13 Oct.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "matches = pd.read_csv('matches.csv')\n",
    "delivery = pd.read_csv('deliveries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  season       city        date                        team1  \\\n",
       "0   1    2017  Hyderabad  2017-04-05          Sunrisers Hyderabad   \n",
       "1   2    2017       Pune  2017-04-06               Mumbai Indians   \n",
       "2   3    2017     Rajkot  2017-04-07                Gujarat Lions   \n",
       "3   4    2017     Indore  2017-04-08       Rising Pune Supergiant   \n",
       "4   5    2017  Bangalore  2017-04-08  Royal Challengers Bangalore   \n",
       "\n",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1       Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2        Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3              Kings XI Punjab              Kings XI Punjab         field   \n",
       "4             Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "\n",
       "   result  dl_applied                       winner  win_by_runs  \\\n",
       "0  normal           0          Sunrisers Hyderabad           35   \n",
       "1  normal           0       Rising Pune Supergiant            0   \n",
       "2  normal           0        Kolkata Knight Riders            0   \n",
       "3  normal           0              Kings XI Punjab            0   \n",
       "4  normal           0  Royal Challengers Bangalore           15   \n",
       "\n",
       "   win_by_wickets player_of_match                                      venue  \\\n",
       "0               0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "1               7       SPD Smith    Maharashtra Cricket Association Stadium   \n",
       "2              10         CA Lynn     Saurashtra Cricket Association Stadium   \n",
       "3               6      GJ Maxwell                     Holkar Cricket Stadium   \n",
       "4               0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "\n",
       "          umpire1        umpire2 umpire3  \n",
       "0     AY Dandekar       NJ Llong     NaN  \n",
       "1  A Nand Kishore         S Ravi     NaN  \n",
       "2     Nitin Menon      CK Nandan     NaN  \n",
       "3    AK Chaudhary  C Shamshuddin     NaN  \n",
       "4             NaN            NaN     NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "match_ids = matches[matches.season >= 2016]['id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "team_1 = 'Chennai Super Kings'\n",
    "team_2 = 'Sunrisers Hyderabad'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "## The plot below suggests that CSK will win the match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Percentage of Winning')"
      ]
     },
     "execution_count": 37,
     "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": 10,
   "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>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "1     2  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "2     3  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "3     4  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "4     5  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "1            0            0             0             0           0   \n",
       "2            0            0             0             4           0   \n",
       "3            0            0             0             0           0   \n",
       "4            0            0             0             0           2   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "1           0               NaN            NaN     NaN  \n",
       "2           4               NaN            NaN     NaN  \n",
       "3           0               NaN            NaN     NaN  \n",
       "4           2               NaN            NaN     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of wides bowled in the match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1st Method: By analysing bowlers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bowler_squad = ['T Natarajan', \"K Ahmed\", \"S Sharma\", \"Rashid Khan\", \"SN Thakur\", \"DL Chahar\", \"KV Sharma\", \"DJ Bravo\", \"RA Jadeja\", \"S Curran\", \"SR Watson\"]\n",
    "bowlers_wide = dict()\n",
    "for bowler in bowler_squad:\n",
    "    no_of_wides_per_match = np.median(delivery[delivery['bowler']==bowler].groupby('match_id').sum()['wide_runs'])\n",
    "    bowlers_wide[bowler] = no_of_wides_per_match\n",
    "\n",
    "sum(bowlers_wide.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2nd Method: By analysing teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2a5572a4f48>"
      ]
     },
     "execution_count": 13,
     "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": [
    "plt.figure(figsize=(10,8))\n",
    "csk_bowling = delivery[delivery['bowling_team']==team_1].groupby('match_id').sum().reset_index()\n",
    "sns.scatterplot(x=csk_bowling.index, y=\"wide_runs\", data=csk_bowling)\n",
    "plt.axhline(y=round(np.mean(csk_bowling['wide_runs'])), color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2a55730c608>"
      ]
     },
     "execution_count": 14,
     "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": [
    "plt.figure(figsize=(10,8))\n",
    "srh_bowling = delivery[delivery['bowling_team']==team_2].groupby('match_id').sum().reset_index()\n",
    "sns.scatterplot(x=srh_bowling.index, y=\"wide_runs\", data=srh_bowling)\n",
    "plt.axhline(y=round(np.mean(srh_bowling['wide_runs'])), color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total wides bowled in the match will be: 7\n"
     ]
    }
   ],
   "source": [
    "print(\"Total wides bowled in the match will be:\", round(np.mean(srh_bowling['wide_runs']))+round(np.mean(csk_bowling['wide_runs'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So, the wides bowled tommorrow will be in range 6 to 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# David Warner Score Probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dw = 'DA Warner'\n",
    "dw_matches = delivery[(delivery.batting_team==team_2) & (delivery.batsman==dw)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'David Warner score in SRH against all CSK matches')"
      ]
     },
     "execution_count": 17,
     "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": [
    "dw_vs_csk = dw_matches[dw_matches['bowling_team']==team_1].groupby('match_id').sum().reset_index()\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.barplot(x='match_id', y='batsman_runs', data=dw_vs_csk)\n",
    "plt.axhline(y=np.mean(dw_vs_csk['batsman_runs']), color='red')\n",
    "plt.text(x=1, y=np.mean(dw_vs_csk['batsman_runs'])+0.5, fontsize=12, s='Average Score: {}'.format(round(np.mean(dw_vs_csk['batsman_runs']))))\n",
    "plt.title('David Warner score in SRH against all CSK matches')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of wickets CSK will loose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'No. of wickets')"
      ]
     },
     "execution_count": 18,
     "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": [
    "plt.figure(figsize=(10,8))\n",
    "csk_matches = delivery[delivery['batting_team']==team_1].groupby('match_id').sum()\n",
    "csk_matches['player_dismissed'].plot(kind='bar')\n",
    "plt.axhline(y=np.mean(csk_matches['player_dismissed']), color='red')\n",
    "plt.text(x=1, y=np.mean(csk_matches['player_dismissed'])+0.5, s= \"Average wickets lose: {} \".format(round(np.mean(csk_matches['player_dismissed']))))\n",
    "plt.title(\"Wickets lost by CSK per match\")\n",
    "plt.ylabel(\"No. of wickets\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total Runs Scored in Tommorrow's Match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "srh_matches = delivery[delivery['batting_team']==team_2].groupby('match_id').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total predicted runs scored totaly in tommorow's match =  336\n",
      "This data was used neglecting the extremes. So, for rough estimation, the predicted runs be more than 336.\n"
     ]
    }
   ],
   "source": [
    "average_runs_csk = csk_matches[csk_matches['total_runs']>100]['total_runs'].mean()\n",
    "average_runs_srh = srh_matches[srh_matches['total_runs']>100]['total_runs'].mean()\n",
    "print(\"Total predicted runs scored totaly in tommorow's match = \", round(average_runs_csk+average_runs_srh))\n",
    "print(\"This data was used neglecting the extremes. So, for rough estimation, the predicted runs be more than 336.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Dataset was filtered and data from 2016 to 2019 was considered and non-required columns were dropped.\n",
    "- Using the team winner of every match played between CSK and SRH, the chances of winning of CSK and SRH was taken out. \n",
    "- The predicted bowlers for today;s match was considered for wide balls prediction. Number of wides generally bowled by bowler in a match was considered and then summed to take out the no. of wides that will be bowled in this match. To recheck this, no. of wides generally bowled by the team per match was considered. \n",
    "- To estimate David Warner runs, the average runs scored by David Warner so far was considered.\n",
    "- To estimate the number of wickets that will be lost by CSK, the average wickets out of CSK per match was considered. \n",
    "\n"
   ]
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