16 Oct.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from statistics import mode\n",
    "import math\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "matches = pd.read_csv('matches.csv')\n",
    "delivery = pd.read_csv('deliveries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "match_ids = matches[matches.season >= 2016]['id'].unique()\n",
    "matches1 = matches[(matches.season >= 2016) & (matches['result']=='normal')]\n",
    "matches1 = matches1.drop(['player_of_match','venue','umpire1','umpire2','umpire3', 'dl_applied', 'season', 'city', 'date', 'result'], axis=1)\n",
    "matches1.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "team_1 = 'Mumbai Indians'\n",
    "team_2 = 'Kolkata Knight Riders'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def team_correction(row):\n",
    "    row = row.strip()\n",
    "    if row == 'Delhi Daredevils':\n",
    "        return 'Delhi Capitals'\n",
    "    if row == 'Rising Pune Supergiants':\n",
    "        return 'Rising Pune Supergiant'\n",
    "    return row\n",
    "\n",
    "for col in ['team1', 'team2', 'winner', 'toss_winner']:\n",
    "    matches1[col] = matches1[col].apply(team_correction)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Winning Probability of Teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Percentage of Winning')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wins = pd.DataFrame(matches1[(matches1['team1'].isin([team_1, team_2])) & (matches1['team2'].isin([team_1, team_2]))]['winner'].value_counts())\n",
    "wins['Percentage Win Chance'] = (wins['winner']/sum(wins['winner']))*100\n",
    "sns.barplot(wins.index, y=wins['Percentage Win Chance'])\n",
    "plt.title('Percentage of Winning')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery = delivery[delivery.match_id.apply(lambda x: True if x in match_ids else False)]\n",
    "for col in ['batting_team', 'bowling_team']:\n",
    "    delivery[col] = delivery[col].apply(team_correction)\n",
    "\n",
    "delivery.fillna(0, inplace=True)\n",
    "delivery['player_dismissed'] = delivery['player_dismissed'].apply(lambda x: 0 if x==0 else 1)\n",
    "\n",
    "delivery['batsman_runs'] = delivery['batsman_runs'].apply(lambda x: x-1 if ((x==7) or (x==5)) else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicted wickets loss by MI between 6-15 overs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since, the wickets lossing probability is generaly less, we will consider value between minimum wickets lost and 25% quartile value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Wickets lost by MI in overs 6-15 is : 2.0\n"
     ]
    }
   ],
   "source": [
    "MI_wk = delivery[delivery['over'].isin(range(6,16))].groupby('match_id').sum()['player_dismissed']\n",
    "print(\"Predicted Wickets lost by MI in overs 6-15 is : {}\".format((MI_wk.min()+MI_wk.describe()[4])/2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicted No. of 4s by KKR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(61, 14.008196721311476, \"Average Number of 4's by KKR per match: 14\")"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kkr_fours = delivery[(delivery['batting_team'] == team_2) & (delivery['batsman_runs']==4)].groupby('match_id').sum()['batsman_runs']/4\n",
    "plt.figure(figsize=(10,8))\n",
    "kkr_fours.plot(kind='bar')\n",
    "plt.xticks([])\n",
    "plt.axhline(y=math.floor(kkr_fours.mean()), color='red')\n",
    "plt.text(x=61, y=kkr_fours.mean()-0.5, s=f\"Average Number of 4's by KKR per match: {math.floor(kkr_fours.mean())}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicted total scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average runs scored in today's match: 335.78688524590166\n"
     ]
    }
   ],
   "source": [
    "avg_score_mi = delivery[delivery['batting_team'].isin([team_1])].groupby('match_id').sum()['total_runs'].mean()\n",
    "avg_score_kkr = delivery[delivery['batting_team'].isin([team_2])].groupby('match_id').sum()['total_runs'].mean()\n",
    "print(\"Average runs scored in today's match:\", avg_score_kkr+avg_score_mi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RG Sharma dismissal probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Disimissal Kind')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sharma_dismmissal_kind = delivery[(delivery['batsman']=='RG Sharma') & (delivery['dismissal_kind']!=0)]['dismissal_kind'].value_counts()\n",
    "sharma_dismmissal_kind.plot(kind='bar')\n",
    "plt.title(\"RG Sharma vs Dismissal Kind\")\n",
    "plt.ylabel(\"Number of times RG Sharma dismissed\")\n",
    "plt.xlabel(\"Disimissal Kind\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Data was cleaned and filtered. The data from the year 2016 to 2019 was taken and all the non-required columns were dropped. Using the \"winner\" column of the dataset, the percentage chances of winning between MI and KKR was calculated. \n",
    "- Since the wickets losing probability is generally less, we considered value between minimum wickets lost and 25% quartile value of the number of lost wickets to estimate the wickets loss of MI during 6 to 15 overs.\n",
    "- The average number of 4s hit by KKR per match was considered to be an estimate of the number of 4s KKR will hit today.\n",
    "- The average scores made by both the teams per match against different teams is considered and summed to predict today's total score.\n",
    "- RG Sharma has more chances of getting dismissed by caught because previous matches the chances of getting caught was high as compared to others. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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