playersAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Player ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Best Position</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Growth</th>\n",
       "      <th>Team</th>\n",
       "      <th>Value</th>\n",
       "      <th>Wage</th>\n",
       "      <th>...</th>\n",
       "      <th>Loan</th>\n",
       "      <th>Total Stats</th>\n",
       "      <th>Passing</th>\n",
       "      <th>Shooting</th>\n",
       "      <th>Pace</th>\n",
       "      <th>Defending</th>\n",
       "      <th>Physical</th>\n",
       "      <th>Dribbling</th>\n",
       "      <th>Contract Start</th>\n",
       "      <th>Contract End</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>238074</td>\n",
       "      <td>R. James</td>\n",
       "      <td>20</td>\n",
       "      <td>RB</td>\n",
       "      <td>80</td>\n",
       "      <td>86</td>\n",
       "      <td>6</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>30500000</td>\n",
       "      <td>61000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2076</td>\n",
       "      <td>77</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>76</td>\n",
       "      <td>79</td>\n",
       "      <td>77</td>\n",
       "      <td>2017</td>\n",
       "      <td>2025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>240950</td>\n",
       "      <td>Pedro Gonçalves</td>\n",
       "      <td>22</td>\n",
       "      <td>CAM</td>\n",
       "      <td>79</td>\n",
       "      <td>87</td>\n",
       "      <td>8</td>\n",
       "      <td>Sporting CP</td>\n",
       "      <td>39500000</td>\n",
       "      <td>16000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "      <td>74</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>83</td>\n",
       "      <td>2020</td>\n",
       "      <td>2025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>253004</td>\n",
       "      <td>Ansu Fati</td>\n",
       "      <td>17</td>\n",
       "      <td>LW</td>\n",
       "      <td>76</td>\n",
       "      <td>90</td>\n",
       "      <td>14</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>17000000</td>\n",
       "      <td>24000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1817</td>\n",
       "      <td>68</td>\n",
       "      <td>74</td>\n",
       "      <td>88</td>\n",
       "      <td>29</td>\n",
       "      <td>53</td>\n",
       "      <td>79</td>\n",
       "      <td>2018</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>237995</td>\n",
       "      <td>D. Lainez</td>\n",
       "      <td>20</td>\n",
       "      <td>CAM</td>\n",
       "      <td>74</td>\n",
       "      <td>84</td>\n",
       "      <td>10</td>\n",
       "      <td>Real Betis</td>\n",
       "      <td>9500000</td>\n",
       "      <td>15000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1873</td>\n",
       "      <td>69</td>\n",
       "      <td>64</td>\n",
       "      <td>79</td>\n",
       "      <td>49</td>\n",
       "      <td>55</td>\n",
       "      <td>80</td>\n",
       "      <td>2019</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>231943</td>\n",
       "      <td>Richarlison</td>\n",
       "      <td>23</td>\n",
       "      <td>ST</td>\n",
       "      <td>81</td>\n",
       "      <td>87</td>\n",
       "      <td>6</td>\n",
       "      <td>Everton</td>\n",
       "      <td>41000000</td>\n",
       "      <td>90000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2114</td>\n",
       "      <td>73</td>\n",
       "      <td>79</td>\n",
       "      <td>83</td>\n",
       "      <td>53</td>\n",
       "      <td>80</td>\n",
       "      <td>83</td>\n",
       "      <td>2018</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Player ID              Name  Age Best Position  Overall  Potential  \\\n",
       "59     238074          R. James   20            RB       80         86   \n",
       "61     240950   Pedro Gonçalves   22           CAM       79         87   \n",
       "62     253004         Ansu Fati   17            LW       76         90   \n",
       "63     237995         D. Lainez   20           CAM       74         84   \n",
       "64     231943       Richarlison   23            ST       81         87   \n",
       "\n",
       "    Growth            Team     Value   Wage  ... Loan Total Stats  Passing  \\\n",
       "59       6       Chelsea    30500000  61000  ...  NaN        2076       77   \n",
       "61       8   Sporting CP    39500000  16000  ...  NaN        2021       78   \n",
       "62      14  FC Barcelona    17000000  24000  ...  NaN        1817       68   \n",
       "63      10    Real Betis     9500000  15000  ...  NaN        1873       69   \n",
       "64       6       Everton    41000000  90000  ...  NaN        2114       73   \n",
       "\n",
       "    Shooting  Pace  Defending  Physical  Dribbling  Contract Start  \\\n",
       "59        58    80         76        79         77           2017    \n",
       "61        75    74         56        57         83           2020    \n",
       "62        74    88         29        53         79           2018    \n",
       "63        64    79         49        55         80           2019    \n",
       "64        79    83         53        80         83           2018    \n",
       "\n",
       "   Contract End  \n",
       "59         2025  \n",
       "61         2025  \n",
       "62         2022  \n",
       "63         2024  \n",
       "64         2024  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def clean_string(my_list,string):\n",
    "    data =[]\n",
    "    for item in my_list:\n",
    "        a = re.sub(string,'',item)\n",
    "        data.append(a)\n",
    "    return data\n",
    "\n",
    "df = pd.read_csv('.\\FIFAPlayersData.csv')\n",
    "\n",
    "#~~~~DATA WRANGLING~~~~~~~~~\n",
    "#deleting old indexes\n",
    "del df['Unnamed: 0']\n",
    "\n",
    "#Renaming columns\n",
    "df.rename(columns={\"col-pi\": \"Player ID\", \"col-tt\": \"Total Stats\",\"col-gu\":\"Growth\",\"col-le\":\"Loan\",\n",
    "                   \"col-pac\":\"Pace\",\"col-sho\":\"Shooting\",\"col-vl\":\"Value\",\"col-bp\":\"Best Position\",\n",
    "                   \"col-wg\":\"Wage\",\"col-def\":\"Defending\",\"col-phy\":\"Physical\",\"col-ae\":\"Age\",\"col-pas\":\"Passing\",\n",
    "                   \"col-dri\":\"Dribbling\",\"col-oa\":\"Overall\",\"col-pt\":\"Potential\"},inplace=True)\n",
    "\n",
    "#Dropping duplicates\n",
    "df.drop_duplicates(subset =\"Player ID\",keep = False, inplace = True)\n",
    "\n",
    "#Reordering columns\n",
    "df = df[['Player ID','Name','Age','Best Position','Overall','Potential','Growth'   ,'Team','Value','Wage',\n",
    "         'Contract' ,'Loan','Total Stats','Passing','Shooting','Pace'  ,'Defending','Physical','Dribbling']]\n",
    "\n",
    "#Cleaning wages and values, turning into int\n",
    "df['Wage'] = df['Wage'].apply(lambda x: re.sub('€','',x))\n",
    "df['Wage'] = df['Wage'].apply(lambda x: re.sub('K','000',x))\n",
    "df['Value'] = df['Value'].apply(lambda x: re.sub('€','',x))\n",
    "df['Value'] = df['Value'].apply(lambda x : re.sub('M','00000',x) if (('M' in x) and ('.' in x))else x)\n",
    "df['Value'] = df['Value'].apply(lambda x : re.sub('[.]','',x))\n",
    "df['Value'] = df['Value'].apply(lambda x : re.sub('M','000000',x))\n",
    "df['Value'] = df['Value'].apply(lambda x : re.sub('K','000',x))\n",
    "\n",
    "df['Wage'] = df['Wage'].astype(int)\n",
    "df['Value'] = df['Value'].astype(int)\n",
    "\n",
    "#Filter players that no longer play, with contract ending earlier than 2021. \n",
    "df[['Contract Start','Contract End']] = df['Contract'].str.split('~',expand=True)\n",
    "\n",
    "#Filter zeros in values/Free agents and players over 40 a players under 70 of overall\n",
    "df = df[df['Value']>0]\n",
    "df = df[df['Age']<40]\n",
    "df = df[df['Overall']>70]\n",
    "\n",
    "#Filter players not playing anymore\n",
    "df['Contract End'] = df['Contract End'].apply(lambda x: \"2021\" if x is None else x)\n",
    "df['Contract End'] = df['Contract End'].astype(int)\n",
    "df = df[df['Contract End']>2020]\n",
    "\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#~~~~DATA EXPLORATION\n",
    "age_dist = sns.displot(df,x=\"Age\",discrete=True)\n",
    "value_dist = sns.displot(df,x=\"Value\", bins=100)\n",
    "overall_dist = sns.displot(df,x=\"Overall\",bins = 20)\n",
    "value_vs_wages_scat=sns.displot(data=df, x=\"Value\", y=\"Wage\")\n",
    "overall_vs_value_scat = sns.displot(data=df, x=\"Overall\", y=\"Value\")\n",
    "sns.displot(data=df, x=\"Potential\", y=\"Value\")\n",
    "\n",
    "#Getting a sorted boxplot Value vs Age\n",
    "grouped = df.groupby('Age')\n",
    "sorted_averages = (\n",
    "    pd.DataFrame({col: vals['Value'] for col, vals in grouped})\n",
    "    .mean()\n",
    "    .sort_values(ascending=False)\n",
    ")\n",
    "boxp_val_vs_age = sns.boxplot(x=\"Age\", y=\"Value\", data=df,order=sorted_averages.index)\n",
    "\n",
    "#Getting a sorted boxplot Value vs Best Position\n",
    "grouped = df.groupby('Best Position')\n",
    "sorted_averages = (\n",
    "    pd.DataFrame({col: vals['Value'] for col, vals in grouped})\n",
    "    .mean()\n",
    "    .sort_values(ascending=False)\n",
    ")\n",
    "boxp_val_vs_bp = sns.boxplot(x=\"Value\", y=\"Best Position\", data=df, order=sorted_averages.index)\n",
    "#Getting a sorted boxplot Wage vs Best Position\n",
    "grouped = df.groupby('Best Position')\n",
    "sorted_averages = (\n",
    "    pd.DataFrame({col: vals['Wage'] for col, vals in grouped})\n",
    "    .mean()\n",
    "    .sort_values(ascending=False)\n",
    ")\n",
    "boxp_val_vs_bp = sns.boxplot(x=\"Wage\", y=\"Best Position\", data=df, order=sorted_averages.index)\n",
    "all_positions = ['ST','CF','LW','RW','CAM','CM','CDM','LM','RM','LWB','LB','CB','RB','RWB','GK']\n",
    "\n",
    "sns.color_palette(\"light:#5A9\", as_cmap=True)\n",
    "g = sns.FacetGrid(df, col=\"Best Position\", hue = 'Age', col_wrap=4, height=3,palette=\"ch:s=.25,rot=-.25\")\n",
    "g.map(sns.scatterplot, \"Value\", \"Overall\", alpha = 0.5)\n",
    "g.add_legend()\n",
    "\n",
    "#Creating groups by Overall\n",
    "df_71_75 = df[df['Overall']<76]\n",
    "df_76_80 = df[(df['Overall']<81)&(df['Overall']>75)]\n",
    "df_81_85 = df[(df['Overall']<86)&(df['Overall']>80)]\n",
    "df_86_90 = df[(df['Overall']<91)&(df['Overall']>85)]\n",
    "df_90 = df[df['Overall']>90]\n",
    "\n",
    "#Visualization of Value and wage per Overall and Position\n",
    "g = sns.FacetGrid(df_86_90, col=\"Overall\", hue = 'Best Position', col_wrap=4, height=3,palette=\"bright\")\n",
    "g.map(sns.scatterplot, \"Value\", \"Wage\", alpha = 0.5)\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_optimal_players(positions,ova_min,ova_max,df):\n",
    "    vw = 0.30 #Value Weight\n",
    "    ww = 0.15 #Wage Weight\n",
    "    ow = 0.35 #Overall Weight\n",
    "    aw = 0.15 #Age Weight\n",
    "    pw = 0.05 #Potential Weight\n",
    "    df_list = []\n",
    "    for pos in positions:\n",
    "        my_df = pd.DataFrame()\n",
    "        my_df = df[(df['Overall']<=ova_max)&(df['Overall']>=ova_min)]\n",
    "        my_df = my_df[my_df['Best Position'] == pos]\n",
    "        my_df['Value Index'] = my_df['Value']/ (my_df['Value'].mean())\n",
    "        my_df['Wage Index']  = my_df['Wage'] / (my_df['Wage'].mean())\n",
    "        my_df['Overall Index'] = (my_df['Overall'].mean()/ my_df['Overall']) * 4\n",
    "        my_df['Age Index'] = my_df['Age']/ (my_df['Age'].mean()) * 4\n",
    "        my_df['Potential Index'] = my_df['Potential']/ (my_df['Potential'].mean()) * 4\n",
    "        my_df['Final Index'] =(my_df['Value Index']* vw)+(my_df['Wage Index']*ww)+(my_df['Overall Index']*ow)+(my_df['Age Index']*aw)+(my_df['Potential Index']*pw)\n",
    "        my_df.sort_values(by='Final Index')\n",
    "        my_df = my_df[['Best Position','Name','Age','Team','Overall','Potential','Value','Wage','Final Index']].sort_values(by='Final Index')\n",
    "        my_df = my_df.head()\n",
    "        df_list.append(my_df)\n",
    "    return pd.concat(df_list)\n",
    "\n",
    "ova_min = 70\n",
    "ova_max = 79\n",
    "position = \"GK\"\n",
    "positions = ['ST','CF','LW','RW','CAM','CM','CDM','LM','RM','LWB','LB','CB','RB','RWB','GK']\n",
    "final =[]\n",
    "final_list_80_100 = top_optimal_players(positions,80,100,df)\n",
    "final_list_70_79 = top_optimal_players(positions,70,79,df)\n",
    "final.append(final_list_80_100)\n",
    "final.append(final_list_70_79)\n",
    "final_list = pd.concat(final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Best Position               Name  Age                         Team  \\\n",
      "569              ST        J. Martínez   27             Atlanta United     \n",
      "519              ST           M. Gómez   23                Valencia CF     \n",
      "785              ST          S. Haller   26                       Ajax     \n",
      "679              ST         C. Bakambu   29    Beijing Sinobo Guoan FC     \n",
      "1109             ST            A. Plea   27   Borussia Mönchengladbach     \n",
      "...             ...                ...  ...                          ...   \n",
      "13206            GK   Ailton Cardenhas   20                     Santos     \n",
      "11858            GK   Eduardo Fonseira   20                Corinthians     \n",
      "14696            GK   Nathael Silbeira   24              RB Bragantino     \n",
      "14862            GK    André Milazisco   24  Club Athletico Paranaense     \n",
      "14863            GK    Wiliam Santinho   24                 Fluminense     \n",
      "\n",
      "       Overall  Potential     Value   Wage  Final Index  \n",
      "569         80         80  23000000  13000     2.406825  \n",
      "519         80         85  32000000  40000     2.446615  \n",
      "785         80         83  28000000  23000     2.447243  \n",
      "679         80         80  21000000  30000     2.461178  \n",
      "1109        80         80  23000000  49000     2.464818  \n",
      "...        ...        ...       ...    ...          ...  \n",
      "13206       71         71   1600000   8000     2.262673  \n",
      "11858       71         71   1600000   8000     2.262673  \n",
      "14696       71         71   1600000   6000     2.325477  \n",
      "14862       72         72   2000000   7000     2.348831  \n",
      "14863       71         71   1600000   9000     2.355113  \n",
      "\n",
      "[149 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "print(final_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving df into a csv datbase\n",
    "final_list.to_csv(\"./FinalResult.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.set_theme(style=\"white\")\n",
    "# sns.relplot(x=\"Overall\", y=\"Value\", hue=\"Best Position\", size=\"Wage\",\n",
    "#             sizes=(40, 400), alpha=.5, palette=\"muted\",\n",
    "#             height=6, data=df_86_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #TESTING RENAMING TECHNIQUE\n",
    "# test = pd.DataFrame({\"A\":[1,2,3],\"B\":[4,5,6]})\n",
    "# test.rename(columns={\"A\": \"bb\"},inplace=True)\n",
    "# print(test)"
   ]
  }
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