Inference_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "510996ab",
   "metadata": {},
   "source": [
    "## Inference notebook for Sledge Hack hackathon\n",
    "- **AUTHOR**: Nikhil Kotra\n",
    "- **AV_username**: nikhil1e9\n",
    "\n",
    "In this notebook I have breakdown my approach step by step to solve **The Sledge Hack hackathon** conducted by **Analytics Vidhya** from 29-09-2023 to 08-10-2023 with full explanation showing all inferences made from the data and how a certain assumption is made based on the analysis of data. This notebook breaks down the solution from my solution notebook which can be found at https://github.com/nikhil-1e9/AV-Hackathon/blob/main/AV_match_prediction.ipynb. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bbee193",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "  <b>NOTE:</b> The original notebook is unaltered after the competition and the predictions file can easily be generated by running the notebook from start to finish. The predictions file generated is exactly the same as submitted in the competition.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a42047c",
   "metadata": {},
   "source": [
    "***This notebook doesn't contain all the models present in the original solution notebook and focuses on only the weighted average model as I selected that model for my final predictions in the original solution. The main focus of this notebook is to explain the data driven decisions which I made and implemented in the solution notebook but wasn't able to explain them there due to time constraints.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d70d58f8",
   "metadata": {
    "id": "d70d58f8"
   },
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dc9ff36a",
   "metadata": {
    "id": "dc9ff36a"
   },
   "outputs": [],
   "source": [
    "# Load data into csv files\n",
    "train = pd.read_csv(\"C:/Users/relat/Downloads/data_zpyYWs0.csv\")\n",
    "sub = pd.read_csv(\"C:/Users/relat/Downloads/sample_submission_Gk1Mne1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dbcdd570",
   "metadata": {
    "id": "dbcdd570",
    "outputId": "903779b5-da6b-4b64-fba8-d40ae1f4818f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>runs_scored</th>\n",
       "      <th>wickets</th>\n",
       "      <th>runs_conceded</th>\n",
       "      <th>catches</th>\n",
       "      <th>stumpings</th>\n",
       "      <th>match_date</th>\n",
       "      <th>opposition</th>\n",
       "      <th>match_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Pat Cummins</td>\n",
       "      <td>DNB</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19 Oct 2011</td>\n",
       "      <td>v South Africa Centurion</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Steve Smith</td>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19 Oct 2011</td>\n",
       "      <td>v South Africa Centurion</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>Mitch Marsh</td>\n",
       "      <td>8*</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19 Oct 2011</td>\n",
       "      <td>v South Africa Centurion</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>David Warner</td>\n",
       "      <td>20</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19 Oct 2011</td>\n",
       "      <td>v South Africa Centurion</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Pat Cummins</td>\n",
       "      <td>11*</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23 Oct 2011</td>\n",
       "      <td>v South Africa Gqeberha</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2570</th>\n",
       "      <td>22</td>\n",
       "      <td>Ravindra Jadeja</td>\n",
       "      <td>DNB</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28 Oct 2009</td>\n",
       "      <td>v Australia Nagpur</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2571</th>\n",
       "      <td>22</td>\n",
       "      <td>Ravindra Jadeja</td>\n",
       "      <td>DNB</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31 Oct 2009</td>\n",
       "      <td>v Australia Delhi</td>\n",
       "      <td>554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2572</th>\n",
       "      <td>22</td>\n",
       "      <td>Ravindra Jadeja</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5 Nov 2009</td>\n",
       "      <td>v Australia Hyderabad</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2573</th>\n",
       "      <td>22</td>\n",
       "      <td>Ravindra Jadeja</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8 Nov 2009</td>\n",
       "      <td>v Australia Guwahati</td>\n",
       "      <td>556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2574</th>\n",
       "      <td>22</td>\n",
       "      <td>Ravindra Jadeja</td>\n",
       "      <td>DNB</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21 Dec 2009</td>\n",
       "      <td>v Sri Lanka Cuttack</td>\n",
       "      <td>557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2575 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      player_id      player_name runs_scored wickets runs_conceded catches  \\\n",
       "0             1      Pat Cummins         DNB       3            28       0   \n",
       "1             2      Steve Smith         DNB       -             -       0   \n",
       "2            10      Mitch Marsh          8*       1            19       1   \n",
       "3            13     David Warner          20       -             -       0   \n",
       "4             1      Pat Cummins         11*       1            73       0   \n",
       "...         ...              ...         ...     ...           ...     ...   \n",
       "2570         22  Ravindra Jadeja         DNB       3            35       0   \n",
       "2571         22  Ravindra Jadeja         DNB       2            41       0   \n",
       "2572         22  Ravindra Jadeja          23       0            44       1   \n",
       "2573         22  Ravindra Jadeja          57       0            36       0   \n",
       "2574         22  Ravindra Jadeja         DNB       4            32       0   \n",
       "\n",
       "     stumpings   match_date                opposition  match_id  \n",
       "0            0  19 Oct 2011  v South Africa Centurion         1  \n",
       "1            0  19 Oct 2011  v South Africa Centurion         1  \n",
       "2            0  19 Oct 2011  v South Africa Centurion         1  \n",
       "3            0  19 Oct 2011  v South Africa Centurion         1  \n",
       "4            0  23 Oct 2011   v South Africa Gqeberha         2  \n",
       "...        ...          ...                       ...       ...  \n",
       "2570         0  28 Oct 2009        v Australia Nagpur       553  \n",
       "2571         0  31 Oct 2009         v Australia Delhi       554  \n",
       "2572         0   5 Nov 2009     v Australia Hyderabad       555  \n",
       "2573         0   8 Nov 2009      v Australia Guwahati       556  \n",
       "2574         0  21 Dec 2009       v Sri Lanka Cuttack       557  \n",
       "\n",
       "[2575 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe08fa93",
   "metadata": {},
   "source": [
    "***train and sub data is same as provided for the competition.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "901fcb47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fcfab49b",
   "metadata": {
    "id": "fcfab49b",
    "outputId": "7690711a-6271-416c-b92b-cd6f788bba3a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2575 entries, 0 to 2574\n",
      "Data columns (total 10 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   player_id      2575 non-null   int64 \n",
      " 1   player_name    2575 non-null   object\n",
      " 2   runs_scored    2575 non-null   object\n",
      " 3   wickets        2575 non-null   object\n",
      " 4   runs_conceded  2575 non-null   object\n",
      " 5   catches        2575 non-null   object\n",
      " 6   stumpings      2575 non-null   object\n",
      " 7   match_date     2575 non-null   object\n",
      " 8   opposition     2575 non-null   object\n",
      " 9   match_id       2575 non-null   int64 \n",
      "dtypes: int64(2), object(8)\n",
      "memory usage: 201.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check data statictics\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e5567193",
   "metadata": {
    "id": "e5567193"
   },
   "outputs": [],
   "source": [
    "# clean runs scored and convert to int\n",
    "train['runs_scored'] = train['runs_scored'].str.replace('*', '', regex=False)\n",
    "train['runs_scored'].replace({'DNB': '-1', 'TDNB': '-1'}, inplace=True)\n",
    "train['runs_scored'] = train['runs_scored'].astype('int')\n",
    "\n",
    "# clean wickets and convert to int\n",
    "train['wickets'] = train['wickets'].str.replace('-', '0', regex=False).astype('int')\n",
    "\n",
    "# convert date to datetime\n",
    "train['match_date'] = pd.to_datetime(train['match_date'])\n",
    "\n",
    "# extract different ground names\n",
    "train['ground'] = train['opposition'].apply(lambda x: x.split()[-1])\n",
    "\n",
    "# extract opponent team\n",
    "train['opposition'] = train['opposition'].apply(lambda x: x.split()[1])\n",
    "train['opposition'].replace({'South': 'South Africa',\n",
    "                             'New': 'New Zealand',\n",
    "                             'West': 'West Indies',\n",
    "                             'Sri': 'Sri Lanka',\n",
    "                             'Hong': 'Hong Kong'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1ef91854",
   "metadata": {},
   "outputs": [],
   "source": [
    "id_name = train.groupby(['player_id']).player_name.first().reset_index()\n",
    "id_dict = id_name.set_index('player_id').to_dict()['player_name']\n",
    "# id_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6915289",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "30f69933",
   "metadata": {},
   "source": [
    "## Making inferences from data\n",
    "In this section I tried to predict the potential playing XI of both teams based on past and historical records for runs socred, wickets taken, stumpings, matches played etc."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c9bc71",
   "metadata": {},
   "source": [
    "### AUSTRALIA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6695b615",
   "metadata": {},
   "source": [
    "#### Matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1adb0383",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id  player_name       \n",
       "13         David Warner          150\n",
       "2          Steve Smith           145\n",
       "11         Glenn Maxwell         129\n",
       "15         Mitchell Starc.       111\n",
       "14         Adam Zampa             85\n",
       "10         Mitch Marsh            79\n",
       "1          Pat Cummins            77\n",
       "8          Josh Hazlewood         74\n",
       "3          Alex Carey             71\n",
       "12         Marcus Stoinis         64\n",
       "9          Travis Head            58\n",
       "6          Marnus Labuschagne     38\n",
       "7          Cameron Green          20\n",
       "5          Sean Abbott            17\n",
       "4          Josh Inglis             8\n",
       "Name: player_id, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total matches played by a player \n",
    "aus_tot_mat = train.groupby(['player_id', 'player_name']).player_id.count()[:15]\n",
    "aus_tot_mat.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a59b1d6",
   "metadata": {},
   "source": [
    "Warner, Smith, Maxwell, Starc, Zampa, Marsh, Cummins, Hazlewood are important and experienced players for AUS."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006aa0b4",
   "metadata": {},
   "source": [
    "#### Runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "729e4437",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id  player_name       \n",
       "13         David Warner          42.633333\n",
       "9          Travis Head           35.534483\n",
       "2          Steve Smith           34.744828\n",
       "6          Marnus Labuschagne    33.315789\n",
       "10         Mitch Marsh           28.189873\n",
       "11         Glenn Maxwell         27.007752\n",
       "3          Alex Carey            25.464789\n",
       "12         Marcus Stoinis        21.781250\n",
       "7          Cameron Green         18.800000\n",
       "4          Josh Inglis           17.875000\n",
       "5          Sean Abbott           13.235294\n",
       "1          Pat Cummins            4.363636\n",
       "15         Mitchell Starc.        3.927928\n",
       "14         Adam Zampa             2.270588\n",
       "8          Josh Hazlewood         0.932432\n",
       "Name: runs_scored, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average runs scored by player in all matches\n",
    "aus_avg_run = train.groupby(['player_id', 'player_name']).runs_scored.mean()[:15]\n",
    "aus_avg_run.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fafb4bdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id  player_name       \n",
       "13         David Warner          6395\n",
       "2          Steve Smith           5038\n",
       "11         Glenn Maxwell         3484\n",
       "10         Mitch Marsh           2227\n",
       "9          Travis Head           2061\n",
       "3          Alex Carey            1808\n",
       "12         Marcus Stoinis        1394\n",
       "6          Marnus Labuschagne    1266\n",
       "15         Mitchell Starc.        436\n",
       "7          Cameron Green          376\n",
       "1          Pat Cummins            336\n",
       "5          Sean Abbott            225\n",
       "14         Adam Zampa             193\n",
       "4          Josh Inglis            143\n",
       "8          Josh Hazlewood          69\n",
       "Name: runs_scored, dtype: int32"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total runs scored by player in all matches\n",
    "aus_run_sum = train.groupby(['player_id', 'player_name']).runs_scored.sum()[:15]\n",
    "aus_run_sum.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "208b6ec0",
   "metadata": {},
   "source": [
    "David Warner, Steve Smith, Glenn Maxwell, Mitch Marsh, Marcus Stoinis, Marnus Labuschagne, Alex Carey, Travis Head are potential options for batting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ec06638",
   "metadata": {},
   "source": [
    "#### Wickets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "730b5a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id  player_name       \n",
       "15         Mitchell Starc.       1.981982\n",
       "14         Adam Zampa            1.670588\n",
       "1          Pat Cummins           1.636364\n",
       "8          Josh Hazlewood        1.567568\n",
       "5          Sean Abbott           1.176471\n",
       "7          Cameron Green         0.800000\n",
       "12         Marcus Stoinis        0.687500\n",
       "10         Mitch Marsh           0.683544\n",
       "11         Glenn Maxwell         0.496124\n",
       "9          Travis Head           0.275862\n",
       "2          Steve Smith           0.193103\n",
       "6          Marnus Labuschagne    0.052632\n",
       "3          Alex Carey            0.000000\n",
       "4          Josh Inglis           0.000000\n",
       "13         David Warner          0.000000\n",
       "Name: wickets, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average wickets taken by player in all matches\n",
    "aus_wic = train.groupby(['player_id', 'player_name']).wickets.mean()[:15]\n",
    "aus_wic.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e3ba83d4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id  player_name       \n",
       "15         Mitchell Starc.       220\n",
       "14         Adam Zampa            142\n",
       "1          Pat Cummins           126\n",
       "8          Josh Hazlewood        116\n",
       "11         Glenn Maxwell          64\n",
       "10         Mitch Marsh            54\n",
       "12         Marcus Stoinis         44\n",
       "2          Steve Smith            28\n",
       "5          Sean Abbott            20\n",
       "7          Cameron Green          16\n",
       "9          Travis Head            16\n",
       "6          Marnus Labuschagne      2\n",
       "3          Alex Carey              0\n",
       "4          Josh Inglis             0\n",
       "13         David Warner            0\n",
       "Name: wickets, dtype: int32"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total wickets taken by player in all matches\n",
    "aus_wic_sum = train.groupby(['player_id', 'player_name']).wickets.sum()[:15]\n",
    "aus_wic_sum.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a349f6b",
   "metadata": {},
   "source": [
    "There needs to be a balance between batters and bowlers. Every good team must have a balance between the number of batters and bowlers to win a match. For bowlers Mitchell Starc, Adam Zampa, Pat Cummins, Josh Hazlewood have the best records. Maxwell, Marsh and Stoinis also have decent wickets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b705eca4",
   "metadata": {},
   "source": [
    "#### Stumpings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "77b3bb99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id  player_name       \n",
       "3          Alex Carey            8\n",
       "1          Pat Cummins           0\n",
       "2          Steve Smith           0\n",
       "4          Josh Inglis           0\n",
       "5          Sean Abbott           0\n",
       "6          Marnus Labuschagne    0\n",
       "7          Cameron Green         0\n",
       "8          Josh Hazlewood        0\n",
       "9          Travis Head           0\n",
       "10         Mitch Marsh           0\n",
       "11         Glenn Maxwell         0\n",
       "12         Marcus Stoinis        0\n",
       "13         David Warner          0\n",
       "14         Adam Zampa            0\n",
       "15         Mitchell Starc.       0\n",
       "Name: stumpings, dtype: int32"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total stumpings done by player in all matches\n",
    "train['stumpings'] = train['stumpings'].str.replace('-', '-1', regex=False).astype('int')\n",
    "aus_st_sum = train.groupby(['player_id', 'player_name']).stumpings.sum()[:15]\n",
    "aus_st_sum.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6c2bd40",
   "metadata": {},
   "source": [
    "Alex Carey is the only wicketkeeper option. So has to be in the team."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "471c9d8c",
   "metadata": {},
   "source": [
    "We can combine the players in three categories based on the above data - Batsmen, Bowlers and All-rounders.\n",
    "- **Batsmen** - David Warner, Steve Smith, Marnus Labuschagne, Alex Carey(wk), Travis Head are potential batsmen\n",
    "- **All rounders** - Glenn Maxwell, Mitch Marsh, Cameron Green, Marcus Stoinis fall in this category and are potential all rounders.\n",
    "- **Bowlers** - Mitchell Starc, Adam Zampa, Josh hazlewood, Pat Cummins(c) are good bowling options."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f7f8121",
   "metadata": {},
   "source": [
    "Similar process can be done for India to predict the potential playing XI."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fbaf80b",
   "metadata": {},
   "source": [
    "### INDIA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f000151f",
   "metadata": {},
   "source": [
    "#### Matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8a3294f3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id  player_name        \n",
       "19         Virat Kohli            281\n",
       "16         Rohit Sharma           251\n",
       "22         Ravindra Jadeja        186\n",
       "28         Ravichandran Ashwin    115\n",
       "27         Mohammed Shami          94\n",
       "26         Kuldeep Yadav           90\n",
       "17         Hardik Pandya           82\n",
       "24         Jasprit Bumrah          78\n",
       "21         KL Rahul                61\n",
       "20         Shreyas Iyer            47\n",
       "23         Shardul Thakur          44\n",
       "18         Shubman Gill            35\n",
       "25         Mohammed Siraj          30\n",
       "30         Suryakumar Yadav        30\n",
       "29         Ishan Kishan            25\n",
       "Name: player_id, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total matches played by a player \n",
    "ind_tot_mat = train.groupby(['player_id', 'player_name']).player_id.count()[15:]\n",
    "ind_tot_mat.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "946f67b1",
   "metadata": {},
   "source": [
    "Kohli, Rohit, Jadeja, Ashwin are senior players with more experience. Gill, Siraj, Surya and Ishan are newer ones."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47dee122",
   "metadata": {},
   "source": [
    "#### Runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aa7456d9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id  player_name        \n",
       "18         Shubman Gill           54.771429\n",
       "19         Virat Kohli            46.516014\n",
       "16         Rohit Sharma           40.254980\n",
       "20         Shreyas Iyer           38.212766\n",
       "21         KL Rahul               37.508197\n",
       "29         Ishan Kishan           35.320000\n",
       "30         Suryakumar Yadav       22.166667\n",
       "17         Hardik Pandya          21.170732\n",
       "22         Ravindra Jadeja        13.854839\n",
       "23         Shardul Thakur          7.045455\n",
       "28         Ravichandran Ashwin     5.695652\n",
       "27         Mohammed Shami          1.702128\n",
       "26         Kuldeep Yadav           1.266667\n",
       "25         Mohammed Siraj          0.666667\n",
       "24         Jasprit Bumrah          0.230769\n",
       "Name: runs_scored, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average runs scored by player in all matches\n",
    "ind_avg_run = train.groupby(['player_id', 'player_name']).runs_scored.mean()[15:]\n",
    "ind_avg_run.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a9764210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id  player_name        \n",
       "19         Virat Kohli            13071\n",
       "16         Rohit Sharma           10104\n",
       "22         Ravindra Jadeja         2577\n",
       "21         KL Rahul                2288\n",
       "18         Shubman Gill            1917\n",
       "20         Shreyas Iyer            1796\n",
       "17         Hardik Pandya           1736\n",
       "29         Ishan Kishan             883\n",
       "30         Suryakumar Yadav         665\n",
       "28         Ravichandran Ashwin      655\n",
       "23         Shardul Thakur           310\n",
       "27         Mohammed Shami           160\n",
       "26         Kuldeep Yadav            114\n",
       "25         Mohammed Siraj            20\n",
       "24         Jasprit Bumrah            18\n",
       "Name: runs_scored, dtype: int32"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total runs scored by player in all matches\n",
    "ind_run_sum = train.groupby(['player_id', 'player_name']).runs_scored.sum()[15:]\n",
    "ind_run_sum.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b165604",
   "metadata": {},
   "source": [
    "Virat Kohli, Rohit Sharma, Ravindra Jadeja, KL Rahul, Shubman Gill, Shreyas Iyer, Hardik Pandya, Ishan Kishan are potential options for batting."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "482947cf",
   "metadata": {},
   "source": [
    "#### Wickets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f6536fc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id  player_name        \n",
       "27         Mohammed Shami         1.819149\n",
       "25         Mohammed Siraj         1.800000\n",
       "26         Kuldeep Yadav          1.688889\n",
       "24         Jasprit Bumrah         1.653846\n",
       "23         Shardul Thakur         1.431818\n",
       "28         Ravichandran Ashwin    1.347826\n",
       "22         Ravindra Jadeja        1.096774\n",
       "17         Hardik Pandya          0.963415\n",
       "16         Rohit Sharma           0.031873\n",
       "19         Virat Kohli            0.014235\n",
       "18         Shubman Gill           0.000000\n",
       "20         Shreyas Iyer           0.000000\n",
       "21         KL Rahul               0.000000\n",
       "29         Ishan Kishan           0.000000\n",
       "30         Suryakumar Yadav       0.000000\n",
       "Name: wickets, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average wickets taken by player in all matches\n",
    "ind_wic = train.groupby(['player_id', 'player_name']).wickets.mean()[15:]\n",
    "ind_wic.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "61f6dcce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id  player_name        \n",
       "22         Ravindra Jadeja        204\n",
       "27         Mohammed Shami         171\n",
       "28         Ravichandran Ashwin    155\n",
       "26         Kuldeep Yadav          152\n",
       "24         Jasprit Bumrah         129\n",
       "17         Hardik Pandya           79\n",
       "23         Shardul Thakur          63\n",
       "25         Mohammed Siraj          54\n",
       "16         Rohit Sharma             8\n",
       "19         Virat Kohli              4\n",
       "18         Shubman Gill             0\n",
       "20         Shreyas Iyer             0\n",
       "21         KL Rahul                 0\n",
       "29         Ishan Kishan             0\n",
       "30         Suryakumar Yadav         0\n",
       "Name: wickets, dtype: int32"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total wickets taken by player in all matches\n",
    "ind_wic_sum = train.groupby(['player_id', 'player_name']).wickets.sum()[15:]\n",
    "ind_wic_sum.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afd8e378",
   "metadata": {},
   "source": [
    "Ravindra Jadeja, Mohammed Shami, Ashwin, Kuldeep Yadav, Bumrah and Pandya have decent amount of wickets. Mohammed Siraj has a also a very good average so he can also be included in the team."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e79fee1",
   "metadata": {},
   "source": [
    "#### Stumpings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e980ddc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id  player_name        \n",
       "21         KL Rahul               4\n",
       "29         Ishan Kishan           1\n",
       "23         Shardul Thakur        -1\n",
       "24         Jasprit Bumrah        -1\n",
       "25         Mohammed Siraj        -1\n",
       "27         Mohammed Shami        -1\n",
       "28         Ravichandran Ashwin   -1\n",
       "30         Suryakumar Yadav      -1\n",
       "16         Rohit Sharma          -2\n",
       "17         Hardik Pandya         -2\n",
       "18         Shubman Gill          -2\n",
       "20         Shreyas Iyer          -2\n",
       "22         Ravindra Jadeja       -2\n",
       "26         Kuldeep Yadav         -2\n",
       "19         Virat Kohli           -3\n",
       "Name: stumpings, dtype: int32"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total stumpings done by player in all matches\n",
    "# train['stumpings'] = train['stumpings'].str.replace('-', '-1', regex=False).astype('int')\n",
    "ind_st_sum = train.groupby(['player_id', 'player_name']).stumpings.sum()[15:]\n",
    "ind_st_sum.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "141eccb6",
   "metadata": {},
   "source": [
    "KL Rahul and Ishan Kishan are potential wicketkeeper options"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d77893c",
   "metadata": {},
   "source": [
    "From above inforamtion we can now segregate the players and make a potential team.\n",
    "- **Batsmen** - Virat Kohli, Rohit Sharma(c), KL Rahul(wk), Ishan Kishan(wk), Shubman Gill, Shreyas Iyer are potential batsmen\n",
    "- **All rounders** - Hardik Pandya, Ravindra Jadeja, Ravi Ashwin fall in this category and are potential all rounders.\n",
    "- **Bowlers** - Kuldeep Yadav, Jasprit Bumrah, Mohammed Shami, Mohammed Siraj are good bowling options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b20f3749",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ed0ed709",
   "metadata": {},
   "source": [
    "## Ground analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0774fe91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Centurion', 'Gqeberha', 'Durban', 'Belfast', \"Lord's\",\n",
       "       'Melbourne', 'Sydney', 'Hobart', 'Auckland', 'Southampton',\n",
       "       'Manchester', 'Leeds', 'Canberra', 'Brisbane', 'Perth', 'Adelaide',\n",
       "       'Hamilton', 'Birmingham', 'Oval', 'Chennai', 'Gardens', 'Indore',\n",
       "       'Bengaluru', 'Nagpur', 'Hyderabad', 'Ranchi', 'Mohali', 'Delhi',\n",
       "       'Dhabi', 'Bristol', 'Nottingham', 'Taunton', 'Wankhede', 'Rajkot',\n",
       "       'Paarl', 'Bloemfontein', 'Pallekele', '(RPS)', 'Cardiff',\n",
       "       'Visakhapatnam', 'Ahmedabad', 'Mirpur', 'Hambantota', 'Harare',\n",
       "       'Sharjah', '(DSC)', 'Wellington', 'Providence', 'Basseterre',\n",
       "       'Bridgetown', 'Benoni', 'Johannesburg', 'Town', 'Potchefstroom',\n",
       "       'Townsville', 'Cairns', 'Chester-le-Street', 'Lahore', 'Dambulla',\n",
       "       'Pune', 'Jaipur', 'Edinburgh', 'Kingstown', 'Islet', 'Karachi',\n",
       "       'Kanpur', 'Cuttack', 'Kingston', 'Bulawayo', 'Spain', 'Sound',\n",
       "       'Dharamsala', 'Kochi', 'Napier', 'Fatullah', 'Guwahati',\n",
       "       'Brabourne', 'Thiruvananthapuram', 'Maunganui', 'Raipur',\n",
       "       'Tarouba', 'Lucknow', 'Christchurch', 'Vadodara', 'Gwalior',\n",
       "       'Chattogram'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.ground.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "370d8506",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id  player_name        \n",
       "20         Shreyas Iyer           70.000000\n",
       "17         Hardik Pandya          61.500000\n",
       "10         Mitch Marsh            47.000000\n",
       "19         Virat Kohli            42.125000\n",
       "11         Glenn Maxwell          39.000000\n",
       "3          Alex Carey             38.000000\n",
       "18         Shubman Gill           37.000000\n",
       "6          Marnus Labuschagne     28.000000\n",
       "5          Sean Abbott            26.000000\n",
       "13         David Warner           24.000000\n",
       "16         Rohit Sharma           19.857143\n",
       "21         KL Rahul               19.000000\n",
       "9          Travis Head            19.000000\n",
       "12         Marcus Stoinis         14.000000\n",
       "28         Ravichandran Ashwin    13.333333\n",
       "22         Ravindra Jadeja        12.666667\n",
       "15         Mitchell Starc.        10.000000\n",
       "1          Pat Cummins             9.000000\n",
       "14         Adam Zampa              7.500000\n",
       "27         Mohammed Shami          7.000000\n",
       "25         Mohammed Siraj          3.000000\n",
       "26         Kuldeep Yadav           1.666667\n",
       "2          Steve Smith             0.500000\n",
       "30         Suryakumar Yadav        0.000000\n",
       "24         Jasprit Bumrah         -1.000000\n",
       "Name: runs_scored, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average runs scored by a player on Chennai pitch\n",
    "train[train.ground=='Chennai'].groupby(['player_id', 'player_name']).runs_scored.mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc54d85",
   "metadata": {},
   "source": [
    "Steve Smith and Suryakumar Yadav can be seen at the bottom. This can happen because of them not playing many matches on this ground. Apart from that Shreyas Iyer, Virat Kohli, Hardik Pandya and Mitch Marsh have performed better on this pitch according to historical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "01623a23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id  player_name        \n",
       "1          Pat Cummins            0\n",
       "2          Steve Smith            0\n",
       "3          Alex Carey             0\n",
       "5          Sean Abbott            1\n",
       "6          Marnus Labuschagne     0\n",
       "9          Travis Head            0\n",
       "10         Mitch Marsh            0\n",
       "11         Glenn Maxwell          0\n",
       "12         Marcus Stoinis         3\n",
       "13         David Warner           0\n",
       "14         Adam Zampa             5\n",
       "15         Mitchell Starc.        0\n",
       "16         Rohit Sharma           0\n",
       "17         Hardik Pandya          5\n",
       "18         Shubman Gill           0\n",
       "19         Virat Kohli            0\n",
       "20         Shreyas Iyer           0\n",
       "21         KL Rahul               0\n",
       "22         Ravindra Jadeja        3\n",
       "24         Jasprit Bumrah         1\n",
       "25         Mohammed Siraj         2\n",
       "26         Kuldeep Yadav          5\n",
       "27         Mohammed Shami         1\n",
       "28         Ravichandran Ashwin    5\n",
       "30         Suryakumar Yadav       0\n",
       "Name: wickets, dtype: int32"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total wickets taken by a player on Chennai pitch\n",
    "train[train.ground=='Chennai'].groupby(['player_id', 'player_name']).wickets.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9abd9a4",
   "metadata": {},
   "source": [
    "Based on the data we can compare between wickets taken by spinners and pacers. Pacers are **Pandya, Siraj ,Shami and Bumrah**. Spinners are **Ashwin, Jadeja, Zampa and Kuldeep**\n",
    "\n",
    "- Wickets by pacers - 5+1+2+1=9\n",
    "- Wickets by spinners - 5+3+5+5=18\n",
    "\n",
    "**This means that spinners has edge over pacers on this pitch.** Let's take a look at the average too for more accurate numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a49c8bb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id  player_name        \n",
       "1          Pat Cummins            0.000000\n",
       "2          Steve Smith            0.000000\n",
       "3          Alex Carey             0.000000\n",
       "5          Sean Abbott            1.000000\n",
       "6          Marnus Labuschagne     0.000000\n",
       "9          Travis Head            0.000000\n",
       "10         Mitch Marsh            0.000000\n",
       "11         Glenn Maxwell          0.000000\n",
       "12         Marcus Stoinis         1.500000\n",
       "13         David Warner           0.000000\n",
       "14         Adam Zampa             2.500000\n",
       "15         Mitchell Starc.        0.000000\n",
       "16         Rohit Sharma           0.000000\n",
       "17         Hardik Pandya          2.500000\n",
       "18         Shubman Gill           0.000000\n",
       "19         Virat Kohli            0.000000\n",
       "20         Shreyas Iyer           0.000000\n",
       "21         KL Rahul               0.000000\n",
       "22         Ravindra Jadeja        1.000000\n",
       "24         Jasprit Bumrah         1.000000\n",
       "25         Mohammed Siraj         2.000000\n",
       "26         Kuldeep Yadav          1.666667\n",
       "27         Mohammed Shami         0.500000\n",
       "28         Ravichandran Ashwin    1.666667\n",
       "30         Suryakumar Yadav       0.000000\n",
       "Name: wickets, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average wickets taken by a player on Chennai pitch\n",
    "train[train.ground=='Chennai'].groupby(['player_id', 'player_name']).wickets.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b7f9806",
   "metadata": {},
   "source": [
    "- Average wickets by pacers - 2.5+1+2+0.5 = 6.00\n",
    "- Average wickets by spinners - 2.5+1+1.66+1.66 = 6.83\n",
    "\n",
    "This also lead to the same conclusion. So it can be safely said that this pitch will likely be better for spin bolwing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ce6f795",
   "metadata": {},
   "source": [
    "### Additional ground data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08c27efb",
   "metadata": {},
   "source": [
    "I have also extracted ground historical data from [espncricinfo](https://www.espncricinfo.com/) site which is analyzed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4bbdf36d",
   "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>Team</th>\n",
       "      <th>Score</th>\n",
       "      <th>Overs</th>\n",
       "      <th>Target</th>\n",
       "      <th>RPO</th>\n",
       "      <th>Inns</th>\n",
       "      <th>Result</th>\n",
       "      <th>Opposition</th>\n",
       "      <th>Match date</th>\n",
       "      <th>Match ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>270/6</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.40</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v India</td>\n",
       "      <td>1987-10-09</td>\n",
       "      <td>ODI # 453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>269</td>\n",
       "      <td>49.5</td>\n",
       "      <td>271.0</td>\n",
       "      <td>5.39</td>\n",
       "      <td>2</td>\n",
       "      <td>lost</td>\n",
       "      <td>v Australia</td>\n",
       "      <td>1987-10-09</td>\n",
       "      <td>ODI # 453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Australia</td>\n",
       "      <td>235/9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.70</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v Zimbabwe</td>\n",
       "      <td>1987-10-13</td>\n",
       "      <td>ODI # 456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>139</td>\n",
       "      <td>42.4</td>\n",
       "      <td>236.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2</td>\n",
       "      <td>lost</td>\n",
       "      <td>v Australia</td>\n",
       "      <td>1987-10-13</td>\n",
       "      <td>ODI # 456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>241/6</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.82</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>1989-10-21</td>\n",
       "      <td>ODI # 578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>West Indies</td>\n",
       "      <td>142</td>\n",
       "      <td>40.3</td>\n",
       "      <td>242.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2</td>\n",
       "      <td>lost</td>\n",
       "      <td>v Australia</td>\n",
       "      <td>1989-10-21</td>\n",
       "      <td>ODI # 578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>West Indies</td>\n",
       "      <td>221</td>\n",
       "      <td>49.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.47</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "      <td>v India</td>\n",
       "      <td>1994-10-23</td>\n",
       "      <td>ODI # 936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>India</td>\n",
       "      <td>225/6</td>\n",
       "      <td>48.2</td>\n",
       "      <td>222.0</td>\n",
       "      <td>4.65</td>\n",
       "      <td>2</td>\n",
       "      <td>won</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>1994-10-23</td>\n",
       "      <td>ODI # 936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>286/9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.72</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "      <td>v Australia</td>\n",
       "      <td>1996-03-11</td>\n",
       "      <td>ODI # 1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Australia</td>\n",
       "      <td>289/4</td>\n",
       "      <td>47.5</td>\n",
       "      <td>287.0</td>\n",
       "      <td>6.04</td>\n",
       "      <td>2</td>\n",
       "      <td>won</td>\n",
       "      <td>v New Zealand</td>\n",
       "      <td>1996-03-11</td>\n",
       "      <td>ODI # 1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>327/5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.54</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v India</td>\n",
       "      <td>1997-05-21</td>\n",
       "      <td>ODI # 1209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>India</td>\n",
       "      <td>292</td>\n",
       "      <td>49.2</td>\n",
       "      <td>328.0</td>\n",
       "      <td>5.91</td>\n",
       "      <td>2</td>\n",
       "      <td>lost</td>\n",
       "      <td>v Pakistan</td>\n",
       "      <td>1997-05-21</td>\n",
       "      <td>ODI # 1209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>226/8</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.52</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v Bangladesh</td>\n",
       "      <td>1998-05-23</td>\n",
       "      <td>ODI # 1333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>198</td>\n",
       "      <td>46.2</td>\n",
       "      <td>226.0</td>\n",
       "      <td>4.27</td>\n",
       "      <td>2</td>\n",
       "      <td>lost</td>\n",
       "      <td>v Kenya</td>\n",
       "      <td>1998-05-23</td>\n",
       "      <td>ODI # 1333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>England</td>\n",
       "      <td>217</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.52</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "      <td>v India</td>\n",
       "      <td>2002-01-25</td>\n",
       "      <td>ODI # 1795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>India</td>\n",
       "      <td>221/6</td>\n",
       "      <td>46.4</td>\n",
       "      <td>218.0</td>\n",
       "      <td>4.73</td>\n",
       "      <td>2</td>\n",
       "      <td>won</td>\n",
       "      <td>v England</td>\n",
       "      <td>2002-01-25</td>\n",
       "      <td>ODI # 1795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>DNB</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>n/r</td>\n",
       "      <td>v India</td>\n",
       "      <td>2003-10-23</td>\n",
       "      <td>ODI # 2051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>India</td>\n",
       "      <td>141/3</td>\n",
       "      <td>26.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1</td>\n",
       "      <td>n/r</td>\n",
       "      <td>v New Zealand</td>\n",
       "      <td>2003-10-23</td>\n",
       "      <td>ODI # 2051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>India</td>\n",
       "      <td>268</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.58</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>2007-01-27</td>\n",
       "      <td>ODI # 2487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>West Indies</td>\n",
       "      <td>270/7</td>\n",
       "      <td>43.4</td>\n",
       "      <td>269.0</td>\n",
       "      <td>6.18</td>\n",
       "      <td>2</td>\n",
       "      <td>won</td>\n",
       "      <td>v India</td>\n",
       "      <td>2007-01-27</td>\n",
       "      <td>ODI # 2487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Asia XI</td>\n",
       "      <td>337/7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.74</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v Africa XI</td>\n",
       "      <td>2007-06-09</td>\n",
       "      <td>ODI # 2588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Africa XI</td>\n",
       "      <td>306</td>\n",
       "      <td>49.5</td>\n",
       "      <td>338.0</td>\n",
       "      <td>6.14</td>\n",
       "      <td>2</td>\n",
       "      <td>lost</td>\n",
       "      <td>v Asia XI</td>\n",
       "      <td>2007-06-09</td>\n",
       "      <td>ODI # 2588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Asia XI</td>\n",
       "      <td>331/8</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.62</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v Africa XI</td>\n",
       "      <td>2007-06-10</td>\n",
       "      <td>ODI # 2589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Africa XI</td>\n",
       "      <td>318/7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>6.36</td>\n",
       "      <td>2</td>\n",
       "      <td>lost</td>\n",
       "      <td>v Asia XI</td>\n",
       "      <td>2007-06-10</td>\n",
       "      <td>ODI # 2589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>103</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.81</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "      <td>v India</td>\n",
       "      <td>2010-12-10</td>\n",
       "      <td>ODI # 3077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>India</td>\n",
       "      <td>107/2</td>\n",
       "      <td>21.1</td>\n",
       "      <td>104.0</td>\n",
       "      <td>5.05</td>\n",
       "      <td>2</td>\n",
       "      <td>won</td>\n",
       "      <td>v New Zealand</td>\n",
       "      <td>2010-12-10</td>\n",
       "      <td>ODI # 3077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>69</td>\n",
       "      <td>23.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.89</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "      <td>v New Zealand</td>\n",
       "      <td>2011-02-20</td>\n",
       "      <td>ODI # 3101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>72/0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2</td>\n",
       "      <td>won</td>\n",
       "      <td>v Kenya</td>\n",
       "      <td>2011-02-20</td>\n",
       "      <td>ODI # 3101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>England</td>\n",
       "      <td>171</td>\n",
       "      <td>45.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.74</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v South Africa</td>\n",
       "      <td>2011-03-06</td>\n",
       "      <td>ODI # 3120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>165</td>\n",
       "      <td>47.4</td>\n",
       "      <td>172.0</td>\n",
       "      <td>3.46</td>\n",
       "      <td>2</td>\n",
       "      <td>lost</td>\n",
       "      <td>v England</td>\n",
       "      <td>2011-03-06</td>\n",
       "      <td>ODI # 3120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>England</td>\n",
       "      <td>243</td>\n",
       "      <td>48.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.99</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>2011-03-17</td>\n",
       "      <td>ODI # 3135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>West Indies</td>\n",
       "      <td>225</td>\n",
       "      <td>44.4</td>\n",
       "      <td>244.0</td>\n",
       "      <td>5.03</td>\n",
       "      <td>2</td>\n",
       "      <td>lost</td>\n",
       "      <td>v England</td>\n",
       "      <td>2011-03-17</td>\n",
       "      <td>ODI # 3135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>India</td>\n",
       "      <td>268</td>\n",
       "      <td>49.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.45</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>2011-03-20</td>\n",
       "      <td>ODI # 3141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>West Indies</td>\n",
       "      <td>188</td>\n",
       "      <td>43.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>4.37</td>\n",
       "      <td>2</td>\n",
       "      <td>lost</td>\n",
       "      <td>v India</td>\n",
       "      <td>2011-03-20</td>\n",
       "      <td>ODI # 3141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>India</td>\n",
       "      <td>267/6</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.34</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>2011-12-11</td>\n",
       "      <td>ODI # 3224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>West Indies</td>\n",
       "      <td>233</td>\n",
       "      <td>44.1</td>\n",
       "      <td>268.0</td>\n",
       "      <td>5.27</td>\n",
       "      <td>2</td>\n",
       "      <td>lost</td>\n",
       "      <td>v India</td>\n",
       "      <td>2011-12-11</td>\n",
       "      <td>ODI # 3224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>India</td>\n",
       "      <td>227/6</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.54</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "      <td>v Pakistan</td>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>ODI # 3314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>228/4</td>\n",
       "      <td>48.1</td>\n",
       "      <td>228.0</td>\n",
       "      <td>4.73</td>\n",
       "      <td>2</td>\n",
       "      <td>won</td>\n",
       "      <td>v India</td>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>ODI # 3314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>India</td>\n",
       "      <td>299/8</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.98</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v South Africa</td>\n",
       "      <td>2015-10-22</td>\n",
       "      <td>ODI # 3698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>264/9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>5.28</td>\n",
       "      <td>2</td>\n",
       "      <td>lost</td>\n",
       "      <td>v India</td>\n",
       "      <td>2015-10-22</td>\n",
       "      <td>ODI # 3698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>India</td>\n",
       "      <td>281/7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.62</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v Australia</td>\n",
       "      <td>2017-09-17</td>\n",
       "      <td>ODI # 3910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Australia</td>\n",
       "      <td>137/9</td>\n",
       "      <td>21.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>6.52</td>\n",
       "      <td>2</td>\n",
       "      <td>lost</td>\n",
       "      <td>v India</td>\n",
       "      <td>2017-09-17</td>\n",
       "      <td>ODI # 3910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>India</td>\n",
       "      <td>287/8</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.74</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>2019-12-15</td>\n",
       "      <td>ODI # 4221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>West Indies</td>\n",
       "      <td>291/2</td>\n",
       "      <td>47.5</td>\n",
       "      <td>288.0</td>\n",
       "      <td>6.08</td>\n",
       "      <td>2</td>\n",
       "      <td>won</td>\n",
       "      <td>v India</td>\n",
       "      <td>2019-12-15</td>\n",
       "      <td>ODI # 4221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Australia</td>\n",
       "      <td>269</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.48</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v India</td>\n",
       "      <td>2023-03-22</td>\n",
       "      <td>ODI # 4545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>India</td>\n",
       "      <td>248</td>\n",
       "      <td>49.1</td>\n",
       "      <td>270.0</td>\n",
       "      <td>5.04</td>\n",
       "      <td>2</td>\n",
       "      <td>lost</td>\n",
       "      <td>v Australia</td>\n",
       "      <td>2023-03-22</td>\n",
       "      <td>ODI # 4545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Team  Score  Overs  Target   RPO  Inns Result      Opposition  \\\n",
       "0      Australia  270/6   50.0     NaN  5.40     1    won         v India   \n",
       "1          India    269   49.5   271.0  5.39     2   lost     v Australia   \n",
       "2      Australia  235/9   50.0     NaN  4.70     1    won      v Zimbabwe   \n",
       "3       Zimbabwe    139   42.4   236.0  3.25     2   lost     v Australia   \n",
       "4      Australia  241/6   50.0     NaN  4.82     1    won   v West Indies   \n",
       "5    West Indies    142   40.3   242.0  3.50     2   lost     v Australia   \n",
       "6    West Indies    221   49.2     NaN  4.47     1   lost         v India   \n",
       "7          India  225/6   48.2   222.0  4.65     2    won   v West Indies   \n",
       "8    New Zealand  286/9   50.0     NaN  5.72     1   lost     v Australia   \n",
       "9      Australia  289/4   47.5   287.0  6.04     2    won   v New Zealand   \n",
       "10      Pakistan  327/5   50.0     NaN  6.54     1    won         v India   \n",
       "11         India    292   49.2   328.0  5.91     2   lost      v Pakistan   \n",
       "12         Kenya  226/8   50.0     NaN  4.52     1    won    v Bangladesh   \n",
       "13    Bangladesh    198   46.2   226.0  4.27     2   lost         v Kenya   \n",
       "14       England    217   48.0     NaN  4.52     1   lost         v India   \n",
       "15         India  221/6   46.4   218.0  4.73     2    won       v England   \n",
       "16   New Zealand    DNB    0.0     NaN     -     0    n/r         v India   \n",
       "17         India  141/3   26.5     NaN  5.25     1    n/r   v New Zealand   \n",
       "18         India    268   48.0     NaN  5.58     1   lost   v West Indies   \n",
       "19   West Indies  270/7   43.4   269.0  6.18     2    won         v India   \n",
       "20       Asia XI  337/7   50.0     NaN  6.74     1    won     v Africa XI   \n",
       "21     Africa XI    306   49.5   338.0  6.14     2   lost       v Asia XI   \n",
       "22       Asia XI  331/8   50.0     NaN  6.62     1    won     v Africa XI   \n",
       "23     Africa XI  318/7   50.0   332.0  6.36     2   lost       v Asia XI   \n",
       "24   New Zealand    103   27.0     NaN  3.81     1   lost         v India   \n",
       "25         India  107/2   21.1   104.0  5.05     2    won   v New Zealand   \n",
       "26         Kenya     69   23.5     NaN  2.89     1   lost   v New Zealand   \n",
       "27   New Zealand   72/0    8.0    70.0  9.00     2    won         v Kenya   \n",
       "28       England    171   45.4     NaN  3.74     1    won  v South Africa   \n",
       "29  South Africa    165   47.4   172.0  3.46     2   lost       v England   \n",
       "30       England    243   48.4     NaN  4.99     1    won   v West Indies   \n",
       "31   West Indies    225   44.4   244.0  5.03     2   lost       v England   \n",
       "32         India    268   49.1     NaN  5.45     1    won   v West Indies   \n",
       "33   West Indies    188   43.0   269.0  4.37     2   lost         v India   \n",
       "34         India  267/6   50.0     NaN  5.34     1    won   v West Indies   \n",
       "35   West Indies    233   44.1   268.0  5.27     2   lost         v India   \n",
       "36         India  227/6   50.0     NaN  4.54     1   lost      v Pakistan   \n",
       "37      Pakistan  228/4   48.1   228.0  4.73     2    won         v India   \n",
       "38         India  299/8   50.0     NaN  5.98     1    won  v South Africa   \n",
       "39  South Africa  264/9   50.0   300.0  5.28     2   lost         v India   \n",
       "40         India  281/7   50.0     NaN  5.62     1    won     v Australia   \n",
       "41     Australia  137/9   21.0   164.0  6.52     2   lost         v India   \n",
       "42         India  287/8   50.0     NaN  5.74     1   lost   v West Indies   \n",
       "43   West Indies  291/2   47.5   288.0  6.08     2    won         v India   \n",
       "44     Australia    269   49.0     NaN  5.48     1    won         v India   \n",
       "45         India    248   49.1   270.0  5.04     2   lost     v Australia   \n",
       "\n",
       "    Match date    Match ID  \n",
       "0   1987-10-09   ODI # 453  \n",
       "1   1987-10-09   ODI # 453  \n",
       "2   1987-10-13   ODI # 456  \n",
       "3   1987-10-13   ODI # 456  \n",
       "4   1989-10-21   ODI # 578  \n",
       "5   1989-10-21   ODI # 578  \n",
       "6   1994-10-23   ODI # 936  \n",
       "7   1994-10-23   ODI # 936  \n",
       "8   1996-03-11  ODI # 1080  \n",
       "9   1996-03-11  ODI # 1080  \n",
       "10  1997-05-21  ODI # 1209  \n",
       "11  1997-05-21  ODI # 1209  \n",
       "12  1998-05-23  ODI # 1333  \n",
       "13  1998-05-23  ODI # 1333  \n",
       "14  2002-01-25  ODI # 1795  \n",
       "15  2002-01-25  ODI # 1795  \n",
       "16  2003-10-23  ODI # 2051  \n",
       "17  2003-10-23  ODI # 2051  \n",
       "18  2007-01-27  ODI # 2487  \n",
       "19  2007-01-27  ODI # 2487  \n",
       "20  2007-06-09  ODI # 2588  \n",
       "21  2007-06-09  ODI # 2588  \n",
       "22  2007-06-10  ODI # 2589  \n",
       "23  2007-06-10  ODI # 2589  \n",
       "24  2010-12-10  ODI # 3077  \n",
       "25  2010-12-10  ODI # 3077  \n",
       "26  2011-02-20  ODI # 3101  \n",
       "27  2011-02-20  ODI # 3101  \n",
       "28  2011-03-06  ODI # 3120  \n",
       "29  2011-03-06  ODI # 3120  \n",
       "30  2011-03-17  ODI # 3135  \n",
       "31  2011-03-17  ODI # 3135  \n",
       "32  2011-03-20  ODI # 3141  \n",
       "33  2011-03-20  ODI # 3141  \n",
       "34  2011-12-11  ODI # 3224  \n",
       "35  2011-12-11  ODI # 3224  \n",
       "36  2012-12-30  ODI # 3314  \n",
       "37  2012-12-30  ODI # 3314  \n",
       "38  2015-10-22  ODI # 3698  \n",
       "39  2015-10-22  ODI # 3698  \n",
       "40  2017-09-17  ODI # 3910  \n",
       "41  2017-09-17  ODI # 3910  \n",
       "42  2019-12-15  ODI # 4221  \n",
       "43  2019-12-15  ODI # 4221  \n",
       "44  2023-03-22  ODI # 4545  \n",
       "45  2023-03-22  ODI # 4545  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_df = pd.read_csv(\"C:/Users/relat/Downloads/Ground_Stats.csv\")\n",
    "ground_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d057f563",
   "metadata": {},
   "source": [
    "This contains all the matches ever played on the Chennai pitch. Original data source is - https://stats.espncricinfo.com/ci/engine/ground/58008.html?class=2;template=results;type=aggregate;view=innings . I extracted the data from the site using excel and converted to dataframe. ***For original solution I did the calculation from the website table itself and didn't extracted to excel or dataframe format.***\n",
    "\n",
    "***This data is used to calculate the average scores for both teams to get an idea of the total runs they can score in the match. I will be taking only the first innings scores as these are more important because the team can score as many runs playing first but in 2nd innings the runs are constrained to only how much runs the opponent team made so will not give an accurate estimate. Also matches with no result are excluded from the count.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d5ba1ceb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Score</th>\n",
       "      <th>Overs</th>\n",
       "      <th>Target</th>\n",
       "      <th>RPO</th>\n",
       "      <th>Inns</th>\n",
       "      <th>Result</th>\n",
       "      <th>Opposition</th>\n",
       "      <th>Match date</th>\n",
       "      <th>Match ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>India</td>\n",
       "      <td>268</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.58</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>2007-01-27</td>\n",
       "      <td>ODI # 2487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>India</td>\n",
       "      <td>268</td>\n",
       "      <td>49.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.45</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>2011-03-20</td>\n",
       "      <td>ODI # 3141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>India</td>\n",
       "      <td>267/6</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.34</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>2011-12-11</td>\n",
       "      <td>ODI # 3224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>India</td>\n",
       "      <td>227/6</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.54</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "      <td>v Pakistan</td>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>ODI # 3314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>India</td>\n",
       "      <td>299/8</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.98</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v South Africa</td>\n",
       "      <td>2015-10-22</td>\n",
       "      <td>ODI # 3698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>India</td>\n",
       "      <td>281/7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.62</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v Australia</td>\n",
       "      <td>2017-09-17</td>\n",
       "      <td>ODI # 3910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>India</td>\n",
       "      <td>287/8</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.74</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>2019-12-15</td>\n",
       "      <td>ODI # 4221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Team  Score  Overs  Target   RPO  Inns Result      Opposition  \\\n",
       "18  India    268   48.0     NaN  5.58     1   lost   v West Indies   \n",
       "32  India    268   49.1     NaN  5.45     1    won   v West Indies   \n",
       "34  India  267/6   50.0     NaN  5.34     1    won   v West Indies   \n",
       "36  India  227/6   50.0     NaN  4.54     1   lost      v Pakistan   \n",
       "38  India  299/8   50.0     NaN  5.98     1    won  v South Africa   \n",
       "40  India  281/7   50.0     NaN  5.62     1    won     v Australia   \n",
       "42  India  287/8   50.0     NaN  5.74     1   lost   v West Indies   \n",
       "\n",
       "    Match date    Match ID  \n",
       "18  2007-01-27  ODI # 2487  \n",
       "32  2011-03-20  ODI # 3141  \n",
       "34  2011-12-11  ODI # 3224  \n",
       "36  2012-12-30  ODI # 3314  \n",
       "38  2015-10-22  ODI # 3698  \n",
       "40  2017-09-17  ODI # 3910  \n",
       "42  2019-12-15  ODI # 4221  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All 1st innings scores for India in Chennai excluding no result matches\n",
    "ind_chn = ground_df[(ground_df['Inns']==1)&(ground_df['Team']=='India')&(ground_df['Result']!='n/r')]\n",
    "ind_chn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a6d13026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "271.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_chn.Score.apply(lambda x: x.split('/')[0]).astype('int').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a830e188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Score</th>\n",
       "      <th>Overs</th>\n",
       "      <th>Target</th>\n",
       "      <th>RPO</th>\n",
       "      <th>Inns</th>\n",
       "      <th>Result</th>\n",
       "      <th>Opposition</th>\n",
       "      <th>Match date</th>\n",
       "      <th>Match ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>270/6</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.40</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v India</td>\n",
       "      <td>1987-10-09</td>\n",
       "      <td>ODI # 453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Australia</td>\n",
       "      <td>235/9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.70</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v Zimbabwe</td>\n",
       "      <td>1987-10-13</td>\n",
       "      <td>ODI # 456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>241/6</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.82</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>1989-10-21</td>\n",
       "      <td>ODI # 578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Australia</td>\n",
       "      <td>269</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.48</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v India</td>\n",
       "      <td>2023-03-22</td>\n",
       "      <td>ODI # 4545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Team  Score  Overs  Target   RPO  Inns Result     Opposition  \\\n",
       "0   Australia  270/6   50.0     NaN  5.40     1    won        v India   \n",
       "2   Australia  235/9   50.0     NaN  4.70     1    won     v Zimbabwe   \n",
       "4   Australia  241/6   50.0     NaN  4.82     1    won  v West Indies   \n",
       "44  Australia    269   49.0     NaN  5.48     1    won        v India   \n",
       "\n",
       "    Match date    Match ID  \n",
       "0   1987-10-09   ODI # 453  \n",
       "2   1987-10-13   ODI # 456  \n",
       "4   1989-10-21   ODI # 578  \n",
       "44  2023-03-22  ODI # 4545  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All 1st innings scores for Australia in Chennai excluding no result matches\n",
    "aus_chn = ground_df[(ground_df['Inns']==1)&(ground_df['Team']=='Australia')&(ground_df['Result']!='n/r')]\n",
    "aus_chn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "65548d41",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "253.75"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aus_chn.Score.apply(lambda x: x.split('/')[0]).astype('int').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff698584",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "83abf24e",
   "metadata": {
    "id": "83abf24e"
   },
   "source": [
    "## Model: Weighted average by date"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a7c5ba",
   "metadata": {
    "id": "15a7c5ba"
   },
   "source": [
    "This model assigns a weight to every individual match_date of each player, with higher weight given to recent matches and lower weight given to older matches. This model tries to capture the form of the player as the runs he scored 5 or 10 years ago don't matter much and the his recent performance depicts his form and he is more likely to perform likewise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0f4ae466",
   "metadata": {
    "id": "0f4ae466",
    "outputId": "35b26e0d-1222-4c49-f33e-867ad8da6231",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>runs_scored</th>\n",
       "      <th>wickets</th>\n",
       "      <th>runs_conceded</th>\n",
       "      <th>catches</th>\n",
       "      <th>stumpings</th>\n",
       "      <th>match_date</th>\n",
       "      <th>opposition</th>\n",
       "      <th>match_id</th>\n",
       "      <th>ground</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Pat Cummins</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-10-19</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>1</td>\n",
       "      <td>Centurion</td>\n",
       "      <td>0.012987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Pat Cummins</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-10-23</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2</td>\n",
       "      <td>Gqeberha</td>\n",
       "      <td>0.025974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>Pat Cummins</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-10-28</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>3</td>\n",
       "      <td>Durban</td>\n",
       "      <td>0.038961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>Pat Cummins</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-06-23</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>4</td>\n",
       "      <td>Belfast</td>\n",
       "      <td>0.051948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>Pat Cummins</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-06-29</td>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>Lord's</td>\n",
       "      <td>0.064935</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>1</td>\n",
       "      <td>Pat Cummins</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-06-24</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>73</td>\n",
       "      <td>(RPS)</td>\n",
       "      <td>0.948052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>1</td>\n",
       "      <td>Pat Cummins</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-11-17</td>\n",
       "      <td>England</td>\n",
       "      <td>74</td>\n",
       "      <td>Adelaide</td>\n",
       "      <td>0.961039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>1</td>\n",
       "      <td>Pat Cummins</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-11-22</td>\n",
       "      <td>England</td>\n",
       "      <td>75</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>0.974026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>1</td>\n",
       "      <td>Pat Cummins</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2023-09-22</td>\n",
       "      <td>India</td>\n",
       "      <td>76</td>\n",
       "      <td>Mohali</td>\n",
       "      <td>0.987013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>1</td>\n",
       "      <td>Pat Cummins</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2023-09-27</td>\n",
       "      <td>India</td>\n",
       "      <td>77</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     player_id  player_name  runs_scored  wickets runs_conceded catches  \\\n",
       "0            1  Pat Cummins           -1        3            28       0   \n",
       "4            1  Pat Cummins           11        1            73       0   \n",
       "7            1  Pat Cummins            6        1            49       0   \n",
       "9            1  Pat Cummins           -1        1            11       0   \n",
       "12           1  Pat Cummins            4        1            53       0   \n",
       "..         ...          ...          ...      ...           ...     ...   \n",
       "582          1  Pat Cummins           -1        2            22       1   \n",
       "591          1  Pat Cummins           -1        3            62       0   \n",
       "601          1  Pat Cummins           -1        2            25       1   \n",
       "612          1  Pat Cummins           21        1            44       0   \n",
       "632          1  Pat Cummins           19        1            59       1   \n",
       "\n",
       "     stumpings match_date    opposition  match_id     ground    weight  \n",
       "0            0 2011-10-19  South Africa         1  Centurion  0.012987  \n",
       "4            0 2011-10-23  South Africa         2   Gqeberha  0.025974  \n",
       "7            0 2011-10-28  South Africa         3     Durban  0.038961  \n",
       "9            0 2012-06-23       Ireland         4    Belfast  0.051948  \n",
       "12           0 2012-06-29       England         5     Lord's  0.064935  \n",
       "..         ...        ...           ...       ...        ...       ...  \n",
       "582          0 2022-06-24     Sri Lanka        73      (RPS)  0.948052  \n",
       "591          0 2022-11-17       England        74   Adelaide  0.961039  \n",
       "601          0 2022-11-22       England        75  Melbourne  0.974026  \n",
       "612          0 2023-09-22         India        76     Mohali  0.987013  \n",
       "632          0 2023-09-27         India        77     Rajkot  1.000000  \n",
       "\n",
       "[77 rows x 12 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights_df = train[train.player_id==1].sort_values('match_date')\n",
    "weights_df['weight'] = [i/len(weights_df) for i in range(1,len(weights_df)+1)]\n",
    "weights_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f3750d9",
   "metadata": {},
   "source": [
    "As seen in above case the weights for the last match played by the player is 1 and keeps on decreasing for older matches. This is calculated as the inverse of the total matches played by the player multiplied by the match number for the specific row. In the above case total matches are 77(n) and each row number from the start (numbered from 1 to n) is multiplied by (1/77) to calclulate the weights for a particular match. After this the weighted average runs and wickets are calculated from the following formulae:\n",
    "$$Wtd\\;avg\\;runs = \\frac{\\sum_{i=1}^n runs_i*wts_i}{\\sum_{i=1}^n wts_i}$$\n",
    "Similarly for wickets\n",
    "$$Wtd\\;avg\\;wickets = \\frac{\\sum_{i=1}^n wickets_i*wts_i}{\\sum_{i=1}^n wts_i}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a13e9ced",
   "metadata": {
    "id": "a13e9ced"
   },
   "outputs": [],
   "source": [
    "# Functions to calculate weighted average runs and weighted average wickets\n",
    "def weighted_mean_runs(df):\n",
    "    return np.sum(df.runs_scored*df.weight)/np.sum(df.weight)\n",
    "def weighted_mean_wickets(df):\n",
    "    return np.sum(df.wickets*df.weight)/np.sum(df.weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "435427bf",
   "metadata": {
    "id": "435427bf"
   },
   "outputs": [],
   "source": [
    "# Making weighted predictions for all players and storing in dataframe\n",
    "def make_wtd_predictions(df):\n",
    "    runs = []\n",
    "    wickets = []\n",
    "    for i in range(1,31):\n",
    "        weights_df = df[df.player_id==i].sort_values('match_date')\n",
    "        weights_df['weight'] = [j/len(weights_df) for j in range(1,len(weights_df)+1)]\n",
    "        \n",
    "        # Excluding DNB and TDNB runs scored which are given by -1\n",
    "        weighted_runs = weighted_mean_runs(weights_df[(weights_df.player_id==i)&(weights_df.runs_scored!=-1)])\n",
    "        runs.append(weighted_runs)\n",
    "\n",
    "        weighted_wickets = weighted_mean_wickets(weights_df[weights_df.player_id==i])\n",
    "        wickets.append(weighted_wickets)\n",
    "\n",
    "    sub_df = sub.copy()\n",
    "    sub_df['runs'] = runs\n",
    "    sub_df['wickets'] = wickets\n",
    "    sub_df['name'] = id_name['player_name']\n",
    "    sub_df = sub_df[['player_id', 'name', 'runs', 'wickets']]\n",
    "    return sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aef48b21",
   "metadata": {
    "id": "aef48b21",
    "outputId": "f5f8f2c3-39f1-48ae-9b6f-59658fd08c62",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_id</th>\n",
       "      <th>name</th>\n",
       "      <th>runs</th>\n",
       "      <th>wickets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Pat Cummins</td>\n",
       "      <td>8.047170</td>\n",
       "      <td>1.607060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Steve Smith</td>\n",
       "      <td>42.301579</td>\n",
       "      <td>0.051488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Alex Carey</td>\n",
       "      <td>27.164066</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Josh Inglis</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Sean Abbott</td>\n",
       "      <td>17.656250</td>\n",
       "      <td>1.241830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Marnus Labuschagne</td>\n",
       "      <td>35.827004</td>\n",
       "      <td>0.045884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Cameron Green</td>\n",
       "      <td>20.910112</td>\n",
       "      <td>0.904762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Josh Hazlewood</td>\n",
       "      <td>5.553525</td>\n",
       "      <td>1.541982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Travis Head</td>\n",
       "      <td>40.410984</td>\n",
       "      <td>0.219755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Mitch Marsh</td>\n",
       "      <td>30.564077</td>\n",
       "      <td>0.579114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Glenn Maxwell</td>\n",
       "      <td>30.087637</td>\n",
       "      <td>0.455218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Marcus Stoinis</td>\n",
       "      <td>18.793085</td>\n",
       "      <td>0.643269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>David Warner</td>\n",
       "      <td>46.413962</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Adam Zampa</td>\n",
       "      <td>6.147770</td>\n",
       "      <td>1.824624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Mitchell Starc.</td>\n",
       "      <td>7.225847</td>\n",
       "      <td>2.004505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Rohit Sharma</td>\n",
       "      <td>47.469936</td>\n",
       "      <td>0.014134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Hardik Pandya</td>\n",
       "      <td>30.524760</td>\n",
       "      <td>0.945930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Shubman Gill</td>\n",
       "      <td>57.796825</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Virat Kohli</td>\n",
       "      <td>51.058276</td>\n",
       "      <td>0.009692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Shreyas Iyer</td>\n",
       "      <td>42.948464</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>41.368480</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Ravindra Jadeja</td>\n",
       "      <td>19.904404</td>\n",
       "      <td>1.064746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Shardul Thakur</td>\n",
       "      <td>11.617329</td>\n",
       "      <td>1.445455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Jasprit Bumrah</td>\n",
       "      <td>3.840580</td>\n",
       "      <td>1.578384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Mohammed Siraj</td>\n",
       "      <td>2.735849</td>\n",
       "      <td>1.963441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>Kuldeep Yadav</td>\n",
       "      <td>4.919484</td>\n",
       "      <td>1.614164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>5.076816</td>\n",
       "      <td>1.843001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>Ravichandran Ashwin</td>\n",
       "      <td>10.524539</td>\n",
       "      <td>1.304798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>Ishan Kishan</td>\n",
       "      <td>38.819149</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>Suryakumar Yadav</td>\n",
       "      <td>22.216895</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    player_id                 name       runs   wickets\n",
       "0           1          Pat Cummins   8.047170  1.607060\n",
       "1           2          Steve Smith  42.301579  0.051488\n",
       "2           3           Alex Carey  27.164066  0.000000\n",
       "3           4          Josh Inglis  20.000000  0.000000\n",
       "4           5          Sean Abbott  17.656250  1.241830\n",
       "5           6   Marnus Labuschagne  35.827004  0.045884\n",
       "6           7        Cameron Green  20.910112  0.904762\n",
       "7           8       Josh Hazlewood   5.553525  1.541982\n",
       "8           9          Travis Head  40.410984  0.219755\n",
       "9          10          Mitch Marsh  30.564077  0.579114\n",
       "10         11        Glenn Maxwell  30.087637  0.455218\n",
       "11         12       Marcus Stoinis  18.793085  0.643269\n",
       "12         13         David Warner  46.413962  0.000000\n",
       "13         14           Adam Zampa   6.147770  1.824624\n",
       "14         15      Mitchell Starc.   7.225847  2.004505\n",
       "15         16         Rohit Sharma  47.469936  0.014134\n",
       "16         17        Hardik Pandya  30.524760  0.945930\n",
       "17         18         Shubman Gill  57.796825  0.000000\n",
       "18         19          Virat Kohli  51.058276  0.009692\n",
       "19         20         Shreyas Iyer  42.948464  0.000000\n",
       "20         21             KL Rahul  41.368480  0.000000\n",
       "21         22      Ravindra Jadeja  19.904404  1.064746\n",
       "22         23       Shardul Thakur  11.617329  1.445455\n",
       "23         24       Jasprit Bumrah   3.840580  1.578384\n",
       "24         25       Mohammed Siraj   2.735849  1.963441\n",
       "25         26        Kuldeep Yadav   4.919484  1.614164\n",
       "26         27       Mohammed Shami   5.076816  1.843001\n",
       "27         28  Ravichandran Ashwin  10.524539  1.304798\n",
       "28         29         Ishan Kishan  38.819149  0.000000\n",
       "29         30     Suryakumar Yadav  22.216895  0.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions with weighted average model\n",
    "sub = make_wtd_predictions(train)\n",
    "sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b9d389d4",
   "metadata": {
    "id": "c7810ae4",
    "outputId": "c002bf6d-5546-4cc6-b63a-0204a4468bd9"
   },
   "outputs": [],
   "source": [
    "# This function calculates the total runs and wickets by top 11 batsman and bowlers for both teams\n",
    "def sanity_check(df):\n",
    "    # Making sure the sum of scores of best 11 batsmen for AUS is around 250-300 range and wickets should not exceed 10\n",
    "    print(\"Total score for AUS by top 11 batsman:\", df[:15].sort_values('runs', ascending=False)[:11].runs.sum())\n",
    "    print(\"Total wickets taken by AUS top 11 bowlers:\", df[:15].sort_values('wickets', ascending=False)[:11].wickets.sum())\n",
    "    # Making sure the sum of scores of best 11 batsmen for IND is around 250-300 range and wickets should not exceed 10\n",
    "    print(\"Total score for IND by top 11 batsman:\", df[15:].sort_values('runs', ascending=False)[:11].runs.sum())\n",
    "    print(\"Total wickets taken by IND top 11 bowlers:\", df[15:].sort_values('wickets', ascending=False)[:11].wickets.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7f5f48",
   "metadata": {},
   "source": [
    "The sanity_check function above gives an idea about the predicted runs and wickets to decide whether these need to be balanced out later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "25fefa88",
   "metadata": {
    "id": "25fefa88",
    "outputId": "82ad7881-944f-45bd-f95b-0e6dca193b14"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total score for AUS by top 11 batsman: 330.1287559651316\n",
      "Total wickets taken by AUS top 11 bowlers: 11.073605156231114\n",
      "Total score for IND by top 11 batsman: 374.2490567534829\n",
      "Total wickets taken by IND top 11 bowlers: 11.78374334781467\n"
     ]
    }
   ],
   "source": [
    "sanity_check(sub)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90ba3dff",
   "metadata": {},
   "source": [
    "These scores seem to be far ahead than the normal range. The function considers the scores and wickets of the top 11 batsmen and top 11 bowlers. This would not be the case in real as not all batsman and all bowlers are playing(there would be a mix of both) therefore these would automatically drop down when we consider the selected 11 players from each team."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8769419e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f8d53c5a",
   "metadata": {},
   "source": [
    "## Final teams\n",
    "Based on the [inferences section](#Making-inferences-from-data) above I came to the conclusion about the potential players who can be playing in the match based on their past records. Australian team include 13 players but we have to drop two of them to make correct predictions. There is a choice between Cameron Green, Travis Head and Marcus Stoinis as other 10 players selected are either experienced or very good players according to historical data (rest of the players like Sean Abbott etc. don't have good historical records). Past records are more in favor of Travis Head as he has more runs and better average than both Cameron Green and Stoinis. But as I was following the news about the match I found out that Travis Head would likely not be playing because of his injury, so this leaves Stoinis and Green. As the model is predicting Green(20.91 runs, 0.90 wickets) to perform better in both batting as well as bowling than Stoinis(18.79 runs, 0.64 wickets), I will select Cameron Green.\n",
    "\n",
    "For Indian team I shortlisted 13 of the 15 available players above who are likely to be playing based on the analysis above. The team contain 4 fast bowlers and 3 spinners. The pitch is more likely to favor spinners based on historical data therefore, of the 4 fast bowlers one can be dropped. Hardik Pandya is all rounder and can't be dropped, Jasprit Bumrah's stats are the best among all so he is the best option. The choice now boils down between Mohammed Shami or Mohammed Siraj. This is a tough choice. As the weighted average model, which give more weight to the recent matches, is predicting Siraj to perform better(Siraj-1.96 wickets, Shami-1.84 wickets) then he must be in better form than Shami. Therefore I will be picking Siraj over Shami. For the last choice Ishan Kishan can be dropped as KL Rahul fulfils his role as wicketkeeper batsman and has a much better track record. But there is news of Shubman Gill also not playing as he is sick so only one of Ishan Kishan and Gill needs to be dropped. As the odds are against Gill who would likely not be playing which leaves Surya KumarYadav as a choice to consider but as far as my knowledge about cricket goes he is not good option because of his batting order. India would likely be in need of top order batsman if Shubman Gill doesn't play and Ishan Kishan fulfils that role. My model also is in favor of Ishan Kishan(38.81 runs) as his predicted runs are greater than Surya's(22.21 runs). so I will go with Ishan Kishan over Surya.\n",
    "\n",
    "This leads to the potential playing XI of both teams which is given below:\n",
    "\n",
    "- **AUS potential playing XI** - 1:Pat Cummins, 2:Steve Smith, 3:Alex Carey, 6:Marnus Labuschagne, 7:Cameron Green, 8:Josh Hazlewood, 10:Mitch Marsh, 11:Glenn Maxwell, 13:David Warner, 14:Adam Zampa, 15:Mitchell Starc\n",
    "\n",
    "- **IND potential playing XI** - 16:Rohit Sharma, 17:Hardik Pandya, 19:Virat Kohli, 20:Shreyas Iyer, 21:KL Rahul, 22:Ravindra Jadeja, 24:Jasprit Bumrah, 25:Mohammed Siraj, 26:Kuldeep Yadav, 28:Ravichandran Ashwin, 29:Ishan Kishan\n",
    "\n",
    "For the excluded players we don't have to include the predictions and in the post processing step we can change these predictions to zeros. \n",
    "\n",
    "- **Excluded Players**: 4: Josh Inglis, 5: Sean Abbott, 9: Travis Head, 12: Marcus Stoinis, 18: Shubman Gill, 23: Shardul Thakur, 27: Mohammed Shami, 30: Suryakumar Yadav\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8760a1c",
   "metadata": {
    "id": "b8760a1c"
   },
   "source": [
    "## Post processing and Final predictions\n",
    "In this section, I will process the predictions from the weighted average model, calculated above, one final time depending on the playing 11 of both teams. Therefore only 22 columns will contain non-zero values and the 8 columns for players not playing will be zero. Also multiplying final predictions with a final weight score to balance the total score. The weight score is calculated from the historical performance of India on the Chennai pitch as calculated in [ground analysis section](#Additional-ground-data) above. This is also explained below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8a929794",
   "metadata": {
    "id": "8a929794",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# ids of players likely to be excluded\n",
    "zero_id = [4, 5, 9, 12, 18, 23, 27, 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ffed688a",
   "metadata": {
    "id": "ffed688a",
    "outputId": "7f1d2651-7cf7-43ed-aff2-2aca9f3f454f"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_id</th>\n",
       "      <th>runs</th>\n",
       "      <th>wickets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8.047170</td>\n",
       "      <td>1.607060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>42.301579</td>\n",
       "      <td>0.051488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>27.164066</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>35.827004</td>\n",
       "      <td>0.045884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>20.910112</td>\n",
       "      <td>0.904762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>5.553525</td>\n",
       "      <td>1.541982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>30.564077</td>\n",
       "      <td>0.579114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>30.087637</td>\n",
       "      <td>0.455218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>46.413962</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>6.147770</td>\n",
       "      <td>1.824624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>7.225847</td>\n",
       "      <td>2.004505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>47.469936</td>\n",
       "      <td>0.014134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>30.524760</td>\n",
       "      <td>0.945930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>51.058276</td>\n",
       "      <td>0.009692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>42.948464</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>41.368480</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>19.904404</td>\n",
       "      <td>1.064746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>3.840580</td>\n",
       "      <td>1.578384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>2.735849</td>\n",
       "      <td>1.963441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>4.919484</td>\n",
       "      <td>1.614164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>10.524539</td>\n",
       "      <td>1.304798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>38.819149</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    player_id       runs   wickets\n",
       "0           1   8.047170  1.607060\n",
       "1           2  42.301579  0.051488\n",
       "2           3  27.164066  0.000000\n",
       "3           4   0.000000  0.000000\n",
       "4           5   0.000000  0.000000\n",
       "5           6  35.827004  0.045884\n",
       "6           7  20.910112  0.904762\n",
       "7           8   5.553525  1.541982\n",
       "8           9   0.000000  0.000000\n",
       "9          10  30.564077  0.579114\n",
       "10         11  30.087637  0.455218\n",
       "11         12   0.000000  0.000000\n",
       "12         13  46.413962  0.000000\n",
       "13         14   6.147770  1.824624\n",
       "14         15   7.225847  2.004505\n",
       "15         16  47.469936  0.014134\n",
       "16         17  30.524760  0.945930\n",
       "17         18   0.000000  0.000000\n",
       "18         19  51.058276  0.009692\n",
       "19         20  42.948464  0.000000\n",
       "20         21  41.368480  0.000000\n",
       "21         22  19.904404  1.064746\n",
       "22         23   0.000000  0.000000\n",
       "23         24   3.840580  1.578384\n",
       "24         25   2.735849  1.963441\n",
       "25         26   4.919484  1.614164\n",
       "26         27   0.000000  0.000000\n",
       "27         28  10.524539  1.304798\n",
       "28         29  38.819149  0.000000\n",
       "29         30   0.000000  0.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make columns zero for players not playing\n",
    "sub.drop('name', axis=1, inplace=True)\n",
    "for i in range(1, 31):\n",
    "    if i in zero_id:\n",
    "        sub.iloc[i-1,1:] = [0, 0]\n",
    "sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "88e78df9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total score for AUS by top 11 batsman: 260.2427496120536\n",
      "Total wickets taken by AUS top 11 bowlers: 9.014635271208288\n",
      "Total score for IND by top 11 batsman: 294.1139203387357\n",
      "Total wickets taken by IND top 11 bowlers: 8.495287682539297\n"
     ]
    }
   ],
   "source": [
    "sanity_check(sub)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad704ad",
   "metadata": {},
   "source": [
    "***Australia's predicted total came out to be around 260 from the model. The average score for Australia as calculated from historical ground data was around 253.75. As these scores are close there is no need for processing.***\n",
    "\n",
    "***India's predicted total score came out to be around 294 from the model. The average score for India as calculated from historical ground data was around 271. The predicted score looks a little off from the average so I introduced a weight factor which is (India's avg score in Chennai/Total predicted score from model) and multiplied it to the predicted runs of Indian team.\n",
    "As I calculated average score in Chennai by hand for the original solution directly from the website table I was getting 270.75 which is shown below. This happened because I accidentally also included the 1st entry which was 2nd Innings score for India and average becomes (269+268+268+267+227+299+281+287)/8 = 270.75. This won't make much of a difference in the final predictions though.***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0fc3e445",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Team</th>\n",
       "      <th>Score</th>\n",
       "      <th>Overs</th>\n",
       "      <th>Target</th>\n",
       "      <th>RPO</th>\n",
       "      <th>Inns</th>\n",
       "      <th>Result</th>\n",
       "      <th>Opposition</th>\n",
       "      <th>Match date</th>\n",
       "      <th>Match ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>269</td>\n",
       "      <td>49.5</td>\n",
       "      <td>271.0</td>\n",
       "      <td>5.39</td>\n",
       "      <td>2</td>\n",
       "      <td>lost</td>\n",
       "      <td>v Australia</td>\n",
       "      <td>1987-10-09</td>\n",
       "      <td>ODI # 453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>India</td>\n",
       "      <td>268</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.58</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>2007-01-27</td>\n",
       "      <td>ODI # 2487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>India</td>\n",
       "      <td>268</td>\n",
       "      <td>49.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.45</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>2011-03-20</td>\n",
       "      <td>ODI # 3141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>India</td>\n",
       "      <td>267/6</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.34</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>2011-12-11</td>\n",
       "      <td>ODI # 3224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>India</td>\n",
       "      <td>227/6</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.54</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "      <td>v Pakistan</td>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>ODI # 3314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>India</td>\n",
       "      <td>299/8</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.98</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v South Africa</td>\n",
       "      <td>2015-10-22</td>\n",
       "      <td>ODI # 3698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>India</td>\n",
       "      <td>281/7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.62</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>v Australia</td>\n",
       "      <td>2017-09-17</td>\n",
       "      <td>ODI # 3910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>India</td>\n",
       "      <td>287/8</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.74</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "      <td>v West Indies</td>\n",
       "      <td>2019-12-15</td>\n",
       "      <td>ODI # 4221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Team  Score  Overs  Target   RPO  Inns Result      Opposition  \\\n",
       "1   India    269   49.5   271.0  5.39     2   lost     v Australia   \n",
       "18  India    268   48.0     NaN  5.58     1   lost   v West Indies   \n",
       "32  India    268   49.1     NaN  5.45     1    won   v West Indies   \n",
       "34  India  267/6   50.0     NaN  5.34     1    won   v West Indies   \n",
       "36  India  227/6   50.0     NaN  4.54     1   lost      v Pakistan   \n",
       "38  India  299/8   50.0     NaN  5.98     1    won  v South Africa   \n",
       "40  India  281/7   50.0     NaN  5.62     1    won     v Australia   \n",
       "42  India  287/8   50.0     NaN  5.74     1   lost   v West Indies   \n",
       "\n",
       "    Match date    Match ID  \n",
       "1   1987-10-09   ODI # 453  \n",
       "18  2007-01-27  ODI # 2487  \n",
       "32  2011-03-20  ODI # 3141  \n",
       "34  2011-12-11  ODI # 3224  \n",
       "36  2012-12-30  ODI # 3314  \n",
       "38  2015-10-22  ODI # 3698  \n",
       "40  2017-09-17  ODI # 3910  \n",
       "42  2019-12-15  ODI # 4221  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_df[(ground_df['Inns']==1)&(ground_df['Team']=='India')&(ground_df['Result']!='n/r')|(ground_df.index==1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd1d935",
   "metadata": {},
   "source": [
    "***I took the integer values of 294.11 -> 294 and 270.75 -> 270. This is why I multiplied the predicted runs for India by 270/294 in the original solution to balance out the predictions which can also be seen below.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d80d5415",
   "metadata": {
    "id": "d80d5415",
    "outputId": "bf31afa8-35a6-4b27-ddf4-847b21f5f002",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Balancing predictions by multiplying by weight factor\n",
    "sub.runs[15:] = sub.runs[15:]*270/294"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9f028285",
   "metadata": {
    "id": "9f028285",
    "outputId": "9b9959cf-367a-48b9-cf00-d27bede5bd77",
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>42.301579</td>\n",
       "      <td>0.051488</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>35.827004</td>\n",
       "      <td>0.045884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>20.910112</td>\n",
       "      <td>0.904762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>5.553525</td>\n",
       "      <td>1.541982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>30.564077</td>\n",
       "      <td>0.579114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>30.087637</td>\n",
       "      <td>0.455218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>46.413962</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>6.147770</td>\n",
       "      <td>1.824624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>7.225847</td>\n",
       "      <td>2.004505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>43.594839</td>\n",
       "      <td>0.014134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>28.032943</td>\n",
       "      <td>0.945930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>46.890253</td>\n",
       "      <td>0.009692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>39.442467</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>37.991461</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>18.279555</td>\n",
       "      <td>1.064746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>3.527063</td>\n",
       "      <td>1.578384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>2.512514</td>\n",
       "      <td>1.963441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>4.517893</td>\n",
       "      <td>1.614164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>9.665393</td>\n",
       "      <td>1.304798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>35.650239</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    player_id       runs   wickets\n",
       "0           1   8.047170  1.607060\n",
       "1           2  42.301579  0.051488\n",
       "2           3  27.164066  0.000000\n",
       "3           4   0.000000  0.000000\n",
       "4           5   0.000000  0.000000\n",
       "5           6  35.827004  0.045884\n",
       "6           7  20.910112  0.904762\n",
       "7           8   5.553525  1.541982\n",
       "8           9   0.000000  0.000000\n",
       "9          10  30.564077  0.579114\n",
       "10         11  30.087637  0.455218\n",
       "11         12   0.000000  0.000000\n",
       "12         13  46.413962  0.000000\n",
       "13         14   6.147770  1.824624\n",
       "14         15   7.225847  2.004505\n",
       "15         16  43.594839  0.014134\n",
       "16         17  28.032943  0.945930\n",
       "17         18   0.000000  0.000000\n",
       "18         19  46.890253  0.009692\n",
       "19         20  39.442467  0.000000\n",
       "20         21  37.991461  0.000000\n",
       "21         22  18.279555  1.064746\n",
       "22         23   0.000000  0.000000\n",
       "23         24   3.527063  1.578384\n",
       "24         25   2.512514  1.963441\n",
       "25         26   4.517893  1.614164\n",
       "26         27   0.000000  0.000000\n",
       "27         28   9.665393  1.304798\n",
       "28         29  35.650239  0.000000\n",
       "29         30   0.000000  0.000000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3261f893",
   "metadata": {
    "id": "b2d1a952"
   },
   "source": [
    "## Final Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6bfc82cd",
   "metadata": {
    "id": "2f5a104e"
   },
   "outputs": [],
   "source": [
    "sub.to_csv(\"submission.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eaace43",
   "metadata": {},
   "source": [
    "These are the exact same predictions as generated by the original code file as well as that were submitted as part of the final solution to the competition. Steps taken for verification of the same:\n",
    "- Went to \"My Submissions\" tab in the Sledge Hack Hackathon and downloaded the Final predictions solution file\n",
    "- Saved the file as last_predictions.csv locally and compared that with this final submission as well as the final predictions generated from the [original](https://github.com/nikhil-1e9/AV-Hackathon/blob/main/AV_match_prediction.ipynb) code.\n",
    "\n",
    "The comparison is shown below:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b4fcb4",
   "metadata": {},
   "source": [
    "## Comparison of all 3 files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0989645f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# av_sub = pd.read_csv(\"last_predictions.csv\")\n",
    "# origin_sub = pd.read_csv(\"match_preds_final.csv\")\n",
    "# sub = pd.read_csv(\"submission.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e7da75",
   "metadata": {},
   "source": [
    "**av_sub** is the file downloaded from My Submissions, **origin_sub** is the file generated from [original](https://github.com/nikhil-1e9/AV-Hackathon/blob/main/AV_match_prediction.ipynb) and **sub** is generated in this notebook itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1bf7b847",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    player_id  runs  wickets\n",
       "0        True  True     True\n",
       "1        True  True     True\n",
       "2        True  True     True\n",
       "3        True  True     True\n",
       "4        True  True     True\n",
       "5        True  True     True\n",
       "6        True  True     True\n",
       "7        True  True     True\n",
       "8        True  True     True\n",
       "9        True  True     True\n",
       "10       True  True     True\n",
       "11       True  True     True\n",
       "12       True  True     True\n",
       "13       True  True     True\n",
       "14       True  True     True\n",
       "15       True  True     True\n",
       "16       True  True     True\n",
       "17       True  True     True\n",
       "18       True  True     True\n",
       "19       True  True     True\n",
       "20       True  True     True\n",
       "21       True  True     True\n",
       "22       True  True     True\n",
       "23       True  True     True\n",
       "24       True  True     True\n",
       "25       True  True     True\n",
       "26       True  True     True\n",
       "27       True  True     True\n",
       "28       True  True     True\n",
       "29       True  True     True"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# av_sub==origin_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e83b3ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id    30\n",
       "runs         30\n",
       "wickets      30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (av_sub == origin_sub).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "eef38a70",
   "metadata": {
    "id": "eef38a70"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id    30\n",
       "runs         30\n",
       "wickets      30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (av_sub == sub).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4c2f0a45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_id    30\n",
       "runs         30\n",
       "wickets      30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (sub==origin_sub).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4346bd0f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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