goalscorers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding the best goalscorers\n",
    "\n",
    "Using the dataframe created before, I have sorted different players and calculated statistics like, goals scored, expected goals, shots taken etc. A player outperforming his expected goal value is highly efficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "def getplayerinfo(id):\n",
    "    return players.loc[players['wyId']==id]\n",
    "sns.set_context('poster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "allshots=pd.read_csv('allshots.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "shots_leagues=allshots.loc[(allshots['competition']==0)|(allshots['competition']==1)|(allshots['competition']==2)|(allshots['competition']==3)|(allshots['competition']==5)]\n",
    "shots_worldcup=allshots.loc[allshots['competition']==4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "players=pd.read_json(r'C:\\Users\\ATHARAVA SHUKLA\\Desktop\\study\\Football\\players.json')\n",
    "teams=pd.read_json(r'C:\\Users\\ATHARAVA SHUKLA\\Desktop\\study\\Football\\teams.json')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "playerId=np.unique(shots_leagues['playerId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "goals=[]\n",
    "expectedgoals=[]\n",
    "headers=[]\n",
    "preferred_foot=[]\n",
    "shots=[]\n",
    "for id in playerId:\n",
    "    temp1=shots_leagues.loc[shots_leagues['playerId']==id,'goal'].sum()\n",
    "    temp2=shots_leagues.loc[shots_leagues['playerId']==id,'xG'].sum()\n",
    "    temp3=(shots_leagues.loc[shots_leagues['playerId']==id]).shape[0]\n",
    "    temp4=shots_leagues.loc[(shots_leagues['playerId']==id)&(shots_leagues['goal']==1),'headers'].sum()\n",
    "    temp5=shots_leagues.loc[(shots_leagues['playerId']==id)&(shots_leagues['goal']==1),'preferrence'].sum()\n",
    "    goals=np.append(goals,temp1)\n",
    "    expectedgoals=np.append(expectedgoals,temp2)\n",
    "    headers=np.append(headers,temp4)\n",
    "    preferred_foot=np.append(preferred_foot,temp5)\n",
    "    shots=np.append(shots,temp3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix=[[playerId,goals,expectedgoals,headers,preferred_foot,shots]]\n",
    "matrix=np.transpose(matrix)\n",
    "matrix=np.reshape(matrix,(np.shape(matrix)[0],np.shape(matrix)[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "goalscoring_leagues = pd.DataFrame(matrix, columns=['playerId','goals','expectedgoals','headers','preferred_foot','shots'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "goalscoring_leagues['unpreferred_foot']=goalscoring_leagues['goals']-goalscoring_leagues['preferred_foot']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "goalscoring_leagues['preferred_foot']=goalscoring_leagues['preferred_foot']-goalscoring_leagues['headers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "xGdifference=goalscoring_leagues.goals-goalscoring_leagues.expectedgoals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "goalscoring_leagues['xGdifference']=xGdifference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "birthDate=players.birthDate\n",
    "birthdates=[]\n",
    "for i in range(len(birthDate)):\n",
    "    temp=birthDate[i][0:4]\n",
    "    temp=int(temp)\n",
    "    temp = 2018-temp\n",
    "    birthdates=np.append(birthdates,temp)\n",
    "players['age']=birthdates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "lastname=[]\n",
    "age=[]\n",
    "for id in playerId:\n",
    "    temp1=players.loc[players['wyId']==id].shortName\n",
    "    temp2=players.loc[players['wyId']==id].age\n",
    "    lastname=np.append(lastname,temp1)\n",
    "    age=np.append(age,temp2)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>playerId</th>\n",
       "      <th>goals</th>\n",
       "      <th>expectedgoals</th>\n",
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      "text/plain": [
       "   playerId  goals  expectedgoals  headers  preferred_foot  shots  \\\n",
       "0       0.0    0.0         0.1673      0.0             0.0    3.0   \n",
       "\n",
       "   unpreferred_foot  xGdifference  \n",
       "0               0.0       -0.1673  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goalscoring_leagues.loc[goalscoring_leagues['playerId']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "goalscoring_leagues=goalscoring_leagues.drop(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "goalscoring_leagues['names']=lastname\n",
    "goalscoring_leagues['age']=age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "goalscoring_leagues['playerId']=goalscoring_leagues['playerId'].astype(int)\n",
    "goalscoring_leagues['goals']=goalscoring_leagues['goals'].astype(int)\n",
    "goalscoring_leagues['headers']=goalscoring_leagues['headers'].astype(int)\n",
    "goalscoring_leagues['preferred_foot']=goalscoring_leagues['preferred_foot'].astype(int)\n",
    "goalscoring_leagues['shots']=goalscoring_leagues['shots'].astype(int)\n",
    "goalscoring_leagues['unpreferred_foot']=goalscoring_leagues['unpreferred_foot'].astype(int)\n",
    "goalscoring_leagues['age']=goalscoring_leagues['age'].astype(int)\n",
    "cols=['playerId', 'names','age','shots', 'goals', 'expectedgoals', 'xGdifference', 'headers', 'preferred_foot',\n",
    "       'unpreferred_foot']\n",
    "goalscoring_leagues=goalscoring_leagues[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "goalscoring_leagues = goalscoring_leagues.sort_values('goals',\n",
    "                                     ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>playerId</th>\n",
       "      <th>names</th>\n",
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       "      <th>unpreferred_foot</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1391</th>\n",
       "      <td>120353</td>\n",
       "      <td>Mohamed Salah</td>\n",
       "      <td>26</td>\n",
       "      <td>136</td>\n",
       "      <td>31</td>\n",
       "      <td>17.4200</td>\n",
       "      <td>13.5800</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>8717</td>\n",
       "      <td>H. Kane</td>\n",
       "      <td>25</td>\n",
       "      <td>162</td>\n",
       "      <td>27</td>\n",
       "      <td>22.5989</td>\n",
       "      <td>4.4011</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>3359</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>31</td>\n",
       "      <td>142</td>\n",
       "      <td>26</td>\n",
       "      <td>17.2299</td>\n",
       "      <td>8.7701</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>7972</td>\n",
       "      <td>L. Su\\u00e1rez</td>\n",
       "      <td>31</td>\n",
       "      <td>103</td>\n",
       "      <td>24</td>\n",
       "      <td>19.0796</td>\n",
       "      <td>4.9204</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>21174</td>\n",
       "      <td>E. Cavani</td>\n",
       "      <td>31</td>\n",
       "      <td>93</td>\n",
       "      <td>24</td>\n",
       "      <td>16.9865</td>\n",
       "      <td>7.0135</td>\n",
       "      <td>4</td>\n",
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       "      <td>3</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>26033</td>\n",
       "      <td>Y. Jouffre</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0571</td>\n",
       "      <td>-0.0571</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028</th>\n",
       "      <td>26024</td>\n",
       "      <td>W. Lautoa</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1785</td>\n",
       "      <td>-0.1785</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1022</th>\n",
       "      <td>25993</td>\n",
       "      <td>Hilton</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9537</td>\n",
       "      <td>-1.9537</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1021</th>\n",
       "      <td>25990</td>\n",
       "      <td>M. Deplagne</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3918</td>\n",
       "      <td>-0.3918</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2113</th>\n",
       "      <td>564512</td>\n",
       "      <td>S. Sanogo</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0838</td>\n",
       "      <td>-0.0838</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2113 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      playerId           names  age  shots  goals  expectedgoals  \\\n",
       "1391    120353   Mohamed Salah   26    136     31        17.4200   \n",
       "434       8717         H. Kane   25    162     27        22.5989   \n",
       "99        3359        L. Messi   31    142     26        17.2299   \n",
       "342       7972  L. Su\\u00e1rez   31    103     24        19.0796   \n",
       "823      21174       E. Cavani   31     93     24        16.9865   \n",
       "...        ...             ...  ...    ...    ...            ...   \n",
       "1030     26033      Y. Jouffre   34      1      0         0.0571   \n",
       "1028     26024       W. Lautoa   31      1      0         0.1785   \n",
       "1022     25993          Hilton   41     18      0         1.9537   \n",
       "1021     25990     M. Deplagne   27      6      0         0.3918   \n",
       "2113    564512       S. Sanogo   17      1      0         0.0838   \n",
       "\n",
       "      xGdifference  headers  preferred_foot  unpreferred_foot  \n",
       "1391       13.5800        2              24                 5  \n",
       "434         4.4011        6              11                10  \n",
       "99          8.7701        0              24                 2  \n",
       "342         4.9204        2              18                 4  \n",
       "823         7.0135        4              17                 3  \n",
       "...            ...      ...             ...               ...  \n",
       "1030       -0.0571        0               0                 0  \n",
       "1028       -0.1785        0               0                 0  \n",
       "1022       -1.9537        0               0                 0  \n",
       "1021       -0.3918        0               0                 0  \n",
       "2113       -0.0838        0               0                 0  \n",
       "\n",
       "[2113 rows x 10 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goalscoring_leagues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
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       "      <td>E. Cavani</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>3322</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>33</td>\n",
       "      <td>151</td>\n",
       "      <td>23</td>\n",
       "      <td>22.5634</td>\n",
       "      <td>0.4366</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1492</th>\n",
       "      <td>206314</td>\n",
       "      <td>M. Icardi</td>\n",
       "      <td>25</td>\n",
       "      <td>87</td>\n",
       "      <td>23</td>\n",
       "      <td>15.1933</td>\n",
       "      <td>7.8067</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>14817</td>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>30</td>\n",
       "      <td>106</td>\n",
       "      <td>23</td>\n",
       "      <td>19.2539</td>\n",
       "      <td>3.7461</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>21384</td>\n",
       "      <td>C. Immobile</td>\n",
       "      <td>28</td>\n",
       "      <td>102</td>\n",
       "      <td>22</td>\n",
       "      <td>13.2412</td>\n",
       "      <td>8.7588</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>25770</td>\n",
       "      <td>F. Thauvin</td>\n",
       "      <td>25</td>\n",
       "      <td>112</td>\n",
       "      <td>19</td>\n",
       "      <td>12.9296</td>\n",
       "      <td>6.0704</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>3840</td>\n",
       "      <td>Iago Aspas</td>\n",
       "      <td>31</td>\n",
       "      <td>85</td>\n",
       "      <td>19</td>\n",
       "      <td>12.5760</td>\n",
       "      <td>6.4240</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>25867</td>\n",
       "      <td>P. Aubameyang</td>\n",
       "      <td>29</td>\n",
       "      <td>73</td>\n",
       "      <td>19</td>\n",
       "      <td>14.3294</td>\n",
       "      <td>4.6706</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>395636</td>\n",
       "      <td>Maxi G\\u00f3mez</td>\n",
       "      <td>22</td>\n",
       "      <td>77</td>\n",
       "      <td>18</td>\n",
       "      <td>13.2691</td>\n",
       "      <td>4.7309</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>8325</td>\n",
       "      <td>S. Ag\\u00fcero</td>\n",
       "      <td>30</td>\n",
       "      <td>86</td>\n",
       "      <td>17</td>\n",
       "      <td>12.8598</td>\n",
       "      <td>4.1402</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>5167</td>\n",
       "      <td>Mariano D\\u00edaz</td>\n",
       "      <td>25</td>\n",
       "      <td>85</td>\n",
       "      <td>17</td>\n",
       "      <td>12.7100</td>\n",
       "      <td>4.2900</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>11066</td>\n",
       "      <td>R. Sterling</td>\n",
       "      <td>24</td>\n",
       "      <td>79</td>\n",
       "      <td>17</td>\n",
       "      <td>14.3467</td>\n",
       "      <td>2.6533</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>89186</td>\n",
       "      <td>P. Dybala</td>\n",
       "      <td>25</td>\n",
       "      <td>87</td>\n",
       "      <td>16</td>\n",
       "      <td>6.9386</td>\n",
       "      <td>9.0614</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>3714</td>\n",
       "      <td>C. Stuani</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>16</td>\n",
       "      <td>11.7730</td>\n",
       "      <td>4.2270</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>7905</td>\n",
       "      <td>R. Lukaku</td>\n",
       "      <td>25</td>\n",
       "      <td>76</td>\n",
       "      <td>16</td>\n",
       "      <td>13.6804</td>\n",
       "      <td>2.3196</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>8327</td>\n",
       "      <td>E. D\\u017eeko</td>\n",
       "      <td>32</td>\n",
       "      <td>144</td>\n",
       "      <td>16</td>\n",
       "      <td>18.6490</td>\n",
       "      <td>-2.6490</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      playerId              names  age  shots  goals  expectedgoals  \\\n",
       "1391    120353      Mohamed Salah   26    136     31        17.4200   \n",
       "434       8717            H. Kane   25    162     27        22.5989   \n",
       "99        3359           L. Messi   31    142     26        17.2299   \n",
       "342       7972     L. Su\\u00e1rez   31    103     24        19.0796   \n",
       "823      21174          E. Cavani   31     93     24        16.9865   \n",
       "77        3322  Cristiano Ronaldo   33    151     23        22.5634   \n",
       "1492    206314          M. Icardi   25     87     23        15.1933   \n",
       "543      14817     R. Lewandowski   30    106     23        19.2539   \n",
       "840      21384        C. Immobile   28    102     22        13.2412   \n",
       "979      25770         F. Thauvin   25    112     19        12.9296   \n",
       "199       3840         Iago Aspas   31     85     19        12.5760   \n",
       "1003     25867      P. Aubameyang   29     73     19        14.3294   \n",
       "2020    395636    Maxi G\\u00f3mez   22     77     18        13.2691   \n",
       "397       8325     S. Ag\\u00fcero   30     86     17        12.8598   \n",
       "271       5167  Mariano D\\u00edaz   25     85     17        12.7100   \n",
       "482      11066        R. Sterling   24     79     17        14.3467   \n",
       "1326     89186          P. Dybala   25     87     16         6.9386   \n",
       "182       3714          C. Stuani   32     64     16        11.7730   \n",
       "314       7905          R. Lukaku   25     76     16        13.6804   \n",
       "399       8327      E. D\\u017eeko   32    144     16        18.6490   \n",
       "\n",
       "      xGdifference  headers  preferred_foot  unpreferred_foot  \n",
       "1391       13.5800        2              24                 5  \n",
       "434         4.4011        6              11                10  \n",
       "99          8.7701        0              24                 2  \n",
       "342         4.9204        2              18                 4  \n",
       "823         7.0135        4              17                 3  \n",
       "77          0.4366        5              11                 7  \n",
       "1492        7.8067        4              16                 3  \n",
       "543         3.7461        5              12                 6  \n",
       "840         8.7588        5              15                 2  \n",
       "979         6.0704        4              12                 3  \n",
       "199         6.4240        3              10                 6  \n",
       "1003        4.6706        2              11                 6  \n",
       "2020        4.7309       10               8                 0  \n",
       "397         4.1402        5              11                 1  \n",
       "271         4.2900        3              12                 2  \n",
       "482         2.6533        0              10                 7  \n",
       "1326        9.0614        0              12                 4  \n",
       "182         4.2270       10               5                 1  \n",
       "314         2.3196        3              11                 2  \n",
       "399        -2.6490        5               3                 8  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top20=goalscoring_leagues[:20]\n",
    "top20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20412c1ec48>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "xGplot=sns.scatterplot(data=top20, y='goals', x='expectedgoals', hue='xGdifference',size='xGdifference', sizes=(20,200))\n",
    "xGplot.set(xlim=(0,25))\n",
    "xGplot.set(ylim=(0,35))\n",
    "plt.legend(bbox_to_anchor=(1.2,0.5),\n",
    "           borderaxespad=0)\n",
    "plt.xlabel('Expected Goals')\n",
    "plt.ylabel('Goals')\n",
    "plt.annotate('Salah', (17.5850, 31), (18,32))\n",
    "plt.annotate('Dybala', (7.4,16))\n",
    "plt.annotate('Ronaldo', (23,23))\n",
    "plt.annotate('Messi', (17.8,26))\n",
    "plt.plot([0, 35], [0, 35], color='blue', linestyle='--', alpha=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Salah is a clear outlier with having outperformed his xG value by 13! Ronaldo has underperformed for a player of his stature with having almost no difference between goals scored and expected goals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "top20_U21=goalscoring_leagues.loc[goalscoring_leagues['age']<=21][:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>playerId</th>\n",
       "      <th>names</th>\n",
       "      <th>age</th>\n",
       "      <th>shots</th>\n",
       "      <th>goals</th>\n",
       "      <th>expectedgoals</th>\n",
       "      <th>xGdifference</th>\n",
       "      <th>headers</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>unpreferred_foot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>353833</td>\n",
       "      <td>K. Mbapp\\u00e9</td>\n",
       "      <td>20</td>\n",
       "      <td>75</td>\n",
       "      <td>13</td>\n",
       "      <td>11.2216</td>\n",
       "      <td>1.7784</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td>340386</td>\n",
       "      <td>Gabriel Jesus</td>\n",
       "      <td>21</td>\n",
       "      <td>53</td>\n",
       "      <td>13</td>\n",
       "      <td>10.1960</td>\n",
       "      <td>2.8040</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>355599</td>\n",
       "      <td>Mikel Oyarzabal</td>\n",
       "      <td>21</td>\n",
       "      <td>57</td>\n",
       "      <td>12</td>\n",
       "      <td>6.3102</td>\n",
       "      <td>5.6898</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1849</th>\n",
       "      <td>300485</td>\n",
       "      <td>Malcom</td>\n",
       "      <td>21</td>\n",
       "      <td>57</td>\n",
       "      <td>10</td>\n",
       "      <td>5.4950</td>\n",
       "      <td>4.5050</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td>351010</td>\n",
       "      <td>P. Cutrone</td>\n",
       "      <td>20</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>8.6006</td>\n",
       "      <td>1.3994</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>388267</td>\n",
       "      <td>L. Bailey</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>9</td>\n",
       "      <td>6.1516</td>\n",
       "      <td>2.8484</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1778</th>\n",
       "      <td>279780</td>\n",
       "      <td>J. Augustin</td>\n",
       "      <td>21</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>4.9193</td>\n",
       "      <td>3.0807</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>258264</td>\n",
       "      <td>L. Jovi\\u0107</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>7.4446</td>\n",
       "      <td>0.5554</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>397178</td>\n",
       "      <td>M. Rashford</td>\n",
       "      <td>21</td>\n",
       "      <td>54</td>\n",
       "      <td>7</td>\n",
       "      <td>4.9100</td>\n",
       "      <td>2.0900</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>350032</td>\n",
       "      <td>C. \\u00dcnder</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "      <td>7</td>\n",
       "      <td>5.4767</td>\n",
       "      <td>1.5233</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1741</th>\n",
       "      <td>274435</td>\n",
       "      <td>E. \\u00dcnal</td>\n",
       "      <td>21</td>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>5.2258</td>\n",
       "      <td>0.7742</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>397046</td>\n",
       "      <td>H. Aouar</td>\n",
       "      <td>20</td>\n",
       "      <td>48</td>\n",
       "      <td>6</td>\n",
       "      <td>4.2334</td>\n",
       "      <td>1.7666</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2079</th>\n",
       "      <td>447804</td>\n",
       "      <td>F. Chiesa</td>\n",
       "      <td>21</td>\n",
       "      <td>104</td>\n",
       "      <td>6</td>\n",
       "      <td>7.6548</td>\n",
       "      <td>-1.6548</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>377071</td>\n",
       "      <td>Richarlison</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>10.4028</td>\n",
       "      <td>-5.4028</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2052</th>\n",
       "      <td>406682</td>\n",
       "      <td>A. Lookman</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>2.1960</td>\n",
       "      <td>2.8040</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2082</th>\n",
       "      <td>448079</td>\n",
       "      <td>I. Sarr</td>\n",
       "      <td>20</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>4.2843</td>\n",
       "      <td>0.7157</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1617</th>\n",
       "      <td>240042</td>\n",
       "      <td>D. Kownacki</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>3.4219</td>\n",
       "      <td>1.5781</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>343951</td>\n",
       "      <td>T. Abraham</td>\n",
       "      <td>21</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>6.2744</td>\n",
       "      <td>-1.2744</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2081</th>\n",
       "      <td>447821</td>\n",
       "      <td>Y. En-Nesyri</td>\n",
       "      <td>21</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0588</td>\n",
       "      <td>-0.0588</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>347200</td>\n",
       "      <td>C. Nkunku</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>2.6354</td>\n",
       "      <td>1.3646</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      playerId            names  age  shots  goals  expectedgoals  \\\n",
       "1946    353833   K. Mbapp\\u00e9   20     75     13        11.2216   \n",
       "1898    340386    Gabriel Jesus   21     53     13        10.1960   \n",
       "1951    355599  Mikel Oyarzabal   21     57     12         6.3102   \n",
       "1849    300485           Malcom   21     57     10         5.4950   \n",
       "1938    351010       P. Cutrone   20     42     10         8.6006   \n",
       "2004    388267        L. Bailey   21     64      9         6.1516   \n",
       "1778    279780      J. Augustin   21     34      8         4.9193   \n",
       "1689    258264    L. Jovi\\u0107   21     35      8         7.4446   \n",
       "2029    397178      M. Rashford   21     54      7         4.9100   \n",
       "1934    350032    C. \\u00dcnder   21     46      7         5.4767   \n",
       "1741    274435     E. \\u00dcnal   21     37      6         5.2258   \n",
       "2025    397046         H. Aouar   20     48      6         4.2334   \n",
       "2079    447804        F. Chiesa   21    104      6         7.6548   \n",
       "1999    377071      Richarlison   21     90      5        10.4028   \n",
       "2052    406682       A. Lookman   20     14      5         2.1960   \n",
       "2082    448079          I. Sarr   20     37      5         4.2843   \n",
       "1617    240042      D. Kownacki   21     21      5         3.4219   \n",
       "1910    343951       T. Abraham   21     39      5         6.2744   \n",
       "2081    447821     Y. En-Nesyri   21     40      4         4.0588   \n",
       "1929    347200        C. Nkunku   21     20      4         2.6354   \n",
       "\n",
       "      xGdifference  headers  preferred_foot  unpreferred_foot  \n",
       "1946        1.7784        0               8                 5  \n",
       "1898        2.8040        3               7                 3  \n",
       "1951        5.6898        0              10                 2  \n",
       "1849        4.5050        0               8                 2  \n",
       "1938        1.3994        3               4                 3  \n",
       "2004        2.8484        0               9                 0  \n",
       "1778        3.0807        0               8                 0  \n",
       "1689        0.5554        2               2                 4  \n",
       "2029        2.0900        2               5                 0  \n",
       "1934        1.5233        0               7                 0  \n",
       "1741        0.7742        2               1                 3  \n",
       "2025        1.7666        0               5                 1  \n",
       "2079       -1.6548        0               5                 1  \n",
       "1999       -5.4028        1               2                 2  \n",
       "2052        2.8040        0               4                 1  \n",
       "2082        0.7157        1               4                 0  \n",
       "1617        1.5781        0               2                 3  \n",
       "1910       -1.2744        1               4                 0  \n",
       "2081       -0.0588        0               2                 2  \n",
       "1929        1.3646        0               3                 1  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top20_U21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x204126fba08>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "xGplot=sns.scatterplot(data=top20_U21, y='goals', x='expectedgoals', hue='xGdifference',size='xGdifference', sizes=(20,200))\n",
    "xGplot.set(xlim=(0,15))\n",
    "xGplot.set(ylim=(0,15))\n",
    "plt.legend(bbox_to_anchor=(1.2,0.8),\n",
    "           borderaxespad=0)\n",
    "plt.xlabel('Expected Goals')\n",
    "plt.ylabel('Goals')\n",
    "plt.annotate('Mbappe', (11.5,12.5))\n",
    "plt.annotate('Jesus', (10,14))\n",
    "plt.annotate('Oyarzabal', (6.5,12.5))\n",
    "plt.annotate('Malcom', (4,10.6))\n",
    "plt.plot([0, 15], [0, 15], color='blue', linestyle='--', alpha=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mbappe was showing signs of greatness at young age, but the player that stands out the most for me is Oyarzabal who has continued a great start to his career and is now one of the captains of Real Sociedad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [],
   "source": [
    "goalscoring_leagues.to_csv('openplaygoals_leagues.csv',index=False,float_format='%.4f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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