Models clean_Nancy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import math\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tools.eval_measures import rmse\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from scipy import stats\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading CSVs and filling NAs w/0s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank=pd.read_csv('all_risk_df_ordered.csv')\n",
    "rank=rank.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>ranking_y</td>    <th>  R-squared:         </th>  <td>   0.305</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.305</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   4319.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 22 Jun 2020</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:24:17</td>     <th>  Log-Likelihood:    </th> <td>-1.4212e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>423944</td>      <th>  AIC:               </th>  <td>2.843e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>423900</td>      <th>  BIC:               </th>  <td>2.843e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    43</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                <td> -214.5617</td> <td>   11.296</td> <td>  -18.995</td> <td> 0.000</td> <td> -236.701</td> <td> -192.422</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>teamId</th>               <td>    0.0171</td> <td>    0.000</td> <td>   55.362</td> <td> 0.000</td> <td>    0.017</td> <td>    0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance1_2_x</th>        <td>   -1.0044</td> <td>    0.052</td> <td>  -19.311</td> <td> 0.000</td> <td>   -1.106</td> <td>   -0.902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance1_3_x</th>        <td>    0.6239</td> <td>    0.063</td> <td>    9.968</td> <td> 0.000</td> <td>    0.501</td> <td>    0.747</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance1_4_x</th>        <td>    0.2525</td> <td>    0.043</td> <td>    5.919</td> <td> 0.000</td> <td>    0.169</td> <td>    0.336</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance2_3_x</th>        <td>    0.8053</td> <td>    0.051</td> <td>   15.831</td> <td> 0.000</td> <td>    0.706</td> <td>    0.905</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance2_4_x</th>        <td>   -1.2859</td> <td>    0.057</td> <td>  -22.739</td> <td> 0.000</td> <td>   -1.397</td> <td>   -1.175</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance3_4_x</th>        <td>    0.4134</td> <td>    0.039</td> <td>   10.654</td> <td> 0.000</td> <td>    0.337</td> <td>    0.489</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance_centroid1_x</th> <td>    2.9352</td> <td>    0.023</td> <td>  126.304</td> <td> 0.000</td> <td>    2.890</td> <td>    2.981</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance_centroid2_x</th> <td>    1.2577</td> <td>    0.044</td> <td>   28.702</td> <td> 0.000</td> <td>    1.172</td> <td>    1.344</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance_centroid3_x</th> <td>   -1.5053</td> <td>    0.047</td> <td>  -32.157</td> <td> 0.000</td> <td>   -1.597</td> <td>   -1.414</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance_centroid4_x</th> <td>    1.4914</td> <td>    0.032</td> <td>   46.498</td> <td> 0.000</td> <td>    1.429</td> <td>    1.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_A_x</th>          <td>   -2.4667</td> <td>    0.710</td> <td>   -3.472</td> <td> 0.001</td> <td>   -3.859</td> <td>   -1.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_B_x</th>          <td>  -34.6906</td> <td>    0.816</td> <td>  -42.536</td> <td> 0.000</td> <td>  -36.289</td> <td>  -33.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_C_x</th>          <td>  -23.5426</td> <td>    0.714</td> <td>  -32.959</td> <td> 0.000</td> <td>  -24.943</td> <td>  -22.143</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_D_x</th>          <td>  -32.2168</td> <td>    0.824</td> <td>  -39.100</td> <td> 0.000</td> <td>  -33.832</td> <td>  -30.602</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_players_y</th>          <td>   -0.1507</td> <td>    0.062</td> <td>   -2.414</td> <td> 0.016</td> <td>   -0.273</td> <td>   -0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance1_2_y</th>        <td>    0.0772</td> <td>    0.002</td> <td>   42.969</td> <td> 0.000</td> <td>    0.074</td> <td>    0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance1_3_y</th>        <td>    0.0165</td> <td>    0.004</td> <td>    4.353</td> <td> 0.000</td> <td>    0.009</td> <td>    0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance1_4_y</th>        <td>   -0.0050</td> <td>    0.004</td> <td>   -1.380</td> <td> 0.168</td> <td>   -0.012</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance2_3_y</th>        <td>    0.0408</td> <td>    0.002</td> <td>   23.565</td> <td> 0.000</td> <td>    0.037</td> <td>    0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance2_4_y</th>        <td>    0.0280</td> <td>    0.002</td> <td>   13.044</td> <td> 0.000</td> <td>    0.024</td> <td>    0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance3_4_y</th>        <td>    0.0250</td> <td>    0.001</td> <td>   17.638</td> <td> 0.000</td> <td>    0.022</td> <td>    0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance_centroid1_y</th> <td>   -0.0178</td> <td>    0.002</td> <td>  -11.543</td> <td> 0.000</td> <td>   -0.021</td> <td>   -0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance_centroid2_y</th> <td>   -0.0113</td> <td>    0.001</td> <td>   -8.376</td> <td> 0.000</td> <td>   -0.014</td> <td>   -0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance_centroid3_y</th> <td>   -0.0007</td> <td>    0.001</td> <td>   -0.533</td> <td> 0.594</td> <td>   -0.003</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance_centroid4_y</th> <td>   -0.0177</td> <td>    0.001</td> <td>  -17.076</td> <td> 0.000</td> <td>   -0.020</td> <td>   -0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_A_y</th>          <td>  217.9898</td> <td>   11.213</td> <td>   19.440</td> <td> 0.000</td> <td>  196.012</td> <td>  239.968</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_B_y</th>          <td>  233.6394</td> <td>   11.224</td> <td>   20.816</td> <td> 0.000</td> <td>  211.641</td> <td>  255.638</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_C_y</th>          <td>  235.2722</td> <td>   11.240</td> <td>   20.931</td> <td> 0.000</td> <td>  213.242</td> <td>  257.303</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cluster_D_y</th>          <td>  266.9636</td> <td>   11.258</td> <td>   23.714</td> <td> 0.000</td> <td>  244.899</td> <td>  289.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>agility_y</th>            <td>   31.8090</td> <td>    0.103</td> <td>  308.115</td> <td> 0.000</td> <td>   31.607</td> <td>   32.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_risk1</th>        <td>   -0.0012</td> <td>    0.001</td> <td>   -2.247</td> <td> 0.025</td> <td>   -0.002</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_risk2</th>        <td>   -0.0027</td> <td>    0.001</td> <td>   -4.587</td> <td> 0.000</td> <td>   -0.004</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_risk3</th>        <td>   -0.0049</td> <td>    0.001</td> <td>   -8.252</td> <td> 0.000</td> <td>   -0.006</td> <td>   -0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_risk4</th>        <td>   -0.0036</td> <td>    0.001</td> <td>   -5.363</td> <td> 0.000</td> <td>   -0.005</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>risk1</th>                <td>    0.1053</td> <td>    0.003</td> <td>   37.118</td> <td> 0.000</td> <td>    0.100</td> <td>    0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>risk2</th>                <td>    0.1260</td> <td>    0.003</td> <td>   41.298</td> <td> 0.000</td> <td>    0.120</td> <td>    0.132</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>risk3</th>                <td>    0.1288</td> <td>    0.003</td> <td>   41.517</td> <td> 0.000</td> <td>    0.123</td> <td>    0.135</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>risk4</th>                <td>    0.1193</td> <td>    0.003</td> <td>   35.391</td> <td> 0.000</td> <td>    0.113</td> <td>    0.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>risk_std1</th>            <td>   -0.0849</td> <td>    0.003</td> <td>  -31.690</td> <td> 0.000</td> <td>   -0.090</td> <td>   -0.080</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>risk_std2</th>            <td>   -0.1078</td> <td>    0.003</td> <td>  -38.051</td> <td> 0.000</td> <td>   -0.113</td> <td>   -0.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>risk_std3</th>            <td>   -0.1049</td> <td>    0.003</td> <td>  -36.096</td> <td> 0.000</td> <td>   -0.111</td> <td>   -0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>risk_std4</th>            <td>   -0.0914</td> <td>    0.003</td> <td>  -28.931</td> <td> 0.000</td> <td>   -0.098</td> <td>   -0.085</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>87931.108</td> <th>  Durbin-Watson:     </th>  <td>   1.771</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>961929.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.686</td>   <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>10.251</td>   <th>  Cond. No.          </th>  <td>2.93e+05</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.93e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              ranking_y   R-squared:                       0.305\n",
       "Model:                            OLS   Adj. R-squared:                  0.305\n",
       "Method:                 Least Squares   F-statistic:                     4319.\n",
       "Date:                Mon, 22 Jun 2020   Prob (F-statistic):               0.00\n",
       "Time:                        16:24:17   Log-Likelihood:            -1.4212e+06\n",
       "No. Observations:              423944   AIC:                         2.843e+06\n",
       "Df Residuals:                  423900   BIC:                         2.843e+06\n",
       "Df Model:                          43                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "const                 -214.5617     11.296    -18.995      0.000    -236.701    -192.422\n",
       "teamId                   0.0171      0.000     55.362      0.000       0.017       0.018\n",
       "distance1_2_x           -1.0044      0.052    -19.311      0.000      -1.106      -0.902\n",
       "distance1_3_x            0.6239      0.063      9.968      0.000       0.501       0.747\n",
       "distance1_4_x            0.2525      0.043      5.919      0.000       0.169       0.336\n",
       "distance2_3_x            0.8053      0.051     15.831      0.000       0.706       0.905\n",
       "distance2_4_x           -1.2859      0.057    -22.739      0.000      -1.397      -1.175\n",
       "distance3_4_x            0.4134      0.039     10.654      0.000       0.337       0.489\n",
       "distance_centroid1_x     2.9352      0.023    126.304      0.000       2.890       2.981\n",
       "distance_centroid2_x     1.2577      0.044     28.702      0.000       1.172       1.344\n",
       "distance_centroid3_x    -1.5053      0.047    -32.157      0.000      -1.597      -1.414\n",
       "distance_centroid4_x     1.4914      0.032     46.498      0.000       1.429       1.554\n",
       "cluster_A_x             -2.4667      0.710     -3.472      0.001      -3.859      -1.074\n",
       "cluster_B_x            -34.6906      0.816    -42.536      0.000     -36.289     -33.092\n",
       "cluster_C_x            -23.5426      0.714    -32.959      0.000     -24.943     -22.143\n",
       "cluster_D_x            -32.2168      0.824    -39.100      0.000     -33.832     -30.602\n",
       "n_players_y             -0.1507      0.062     -2.414      0.016      -0.273      -0.028\n",
       "distance1_2_y            0.0772      0.002     42.969      0.000       0.074       0.081\n",
       "distance1_3_y            0.0165      0.004      4.353      0.000       0.009       0.024\n",
       "distance1_4_y           -0.0050      0.004     -1.380      0.168      -0.012       0.002\n",
       "distance2_3_y            0.0408      0.002     23.565      0.000       0.037       0.044\n",
       "distance2_4_y            0.0280      0.002     13.044      0.000       0.024       0.032\n",
       "distance3_4_y            0.0250      0.001     17.638      0.000       0.022       0.028\n",
       "distance_centroid1_y    -0.0178      0.002    -11.543      0.000      -0.021      -0.015\n",
       "distance_centroid2_y    -0.0113      0.001     -8.376      0.000      -0.014      -0.009\n",
       "distance_centroid3_y    -0.0007      0.001     -0.533      0.594      -0.003       0.002\n",
       "distance_centroid4_y    -0.0177      0.001    -17.076      0.000      -0.020      -0.016\n",
       "cluster_A_y            217.9898     11.213     19.440      0.000     196.012     239.968\n",
       "cluster_B_y            233.6394     11.224     20.816      0.000     211.641     255.638\n",
       "cluster_C_y            235.2722     11.240     20.931      0.000     213.242     257.303\n",
       "cluster_D_y            266.9636     11.258     23.714      0.000     244.899     289.028\n",
       "agility_y               31.8090      0.103    308.115      0.000      31.607      32.011\n",
       "landing_risk1           -0.0012      0.001     -2.247      0.025      -0.002      -0.000\n",
       "landing_risk2           -0.0027      0.001     -4.587      0.000      -0.004      -0.002\n",
       "landing_risk3           -0.0049      0.001     -8.252      0.000      -0.006      -0.004\n",
       "landing_risk4           -0.0036      0.001     -5.363      0.000      -0.005      -0.002\n",
       "risk1                    0.1053      0.003     37.118      0.000       0.100       0.111\n",
       "risk2                    0.1260      0.003     41.298      0.000       0.120       0.132\n",
       "risk3                    0.1288      0.003     41.517      0.000       0.123       0.135\n",
       "risk4                    0.1193      0.003     35.391      0.000       0.113       0.126\n",
       "risk_std1               -0.0849      0.003    -31.690      0.000      -0.090      -0.080\n",
       "risk_std2               -0.1078      0.003    -38.051      0.000      -0.113      -0.102\n",
       "risk_std3               -0.1049      0.003    -36.096      0.000      -0.111      -0.099\n",
       "risk_std4               -0.0914      0.003    -28.931      0.000      -0.098      -0.085\n",
       "==============================================================================\n",
       "Omnibus:                    87931.108   Durbin-Watson:                   1.771\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           961929.041\n",
       "Skew:                           0.686   Prob(JB):                         0.00\n",
       "Kurtosis:                      10.251   Cond. No.                     2.93e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.93e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_df=rank.copy()\n",
    "copy1_df=rank.copy()\n",
    "x_df=rank.drop([\"ranking_y\"],axis=1)\n",
    "y_df=copy_df[\"ranking_y\"]\n",
    "X_df=x_df.iloc[:,2:]\n",
    "\n",
    "X_df=sm.add_constant(X_df)\n",
    "model = sm.OLS(y_df,X_df.astype(float)).fit()\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RMSE for Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.913142137301564"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(X_df)\n",
    "rms = sqrt(mean_squared_error(y_df, y_pred))\n",
    "rms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification for Top 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy_df['in Top 5']=np.where(copy_df['ranking_y']<=5, 1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_df=copy_df.drop([\"ranking_y\",'in Top 5'],axis=1)\n",
    "y_df=copy_df['in Top 5']\n",
    "X_df=x_df.iloc[:,2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling with Standard Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_scaler=preprocessing.StandardScaler()\n",
    "X_df=std_scaler.fit_transform(X_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialising DFs for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_w1=pd.DataFrame()\n",
    "res_w2=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(class_weight='balanced', solver='liblinear')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_size=0.2\n",
    "seed=7\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_df, y_df, test_size=test_size, random_state=seed)\n",
    "model = LogisticRegression(solver='liblinear',class_weight='balanced')\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "[[45768 20801]\n",
      " [ 4487 13733]]\n",
      "\n",
      "Accuracy 70.17538\n",
      "Accuracy check with conf. matrix 70.17538\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:143: FutureWarning: The sklearn.metrics.classification module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/sklearn/base.py:213: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  FutureWarning)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/yellowbrick/classifier/base.py:232: YellowbrickWarning: could not determine class_counts_ from previously fitted classifier\n",
      "  YellowbrickWarning,\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d3f3350>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = model.predict(X_test)\n",
    "\n",
    "c_matrix=confusion_matrix(y_test, y_predicted)\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "print(c_matrix)\n",
    "\n",
    "print()\n",
    "print(f'Accuracy {model.score(X_test, y_test)*100:.5f}')\n",
    "print(f'Accuracy check with conf. matrix {(c_matrix[0,0]+c_matrix[1,1])/c_matrix.sum()*100:.5f}')\n",
    "\n",
    "\n",
    "#using yellowbrick\n",
    "import yellowbrick\n",
    "from yellowbrick.classifier import ConfusionMatrix\n",
    "cm = ConfusionMatrix(model, classes=[\"No Top 5\",\"Top 5\"])\n",
    "\n",
    "cm.score(X_test, y_test)\n",
    "cm.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking for overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN METRICS\n",
      "Accuracy: 0.7017528858486533\n",
      "F1 Score: 0.5224218845903249\n",
      "Average precision-recall score: 0.35\n",
      "AUC: 0.7215772333596369\n"
     ]
    }
   ],
   "source": [
    "y_pred_t = model.predict(X_train)\n",
    "print('TRAIN METRICS')\n",
    "print(f'Accuracy: {metrics.accuracy_score(y_train, y_pred_t)}')\n",
    "print(f'F1 Score: {metrics.f1_score(y_train, y_pred_t)}')\n",
    "metrics.confusion_matrix(y_train,y_pred_t)\n",
    "average_precision = average_precision_score(y_train, y_pred_t)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision))\n",
    "\n",
    "print(f'AUC: {roc_auc_score(y_train, y_pred_t)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEST METRICS\n",
      "Accuracy: 0.7017537652289801\n",
      "F1 Score: 0.5206429844182431\n",
      "Average precision-recall score: 0.35\n",
      "AUC: 0.7206296949234801\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "print('TEST METRICS')\n",
    "print(f'Accuracy: {metrics.accuracy_score(y_test, y_pred)}')\n",
    "print(f'F1 Score: {metrics.f1_score(y_test, y_pred)}')\n",
    "metrics.confusion_matrix(y_test,y_pred)\n",
    "average_precision = average_precision_score(y_test, y_pred)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision))\n",
    "print(f'AUC: {roc_auc_score(y_test, y_pred)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Max Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7851726049369612"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = LogisticRegression(random_state=1,class_weight='balanced')\n",
    "model2 = tree.DecisionTreeClassifier(random_state=1,class_weight='balanced')\n",
    "model = VotingClassifier(estimators=[('lr', model1), ('dt', model2)], voting='hard')\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()\n",
    "binary_encoded_y = pd.Series(encoder.fit_transform(y_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X, test_X, train_y, test_y = train_test_split(X_df, binary_encoded_y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),\n",
       "                   n_estimators=200)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1),n_estimators=200)\n",
    "classifier.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = classifier.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7985205593191553\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(test_y, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[78687,  4288],\n",
       "       [17066,  5945]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(test_y, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.51010654, -1.21890519, -1.22767395, ..., -0.69108713,\n",
       "        -0.66832681, -0.37131288],\n",
       "       [-0.49008428, -1.21890519, -1.22767395, ...,  0.28298286,\n",
       "        -0.44358468, -0.5639839 ],\n",
       "       [-0.47006201,  1.22551731,  1.01195896, ..., -0.28229106,\n",
       "        -0.58937865, -0.51110275],\n",
       "       ...,\n",
       "       [ 3.51436925,  0.47552483,  1.36174967, ..., -0.59322215,\n",
       "        -0.1565663 , -0.23856049],\n",
       "       [ 3.53439152,  0.47398567,  0.83450407, ..., -0.54128426,\n",
       "        -0.16747855, -0.42786141],\n",
       "       [ 3.55441379, -0.01403049,  0.25697131, ..., -0.49616773,\n",
       "        -0.20376457, -0.41812552]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    332585\n",
       "1     91359\n",
       "Name: in Top 5, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Upsampling SMOTE Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE(random_state=12)\n",
    "x_train_res, y_train_res = sm.fit_sample(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),\n",
       "                   n_estimators=200)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1),n_estimators=200)\n",
    "classifier.fit(x_train_res, y_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = classifier.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7438907025456193\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(test_y, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.07053922, -1.21890519, -1.22767395, ..., -0.54275211,\n",
       "        -0.39849939, -0.5639839 ],\n",
       "       [-0.2898616 ,  0.10303912,  0.29946494, ..., -0.69108713,\n",
       "        -0.66832681, -0.5639839 ],\n",
       "       [-0.26983933,  0.42787812,  0.5323468 , ..., -0.69108713,\n",
       "        -0.66832681, -0.5639839 ],\n",
       "       ...,\n",
       "       [-0.28477661,  1.94928559,  2.18249475, ..., -0.69108713,\n",
       "        -0.66832681, -0.5639839 ],\n",
       "       [-0.31332102,  0.3365026 ,  0.3085196 , ...,  0.96481253,\n",
       "         1.30002077,  1.55112908],\n",
       "       [-0.40191209, -1.21890519, -1.22767395, ...,  0.07560855,\n",
       "         0.48083528, -0.5639839 ]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    499220.000000\n",
       "mean         -0.069257\n",
       "std           0.897291\n",
       "min          -0.510107\n",
       "25%          -0.409995\n",
       "50%          -0.289862\n",
       "75%          -0.149706\n",
       "max           3.694570\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(x_train_res)[0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4.239440e+05\n",
       "mean    -1.085104e-16\n",
       "std      1.000001e+00\n",
       "min     -5.101065e-01\n",
       "25%     -4.099952e-01\n",
       "50%     -2.698393e-01\n",
       "75%     -1.096612e-01\n",
       "max      3.694570e+00\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_df)[0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.51010654, -1.21890519, -1.22767395, ..., -0.69108713,\n",
       "        -0.66832681, -0.37131288],\n",
       "       [-0.49008428, -1.21890519, -1.22767395, ...,  0.28298286,\n",
       "        -0.44358468, -0.5639839 ],\n",
       "       [-0.47006201,  1.22551731,  1.01195896, ..., -0.28229106,\n",
       "        -0.58937865, -0.51110275],\n",
       "       ...,\n",
       "       [ 3.51436925,  0.47552483,  1.36174967, ..., -0.59322215,\n",
       "        -0.1565663 , -0.23856049],\n",
       "       [ 3.53439152,  0.47398567,  0.83450407, ..., -0.54128426,\n",
       "        -0.16747855, -0.42786141],\n",
       "       [ 3.55441379, -0.01403049,  0.25697131, ..., -0.49616773,\n",
       "        -0.20376457, -0.41812552]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "teams_f=copy1_df.replace(99,np.nan).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    277885.0\n",
       "mean          4.0\n",
       "std           0.0\n",
       "min           4.0\n",
       "25%           4.0\n",
       "50%           4.0\n",
       "75%           4.0\n",
       "max           4.0\n",
       "Name: n_players_y, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams_f.n_players_y.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.18538657134516412, 0.0)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "stats.pearsonr(copy1_df.n_players_y, copy1_df.ranking_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "teamId                  0.194063\n",
       "distance1_2_x          -0.054573\n",
       "distance1_3_x          -0.015644\n",
       "distance1_4_x          -0.019530\n",
       "distance2_3_x           0.069620\n",
       "distance2_4_x           0.080956\n",
       "distance3_4_x           0.107803\n",
       "distance_centroid1_x    0.120875\n",
       "distance_centroid2_x   -0.045753\n",
       "distance_centroid3_x   -0.008070\n",
       "distance_centroid4_x    0.095208\n",
       "cluster_A_x             0.003181\n",
       "cluster_B_x            -0.081307\n",
       "cluster_C_x             0.066099\n",
       "cluster_D_x             0.107778\n",
       "n_players_y                  NaN\n",
       "distance1_2_y           0.182546\n",
       "distance1_3_y           0.153502\n",
       "distance1_4_y           0.172881\n",
       "distance2_3_y           0.114823\n",
       "distance2_4_y           0.124367\n",
       "distance3_4_y           0.129793\n",
       "distance_centroid1_y    0.136212\n",
       "distance_centroid2_y    0.180789\n",
       "distance_centroid3_y    0.139743\n",
       "distance_centroid4_y    0.058441\n",
       "cluster_A_y            -0.163432\n",
       "cluster_B_y             0.122436\n",
       "cluster_C_y             0.167714\n",
       "cluster_D_y             0.173475\n",
       "agility_y               0.296785\n",
       "ranking_y               1.000000\n",
       "landing_risk1          -0.049426\n",
       "landing_risk2          -0.045801\n",
       "landing_risk3          -0.050827\n",
       "landing_risk4          -0.050987\n",
       "risk1                   0.017664\n",
       "risk2                   0.018364\n",
       "risk3                   0.017439\n",
       "risk4                   0.017719\n",
       "risk_std1              -0.044731\n",
       "risk_std2              -0.043611\n",
       "risk_std3              -0.045036\n",
       "risk_std4              -0.043571\n",
       "Name: ranking_y, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams_f[teams_f.columns[1:]].corr()['ranking_y'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c632650>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn\n",
    "corr_df=teams_f.corr(method='pearson')\n",
    "seaborn.heatmap(corr_df,linewidths=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>Unnamed: 0</th>\n",
       "      <th>teamId</th>\n",
       "      <th>distance1_2_x</th>\n",
       "      <th>distance1_3_x</th>\n",
       "      <th>distance1_4_x</th>\n",
       "      <th>distance2_3_x</th>\n",
       "      <th>distance2_4_x</th>\n",
       "      <th>distance3_4_x</th>\n",
       "      <th>distance_centroid1_x</th>\n",
       "      <th>distance_centroid2_x</th>\n",
       "      <th>...</th>\n",
       "      <th>landing_risk3</th>\n",
       "      <th>landing_risk4</th>\n",
       "      <th>risk1</th>\n",
       "      <th>risk2</th>\n",
       "      <th>risk3</th>\n",
       "      <th>risk4</th>\n",
       "      <th>risk_std1</th>\n",
       "      <th>risk_std2</th>\n",
       "      <th>risk_std3</th>\n",
       "      <th>risk_std4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>teamId</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance1_2_x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.656079</td>\n",
       "      <td>0.607269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.954867</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance1_3_x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.656079</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.737945</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.623837</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance1_4_x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.607269</td>\n",
       "      <td>0.737945</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance2_3_x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.867308</td>\n",
       "      <td>0.633628</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance2_4_x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.867308</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.717462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance3_4_x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.633628</td>\n",
       "      <td>0.717462</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_centroid1_x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_centroid2_x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.954867</td>\n",
       "      <td>0.623837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_centroid3_x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.899644</td>\n",
       "      <td>0.705430</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.633234</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_centroid4_x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_A_x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_B_x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_C_x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_D_x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_players_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance1_2_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.616193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance1_3_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.679312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance1_4_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance2_3_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance2_4_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance3_4_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_centroid1_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_centroid2_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.604513</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.629643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_centroid3_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.671972</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_centroid4_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.619851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_A_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_B_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_C_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_D_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agility_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ranking_y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>landing_risk1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.643473</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>landing_risk2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.601291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.643047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>landing_risk3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.640721</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>landing_risk4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.605042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.649076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.640199</td>\n",
       "      <td>0.637269</td>\n",
       "      <td>0.628830</td>\n",
       "      <td>0.927427</td>\n",
       "      <td>0.618206</td>\n",
       "      <td>0.618178</td>\n",
       "      <td>0.609677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.640199</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.638208</td>\n",
       "      <td>0.631757</td>\n",
       "      <td>0.619024</td>\n",
       "      <td>0.926982</td>\n",
       "      <td>0.617448</td>\n",
       "      <td>0.612011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.637269</td>\n",
       "      <td>0.638208</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.632788</td>\n",
       "      <td>0.617418</td>\n",
       "      <td>0.616159</td>\n",
       "      <td>0.926372</td>\n",
       "      <td>0.611144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.605042</td>\n",
       "      <td>0.628830</td>\n",
       "      <td>0.631757</td>\n",
       "      <td>0.632788</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.609324</td>\n",
       "      <td>0.611589</td>\n",
       "      <td>0.612294</td>\n",
       "      <td>0.927203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk_std1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.927427</td>\n",
       "      <td>0.619024</td>\n",
       "      <td>0.617418</td>\n",
       "      <td>0.609324</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.648762</td>\n",
       "      <td>0.650308</td>\n",
       "      <td>0.640796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk_std2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.618206</td>\n",
       "      <td>0.926982</td>\n",
       "      <td>0.616159</td>\n",
       "      <td>0.611589</td>\n",
       "      <td>0.648762</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.647379</td>\n",
       "      <td>0.643153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk_std3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.640721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.618178</td>\n",
       "      <td>0.617448</td>\n",
       "      <td>0.926372</td>\n",
       "      <td>0.612294</td>\n",
       "      <td>0.650308</td>\n",
       "      <td>0.647379</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.643023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk_std4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.649076</td>\n",
       "      <td>0.609677</td>\n",
       "      <td>0.612011</td>\n",
       "      <td>0.611144</td>\n",
       "      <td>0.927203</td>\n",
       "      <td>0.640796</td>\n",
       "      <td>0.643153</td>\n",
       "      <td>0.643023</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Unnamed: 0    teamId  distance1_2_x  distance1_3_x  \\\n",
       "Unnamed: 0                   1.0       NaN            NaN            NaN   \n",
       "teamId                       NaN  1.000000            NaN            NaN   \n",
       "distance1_2_x                NaN       NaN       1.000000       0.656079   \n",
       "distance1_3_x                NaN       NaN       0.656079       1.000000   \n",
       "distance1_4_x                NaN       NaN       0.607269       0.737945   \n",
       "distance2_3_x                NaN       NaN            NaN            NaN   \n",
       "distance2_4_x                NaN       NaN            NaN            NaN   \n",
       "distance3_4_x                NaN       NaN            NaN            NaN   \n",
       "distance_centroid1_x         NaN       NaN            NaN            NaN   \n",
       "distance_centroid2_x         NaN       NaN       0.954867       0.623837   \n",
       "distance_centroid3_x         NaN       NaN            NaN       0.899644   \n",
       "distance_centroid4_x         NaN       NaN            NaN            NaN   \n",
       "cluster_A_x                  NaN       NaN            NaN            NaN   \n",
       "cluster_B_x                  NaN       NaN            NaN            NaN   \n",
       "cluster_C_x                  NaN       NaN            NaN            NaN   \n",
       "cluster_D_x                  NaN       NaN            NaN            NaN   \n",
       "n_players_y                  NaN       NaN            NaN            NaN   \n",
       "distance1_2_y                NaN       NaN            NaN            NaN   \n",
       "distance1_3_y                NaN       NaN            NaN            NaN   \n",
       "distance1_4_y                NaN       NaN            NaN            NaN   \n",
       "distance2_3_y                NaN       NaN            NaN            NaN   \n",
       "distance2_4_y                NaN       NaN            NaN            NaN   \n",
       "distance3_4_y                NaN       NaN            NaN            NaN   \n",
       "distance_centroid1_y         NaN       NaN            NaN            NaN   \n",
       "distance_centroid2_y         NaN  0.604513            NaN            NaN   \n",
       "distance_centroid3_y         NaN       NaN            NaN            NaN   \n",
       "distance_centroid4_y         NaN       NaN            NaN            NaN   \n",
       "cluster_A_y                  NaN       NaN            NaN            NaN   \n",
       "cluster_B_y                  NaN       NaN            NaN            NaN   \n",
       "cluster_C_y                  NaN       NaN            NaN            NaN   \n",
       "cluster_D_y                  NaN       NaN            NaN            NaN   \n",
       "agility_y                    NaN       NaN            NaN            NaN   \n",
       "ranking_y                    NaN       NaN            NaN            NaN   \n",
       "landing_risk1                NaN       NaN            NaN            NaN   \n",
       "landing_risk2                NaN       NaN            NaN            NaN   \n",
       "landing_risk3                NaN       NaN            NaN            NaN   \n",
       "landing_risk4                NaN       NaN            NaN            NaN   \n",
       "risk1                        NaN       NaN            NaN            NaN   \n",
       "risk2                        NaN       NaN            NaN            NaN   \n",
       "risk3                        NaN       NaN            NaN            NaN   \n",
       "risk4                        NaN       NaN            NaN            NaN   \n",
       "risk_std1                    NaN       NaN            NaN            NaN   \n",
       "risk_std2                    NaN       NaN            NaN            NaN   \n",
       "risk_std3                    NaN       NaN            NaN            NaN   \n",
       "risk_std4                    NaN       NaN            NaN            NaN   \n",
       "\n",
       "                      distance1_4_x  distance2_3_x  distance2_4_x  \\\n",
       "Unnamed: 0                      NaN            NaN            NaN   \n",
       "teamId                          NaN            NaN            NaN   \n",
       "distance1_2_x              0.607269            NaN            NaN   \n",
       "distance1_3_x              0.737945            NaN            NaN   \n",
       "distance1_4_x              1.000000            NaN            NaN   \n",
       "distance2_3_x                   NaN       1.000000       0.867308   \n",
       "distance2_4_x                   NaN       0.867308       1.000000   \n",
       "distance3_4_x                   NaN       0.633628       0.717462   \n",
       "distance_centroid1_x            NaN            NaN            NaN   \n",
       "distance_centroid2_x            NaN            NaN            NaN   \n",
       "distance_centroid3_x       0.705430            NaN            NaN   \n",
       "distance_centroid4_x            NaN            NaN            NaN   \n",
       "cluster_A_x                     NaN            NaN            NaN   \n",
       "cluster_B_x                     NaN            NaN            NaN   \n",
       "cluster_C_x                     NaN            NaN            NaN   \n",
       "cluster_D_x                     NaN            NaN            NaN   \n",
       "n_players_y                     NaN            NaN            NaN   \n",
       "distance1_2_y                   NaN            NaN            NaN   \n",
       "distance1_3_y                   NaN            NaN            NaN   \n",
       "distance1_4_y                   NaN            NaN            NaN   \n",
       "distance2_3_y                   NaN            NaN            NaN   \n",
       "distance2_4_y                   NaN            NaN            NaN   \n",
       "distance3_4_y                   NaN            NaN            NaN   \n",
       "distance_centroid1_y            NaN            NaN            NaN   \n",
       "distance_centroid2_y            NaN            NaN            NaN   \n",
       "distance_centroid3_y            NaN            NaN            NaN   \n",
       "distance_centroid4_y            NaN            NaN            NaN   \n",
       "cluster_A_y                     NaN            NaN            NaN   \n",
       "cluster_B_y                     NaN            NaN            NaN   \n",
       "cluster_C_y                     NaN            NaN            NaN   \n",
       "cluster_D_y                     NaN            NaN            NaN   \n",
       "agility_y                       NaN            NaN            NaN   \n",
       "ranking_y                       NaN            NaN            NaN   \n",
       "landing_risk1                   NaN            NaN            NaN   \n",
       "landing_risk2                   NaN            NaN            NaN   \n",
       "landing_risk3                   NaN            NaN            NaN   \n",
       "landing_risk4                   NaN            NaN            NaN   \n",
       "risk1                           NaN            NaN            NaN   \n",
       "risk2                           NaN            NaN            NaN   \n",
       "risk3                           NaN            NaN            NaN   \n",
       "risk4                           NaN            NaN            NaN   \n",
       "risk_std1                       NaN            NaN            NaN   \n",
       "risk_std2                       NaN            NaN            NaN   \n",
       "risk_std3                       NaN            NaN            NaN   \n",
       "risk_std4                       NaN            NaN            NaN   \n",
       "\n",
       "                      distance3_4_x  distance_centroid1_x  \\\n",
       "Unnamed: 0                      NaN                   NaN   \n",
       "teamId                          NaN                   NaN   \n",
       "distance1_2_x                   NaN                   NaN   \n",
       "distance1_3_x                   NaN                   NaN   \n",
       "distance1_4_x                   NaN                   NaN   \n",
       "distance2_3_x              0.633628                   NaN   \n",
       "distance2_4_x              0.717462                   NaN   \n",
       "distance3_4_x              1.000000                   NaN   \n",
       "distance_centroid1_x            NaN              1.000000   \n",
       "distance_centroid2_x            NaN                   NaN   \n",
       "distance_centroid3_x            NaN                   NaN   \n",
       "distance_centroid4_x            NaN                   NaN   \n",
       "cluster_A_x                     NaN                   NaN   \n",
       "cluster_B_x                     NaN                   NaN   \n",
       "cluster_C_x                     NaN                   NaN   \n",
       "cluster_D_x                     NaN                   NaN   \n",
       "n_players_y                     NaN                   NaN   \n",
       "distance1_2_y                   NaN              0.616193   \n",
       "distance1_3_y                   NaN              0.679312   \n",
       "distance1_4_y                   NaN                   NaN   \n",
       "distance2_3_y                   NaN                   NaN   \n",
       "distance2_4_y                   NaN                   NaN   \n",
       "distance3_4_y                   NaN                   NaN   \n",
       "distance_centroid1_y            NaN                   NaN   \n",
       "distance_centroid2_y            NaN              0.629643   \n",
       "distance_centroid3_y            NaN              0.671972   \n",
       "distance_centroid4_y            NaN              0.619851   \n",
       "cluster_A_y                     NaN                   NaN   \n",
       "cluster_B_y                     NaN                   NaN   \n",
       "cluster_C_y                     NaN                   NaN   \n",
       "cluster_D_y                     NaN                   NaN   \n",
       "agility_y                       NaN                   NaN   \n",
       "ranking_y                       NaN                   NaN   \n",
       "landing_risk1                   NaN                   NaN   \n",
       "landing_risk2                   NaN                   NaN   \n",
       "landing_risk3                   NaN                   NaN   \n",
       "landing_risk4                   NaN                   NaN   \n",
       "risk1                           NaN                   NaN   \n",
       "risk2                           NaN                   NaN   \n",
       "risk3                           NaN                   NaN   \n",
       "risk4                           NaN                   NaN   \n",
       "risk_std1                       NaN                   NaN   \n",
       "risk_std2                       NaN                   NaN   \n",
       "risk_std3                       NaN                   NaN   \n",
       "risk_std4                       NaN                   NaN   \n",
       "\n",
       "                      distance_centroid2_x  ...  landing_risk3  landing_risk4  \\\n",
       "Unnamed: 0                             NaN  ...            NaN            NaN   \n",
       "teamId                                 NaN  ...            NaN            NaN   \n",
       "distance1_2_x                     0.954867  ...            NaN            NaN   \n",
       "distance1_3_x                     0.623837  ...            NaN            NaN   \n",
       "distance1_4_x                          NaN  ...            NaN            NaN   \n",
       "distance2_3_x                          NaN  ...            NaN            NaN   \n",
       "distance2_4_x                          NaN  ...            NaN            NaN   \n",
       "distance3_4_x                          NaN  ...            NaN            NaN   \n",
       "distance_centroid1_x                   NaN  ...            NaN            NaN   \n",
       "distance_centroid2_x              1.000000  ...            NaN            NaN   \n",
       "distance_centroid3_x              0.633234  ...            NaN            NaN   \n",
       "distance_centroid4_x                   NaN  ...            NaN            NaN   \n",
       "cluster_A_x                            NaN  ...            NaN            NaN   \n",
       "cluster_B_x                            NaN  ...            NaN            NaN   \n",
       "cluster_C_x                            NaN  ...            NaN            NaN   \n",
       "cluster_D_x                            NaN  ...            NaN            NaN   \n",
       "n_players_y                            NaN  ...            NaN            NaN   \n",
       "distance1_2_y                          NaN  ...            NaN            NaN   \n",
       "distance1_3_y                          NaN  ...            NaN            NaN   \n",
       "distance1_4_y                          NaN  ...            NaN            NaN   \n",
       "distance2_3_y                          NaN  ...            NaN            NaN   \n",
       "distance2_4_y                          NaN  ...            NaN            NaN   \n",
       "distance3_4_y                          NaN  ...            NaN            NaN   \n",
       "distance_centroid1_y                   NaN  ...            NaN            NaN   \n",
       "distance_centroid2_y                   NaN  ...            NaN            NaN   \n",
       "distance_centroid3_y                   NaN  ...            NaN            NaN   \n",
       "distance_centroid4_y                   NaN  ...            NaN            NaN   \n",
       "cluster_A_y                            NaN  ...            NaN            NaN   \n",
       "cluster_B_y                            NaN  ...            NaN            NaN   \n",
       "cluster_C_y                            NaN  ...            NaN            NaN   \n",
       "cluster_D_y                            NaN  ...            NaN            NaN   \n",
       "agility_y                              NaN  ...            NaN            NaN   \n",
       "ranking_y                              NaN  ...            NaN            NaN   \n",
       "landing_risk1                          NaN  ...            NaN            NaN   \n",
       "landing_risk2                          NaN  ...            NaN            NaN   \n",
       "landing_risk3                          NaN  ...       1.000000            NaN   \n",
       "landing_risk4                          NaN  ...            NaN       1.000000   \n",
       "risk1                                  NaN  ...            NaN            NaN   \n",
       "risk2                                  NaN  ...            NaN            NaN   \n",
       "risk3                                  NaN  ...            NaN            NaN   \n",
       "risk4                                  NaN  ...            NaN       0.605042   \n",
       "risk_std1                              NaN  ...            NaN            NaN   \n",
       "risk_std2                              NaN  ...            NaN            NaN   \n",
       "risk_std3                              NaN  ...       0.640721            NaN   \n",
       "risk_std4                              NaN  ...            NaN       0.649076   \n",
       "\n",
       "                         risk1     risk2     risk3     risk4  risk_std1  \\\n",
       "Unnamed: 0                 NaN       NaN       NaN       NaN        NaN   \n",
       "teamId                     NaN       NaN       NaN       NaN        NaN   \n",
       "distance1_2_x              NaN       NaN       NaN       NaN        NaN   \n",
       "distance1_3_x              NaN       NaN       NaN       NaN        NaN   \n",
       "distance1_4_x              NaN       NaN       NaN       NaN        NaN   \n",
       "distance2_3_x              NaN       NaN       NaN       NaN        NaN   \n",
       "distance2_4_x              NaN       NaN       NaN       NaN        NaN   \n",
       "distance3_4_x              NaN       NaN       NaN       NaN        NaN   \n",
       "distance_centroid1_x       NaN       NaN       NaN       NaN        NaN   \n",
       "distance_centroid2_x       NaN       NaN       NaN       NaN        NaN   \n",
       "distance_centroid3_x       NaN       NaN       NaN       NaN        NaN   \n",
       "distance_centroid4_x       NaN       NaN       NaN       NaN        NaN   \n",
       "cluster_A_x                NaN       NaN       NaN       NaN        NaN   \n",
       "cluster_B_x                NaN       NaN       NaN       NaN        NaN   \n",
       "cluster_C_x                NaN       NaN       NaN       NaN        NaN   \n",
       "cluster_D_x                NaN       NaN       NaN       NaN        NaN   \n",
       "n_players_y                NaN       NaN       NaN       NaN        NaN   \n",
       "distance1_2_y              NaN       NaN       NaN       NaN        NaN   \n",
       "distance1_3_y              NaN       NaN       NaN       NaN        NaN   \n",
       "distance1_4_y              NaN       NaN       NaN       NaN        NaN   \n",
       "distance2_3_y              NaN       NaN       NaN       NaN        NaN   \n",
       "distance2_4_y              NaN       NaN       NaN       NaN        NaN   \n",
       "distance3_4_y              NaN       NaN       NaN       NaN        NaN   \n",
       "distance_centroid1_y       NaN       NaN       NaN       NaN        NaN   \n",
       "distance_centroid2_y       NaN       NaN       NaN       NaN        NaN   \n",
       "distance_centroid3_y       NaN       NaN       NaN       NaN        NaN   \n",
       "distance_centroid4_y       NaN       NaN       NaN       NaN        NaN   \n",
       "cluster_A_y                NaN       NaN       NaN       NaN        NaN   \n",
       "cluster_B_y                NaN       NaN       NaN       NaN        NaN   \n",
       "cluster_C_y                NaN       NaN       NaN       NaN        NaN   \n",
       "cluster_D_y                NaN       NaN       NaN       NaN        NaN   \n",
       "agility_y                  NaN       NaN       NaN       NaN        NaN   \n",
       "ranking_y                  NaN       NaN       NaN       NaN        NaN   \n",
       "landing_risk1              NaN       NaN       NaN       NaN   0.643473   \n",
       "landing_risk2              NaN  0.601291       NaN       NaN        NaN   \n",
       "landing_risk3              NaN       NaN       NaN       NaN        NaN   \n",
       "landing_risk4              NaN       NaN       NaN  0.605042        NaN   \n",
       "risk1                 1.000000  0.640199  0.637269  0.628830   0.927427   \n",
       "risk2                 0.640199  1.000000  0.638208  0.631757   0.619024   \n",
       "risk3                 0.637269  0.638208  1.000000  0.632788   0.617418   \n",
       "risk4                 0.628830  0.631757  0.632788  1.000000   0.609324   \n",
       "risk_std1             0.927427  0.619024  0.617418  0.609324   1.000000   \n",
       "risk_std2             0.618206  0.926982  0.616159  0.611589   0.648762   \n",
       "risk_std3             0.618178  0.617448  0.926372  0.612294   0.650308   \n",
       "risk_std4             0.609677  0.612011  0.611144  0.927203   0.640796   \n",
       "\n",
       "                      risk_std2  risk_std3  risk_std4  \n",
       "Unnamed: 0                  NaN        NaN        NaN  \n",
       "teamId                      NaN        NaN        NaN  \n",
       "distance1_2_x               NaN        NaN        NaN  \n",
       "distance1_3_x               NaN        NaN        NaN  \n",
       "distance1_4_x               NaN        NaN        NaN  \n",
       "distance2_3_x               NaN        NaN        NaN  \n",
       "distance2_4_x               NaN        NaN        NaN  \n",
       "distance3_4_x               NaN        NaN        NaN  \n",
       "distance_centroid1_x        NaN        NaN        NaN  \n",
       "distance_centroid2_x        NaN        NaN        NaN  \n",
       "distance_centroid3_x        NaN        NaN        NaN  \n",
       "distance_centroid4_x        NaN        NaN        NaN  \n",
       "cluster_A_x                 NaN        NaN        NaN  \n",
       "cluster_B_x                 NaN        NaN        NaN  \n",
       "cluster_C_x                 NaN        NaN        NaN  \n",
       "cluster_D_x                 NaN        NaN        NaN  \n",
       "n_players_y                 NaN        NaN        NaN  \n",
       "distance1_2_y               NaN        NaN        NaN  \n",
       "distance1_3_y               NaN        NaN        NaN  \n",
       "distance1_4_y               NaN        NaN        NaN  \n",
       "distance2_3_y               NaN        NaN        NaN  \n",
       "distance2_4_y               NaN        NaN        NaN  \n",
       "distance3_4_y               NaN        NaN        NaN  \n",
       "distance_centroid1_y        NaN        NaN        NaN  \n",
       "distance_centroid2_y        NaN        NaN        NaN  \n",
       "distance_centroid3_y        NaN        NaN        NaN  \n",
       "distance_centroid4_y        NaN        NaN        NaN  \n",
       "cluster_A_y                 NaN        NaN        NaN  \n",
       "cluster_B_y                 NaN        NaN        NaN  \n",
       "cluster_C_y                 NaN        NaN        NaN  \n",
       "cluster_D_y                 NaN        NaN        NaN  \n",
       "agility_y                   NaN        NaN        NaN  \n",
       "ranking_y                   NaN        NaN        NaN  \n",
       "landing_risk1               NaN        NaN        NaN  \n",
       "landing_risk2          0.643047        NaN        NaN  \n",
       "landing_risk3               NaN   0.640721        NaN  \n",
       "landing_risk4               NaN        NaN   0.649076  \n",
       "risk1                  0.618206   0.618178   0.609677  \n",
       "risk2                  0.926982   0.617448   0.612011  \n",
       "risk3                  0.616159   0.926372   0.611144  \n",
       "risk4                  0.611589   0.612294   0.927203  \n",
       "risk_std1              0.648762   0.650308   0.640796  \n",
       "risk_std2              1.000000   0.647379   0.643153  \n",
       "risk_std3              0.647379   1.000000   0.643023  \n",
       "risk_std4              0.643153   0.643023   1.000000  \n",
       "\n",
       "[45 rows x 45 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_df[corr_df>0.6]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "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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