{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dc5c5de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# How expensive is it to maintain or improve your standing in German football?\n",
    "# Attempt at model building from 2017/18 and 2018/19 results and the finances from the 2017/18 season\n",
    "\n",
    "# x0\n",
    "#\n",
    "points_2017_18 = { \"Bayern\":      84, \\\n",
    "                   \"Schalke\":     63, \\\n",
    "                   \"Hoffenheim\":  55, \\\n",
    "                   \"Dortmund\":    55, \\\n",
    "                   \"Leverkusen\":  55, \\\n",
    "                   \"Leipzig\":     53, \\\n",
    "                   \"Stuttgart\":   51, \\\n",
    "                   \"Frankfurt\":   49, \\\n",
    "                   \"M'gladbach\":  47, \\\n",
    "                   \"Hertha\":      43, \\\n",
    "                   \"Bremen\":      42, \\\n",
    "                   \"Augsburg\":    41, \\\n",
    "                   \"Hannover\":    39, \\\n",
    "                   \"Mainz\":       36, \\\n",
    "                   \"Freiburg\":    36, \\\n",
    "                   \"Wolfsburg\":   33 }  # Hamburg (31) and Köln (22) relegated\n",
    "\n",
    "# y\n",
    "#\n",
    "points_2018_19 = { \"Bayern\":      78, \\\n",
    "                   \"Dortmund\":    76, \\\n",
    "                   \"Leipzig\":     66, \\\n",
    "                   \"Leverkusen\":  58, \\\n",
    "                   \"M'gladbach\":  55, \\\n",
    "                   \"Wolfsburg\":   55, \\\n",
    "                   \"Frankfurt\":   54, \\\n",
    "                   \"Bremen\":      53, \\\n",
    "                   \"Hoffenheim\":  51, \\\n",
    "                   \"Hertha\":      43, \\\n",
    "                   \"Mainz\":       43, \\\n",
    "                   \"Freiburg\":    36, \\\n",
    "                   \"Schalke\":     33, \\\n",
    "                   \"Augsburg\":    32, \\\n",
    "                   \"Stuttgart\":   28, \\\n",
    "                   \"Hannover\":    21 }  # Düsseldorf (44) and Nürnberg (19) new in this season\n",
    "\n",
    "# For the below, source of data: Deutsche Fußball Liga,\n",
    "# URL: https://media.dfl.de/sites/2/2019/05/Clubs-der-Bundesliga-2019-20-Ver%C3%B6ffentlichung-der-Angaben-gem%C3%A4%C3%9F-%C2%A78-Nr-6-j-und-k-LO.pdf\n",
    "# and https://media.dfl.de/sites/2/2019/05/Clubs-der-2.-Bundesliga-2019-20-Ver%C3%B6ffentlichung-der-Angaben-gem%C3%A4%C3%9F-%C2%A78-Nr-6-j-und-k-LO.pdf\n",
    "\n",
    "balance_2017_18 = { \"Augsburg\":   123073000, \\\n",
    "                    \"Bayern\":     736764000, \\\n",
    "                    \"Bremen\":      52078000, \\\n",
    "                    \"Dortmund\":   478331000, \\\n",
    "                    \"Frankfurt\":   97530000, \\\n",
    "                    \"Freiburg\":    97021000, \\\n",
    "                    \"Hannover\":    96769000, \\\n",
    "                    \"Hertha\":      47567000, \\\n",
    "                    \"Hoffenheim\": 260481000, \\\n",
    "                    \"Leipzig\":    247316000, \\\n",
    "                    \"Leverkusen\": 311343000, \\\n",
    "                    \"Mainz\":       75826000, \\\n",
    "                    \"M'gladbach\": 214949000, \\\n",
    "                    \"Schalke\":    273603000, \\\n",
    "                    \"Stuttgart\":  130715000, \\\n",
    "                    \"Wolfsburg\":  268504000 }\n",
    "total_bundesliga_funds = sum(balance_2017_18.values())\n",
    "\n",
    "# x1\n",
    "#\n",
    "relative_balance_2017_18 = {key: val/sum(balance_2017_18.values()) \\\n",
    "                            for key, val in balance_2017_18.items()}\n",
    "\n",
    "training = [\"Augsburg\", \"Bayern\",   \"Bremen\",  \"Dortmund\",  \"Frankfurt\", \\\n",
    "            \"Freiburg\", \"Hannover\", \"Hertha\", \"Hoffenheim\", \"Leipzig\" ]\n",
    "validation = [\"Leverkusen\", \"Mainz\", \"M'gladbach\"]\n",
    "test = [\"Schalke\", \"Stuttgart\", \"Wolfsburg\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "38da62b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_training_mean: 51.0\n"
     ]
    }
   ],
   "source": [
    "# *** MODEL 0 ***\n",
    "#\n",
    "# Average from training data, no correlation between y and x0 and/or x1 assumed\n",
    "\n",
    "y_training_mean = sum([points_2018_19[club] for club in training]) / len(training)\n",
    "print(\"y_training_mean:\", y_training_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ff8efcbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x0_array:\n",
      " [[ 1. 41.]\n",
      " [ 1. 84.]\n",
      " [ 1. 42.]\n",
      " [ 1. 55.]\n",
      " [ 1. 49.]\n",
      " [ 1. 36.]\n",
      " [ 1. 39.]\n",
      " [ 1. 43.]\n",
      " [ 1. 55.]\n",
      " [ 1. 53.]]\n",
      "\n",
      "y_array:\n",
      " [32 78 53 76 54 36 21 43 51 66]\n",
      "\n",
      "***\n",
      "Regression:\n",
      "                             OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.622\n",
      "Model:                            OLS   Adj. R-squared:                  0.574\n",
      "Method:                 Least Squares   F-statistic:                     13.14\n",
      "Date:                Tue, 07 Dec 2021   Prob (F-statistic):            0.00674\n",
      "Time:                        10:33:25   Log-Likelihood:                -38.081\n",
      "No. Observations:                  10   AIC:                             80.16\n",
      "Df Residuals:                       8   BIC:                             80.77\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -1.8644     15.085     -0.124      0.905     -36.651      32.923\n",
      "x1             1.0637      0.293      3.625      0.007       0.387       1.740\n",
      "==============================================================================\n",
      "Omnibus:                        0.095   Durbin-Watson:                   1.425\n",
      "Prob(Omnibus):                  0.954   Jarque-Bera (JB):                0.306\n",
      "Skew:                           0.114   Prob(JB):                        0.858\n",
      "Kurtosis:                       2.173   Cond. No.                         201.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/horsch/.conda/envs/local/lib/python3.9/site-packages/scipy/stats/stats.py:1541: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    }
   ],
   "source": [
    "# *** MODEL I ***\n",
    "\n",
    "# First, only correlate 2017/18 to 2018/19 results, using statsmodels\n",
    "#\n",
    "# Linear regression y = a*x0 + b\n",
    "#\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "\n",
    "x0_array = sm.add_constant(np.asarray([points_2017_18[club] for club in training]))\n",
    "y_array = np.asarray([points_2018_19[club] for club in training])\n",
    "print(\"x0_array:\\n\", x0_array)\n",
    "print(\"\\ny_array:\\n\", y_array)\n",
    "\n",
    "fit = sm.OLS(y_array, x0_array).fit()\n",
    "print(\"\\n***\\nRegression:\\n\", fit.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6d0bd933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='points in season 2017/18', ylabel='points in season 2018/19'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# *** MODEL I ***\n",
    "\n",
    "# Correlate 2017/18 to 2018/19 results graphically, using seaborn\n",
    "#\n",
    "# Linear regression y = a*x0 + b\n",
    "#\n",
    "import seaborn as sbn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(13, 8)\n",
    "plt.xticks(fontsize=18, color=\"#322300\")\n",
    "plt.yticks(fontsize=18, color=\"#322300\")\n",
    "ax.set_xlabel(\"points in season 2017/18\", fontsize=24, color=\"#322300\")\n",
    "ax.set_ylabel(\"points in season 2018/19\", fontsize=24, color=\"#322300\")\n",
    "\n",
    "sbn.regplot(x=[points_2017_18[club] for club in training], \\\n",
    "            y=[points_2018_19[club] for club in training], \\\n",
    "            color='#322300', order=1, scatter_kws={'s':40}, ci=None, truncate=False)  # black for training data\n",
    "sbn.scatterplot(x=[points_2017_18[club] for club in validation], \\\n",
    "                y=[points_2018_19[club] for club in validation], color=\"#c9211e\", s=80)  # red for val data\n",
    "sbn.scatterplot(x=[points_2017_18[club] for club in test], \\\n",
    "                y=[points_2018_19[club] for club in test], color=\"#2f3ef0\", s=120)  # blue for test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f076d570",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x1_array:\n",
      " [[1.         0.03504486]\n",
      " [1.         0.2097925 ]\n",
      " [1.         0.01482914]\n",
      " [1.         0.13620407]\n",
      " [1.         0.02777153]\n",
      " [1.         0.02762659]\n",
      " [1.         0.02755484]\n",
      " [1.         0.01354464]\n",
      " [1.         0.0741716 ]\n",
      " [1.         0.07042288]]\n",
      "\n",
      "y_array:\n",
      " [32 78 53 76 54 36 21 43 51 66]\n",
      "\n",
      "***\n",
      "Regression:\n",
      "                             OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.592\n",
      "Model:                            OLS   Adj. R-squared:                  0.541\n",
      "Method:                 Least Squares   F-statistic:                     11.59\n",
      "Date:                Tue, 07 Dec 2021   Prob (F-statistic):            0.00930\n",
      "Time:                        10:34:03   Log-Likelihood:                -38.461\n",
      "No. Observations:                  10   AIC:                             80.92\n",
      "Df Residuals:                       8   BIC:                             81.53\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         36.6011      5.824      6.284      0.000      23.170      50.032\n",
      "x1           226.0564     66.397      3.405      0.009      72.945     379.168\n",
      "==============================================================================\n",
      "Omnibus:                        0.766   Durbin-Watson:                   1.478\n",
      "Prob(Omnibus):                  0.682   Jarque-Bera (JB):                0.676\n",
      "Skew:                          -0.427   Prob(JB):                        0.713\n",
      "Kurtosis:                       2.054   Cond. No.                         16.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/horsch/.conda/envs/local/lib/python3.9/site-packages/scipy/stats/stats.py:1541: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    }
   ],
   "source": [
    "# *** MODEL II ***\n",
    "\n",
    "# Correlate 2017/18 funding to 2018/19 results, using statsmodels\n",
    "#\n",
    "# Linear regression y = a*x1 + b\n",
    "#\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "\n",
    "x1_array = sm.add_constant(np.asarray([relative_balance_2017_18[club] for club in training]))\n",
    "y_array = np.asarray([points_2018_19[club] for club in training])\n",
    "print(\"x1_array:\\n\", x1_array)\n",
    "print(\"\\ny_array:\\n\", y_array)\n",
    "\n",
    "fit = sm.OLS(y_array, x1_array).fit()\n",
    "print(\"\\n***\\nRegression:\\n\", fit.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "073d2523",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='fraction of total funds in season 2017/18', ylabel='points in season 2018/19'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# *** MODEL II ***\n",
    "\n",
    "# Correlate 2017/18 funding to 2018/19 results graphically, using seaborn\n",
    "#\n",
    "# Linear regression y = a*x1 + b\n",
    "#\n",
    "import seaborn as sbn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(13, 8)\n",
    "plt.xticks(fontsize=18, color=\"#322300\")\n",
    "plt.yticks(fontsize=18, color=\"#322300\")\n",
    "ax.set_xlabel(\"fraction of total funds in season 2017/18\", fontsize=24, color=\"#322300\")\n",
    "ax.set_ylabel(\"points in season 2018/19\", fontsize=24, color=\"#322300\")\n",
    "\n",
    "sbn.regplot(x=[relative_balance_2017_18[club] for club in training],\\\n",
    "            y=[points_2018_19[club] for club in training], \\\n",
    "            color='#322300', order=1, scatter_kws={'s':40}, ci=None, truncate=False)  # black for training data\n",
    "sbn.scatterplot(x=[relative_balance_2017_18[club] for club in validation], \\\n",
    "                y=[points_2018_19[club] for club in validation], color=\"#c9211e\", s=80)  # red for val data\n",
    "sbn.scatterplot(x=[relative_balance_2017_18[club] for club in test], \\\n",
    "                y=[points_2018_19[club] for club in test], color=\"#2f3ef0\", s=120)  # blue for test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "95f75942",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_array:\n",
      " [[1.00000000e+00 4.10000000e+01 3.50448621e-02]\n",
      " [1.00000000e+00 8.40000000e+01 2.09792504e-01]\n",
      " [1.00000000e+00 4.20000000e+01 1.48291366e-02]\n",
      " [1.00000000e+00 5.50000000e+01 1.36204074e-01]\n",
      " [1.00000000e+00 4.90000000e+01 2.77715291e-02]\n",
      " [1.00000000e+00 3.60000000e+01 2.76265921e-02]\n",
      " [1.00000000e+00 3.90000000e+01 2.75548355e-02]\n",
      " [1.00000000e+00 4.30000000e+01 1.35446358e-02]\n",
      " [1.00000000e+00 5.50000000e+01 7.41715952e-02]\n",
      " [1.00000000e+00 5.30000000e+01 7.04228801e-02]]\n",
      "\n",
      "y_array:\n",
      " [32 78 53 76 54 36 21 43 51 66]\n",
      "\n",
      "***\n",
      "Regression:\n",
      "                             OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.632\n",
      "Model:                            OLS   Adj. R-squared:                  0.527\n",
      "Method:                 Least Squares   F-statistic:                     6.016\n",
      "Date:                Tue, 07 Dec 2021   Prob (F-statistic):             0.0302\n",
      "Time:                        10:34:13   Log-Likelihood:                -37.939\n",
      "No. Observations:                  10   AIC:                             81.88\n",
      "Df Residuals:                       7   BIC:                             82.78\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         10.0256     30.828      0.325      0.755     -62.872      82.923\n",
      "x1             0.7212      0.821      0.878      0.409      -1.220       2.663\n",
      "x2            80.5197    178.867      0.450      0.666    -342.433     503.472\n",
      "==============================================================================\n",
      "Omnibus:                        0.565   Durbin-Watson:                   1.407\n",
      "Prob(Omnibus):                  0.754   Jarque-Bera (JB):                0.535\n",
      "Skew:                          -0.179   Prob(JB):                        0.765\n",
      "Kurtosis:                       1.924   Cond. No.                     2.29e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.29e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/horsch/.conda/envs/local/lib/python3.9/site-packages/scipy/stats/stats.py:1541: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    }
   ],
   "source": [
    "# *** MODEL III ***\n",
    "\n",
    "# Correlate 2017/18 results (x0) and 2017/18 funding (x1) to 2018/19 results (y), using statsmodels\n",
    "#\n",
    "# Linear regression y = a*x0 + b*x1 + c\n",
    "#\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "\n",
    "x_array = np.asarray( [[1, points_2017_18[club], relative_balance_2017_18[club]] for club in training] )\n",
    "y_array = np.asarray( [points_2018_19[club] for club in training] )\n",
    "print(\"x_array:\\n\", x_array)\n",
    "print(\"\\ny_array:\\n\", y_array)\n",
    "\n",
    "fit = sm.OLS(y_array, x_array).fit()\n",
    "print(\"\\n***\\nRegression:\\n\", fit.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c7f7723a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for validation and testing, determine the root mean square deviation between\n",
    "# data estimated using a correlation expression and the actual data\n",
    "#\n",
    "def root_mean_square_deviation(xdata, ydata, estimator, coeff):\n",
    "    ydata_estimated = [estimator(coeff, x) for x in xdata]\n",
    "    square_deviation = [(ydata[i] - ydata_estimated[i])**2 for i in range(len(ydata))]\n",
    "    mean_square_deviation = sum(square_deviation) / len(square_deviation)\n",
    "    return mean_square_deviation**0.5\n",
    "\n",
    "# model 0: y = c[0]\n",
    "#\n",
    "# null hypothesis, data are merely fluctuating around a constant value; averages determined above\n",
    "#\n",
    "def model_0(coeff, x):\n",
    "    return coeff[0]\n",
    "model_0_coeff = [y_training_mean]\n",
    "\n",
    "# model I: y = c[0] + c[1]*x[0]\n",
    "#\n",
    "# only the score from the previous year is taken into account\n",
    "# \n",
    "def model_I(coeff, x):\n",
    "    return coeff[0] + coeff[1]*x[0]\n",
    "model_I_coeff = [-1.8644, 1.0637]\n",
    "\n",
    "# model II: y = c[0] + c[1]*x[1]\n",
    "#\n",
    "# only the funding from the previous year is taken into account\n",
    "# \n",
    "def model_II(coeff, x):\n",
    "    return coeff[0] + coeff[1]*x[1]\n",
    "model_II_coeff = [36.6011, 226.0564]\n",
    "\n",
    "# model III: y = c[0] + c[1]*x[0] + c[2]*x[1]\n",
    "#\n",
    "# linear regression in both of the data items from previous year\n",
    "# \n",
    "def model_III(coeff, x):\n",
    "    return coeff[0] + coeff[1]*x[0] + coeff[1]*x[1]\n",
    "model_III_coeff = [10.0256, 0.7212, 80.5197]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "836e0513",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** MODEL VALIDATION ***\n",
      "\n",
      "x_val_dataset:\n",
      " [[55, 0.08865447752906572], [36, 0.021591345921118946], [47, 0.06120642278899845]]\n",
      "\n",
      "y_val_dataset:\n",
      " [58, 43, 55]\n",
      "Root mean square deviation between model 0 predictions and validation data:\t 6.6\n",
      "Root mean square deviation between model I predictions and validation data:\t 5.5\n",
      "Root mean square deviation between model II predictions and validation data:\t 2.9\n",
      "Root mean square deviation between model III predictions and validation data:\t 8.9\n"
     ]
    }
   ],
   "source": [
    "print(\"*** MODEL VALIDATION ***\")\n",
    "\n",
    "x_val_dataset = [[points_2017_18[club], relative_balance_2017_18[club]] for club in validation]\n",
    "y_val_dataset = [points_2018_19[club] for club in validation]\n",
    "print(\"\\nx_val_dataset:\\n\", x_val_dataset)\n",
    "print(\"\\ny_val_dataset:\\n\", y_val_dataset)\n",
    "\n",
    "print(\"Root mean square deviation between model 0 predictions and validation data:\\t\", \\\n",
    "     round(root_mean_square_deviation(x_val_dataset, y_val_dataset, model_0, model_0_coeff), 1))\n",
    "print(\"Root mean square deviation between model I predictions and validation data:\\t\", \\\n",
    "     round(root_mean_square_deviation(x_val_dataset, y_val_dataset, model_I, model_I_coeff), 1))\n",
    "print(\"Root mean square deviation between model II predictions and validation data:\\t\", \\\n",
    "     round(root_mean_square_deviation(x_val_dataset, y_val_dataset, model_II, model_II_coeff), 1))\n",
    "print(\"Root mean square deviation between model III predictions and validation data:\\t\", \\\n",
    "     round(root_mean_square_deviation(x_val_dataset, y_val_dataset, model_III, model_III_coeff), 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a7c31e36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='actual data (points in season 2018/19)', ylabel='correlated (training) and predicted (validation)'>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize actual vs. correlated data for the training data sets,\n",
    "# and actual vs. predicted data for the validation data sets\n",
    "#\n",
    "import seaborn as sbn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x_training_dataset = [[points_2017_18[club], relative_balance_2017_18[club]] for club in training]\n",
    "x_val_dataset = [[points_2017_18[club], relative_balance_2017_18[club]] for club in validation]\n",
    "x_test_dataset = [[points_2017_18[club], relative_balance_2017_18[club]] for club in test]\n",
    "\n",
    "y_training_dataset = [points_2018_19[club] for club in training]\n",
    "y_val_dataset = [points_2018_19[club] for club in validation]\n",
    "y_test_dataset = [points_2018_19[club] for club in test]  # not shown here, but below\n",
    "\n",
    "y_training_correlation_I = [model_I(model_I_coeff, x) for x in x_training_dataset]\n",
    "y_training_correlation_II = [model_II(model_II_coeff, x) for x in x_training_dataset]\n",
    "y_training_correlation_III = [model_III(model_III_coeff, x) for x in x_training_dataset]\n",
    "y_val_prediction_I = [model_I(model_I_coeff, x) for x in x_val_dataset]\n",
    "y_val_prediction_II = [model_II(model_II_coeff, x) for x in x_val_dataset]\n",
    "y_val_prediction_III = [model_III(model_III_coeff, x) for x in x_val_dataset]\n",
    "y_test_prediction_II = [model_II(model_II_coeff, x) for x in x_test_dataset]  # not shown here, but below\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(12, 12)\n",
    "plt.xticks(fontsize=18, color=\"#322300\")\n",
    "plt.yticks(fontsize=18, color=\"#322300\")\n",
    "ax.set_xlabel(\"actual data (points in season 2018/19)\", fontsize=24, color=\"#322300\")\n",
    "ax.set_ylabel(\"correlated (training) and predicted (validation)\",\\\n",
    "              fontsize=24, color=\"#322300\")\n",
    "\n",
    "# black for the x=y diagonal, representing perfect agreement\n",
    "#\n",
    "sbn.lineplot(x=[20, 80], y=[20, 80], color='#322300', ax=ax)\n",
    "\n",
    "# green for model I\n",
    "#\n",
    "sbn.scatterplot(x=y_training_dataset, \\\n",
    "                y=y_training_correlation_I, color=\"#005528\", s=40)\n",
    "sbn.scatterplot(x=y_val_dataset, \\\n",
    "                y=y_val_prediction_I, color=\"#005528\", s=80)\n",
    "\n",
    "# light blue for model II\n",
    "#\n",
    "sbn.scatterplot(x=y_training_dataset, \\\n",
    "                y=y_training_correlation_II, color=\"#2f3ef0\", s=40)\n",
    "sbn.scatterplot(x=y_val_dataset, \\\n",
    "                y=y_val_prediction_II, color=\"#2f3ef0\", s=80)\n",
    "\n",
    "# red for model III\n",
    "#\n",
    "sbn.scatterplot(x=y_training_dataset, \\\n",
    "                y=y_training_correlation_III, color=\"#c9211e\", s=40)\n",
    "sbn.scatterplot(x=y_val_dataset, \\\n",
    "                y=y_val_prediction_III, color=\"#c9211e\", s=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b111cdb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** TEST OF MODEL II ***\n",
      "\n",
      "x_test_dataset:\n",
      " [[63, 0.07790806607306079], [51, 0.03722091079681195], [33, 0.07645613305731704]]\n",
      "\n",
      "y_test_dataset:\n",
      " [33, 28, 55]\n",
      "\n",
      "Root mean square deviation between model II predictions and test data:\t 15.7\n",
      "Double root mean square deviation for indicating the margin of error:\t+- 31.4\n"
     ]
    }
   ],
   "source": [
    "print(\"*** TEST OF MODEL II ***\")\n",
    "\n",
    "x_test_dataset = [[points_2017_18[club], relative_balance_2017_18[club]] for club in test]\n",
    "y_test_dataset = [points_2018_19[club] for club in test]\n",
    "print(\"\\nx_test_dataset:\\n\", x_test_dataset)\n",
    "print(\"\\ny_test_dataset:\\n\", y_test_dataset)\n",
    "\n",
    "rmsd = root_mean_square_deviation(x_test_dataset, y_test_dataset, model_II, model_II_coeff)\n",
    "print(\"\\nRoot mean square deviation between model II predictions and test data:\\t\", round(rmsd, 1))\n",
    "print(\"Double root mean square deviation for indicating the margin of error:\\t+-\", round(2*rmsd, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b3e10d61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='actual data (points in season 2018/19)', ylabel='correlated (training) and predicted (validation/test)'>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize actual vs. correlated data for the training data sets,\n",
    "# and actual vs. predicted data for the validation and test data sets\n",
    "#\n",
    "import seaborn as sbn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(12, 12)\n",
    "plt.xticks(fontsize=18, color=\"#322300\")\n",
    "plt.yticks(fontsize=18, color=\"#322300\")\n",
    "ax.set_xlabel(\"actual data (points in season 2018/19)\", fontsize=24, color=\"#322300\")\n",
    "ax.set_ylabel(\"correlated (training) and predicted (validation/test)\",\\\n",
    "              fontsize=24, color=\"#322300\")\n",
    "\n",
    "# black for the x=y diagonal, representing perfect agreement\n",
    "#\n",
    "sbn.lineplot(x=[20, 80], y=[20, 80], color='#322300', ax=ax)\n",
    "\n",
    "# light blue for model II\n",
    "#\n",
    "sbn.scatterplot(x=y_training_dataset, \\\n",
    "                y=y_training_correlation_II, color=\"#2f3ef0\", s=40)\n",
    "sbn.scatterplot(x=y_val_dataset, \\\n",
    "                y=y_val_prediction_II, color=\"#2f3ef0\", s=80)\n",
    "sbn.scatterplot(x=y_test_dataset, \\\n",
    "                y=y_test_prediction_II, color=\"#2f3ef0\", s=120)\n",
    "\n",
    "# diagonal transposed by margin of error of the correlation\n",
    "#\n",
    "sbn.lineplot(x=[20, 80], y=[20 - 2*rmsd, 80 - 2*rmsd], color='#322300', ax=ax)\n",
    "sbn.lineplot(x=[20, 80], y=[20 + 2*rmsd, 80 + 2*rmsd], color='#322300', ax=ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "62b8cc2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='fraction of total funds in season 2017/18', ylabel='points in season 2018/19'>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display model II with margin of error\n",
    "#\n",
    "import seaborn as sbn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(13, 8)\n",
    "plt.xticks(fontsize=18, color=\"#322300\")\n",
    "plt.yticks(fontsize=18, color=\"#322300\")\n",
    "ax.set_xlabel(\"fraction of total funds in season 2017/18\", fontsize=24, color=\"#322300\")\n",
    "ax.set_ylabel(\"points in season 2018/19\", fontsize=24, color=\"#322300\")\n",
    "\n",
    "sbn.scatterplot(x=[relative_balance_2017_18[club] for club in training], \\\n",
    "                y=[points_2018_19[club] for club in training], color=\"#322300\", s=40)  # training data\n",
    "sbn.scatterplot(x=[relative_balance_2017_18[club] for club in validation], \\\n",
    "                y=[points_2018_19[club] for club in validation], color=\"#322300\", s=80)  # validation data\n",
    "sbn.scatterplot(x=[relative_balance_2017_18[club] for club in test], \\\n",
    "                y=[points_2018_19[club] for club in test], color=\"#322300\", s=120)  # test data\n",
    "\n",
    "plot_range = np.arange(0, 0.23, 0.01)\n",
    "model_II_plot = [model_II(model_II_coeff, [0, x1]) for x1 in plot_range]\n",
    "model_II_plot_lower = [model_II(model_II_coeff, [0, x1]) - 2*rmsd for x1 in plot_range]\n",
    "model_II_plot_upper = [model_II(model_II_coeff, [0, x1]) + 2*rmsd for x1 in plot_range]\n",
    "\n",
    "sbn.lineplot(x=plot_range, y=model_II_plot, color='#2f3ef0', ax=ax)\n",
    "sbn.lineplot(x=plot_range, y=model_II_plot_lower, color='#2f3ef0', ax=ax)\n",
    "sbn.lineplot(x=plot_range, y=model_II_plot_upper, color='#2f3ef0', ax=ax)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}