Hold analyse.ipynb

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<<<<<<< HEAD
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 5,
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     "text": [
      "C:\\Users\\Seamus-admin\\AppData\\Local\\Temp\\2\\ipykernel_10276\\1851407993.py:158: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  possession_length = df.groupby(['label','date','sequenceId']).apply(lambda group: (\n"
     ]
    },
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     "text": [
      "sorteret efter xg difference\n",
      "     team_name  xg_difference  possession_xg  xg_against\n",
      "8      Kolding       5.386876       7.320611   -1.933734\n",
      "6      Horsens       3.894675       9.242919   -5.348244\n",
      "4     Hillerød       3.695732       7.869055   -4.173322\n",
      "7     Hvidovre       2.843130       7.072814   -4.229685\n",
      "1      Esbjerg       2.445090      10.078211   -7.633121\n",
      "9           OB       1.729480       6.930820   -5.201340\n",
      "10    Roskilde       1.255035       6.243354   -4.988318\n",
      "2   Fredericia      -1.047679       7.386744   -8.434424\n",
      "5        Hobro      -1.432092       6.440169   -7.872262\n",
      "11  Vendsyssel      -2.224167       5.096898   -7.321066\n",
      "0         B 93      -4.043250       7.950390  -11.993640\n",
      "3      HB Køge      -9.740061       3.823370  -13.563430\n",
      "sorteret efter samlet\n",
      "     team_name  xg_difference  possession_xg  xg_against\n",
      "1      Esbjerg       2.445090      10.078211   -7.633121\n",
      "6      Horsens       3.894675       9.242919   -5.348244\n",
      "0         B 93      -4.043250       7.950390  -11.993640\n",
      "4     Hillerød       3.695732       7.869055   -4.173322\n",
      "2   Fredericia      -1.047679       7.386744   -8.434424\n",
      "8      Kolding       5.386876       7.320611   -1.933734\n",
      "7     Hvidovre       2.843130       7.072814   -4.229685\n",
      "9           OB       1.729480       6.930820   -5.201340\n",
      "5        Hobro      -1.432092       6.440169   -7.872262\n",
      "10    Roskilde       1.255035       6.243354   -4.988318\n",
      "11  Vendsyssel      -2.224167       5.096898   -7.321066\n",
      "3      HB Køge      -9.740061       3.823370  -13.563430\n",
      "sorteret efter xg against\n",
      "     team_name  xg_difference  possession_xg  xg_against\n",
      "8      Kolding       5.386876       7.320611   -1.933734\n",
      "4     Hillerød       3.695732       7.869055   -4.173322\n",
      "7     Hvidovre       2.843130       7.072814   -4.229685\n",
      "10    Roskilde       1.255035       6.243354   -4.988318\n",
      "9           OB       1.729480       6.930820   -5.201340\n",
      "6      Horsens       3.894675       9.242919   -5.348244\n",
      "11  Vendsyssel      -2.224167       5.096898   -7.321066\n",
      "1      Esbjerg       2.445090      10.078211   -7.633121\n",
      "5        Hobro      -1.432092       6.440169   -7.872262\n",
      "2   Fredericia      -1.047679       7.386744   -8.434424\n",
      "0         B 93      -4.043250       7.950390  -11.993640\n",
      "3      HB Køge      -9.740061       3.823370  -13.563430\n",
      "sorteret efter xG share\n",
      "            xg_difference  possession_xg  xg_against  xg share\n",
      "team_name                                                     \n",
      "B 93                -4.04           7.95      -11.99      0.55\n",
      "Roskilde             1.26           6.24       -4.99      0.54\n",
      "Esbjerg              2.45          10.08       -7.63      0.54\n",
      "Kolding              5.39           7.32       -1.93      0.47\n",
      "Hillerød             3.70           7.87       -4.17      0.41\n",
      "Hvidovre             2.84           7.07       -4.23      0.39\n",
      "Horsens              3.89           9.24       -5.35      0.39\n",
      "Fredericia          -1.05           7.39       -8.43      0.38\n",
      "HB Køge             -9.74           3.82      -13.56      0.38\n",
      "Vendsyssel          -2.22           5.10       -7.32      0.34\n",
      "Hobro               -1.43           6.44       -7.87      0.32\n",
      "OB                   1.73           6.93       -5.20      0.31\n"
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>>>>>>> cd8d02550d6dc189057885637715ce226201124c
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import table\n",
    "import numpy as np\n",
    "import os\n",
    "import psycopg2\n",
    "import pandas as pd\n",
    "import json\n",
    "\n",
<<<<<<< HEAD
    "df = pd.read_csv(r'C:\\Users\\SéamusPeareBartholdy\\Documents\\GitHub\\AC-Horsens-First-Team\\DNK_1_Division_2024_2025\\Horsens\\Horsens_possession_data.csv')"
   ]
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   "execution_count": 30,
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     "text": [
      "Index(['team_name', 'id', 'eventId', 'typeId', 'timeMin', 'timeSec', 'outcome',\n",
      "       'x', 'y', 'playerName', 'sequenceId', 'possessionId', 'keyPass',\n",
      "       'assist', 'label', 'date', '140.0', '141.0', '318.0', '321.0', '210.0',\n",
      "       '213.0', '22.0', '23.0', '6.0', '9.0', '24.0', '25.0', '26.0', '107.0',\n",
      "       'sequence_xg'],\n",
      "      dtype='object')\n"
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    }
   ],
   "source": [
    "print(df.columns)\n",
    "df = df[df['team_name'].str.contains('Horsens')]\n",
    "df = df[df['label'].str.contains('Hobro|OB|B 93')]\n",
    "df['possession_xg'] = df.groupby('possessionId')['321.0'].transform('max')\n"
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      "      possessionId  pass_count             label  timeMin  timeSec  \\\n",
      "1153         107.0           4     Horsens vs OB       54        7   \n",
      "1152         107.0           4     Horsens vs OB       53        5   \n",
      "1151         107.0           4     Horsens vs OB       52       59   \n",
      "1150         107.0           4     Horsens vs OB       52       57   \n",
      "1149         107.0           4     Horsens vs OB       52       56   \n",
      "...            ...         ...               ...      ...      ...   \n",
      "1997         202.0           4   B 93 vs Horsens       87       33   \n",
      "1996         202.0           4   B 93 vs Horsens       87       32   \n",
      "2000         202.0           4  Horsens vs Hobro       84       52   \n",
      "2001         202.0           4  Horsens vs Hobro       84       54   \n",
      "1999         202.0           4  Horsens vs Hobro       84       46   \n",
      "\n",
      "      possession_xg  \n",
      "1153       0.788400  \n",
      "1152       0.788400  \n",
      "1151       0.788400  \n",
      "1150       0.788400  \n",
      "1149       0.788400  \n",
      "...             ...  \n",
      "1997       0.008225  \n",
      "1996       0.008225  \n",
      "2000       0.008225  \n",
      "2001       0.008225  \n",
      "1999       0.008225  \n",
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            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Pass Count vs xG Regression Plot"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Number of Passes"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "possession_xg"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "\n",
    "\n",
    "#sequences_with_xg = df[df['sequence_xg'].notna()]\n",
    "sequences_with_xg = df.copy()\n",
    "# Counting the number of passes (typeId == 1) in each sequence\n",
    "pass_counts = sequences_with_xg[sequences_with_xg['typeId'] == 1].groupby('possessionId').size().reset_index(name='pass_count')\n",
    "\n",
    "# Create a histogram using Plotly\n",
    "fig = go.Figure(data=[go.Histogram(x=pass_counts['pass_count'], nbinsx=50)])\n",
    "\n",
    "# Update the layout with titles\n",
    "fig.update_layout(\n",
    "    title=\"Histogram of Pass Counts per Sequence\",\n",
    "    xaxis_title=\"Number of Passes\",\n",
    "    yaxis_title=\"Frequency\"\n",
    ")\n",
    "\n",
    "# Show the figure\n",
    "fig.show()\n",
    "xg_pass_data = pd.merge(pass_counts, \n",
    "                        sequences_with_xg[['label','timeMin','timeSec','possessionId', 'possession_xg']].drop_duplicates(), \n",
    "                        on='possessionId')\n",
    "\n",
    "# Filter rows where possession_xg is not null\n",
    "xg_pass_data = xg_pass_data[xg_pass_data['possession_xg'].notna()]\n",
    "\n",
    "# Sort by possession_xg\n",
    "xg_pass_data = xg_pass_data.sort_values(by='possession_xg',ascending=False)\n",
    "\n",
    "# Print the resulting DataFrame\n",
    "print(xg_pass_data)\n",
    "fig = px.scatter(xg_pass_data, x='pass_count', y='possession_xg', trendline=\"ols\", \n",
    "                 labels={\"pass_count\": \"Number of Passes\", \"xg\": \"xG\"},\n",
    "                 title=\"Pass Count vs xG Regression Plot\")\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
=======
    "db_navn = 'AC Horsens'\n",
    "db_brugernavn = 'postgres'\n",
    "db_adgangskode = 'ACHorsens'\n",
    "db_host = 'localhost'\n",
    "conn = psycopg2.connect(\n",
    "    dbname=db_navn,\n",
    "    user=db_brugernavn,\n",
    "    password=db_adgangskode,\n",
    "    host=db_host\n",
    ")\n",
    "\n",
    "cur = conn.cursor()\n",
    "cur.execute(\"SELECT schema_name FROM information_schema.schemata\")\n",
    "schemas = cur.fetchall()\n",
    "\n",
    "def map_to_unified_columns_possession(df):\n",
    "    # Create a dictionary mapping the original column names to the unified column names\n",
    "    column_mapping = {\n",
    "        'contestantId': 'contestantId',\n",
    "        'team_name': 'team_name',\n",
    "        'id': 'id',\n",
    "        'eventId': 'eventId',\n",
    "        'typeId': 'typeId',\n",
    "        'periodId': 'periodId',\n",
    "        'timeMin': 'timeMin',\n",
    "        'timeSec': 'timeSec',\n",
    "        'outcome': 'outcome',\n",
    "        'x': 'x',\n",
    "        'y': 'y',\n",
    "        'timeStamp': 'timeStamp',\n",
    "        'lastModified': 'lastModified',\n",
    "        'playerId': 'playerId',\n",
    "        'playerName': 'playerName',\n",
    "        'sequenceId': 'sequenceId',\n",
    "        'possessionId': 'possessionId',\n",
    "        'keyPass': 'keyPass',\n",
    "        'assist': 'assist',\n",
    "        '140.0': '140.0',\n",
    "        '141.0': '141.0',\n",
    "        '318.0': '318.0',\n",
    "        '321.0': '321.0',\n",
    "        '210.0': '210',\n",
    "        '22.0': '22.0',\n",
    "        '23.0': '23.0',\n",
    "        '5.0' : '5.0',\n",
    "        '6.0' : '6.0',\n",
    "        '9.0' : '9.0',\n",
    "        '24.0': '24.0',\n",
    "        '25.0': '25.0',\n",
    "        '26.0': '26.0',\n",
    "        '107.0':'107.0',\n",
    "        '210.0':'210.0',\n",
    "        '213.0':'213.0',\n",
    "        'match_id': 'match_id',\n",
    "        'label': 'label',\n",
    "        'date': 'date',\n",
    "    }\n",
    "    for col in column_mapping.values():\n",
    "        if col not in df.columns:\n",
    "            df[col] = None\n",
    "\n",
    "    # Rename columns using the mapping dictionary\n",
    "    unified_df = df.rename(columns=column_mapping)\n",
    "\n",
    "    return unified_df\n",
    "\n",
    "def fetch_and_save_possession_data_for_nordic_bet_teams():\n",
    "    try:\n",
    "        conn = psycopg2.connect(\n",
    "            dbname=db_navn,\n",
    "            user=db_brugernavn,\n",
    "            password=db_adgangskode,\n",
    "            host=db_host,\n",
    "            port=\"5432\"\n",
    "        )\n",
    "        cur = conn.cursor()\n",
    "\n",
    "        cur.execute('SELECT table_name FROM information_schema.tables WHERE table_schema = %s', ('DNK_1_Division_2024_2025',))\n",
    "        tables = cur.fetchall()\n",
    "\n",
    "        relevant_tables = [table[0] for table in tables if table[0] and 'possession_data' in table[0]]\n",
    "\n",
    "        all_data_frames = []\n",
    "        for table_name in relevant_tables:\n",
    "            cur.execute(f'SELECT * FROM \"DNK_1_Division_2024_2025\".\"{table_name}\"')\n",
    "            data = cur.fetchall()\n",
    "            column_names = [desc[0] for desc in cur.description]\n",
    "\n",
    "            # Create a DataFrame with the fetched data\n",
    "            df = pd.DataFrame(data, columns=column_names)\n",
    "\n",
    "            # Map data to the correct columns in the unified DataFrame\n",
    "            unified_df = map_to_unified_columns_possession(df)\n",
    "\n",
    "            all_data_frames.append(unified_df)\n",
    "\n",
    "        # Concatenate all DataFrames into one\n",
    "        combined_df = pd.concat(all_data_frames, ignore_index=True)\n",
    "    \n",
    "    except Exception as e:\n",
    "        print(f\"Error fetching and saving data for : {e}\")\n",
    "    finally:\n",
    "        if 'cur' in locals() and cur is not None:\n",
    "            cur.close()\n",
    "        if 'conn' in locals() and conn is not None:\n",
    "            conn.close()\n",
    "    return combined_df\n",
    "\n",
    "df = fetch_and_save_possession_data_for_nordic_bet_teams()\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Reset index to avoid ambiguity with 'label'\n",
    "df = df.reset_index(drop=True)\n",
    "\n",
    "def filter_out_possession_ids(df):\n",
    "    # List of columns to check for 'true' values as strings\n",
    "    columns_to_check = ['5.0', '6.0', '9.0', '24.0', '25.0', '26.0', '107.0']\n",
    "    \n",
    "    # Convert columns to string explicitly\n",
    "    df[columns_to_check] = df[columns_to_check].astype(str)\n",
    "    \n",
    "    # Check if any of the columns contain 'true'\n",
    "    filter_condition = df[columns_to_check].apply(lambda x: x == 'true').any(axis=1)\n",
    "    \n",
    "    # Get the unique combinations of 'date', 'label', and 'sequenceId' that should be excluded\n",
    "    exclusion_combinations = df.loc[filter_condition, ['date', 'label', 'sequenceId']].drop_duplicates()\n",
    "\n",
    "    # Filter the DataFrame to exclude rows where 'date', 'label', and 'sequenceId' match any of the exclusion combinations\n",
    "    filtered_data = df.merge(exclusion_combinations, on=['date', 'label', 'sequenceId'], how='left', indicator=True)\n",
    "    \n",
    "    # Only keep rows that are not part of the exclusion combinations\n",
    "    filtered_data = filtered_data[filtered_data['_merge'] == 'left_only'].drop('_merge', axis=1)\n",
    "    \n",
    "    return filtered_data\n",
    "\n",
    "# Apply filter function\n",
    "df = filter_out_possession_ids(df)\n",
    "\n",
    "def calculate_possession_length(df):\n",
    "    # Ensure timeMin and timeSec are numeric\n",
    "    df['timeMin'] = pd.to_numeric(df['timeMin'], errors='coerce')\n",
    "    df['timeSec'] = pd.to_numeric(df['timeSec'], errors='coerce')\n",
    "\n",
    "    # Group by sequenceId to get the time ranges for each possession\n",
    "    possession_length = df.groupby(['label','date','sequenceId']).apply(lambda group: (\n",
    "        (group['timeMin'].max() * 60 + group['timeSec'].max()) - \n",
    "        (group['timeMin'].min() * 60 + group['timeSec'].min())\n",
    "    ))\n",
    "    # Rename the series for clarity\n",
    "    possession_length.name = 'possession_length'\n",
    "    \n",
    "    return possession_length\n",
    "\n",
    "sequence_length = calculate_possession_length(df)\n",
    "\n",
    "def add_start_possession_distance(df):\n",
    "    # Find the rows where possession_index == 1 (start of possession)\n",
    "    start_distance = df[df['possession_index'] == 1][['label', 'date', 'sequenceId', 'distance to opp goal']].copy()\n",
    "    \n",
    "    # Rename the column to indicate that it’s the starting possession distance\n",
    "    start_distance.rename(columns={'distance to opp goal': 'start_possession_distance'}, inplace=True)\n",
    "    \n",
    "    # Merge this start distance back into the original DataFrame, based on sequenceId, label, and date\n",
    "    df = df.merge(start_distance, on=['label', 'date', 'sequenceId'], how='left')\n",
    "    \n",
    "    return df\n",
    "\n",
    "def length_to_opp_goal(df):\n",
    "    # Ensure x and y are numeric\n",
    "    df['x'] = pd.to_numeric(df['x'], errors='coerce')\n",
    "    df['y'] = pd.to_numeric(df['y'], errors='coerce')\n",
    "\n",
    "    # Calculate distance to opponent's goal\n",
    "    goal_x = 100\n",
    "    goal_y = 50\n",
    "    df['distance to opp goal'] = np.sqrt((goal_x - df['x'])**2 + (goal_y - df['y'])**2)\n",
    "    return df\n",
    "\n",
    "df = length_to_opp_goal(df)\n",
    "\n",
    "\n",
    "\n",
    "# Merge possession length\n",
    "df = df.merge(sequence_length, on=['label','date','sequenceId'], how='left')\n",
    "\n",
    "# Calculate possession index\n",
    "df['possession_index'] = df.groupby(['label','date','sequenceId']).cumcount() + 1\n",
    "\n",
    "# Add start possession distance\n",
    "df = add_start_possession_distance(df)\n",
    "\n",
    "# Convert '321.0' to float\n",
    "df['321.0'] = pd.to_numeric(df['321.0'], errors='coerce')\n",
    "\n",
    "# Calculate possession xG\n",
    "possession_xg = df.groupby(['label','date','sequenceId'])['321.0'].max().reset_index()\n",
    "\n",
    "# Rename the '321.0' column to 'possession_xg' for clarity\n",
    "possession_xg.rename(columns={'321.0': 'possession_xg'}, inplace=True)\n",
    "\n",
    "# Merge this new 'possession_xg' back into the original DataFrame\n",
    "df = df.merge(possession_xg, on=['label','date','sequenceId'], how='left')\n",
    "\n",
    "# Apply conditions for filtering\n",
    "condition1 = (df['start_possession_distance'].astype(float) <= 30) & (df['possession_length'].astype(int) <= 5)\n",
    "condition2 = (df['start_possession_distance'].astype(float) >= 30) & (df['start_possession_distance'].astype(float) <= 60) & (df['possession_length'].astype(int) <= 8)\n",
    "condition3 = (df['start_possession_distance'].astype(float) >= 60) & (df['possession_length'].astype(int) <= 11)\n",
    "\n",
    "# Combine all conditions with OR (|)\n",
    "df1 = df[condition1 | condition2 | condition3]\n",
    "# Filter based on team name and possession_xg > 0\n",
    "\n",
    "# Select relevant columns\n",
    "#df1 = df1[['timeMin', 'timeSec', 'x', 'y','team_name', 'playerName', 'label', 'distance to opp goal', 'start_possession_distance', 'possession_length', 'possession_index', 'possession_xg']]\n",
    "\n",
    "# Print the final DataFrame\n",
    "df1 = df1[df1['possession_xg'] > 0]\n",
    "df1 = df1[df1['possession_index'] == 1]\n",
    "\n",
    "df_by_match = df1.groupby(['label','date'])['possession_xg'].sum().reset_index()\n",
    "\n",
    "df_by_team = df1.groupby(['team_name','date','label'])['possession_xg'].sum().reset_index()\n",
    "df_by_team['match_possession_xg'] = df_by_team.groupby(['label','date'])['possession_xg'].transform('sum')\n",
    "df_by_team['xg_difference'] = df_by_team['possession_xg'] - df_by_team['match_possession_xg'] + df_by_team['possession_xg']\n",
    "df_by_team['xg_against'] = df_by_team['possession_xg'] - df_by_team['match_possession_xg']\n",
    "difference_df = df_by_team.groupby(['team_name'])['xg_difference'].sum().reset_index()\n",
    "xg_against = df_by_team.groupby(['team_name'])['xg_against'].sum().reset_index()\n",
    "total_df = df_by_team.groupby(['team_name'])['possession_xg'].sum().reset_index()\n",
    "combined_df = difference_df.merge(total_df, on='team_name')\n",
    "combined_df = combined_df.merge(xg_against, on='team_name')\n",
    "combined_df = combined_df.sort_values('xg_difference', ascending=False)\n",
    "print('sorteret efter xg difference')\n",
    "print(combined_df)\n",
    "combined_df = combined_df.sort_values('possession_xg', ascending=False)\n",
    "print('sorteret efter samlet')\n",
    "print(combined_df)\n",
    "combined_df = combined_df.sort_values('xg_against', ascending=False)\n",
    "print('sorteret efter xg against')\n",
    "print(combined_df)\n",
    "df_xg = pd.read_csv('C:/Users/Seamus-admin/Documents/GitHub/AC-Horsens-First-Team/DNK_1_Division_2024_2025/xg_all DNK_1_Division_2024_2025.csv')\n",
    "df_xg['321'] = df_xg['321'].astype(float)\n",
    "df_xg = df_xg.groupby('team_name').sum('321')\n",
    "df_xg = df_xg.rename(columns={'321': 'total_xg'})\n",
    "combined_df = combined_df.merge(df_xg,on='team_name')\n",
    "combined_df['xg share'] = combined_df['possession_xg']/combined_df['total_xg']\n",
    "combined_df = combined_df[['team_name','xg_difference','possession_xg','xg_against','xg share']]\n",
    "combined_df = combined_df.set_index('team_name')\n",
    "combined_df = combined_df.round(2)\n",
    "print('sorteret efter xG share')\n",
    "combined_df = combined_df.sort_values('xg share',ascending=False)\n",
    "print(combined_df)\n"
>>>>>>> cd8d02550d6dc189057885637715ce226201124c
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