "      <td>0.153420</td>\n",
       "      <td>0.035294</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex Cejka</th>\n",
       "      <td>170.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>...</td>\n",
       "      <td>487.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2.778523</td>\n",
       "      <td>0.248322</td>\n",
       "      <td>0.162029</td>\n",
       "      <td>0.002525</td>\n",
       "      <td>0.133838</td>\n",
       "      <td>0.014374</td>\n",
       "      <td>0.363450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex Noren</th>\n",
       "      <td>147.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>...</td>\n",
       "      <td>484.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.090323</td>\n",
       "      <td>0.001174</td>\n",
       "      <td>0.148474</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.435950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex Prugh</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>378.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2.741379</td>\n",
       "      <td>0.155172</td>\n",
       "      <td>0.114894</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.158065</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.386243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anders Albertson</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>316.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2.604167</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.119171</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.159844</td>\n",
       "      <td>0.018987</td>\n",
       "      <td>0.367089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrew Landry</th>\n",
       "      <td>207.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>884.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>...</td>\n",
       "      <td>610.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>2.588542</td>\n",
       "      <td>0.369792</td>\n",
       "      <td>0.129243</td>\n",
       "      <td>0.001442</td>\n",
       "      <td>0.168750</td>\n",
       "      <td>0.011475</td>\n",
       "      <td>0.360656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrew Putnam</th>\n",
       "      <td>207.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>967.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>...</td>\n",
       "      <td>672.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2.397129</td>\n",
       "      <td>0.344498</td>\n",
       "      <td>0.172869</td>\n",
       "      <td>0.001772</td>\n",
       "      <td>0.181657</td>\n",
       "      <td>0.007440</td>\n",
       "      <td>0.415179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrew Yun</th>\n",
       "      <td>176.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>...</td>\n",
       "      <td>146.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.911111</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.160221</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.138716</td>\n",
       "      <td>0.006849</td>\n",
       "      <td>0.308219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anirban Lahiri</th>\n",
       "      <td>227.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>906.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>...</td>\n",
       "      <td>588.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>2.836842</td>\n",
       "      <td>0.384211</td>\n",
       "      <td>0.142298</td>\n",
       "      <td>0.000484</td>\n",
       "      <td>0.162633</td>\n",
       "      <td>0.008503</td>\n",
       "      <td>0.367347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin Cook</th>\n",
       "      <td>264.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1179.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>...</td>\n",
       "      <td>634.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2.669951</td>\n",
       "      <td>0.334975</td>\n",
       "      <td>0.167277</td>\n",
       "      <td>0.000909</td>\n",
       "      <td>0.169468</td>\n",
       "      <td>0.012618</td>\n",
       "      <td>0.440063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beau Hossler</th>\n",
       "      <td>239.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>...</td>\n",
       "      <td>788.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>2.582645</td>\n",
       "      <td>0.355372</td>\n",
       "      <td>0.137718</td>\n",
       "      <td>0.001156</td>\n",
       "      <td>0.176879</td>\n",
       "      <td>0.019036</td>\n",
       "      <td>0.425127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ben Crane</th>\n",
       "      <td>145.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>...</td>\n",
       "      <td>456.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.531915</td>\n",
       "      <td>0.276596</td>\n",
       "      <td>0.116402</td>\n",
       "      <td>0.001320</td>\n",
       "      <td>0.161056</td>\n",
       "      <td>0.013158</td>\n",
       "      <td>0.379386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ben Martin</th>\n",
       "      <td>168.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>...</td>\n",
       "      <td>202.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>0.390625</td>\n",
       "      <td>0.134100</td>\n",
       "      <td>0.001451</td>\n",
       "      <td>0.168360</td>\n",
       "      <td>0.009901</td>\n",
       "      <td>0.391089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ben Silverman</th>\n",
       "      <td>213.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>909.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>...</td>\n",
       "      <td>620.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2.607143</td>\n",
       "      <td>0.367347</td>\n",
       "      <td>0.145019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.157447</td>\n",
       "      <td>0.019355</td>\n",
       "      <td>0.395161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bill Haas</th>\n",
       "      <td>197.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>801.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>...</td>\n",
       "      <td>581.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>2.741758</td>\n",
       "      <td>0.241758</td>\n",
       "      <td>0.097297</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>0.135038</td>\n",
       "      <td>0.025818</td>\n",
       "      <td>0.423408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Billy Horschel</th>\n",
       "      <td>188.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>896.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>...</td>\n",
       "      <td>725.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2.432432</td>\n",
       "      <td>0.175676</td>\n",
       "      <td>0.167235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.150084</td>\n",
       "      <td>0.024828</td>\n",
       "      <td>0.401379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Billy Hurley III</th>\n",
       "      <td>203.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>197.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3.171875</td>\n",
       "      <td>0.281250</td>\n",
       "      <td>0.123552</td>\n",
       "      <td>0.001437</td>\n",
       "      <td>0.153736</td>\n",
       "      <td>0.025381</td>\n",
       "      <td>0.355330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blayne Barber</th>\n",
       "      <td>168.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>...</td>\n",
       "      <td>194.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2.507463</td>\n",
       "      <td>0.313433</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.001351</td>\n",
       "      <td>0.166216</td>\n",
       "      <td>0.010309</td>\n",
       "      <td>0.469072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brady Schnell</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>276.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2.761905</td>\n",
       "      <td>0.261905</td>\n",
       "      <td>0.142012</td>\n",
       "      <td>0.002227</td>\n",
       "      <td>0.155902</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.311594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Branden Grace</th>\n",
       "      <td>174.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>683.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>...</td>\n",
       "      <td>594.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2.954545</td>\n",
       "      <td>0.278409</td>\n",
       "      <td>0.124825</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.149382</td>\n",
       "      <td>0.033670</td>\n",
       "      <td>0.405724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brandon Harkins</th>\n",
       "      <td>268.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1031.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>...</td>\n",
       "      <td>728.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.960699</td>\n",
       "      <td>0.388646</td>\n",
       "      <td>0.146394</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.185801</td>\n",
       "      <td>0.030220</td>\n",
       "      <td>0.434066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brandt Snedeker</th>\n",
       "      <td>205.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>905.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>...</td>\n",
       "      <td>720.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2.583333</td>\n",
       "      <td>0.271930</td>\n",
       "      <td>0.114504</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>0.172679</td>\n",
       "      <td>0.019444</td>\n",
       "      <td>0.427778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brendan Steele</th>\n",
       "      <td>199.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>738.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>...</td>\n",
       "      <td>572.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2.707602</td>\n",
       "      <td>0.380117</td>\n",
       "      <td>0.101471</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>0.161008</td>\n",
       "      <td>0.020979</td>\n",
       "      <td>0.416084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Si Woo Kim</th>\n",
       "      <td>294.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1134.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>...</td>\n",
       "      <td>770.0</td>\n",
       "      <td>376.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>2.551724</td>\n",
       "      <td>0.353448</td>\n",
       "      <td>0.134989</td>\n",
       "      <td>0.000806</td>\n",
       "      <td>0.171774</td>\n",
       "      <td>0.014286</td>\n",
       "      <td>0.488312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stephan Jaeger</th>\n",
       "      <td>201.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>...</td>\n",
       "      <td>612.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>2.606218</td>\n",
       "      <td>0.248705</td>\n",
       "      <td>0.123883</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140933</td>\n",
       "      <td>0.021242</td>\n",
       "      <td>0.457516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve Wheatcroft</th>\n",
       "      <td>137.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>621.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>190.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2.362069</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>0.103004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156200</td>\n",
       "      <td>0.015789</td>\n",
       "      <td>0.421053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stewart Cink</th>\n",
       "      <td>234.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>955.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>...</td>\n",
       "      <td>262.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2.689655</td>\n",
       "      <td>0.241379</td>\n",
       "      <td>0.140401</td>\n",
       "      <td>0.001047</td>\n",
       "      <td>0.176963</td>\n",
       "      <td>0.045802</td>\n",
       "      <td>0.374046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sung Kang</th>\n",
       "      <td>247.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>918.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>...</td>\n",
       "      <td>716.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>2.656109</td>\n",
       "      <td>0.407240</td>\n",
       "      <td>0.146532</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154983</td>\n",
       "      <td>0.034916</td>\n",
       "      <td>0.430168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sungjae Im</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>570.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.363636</td>\n",
       "      <td>0.295455</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.002123</td>\n",
       "      <td>0.180467</td>\n",
       "      <td>0.028070</td>\n",
       "      <td>0.498246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Talor Gooch</th>\n",
       "      <td>198.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>778.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>...</td>\n",
       "      <td>459.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2.589041</td>\n",
       "      <td>0.308219</td>\n",
       "      <td>0.140917</td>\n",
       "      <td>0.000633</td>\n",
       "      <td>0.165190</td>\n",
       "      <td>0.026144</td>\n",
       "      <td>0.450980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ted Potter, Jr.</th>\n",
       "      <td>260.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>955.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>624.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>2.953846</td>\n",
       "      <td>0.507692</td>\n",
       "      <td>0.135723</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.171021</td>\n",
       "      <td>0.006410</td>\n",
       "      <td>0.378205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tiger Woods</th>\n",
       "      <td>153.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>754.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>...</td>\n",
       "      <td>406.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.320312</td>\n",
       "      <td>0.296875</td>\n",
       "      <td>0.177734</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.191198</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.453202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom Hoge</th>\n",
       "      <td>254.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1008.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>...</td>\n",
       "      <td>763.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>2.736402</td>\n",
       "      <td>0.334728</td>\n",
       "      <td>0.127461</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.019659</td>\n",
       "      <td>0.382700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom Lovelady</th>\n",
       "      <td>231.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>875.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>...</td>\n",
       "      <td>256.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2.851852</td>\n",
       "      <td>0.456790</td>\n",
       "      <td>0.100917</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.188571</td>\n",
       "      <td>0.035156</td>\n",
       "      <td>0.464844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tommy Fleetwood</th>\n",
       "      <td>172.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>738.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>478.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2.441558</td>\n",
       "      <td>0.259740</td>\n",
       "      <td>0.118893</td>\n",
       "      <td>0.002976</td>\n",
       "      <td>0.180357</td>\n",
       "      <td>0.046025</td>\n",
       "      <td>0.437238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tony Finau</th>\n",
       "      <td>230.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>...</td>\n",
       "      <td>690.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>2.403670</td>\n",
       "      <td>0.266055</td>\n",
       "      <td>0.133028</td>\n",
       "      <td>0.000423</td>\n",
       "      <td>0.179509</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.469565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trey Mullinax</th>\n",
       "      <td>195.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>751.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>...</td>\n",
       "      <td>622.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>2.567839</td>\n",
       "      <td>0.371859</td>\n",
       "      <td>0.117139</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>0.167520</td>\n",
       "      <td>0.025723</td>\n",
       "      <td>0.464630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Troy Merritt</th>\n",
       "      <td>242.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>915.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>536.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>2.634731</td>\n",
       "      <td>0.227545</td>\n",
       "      <td>0.131222</td>\n",
       "      <td>0.002214</td>\n",
       "      <td>0.177643</td>\n",
       "      <td>0.033582</td>\n",
       "      <td>0.449627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyler Duncan</th>\n",
       "      <td>265.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>715.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>2.790393</td>\n",
       "      <td>0.344978</td>\n",
       "      <td>0.122162</td>\n",
       "      <td>0.004029</td>\n",
       "      <td>0.155520</td>\n",
       "      <td>0.022378</td>\n",
       "      <td>0.401399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyrone Van Aswegen</th>\n",
       "      <td>246.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>...</td>\n",
       "      <td>293.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2.645161</td>\n",
       "      <td>0.344086</td>\n",
       "      <td>0.145889</td>\n",
       "      <td>0.000996</td>\n",
       "      <td>0.144422</td>\n",
       "      <td>0.013652</td>\n",
       "      <td>0.402730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyrrell Hatton</th>\n",
       "      <td>127.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>606.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>462.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.595890</td>\n",
       "      <td>0.390411</td>\n",
       "      <td>0.118151</td>\n",
       "      <td>0.000632</td>\n",
       "      <td>0.174463</td>\n",
       "      <td>0.015152</td>\n",
       "      <td>0.393939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vaughn Taylor</th>\n",
       "      <td>206.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>873.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>...</td>\n",
       "      <td>692.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2.389140</td>\n",
       "      <td>0.366516</td>\n",
       "      <td>0.168715</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.178586</td>\n",
       "      <td>0.018786</td>\n",
       "      <td>0.423410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Webb Simpson</th>\n",
       "      <td>186.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1036.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>576.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.121859</td>\n",
       "      <td>0.003142</td>\n",
       "      <td>0.181777</td>\n",
       "      <td>0.046875</td>\n",
       "      <td>0.420139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes Roach</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>294.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.116022</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.130705</td>\n",
       "      <td>0.034014</td>\n",
       "      <td>0.435374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wesley Bryan</th>\n",
       "      <td>183.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>732.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>210.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2.731343</td>\n",
       "      <td>0.373134</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.004098</td>\n",
       "      <td>0.157104</td>\n",
       "      <td>0.004762</td>\n",
       "      <td>0.380952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whee Kim</th>\n",
       "      <td>238.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>654.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>2.763819</td>\n",
       "      <td>0.502513</td>\n",
       "      <td>0.130923</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.174976</td>\n",
       "      <td>0.016820</td>\n",
       "      <td>0.363914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>William McGirt</th>\n",
       "      <td>179.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>849.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>...</td>\n",
       "      <td>238.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.294872</td>\n",
       "      <td>0.256410</td>\n",
       "      <td>0.141956</td>\n",
       "      <td>0.001178</td>\n",
       "      <td>0.142521</td>\n",
       "      <td>0.016807</td>\n",
       "      <td>0.390756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyndham Clark</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>408.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2.492063</td>\n",
       "      <td>0.412698</td>\n",
       "      <td>0.124514</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.191679</td>\n",
       "      <td>0.024510</td>\n",
       "      <td>0.514706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xander Schauffele</th>\n",
       "      <td>255.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1036.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>...</td>\n",
       "      <td>658.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2.453659</td>\n",
       "      <td>0.273171</td>\n",
       "      <td>0.123134</td>\n",
       "      <td>0.001346</td>\n",
       "      <td>0.187163</td>\n",
       "      <td>0.033435</td>\n",
       "      <td>0.480243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xinjun Zhang</th>\n",
       "      <td>196.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>813.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>...</td>\n",
       "      <td>234.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2.613333</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.141914</td>\n",
       "      <td>0.001230</td>\n",
       "      <td>0.156212</td>\n",
       "      <td>0.029915</td>\n",
       "      <td>0.410256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zac Blair</th>\n",
       "      <td>136.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>621.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>...</td>\n",
       "      <td>190.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2.344828</td>\n",
       "      <td>0.275862</td>\n",
       "      <td>0.158798</td>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.152979</td>\n",
       "      <td>0.010526</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zach Johnson</th>\n",
       "      <td>203.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>958.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>...</td>\n",
       "      <td>584.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>2.507692</td>\n",
       "      <td>0.261538</td>\n",
       "      <td>0.125635</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>0.167914</td>\n",
       "      <td>0.035959</td>\n",
       "      <td>0.386986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zecheng Dou</th>\n",
       "      <td>165.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>...</td>\n",
       "      <td>161.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3.367347</td>\n",
       "      <td>0.510204</td>\n",
       "      <td>0.140704</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.018634</td>\n",
       "      <td>0.335404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    B_2018  Rounds_2018  P3b_2018  P3H_2018  P4e_2018  \\\n",
       "PLAYER NAME                                                             \n",
       "Aaron Baddeley       205.0         78.0      25.0     317.0       1.0   \n",
       "Aaron Wise           234.0         88.0      47.0     357.0       1.0   \n",
       "Abraham Ancer        231.0         96.0      49.0     386.0       0.0   \n",
       "Adam Hadwin          230.0         89.0      46.0     352.0       1.0   \n",
       "Adam Long              0.0          0.0       0.0       0.0       0.0   \n",
       "Adam Schenk          217.0         80.0      39.0     325.0       2.0   \n",
       "Adam Scott           208.0         78.0      56.0     313.0       1.0   \n",
       "Adam Svensson          0.0          0.0       0.0       0.0       0.0   \n",
       "Alex Cejka           170.0         69.0      43.0     281.0       4.0   \n",
       "Alex Noren           147.0         60.0      24.0     236.0       0.0   \n",
       "Alex Prugh             0.0          0.0       0.0       0.0       0.0   \n",
       "Anders Albertson       0.0          0.0       0.0       0.0       0.0   \n",
       "Andrew Landry        207.0         80.0      45.0     318.0       3.0   \n",
       "Andrew Putnam        207.0         89.0      60.0     359.0       2.0   \n",
       "Andrew Yun           176.0         45.0      29.0     181.0       0.0   \n",
       "Anirban Lahiri       227.0         82.0      47.0     326.0       1.0   \n",
       "Austin Cook          264.0        107.0      71.0     425.0       0.0   \n",
       "Beau Hossler         239.0        104.0      64.0     415.0       1.0   \n",
       "Ben Crane            145.0         59.0      32.0     237.0       2.0   \n",
       "Ben Martin           168.0         64.0      35.0     261.0       1.0   \n",
       "Ben Silverman        213.0         84.0      45.0     339.0       0.0   \n",
       "Bill Haas            197.0         74.0      34.0     300.0       0.0   \n",
       "Billy Horschel       188.0         82.0      51.0     327.0       0.0   \n",
       "Billy Hurley III     203.0         64.0      32.0     259.0       1.0   \n",
       "Blayne Barber        168.0         67.0      40.0     272.0       1.0   \n",
       "Brady Schnell          0.0          0.0       0.0       0.0       0.0   \n",
       "Branden Grace        174.0         64.0      37.0     259.0       0.0   \n",
       "Brandon Harkins      268.0         95.0      64.0     383.0       0.0   \n",
       "Brandt Snedeker      205.0         82.0      49.0     331.0       2.0   \n",
       "Brendan Steele       199.0         67.0      27.0     260.0       1.0   \n",
       "...                    ...          ...       ...       ...       ...   \n",
       "Si Woo Kim           294.0        104.0      57.0     412.0       0.0   \n",
       "Stephan Jaeger       201.0         75.0      33.0     305.0       0.0   \n",
       "Steve Wheatcroft     137.0         58.0      24.0     233.0       0.0   \n",
       "Stewart Cink         234.0         87.0      49.0     349.0       1.0   \n",
       "Sung Kang            247.0         85.0      49.0     340.0       0.0   \n",
       "Sungjae Im             0.0          0.0       0.0       0.0       0.0   \n",
       "Talor Gooch          198.0         72.0      49.0     291.0       1.0   \n",
       "Ted Potter, Jr.      260.0         87.0      54.0     351.0       4.0   \n",
       "Tiger Woods          153.0         68.0      37.0     272.0       0.0   \n",
       "Tom Hoge             254.0         93.0      47.0     375.0       0.0   \n",
       "Tom Lovelady         231.0         81.0      33.0     327.0       0.0   \n",
       "Tommy Fleetwood      172.0         66.0      39.0     260.0       1.0   \n",
       "Tony Finau           230.0         98.0      52.0     390.0       1.0   \n",
       "Trey Mullinax        195.0         69.0      43.0     281.0       1.0   \n",
       "Troy Merritt         242.0         85.0      45.0     343.0       4.0   \n",
       "Tyler Duncan         265.0         93.0      47.0     375.0       2.0   \n",
       "Tyrone Van Aswegen   246.0         93.0      55.0     377.0       1.0   \n",
       "Tyrrell Hatton       127.0         54.0      21.0     212.0       1.0   \n",
       "Vaughn Taylor        206.0         79.0      55.0     317.0       0.0   \n",
       "Webb Simpson         186.0         92.0      37.0     368.0       3.0   \n",
       "Wes Roach              0.0          0.0       0.0       0.0       0.0   \n",
       "Wesley Bryan         183.0         67.0      33.0     264.0       3.0   \n",
       "Whee Kim             238.0         89.0      47.0     360.0       0.0   \n",
       "William McGirt       179.0         78.0      45.0     317.0       1.0   \n",
       "Wyndham Clark          0.0          0.0       0.0       0.0       0.0   \n",
       "Xander Schauffele    255.0         93.0      45.0     364.0       1.0   \n",
       "Xinjun Zhang         196.0         75.0      43.0     303.0       1.0   \n",
       "Zac Blair            136.0         58.0      37.0     233.0       1.0   \n",
       "Zach Johnson         203.0         87.0      47.0     348.0       1.0   \n",
       "Zecheng Dou          165.0         49.0      28.0     199.0       0.0   \n",
       "\n",
       "                    P4H_2018  P5e_2018  P5H_2018  P4b_2018  P5b_2018  ...  \\\n",
       "PLAYER NAME                                                           ...   \n",
       "Aaron Baddeley         843.0       8.0     244.0     142.0      93.0  ...   \n",
       "Aaron Wise             959.0       5.0     268.0     196.0     119.0  ...   \n",
       "Abraham Ancer         1044.0       5.0     298.0     174.0     126.0  ...   \n",
       "Adam Hadwin            966.0       5.0     284.0     171.0     103.0  ...   \n",
       "Adam Long                0.0       0.0       0.0       0.0       0.0  ...   \n",
       "Adam Schenk            861.0       5.0     254.0     123.0     101.0  ...   \n",
       "Adam Scott             850.0       5.0     241.0     136.0     101.0  ...   \n",
       "Adam Svensson            0.0       0.0       0.0       0.0       0.0  ...   \n",
       "Alex Cejka             746.0       5.0     215.0     108.0      85.0  ...   \n",
       "Alex Noren             668.0       5.0     176.0     105.0      79.0  ...   \n",
       "Alex Prugh               0.0       0.0       0.0       0.0       0.0  ...   \n",
       "Anders Albertson         0.0       0.0       0.0       0.0       0.0  ...   \n",
       "Andrew Landry          884.0       5.0     238.0     153.0      82.0  ...   \n",
       "Andrew Putnam          967.0       3.0     276.0     158.0     105.0  ...   \n",
       "Andrew Yun             483.0       1.0     146.0      67.0      45.0  ...   \n",
       "Anirban Lahiri         906.0       3.0     244.0     154.0      92.0  ...   \n",
       "Austin Cook           1179.0       4.0     322.0     207.0     141.0  ...   \n",
       "Beau Hossler          1129.0       9.0     328.0     195.0     135.0  ...   \n",
       "Ben Crane              635.0       4.0     190.0     104.0      65.0  ...   \n",
       "Ben Martin             689.0       2.0     202.0     116.0      79.0  ...   \n",
       "Ben Silverman          909.0       4.0     264.0     157.0     109.0  ...   \n",
       "Bill Haas              801.0       7.0     231.0     106.0      86.0  ...   \n",
       "Billy Horschel         896.0       8.0     253.0     151.0     119.0  ...   \n",
       "Billy Hurley III       696.0       5.0     197.0     107.0      70.0  ...   \n",
       "Blayne Barber          740.0       2.0     194.0     123.0      91.0  ...   \n",
       "Brady Schnell            0.0       0.0       0.0       0.0       0.0  ...   \n",
       "Branden Grace          683.0       6.0     210.0     114.0      85.0  ...   \n",
       "Brandon Harkins       1031.0      14.0     296.0     192.0     118.0  ...   \n",
       "Brandt Snedeker        905.0       8.0     240.0     158.0      90.0  ...   \n",
       "Brendan Steele         738.0       6.0     208.0     122.0      84.0  ...   \n",
       "...                      ...       ...       ...       ...       ...  ...   \n",
       "Si Woo Kim            1134.0       5.0     326.0     174.0     142.0  ...   \n",
       "Stephan Jaeger         805.0       5.0     240.0     117.0     108.0  ...   \n",
       "Steve Wheatcroft       621.0       3.0     190.0      97.0      80.0  ...   \n",
       "Stewart Cink           955.0      12.0     262.0     169.0      98.0  ...   \n",
       "Sung Kang              918.0       7.0     272.0     139.0     104.0  ...   \n",
       "Sungjae Im               0.0       0.0       0.0       0.0       0.0  ...   \n",
       "Talor Gooch            778.0       6.0     227.0     107.0      89.0  ...   \n",
       "Ted Potter, Jr.        955.0       2.0     260.0     156.0     102.0  ...   \n",
       "Tiger Woods            754.0       6.0     198.0     147.0      90.0  ...   \n",
       "Tom Hoge              1008.0       7.0     291.0     185.0     122.0  ...   \n",
       "Tom Lovelady           875.0       9.0     256.0     165.0     119.0  ...   \n",
       "Tommy Fleetwood        738.0       8.0     190.0     147.0      99.0  ...   \n",
       "Tony Finau            1076.0      14.0     298.0     208.0     146.0  ...   \n",
       "Trey Mullinax          751.0       4.0     210.0     130.0      91.0  ...   \n",
       "Troy Merritt           915.0       6.0     272.0     153.0     117.0  ...   \n",
       "Tyler Duncan          1020.0       8.0     279.0     162.0     117.0  ...   \n",
       "Tyrone Van Aswegen    1004.0       4.0     293.0     145.0     118.0  ...   \n",
       "Tyrrell Hatton         606.0       3.0     154.0     100.0      60.0  ...   \n",
       "Vaughn Taylor          873.0       3.0     232.0     149.0     105.0  ...   \n",
       "Webb Simpson          1036.0      15.0     252.0     191.0     102.0  ...   \n",
       "Wes Roach                0.0       0.0       0.0       0.0       0.0  ...   \n",
       "Wesley Bryan           732.0       1.0     210.0     115.0      80.0  ...   \n",
       "Whee Kim               950.0       9.0     292.0     176.0     100.0  ...   \n",
       "William McGirt         849.0       4.0     238.0     121.0      93.0  ...   \n",
       "Wyndham Clark            0.0       0.0       0.0       0.0       0.0  ...   \n",
       "Xander Schauffele     1036.0       6.0     274.0     177.0     132.0  ...   \n",
       "Xinjun Zhang           813.0       7.0     234.0     127.0      96.0  ...   \n",
       "Zac Blair              621.0       2.0     190.0      95.0      76.0  ...   \n",
       "Zach Johnson           958.0       7.0     260.0     185.0     104.0  ...   \n",
       "Zecheng Dou            522.0       3.0     161.0      72.0      54.0  ...   \n",
       "\n",
       "                      P5H    P5b  Real_Rounds       B/R      DB/R      3b/H  \\\n",
       "PLAYER NAME                                                                   \n",
       "Aaron Baddeley      628.0  247.0        133.0  2.420213  0.271277  0.104084   \n",
       "Aaron Wise          616.0  289.0        141.0  2.649485  0.355670  0.153946   \n",
       "Abraham Ancer       754.0  316.0        168.0  2.437500  0.275000  0.134096   \n",
       "Adam Hadwin         700.0  303.0        153.0  2.663594  0.276498  0.137931   \n",
       "Adam Long           360.0  116.0         56.0  2.625000  0.410714  0.102222   \n",
       "Adam Schenk         786.0  325.0        160.0  2.495833  0.325000  0.136972   \n",
       "Adam Scott          537.0  263.0        121.0  2.597561  0.280488  0.154780   \n",
       "Adam Svensson       340.0  136.0         51.0  2.823529  0.274510  0.144928   \n",
       "Alex Cejka          487.0  177.0        109.0  2.778523  0.248322  0.162029   \n",
       "Alex Noren          484.0  211.0        108.0  2.750000  0.346154  0.090323   \n",
       "Alex Prugh          378.0  146.0         58.0  2.741379  0.155172  0.114894   \n",
       "Anders Albertson    316.0  116.0         48.0  2.604167  0.291667  0.119171   \n",
       "Andrew Landry       610.0  220.0        136.0  2.588542  0.369792  0.129243   \n",
       "Andrew Putnam       672.0  279.0        149.0  2.397129  0.344498  0.172869   \n",
       "Andrew Yun          146.0   45.0         45.0  3.911111  0.466667  0.160221   \n",
       "Anirban Lahiri      588.0  216.0        136.0  2.836842  0.384211  0.142298   \n",
       "Austin Cook         634.0  279.0        155.0  2.669951  0.334975  0.167277   \n",
       "Beau Hossler        788.0  335.0        173.0  2.582645  0.355372  0.137718   \n",
       "Ben Crane           456.0  173.0        100.0  2.531915  0.276596  0.116402   \n",
       "Ben Martin          202.0   79.0         64.0  2.625000  0.390625  0.134100   \n",
       "Ben Silverman       620.0  245.0        140.0  2.607143  0.367347  0.145019   \n",
       "Bill Haas           581.0  246.0        128.0  2.741758  0.241758  0.097297   \n",
       "Billy Horschel      725.0  291.0        152.0  2.432432  0.175676  0.167235   \n",
       "Billy Hurley III    197.0   70.0         64.0  3.171875  0.281250  0.123552   \n",
       "Blayne Barber       194.0   91.0         67.0  2.507463  0.313433  0.147059   \n",
       "Brady Schnell       276.0   86.0         42.0  2.761905  0.261905  0.142012   \n",
       "Branden Grace       594.0  241.0        120.0  2.954545  0.278409  0.124825   \n",
       "Brandon Harkins     728.0  316.0        162.0  2.960699  0.388646  0.146394   \n",
       "Brandt Snedeker     720.0  308.0        155.0  2.583333  0.271930  0.114504   \n",
       "Brendan Steele      572.0  238.0        119.0  2.707602  0.380117  0.101471   \n",
       "...                   ...    ...          ...       ...       ...       ...   \n",
       "Si Woo Kim          770.0  376.0        168.0  2.551724  0.353448  0.134989   \n",
       "Stephan Jaeger      612.0  280.0        134.0  2.606218  0.248705  0.123883   \n",
       "Steve Wheatcroft    190.0   80.0         58.0  2.362069  0.379310  0.103004   \n",
       "Stewart Cink        262.0   98.0         87.0  2.689655  0.241379  0.140401   \n",
       "Sung Kang           716.0  308.0        153.0  2.656109  0.407240  0.146532   \n",
       "Sungjae Im          570.0  284.0         88.0  2.363636  0.295455  0.142857   \n",
       "Talor Gooch         459.0  207.0        109.0  2.589041  0.308219  0.140917   \n",
       "Ted Potter, Jr.     624.0  236.0        141.0  2.953846  0.507692  0.135723   \n",
       "Tiger Woods         406.0  184.0         98.0  2.320312  0.296875  0.177734   \n",
       "Tom Hoge            763.0  292.0        166.0  2.736402  0.334728  0.127461   \n",
       "Tom Lovelady        256.0  119.0         81.0  2.851852  0.456790  0.100917   \n",
       "Tommy Fleetwood     478.0  209.0        110.0  2.441558  0.259740  0.118893   \n",
       "Tony Finau          690.0  324.0        158.0  2.403670  0.266055  0.133028   \n",
       "Trey Mullinax       622.0  289.0        134.0  2.567839  0.371859  0.117139   \n",
       "Troy Merritt        536.0  241.0        126.0  2.634731  0.227545  0.131222   \n",
       "Tyler Duncan        715.0  287.0        161.0  2.790393  0.344978  0.122162   \n",
       "Tyrone Van Aswegen  293.0  118.0         93.0  2.645161  0.344086  0.145889   \n",
       "Tyrrell Hatton      462.0  182.0        100.0  2.595890  0.390411  0.118151   \n",
       "Vaughn Taylor       692.0  293.0        150.0  2.389140  0.366516  0.168715   \n",
       "Webb Simpson        576.0  242.0        146.0  2.100000  0.180000  0.121859   \n",
       "Wes Roach           294.0  128.0         45.0  2.600000  0.200000  0.116022   \n",
       "Wesley Bryan        210.0   80.0         67.0  2.731343  0.373134  0.125000   \n",
       "Whee Kim            654.0  238.0        144.0  2.763819  0.502513  0.130923   \n",
       "William McGirt      238.0   93.0         78.0  2.294872  0.256410  0.141956   \n",
       "Wyndham Clark       408.0  210.0         63.0  2.492063  0.412698  0.124514   \n",
       "Xander Schauffele   658.0  316.0        149.0  2.453659  0.273171  0.123134   \n",
       "Xinjun Zhang        234.0   96.0         75.0  2.613333  0.520000  0.141914   \n",
       "Zac Blair           190.0   76.0         58.0  2.344828  0.275862  0.158798   \n",
       "Zach Johnson        584.0  226.0        141.0  2.507692  0.261538  0.125635   \n",
       "Zecheng Dou         161.0   54.0         49.0  3.367347  0.510204  0.140704   \n",
       "\n",
       "                        4e/H      4b/H      5e/H      5b/H  \n",
       "PLAYER NAME                                                 \n",
       "Aaron Baddeley      0.001502  0.175263  0.022293  0.393312  \n",
       "Aaron Wise          0.000476  0.197813  0.014610  0.469156  \n",
       "Abraham Ancer       0.000768  0.183564  0.019894  0.419098  \n",
       "Adam Hadwin         0.001284  0.172517  0.027143  0.432857  \n",
       "Adam Long           0.003317  0.154229  0.038889  0.322222  \n",
       "Adam Schenk         0.001561  0.158018  0.026718  0.413486  \n",
       "Adam Scott          0.002847  0.168565  0.027933  0.489758  \n",
       "Adam Svensson       0.001848  0.153420  0.035294  0.400000  \n",
       "Alex Cejka          0.002525  0.133838  0.014374  0.363450  \n",
       "Alex Noren          0.001174  0.148474  0.022727  0.435950  \n",
       "Alex Prugh          0.000000  0.158065  0.031746  0.386243  \n",
       "Anders Albertson    0.000000  0.159844  0.018987  0.367089  \n",
       "Andrew Landry       0.001442  0.168750  0.011475  0.360656  \n",
       "Andrew Putnam       0.001772  0.181657  0.007440  0.415179  \n",
       "Andrew Yun          0.000000  0.138716  0.006849  0.308219  \n",
       "Anirban Lahiri      0.000484  0.162633  0.008503  0.367347  \n",
       "Austin Cook         0.000909  0.169468  0.012618  0.440063  \n",
       "Beau Hossler        0.001156  0.176879  0.019036  0.425127  \n",
       "Ben Crane           0.001320  0.161056  0.013158  0.379386  \n",
       "Ben Martin          0.001451  0.168360  0.009901  0.391089  \n",
       "Ben Silverman       0.000000  0.157447  0.019355  0.395161  \n",
       "Bill Haas           0.001023  0.135038  0.025818  0.423408  \n",
       "Billy Horschel      0.000000  0.150084  0.024828  0.401379  \n",
       "Billy Hurley III    0.001437  0.153736  0.025381  0.355330  \n",
       "Blayne Barber       0.001351  0.166216  0.010309  0.469072  \n",
       "Brady Schnell       0.002227  0.155902  0.021739  0.311594  \n",
       "Branden Grace       0.000000  0.149382  0.033670  0.405724  \n",
       "Brandon Harkins     0.000000  0.185801  0.030220  0.434066  \n",
       "Brandt Snedeker     0.000811  0.172679  0.019444  0.427778  \n",
       "Brendan Steele      0.000548  0.161008  0.020979  0.416084  \n",
       "...                      ...       ...       ...       ...  \n",
       "Si Woo Kim          0.000806  0.171774  0.014286  0.488312  \n",
       "Stephan Jaeger      0.000000  0.140933  0.021242  0.457516  \n",
       "Steve Wheatcroft    0.000000  0.156200  0.015789  0.421053  \n",
       "Stewart Cink        0.001047  0.176963  0.045802  0.374046  \n",
       "Sung Kang           0.000000  0.154983  0.034916  0.430168  \n",
       "Sungjae Im          0.002123  0.180467  0.028070  0.498246  \n",
       "Talor Gooch         0.000633  0.165190  0.026144  0.450980  \n",
       "Ted Potter, Jr.     0.001900  0.171021  0.006410  0.378205  \n",
       "Tiger Woods         0.000000  0.191198  0.034483  0.453202  \n",
       "Tom Hoge            0.000000  0.166667  0.019659  0.382700  \n",
       "Tom Lovelady        0.000000  0.188571  0.035156  0.464844  \n",
       "Tommy Fleetwood     0.002976  0.180357  0.046025  0.437238  \n",
       "Tony Finau          0.000423  0.179509  0.043478  0.469565  \n",
       "Trey Mullinax       0.000465  0.167520  0.025723  0.464630  \n",
       "Troy Merritt        0.002214  0.177643  0.033582  0.449627  \n",
       "Tyler Duncan        0.004029  0.155520  0.022378  0.401399  \n",
       "Tyrone Van Aswegen  0.000996  0.144422  0.013652  0.402730  \n",
       "Tyrrell Hatton      0.000632  0.174463  0.015152  0.393939  \n",
       "Vaughn Taylor       0.000000  0.178586  0.018786  0.423410  \n",
       "Webb Simpson        0.003142  0.181777  0.046875  0.420139  \n",
       "Wes Roach           0.000000  0.130705  0.034014  0.435374  \n",
       "Wesley Bryan        0.004098  0.157104  0.004762  0.380952  \n",
       "Whee Kim            0.000000  0.174976  0.016820  0.363914  \n",
       "William McGirt      0.001178  0.142521  0.016807  0.390756  \n",
       "Wyndham Clark       0.000000  0.191679  0.024510  0.514706  \n",
       "Xander Schauffele   0.001346  0.187163  0.033435  0.480243  \n",
       "Xinjun Zhang        0.001230  0.156212  0.029915  0.410256  \n",
       "Zac Blair           0.001610  0.152979  0.010526  0.400000  \n",
       "Zach Johnson        0.000468  0.167914  0.035959  0.386986  \n",
       "Zecheng Dou         0.000000  0.137931  0.018634  0.335404  \n",
       "\n",
       "[240 rows x 41 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_players, tmp = df_stats.shape\n",
    "birdies = np.empty([num_players, sims, 4])\n",
    "for player_idx in range(num_players):\n",
    "    cur_birdies3 = np.random.poisson(df_stats.iloc[player_idx]['3b/H']*par3, [1, sims, 4])\n",
    "    cur_birdies4 = np.random.poisson(df_stats.iloc[player_idx]['4b/H']*par4, [1, sims, 4])\n",
    "    cur_birdies5 = np.random.poisson(df_stats.iloc[player_idx]['5b/H']*par5, [1, sims, 4])\n",
    "    cur_birdies = cur_birdies3+cur_birdies4+cur_birdies5\n",
    "    birdies[player_idx,:,:] = cur_birdies\n",
    "    \n",
    "eagles = np.empty([num_players, sims, 4])\n",
    "for player_idx in range(num_players):\n",
    "    cur_eagles4 = np.random.poisson(df_stats.iloc[player_idx]['4e/H']*par4, [1, sims, 4])\n",
    "    cur_eagles5 = np.random.poisson(df_stats.iloc[player_idx]['5e/H']*par5, [1, sims, 4])\n",
    "    cur_eagles = cur_eagles4+cur_eagles5\n",
    "    eagles[player_idx,:,:] = cur_eagles\n",
    "\n",
    "bogeys = np.empty([num_players, sims, 4])\n",
    "for player_idx in range(num_players):\n",
    "    cur_bogeys = np.random.poisson(df_stats.iloc[player_idx]['B/R'], [1, sims, 4])\n",
    "    bogeys[player_idx,:,:] = cur_bogeys\n",
    "    \n",
    "doublebogeys = np.empty([num_players, sims, 4])\n",
    "for player_idx in range(num_players):\n",
    "    cur_doublebogeys = np.random.poisson(df_stats.iloc[player_idx]['DB/R'], [1, sims, 4])\n",
    "    doublebogeys[player_idx,:,:] = cur_doublebogeys\n",
    "    \n",
    "pars = np.full([num_players, sims, 4], 18)\n",
    "pars = pars-birdies-eagles-bogeys-doublebogeys\n",
    "pars[pars<0]=0\n",
    "\n",
    "#%% Get the scores for each round\n",
    "scores = np.full([num_players, sims, 4], par)\n",
    "scores = scores - birdies - 2*eagles + bogeys +2*doublebogeys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Player Simulated Rounds:\n",
    "get_rounds = False\n",
    "if get_rounds:\n",
    "    m, n, r = scores.shape\n",
    "    out_arr =  np.column_stack((np.repeat(np.arange(m),n), scores.reshape(m*n, -1)))\n",
    "    out_df = pd.DataFrame(out_arr, columns=[\"Index\", \"round1\", \"round2\", \"round3\", \"round4\"])\n",
    "    print(out_df.head())\n",
    "    df_stats.head()\n",
    "    df_converted = df_stats.reset_index()\n",
    "    merged = pd.merge(df_converted, out_df, left_index=True, right_on=\"Index\")\n",
    "    print(merged.columns)\n",
    "    merged.head()\n",
    "    merged[merged[\"PLAYER NAME\"]=='Dustin Johnson'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6., 3., 4., 6.],\n",
       "       [6., 4., 6., 0.],\n",
       "       [7., 6., 5., 4.],\n",
       "       [2., 7., 5., 2.],\n",
       "       [3., 2., 4., 9.]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "birdies[0, 0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71.0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import rankdata\n",
    "#To finalize the scores array, \n",
    "#we have to go back and make the cut so that \n",
    "#only the top 70 golfers move on to rounds 3 and 4. \n",
    "#We use SciPy for this task. Keep in mind that it's \n",
    "#the top 70 golfers + ties that move on (meaning it \n",
    "#could be more than 70 that actually move on).\n",
    "\n",
    "made_cut=np.empty([num_players, sims])\n",
    "for cur_sim in range(sims):\n",
    "    after2 = np.sum(scores[:,cur_sim,0:2], axis=1)\n",
    "    made_cut[:,cur_sim] = rankdata(after2, method='min')<=70\n",
    "missed_cut = (made_cut - 1) * (-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., ..., 0., 0., 1.],\n",
       "       [0., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 0., 1., ..., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 0., 1., ..., 0., 1., 1.],\n",
       "       [1., 1., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "made_cut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "240"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "dk_scores = np.zeros([num_players, sims, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "dk_scores = pts_b*birdies + pts_e*eagles + pts_B*bogeys + pts_DB*doublebogeys + pts_p*pars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[67., 70., 69., 65.],\n",
       "        [68., 69., 68., 73.],\n",
       "        [66., 72., 69., 70.],\n",
       "        ...,\n",
       "        [72., 73., 71., 69.],\n",
       "        [71., 74., 72., 74.],\n",
       "        [71., 69., 69., 71.]],\n",
       "\n",
       "       [[78., 68., 68., 73.],\n",
       "        [70., 67., 71., 68.],\n",
       "        [69., 67., 71., 71.],\n",
       "        ...,\n",
       "        [64., 69., 73., 78.],\n",
       "        [68., 72., 70., 72.],\n",
       "        [68., 70., 73., 72.]],\n",
       "\n",
       "       [[74., 66., 69., 71.],\n",
       "        [71., 72., 66., 72.],\n",
       "        [70., 67., 71., 76.],\n",
       "        ...,\n",
       "        [71., 72., 73., 71.],\n",
       "        [74., 70., 77., 72.],\n",
       "        [67., 71., 64., 73.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[72., 70., 73., 69.],\n",
       "        [71., 72., 72., 69.],\n",
       "        [67., 68., 71., 67.],\n",
       "        ...,\n",
       "        [76., 71., 73., 71.],\n",
       "        [65., 65., 75., 69.],\n",
       "        [69., 71., 70., 73.]],\n",
       "\n",
       "       [[69., 70., 66., 73.],\n",
       "        [69., 71., 70., 71.],\n",
       "        [71., 73., 69., 70.],\n",
       "        ...,\n",
       "        [68., 72., 70., 70.],\n",
       "        [72., 71., 69., 76.],\n",
       "        [73., 72., 75., 69.]],\n",
       "\n",
       "       [[75., 72., 77., 81.],\n",
       "        [72., 70., 74., 73.],\n",
       "        [70., 71., 74., 72.],\n",
       "        ...,\n",
       "        [77., 74., 73., 70.],\n",
       "        [76., 71., 73., 72.],\n",
       "        [74., 72., 71., 75.]]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now lets add the streak bonus\n",
    "def streak_bonus_tmp(be):\n",
    "    prob=streak_prob[be]\n",
    "    return np.random.binomial(1,prob)\n",
    "streak_bonus = np.vectorize(streak_bonus_tmp )\n",
    "bir_eag = birdies+eagles\n",
    "bir_eag[bir_eag>18] = 18 #need to cap it at 18\n",
    "streak_pts = streak_bonus(bir_eag)\n",
    "dk_scores = dk_scores + streak_pts*pts_streak #adds the streak to each day that has it\n",
    "\n",
    "#need to add bogey free bonus\n",
    "bogeyFree = 1*(bogeys==0)\n",
    "doublebogeyFree = 1*(doublebogeys==0)\n",
    "allBFree = bogeyFree*doublebogeyFree\n",
    "dk_scores = dk_scores + pts_BF*bogeyFree\n",
    "\n",
    "#now lets add the placement bonus\n",
    "total_score = np.sum(scores, axis=2)\n",
    "#need to make sure we dont give placement bonuses to those missing the cut so we add 1000 strokes\n",
    "total_score = total_score + missed_cut*1000\n",
    "ranking = np.empty([num_players, sims])\n",
    "for cur_sim in range(sims):\n",
    "    ranking[:,cur_sim] = rankdata(total_score[:,cur_sim], method='min')\n",
    "dk_placement_score = pts_place(ranking)\n",
    "dk_scores[:,:,0] = dk_scores[:,:,0] + dk_placement_score #might as well add it onto the first day\n",
    "\n",
    "#Now we need to add a bonus score if players have all 4 rounds below 70\n",
    "A4U70_array=np.max(scores, axis=2)<70 \n",
    "dk_scores[:,:,2] = dk_scores[:,:,2] + A4U70_array*pts_4U70 #we put it in this on the 3rd round so that it gets zeroed out if CUT\n",
    "\n",
    "#Now we need to 0 out the scores from days 3 and 4 for those that did not make the cut\n",
    "dk_scores[:,:,2] = dk_scores[:,:,2]*made_cut\n",
    "dk_scores[:,:,3] = dk_scores[:,:,3]*made_cut\n",
    "\n",
    "#Finally, we compress the scores\n",
    "dk_final_score = np.sum(dk_scores, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Now let's calculate the statistics of importance\n",
    "dk_median = np.median(dk_final_score, axis=1)\n",
    "dk_mean = np.mean(dk_final_score, axis=1)\n",
    "dk_max = np.max(dk_final_score, axis=1)\n",
    "dk_min = np.min(dk_final_score, axis=1)\n",
    "dk_25 = np.percentile(dk_final_score, 25, axis=1)\n",
    "dk_75 = np.percentile(dk_final_score, 75, axis=1)\n",
    "dk_makecut = np.mean(made_cut, axis=1)\n",
    "\n",
    "#%% Now let's create the dataframe that we will pass to the linear program\n",
    "df_results = pd.DataFrame(index=df_stats.index.copy())\n",
    "#df_results['Salary']=df_dk['Salary']\n",
    "df_results['dk_max']=dk_max\n",
    "df_results['dk_min']=dk_min\n",
    "df_results['dk_median']=dk_median\n",
    "df_results['dk_mean']=dk_mean\n",
    "df_results['dk_75']=dk_75\n",
    "df_results['dk_25']=dk_25\n",
    "df_results['dk_makecut']=dk_makecut\n",
    "df_results['Rounds']=df_stats['Real_Rounds']\n",
    "#df_results['Have_Data']=df_stats['Real']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results.sort_values(by='dk_mean', ascending=False).to_excel(\"draft_kings_3m_open.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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