035_scores_k1_lite.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from sklearn.metrics import accuracy_score, f1_score, r2_score, mean_absolute_error\n",
    "import time\n",
    "import json\n",
    "from lib.llm_utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "MODEL_NAME = 'llm_v1_tokens_v2_lite'\n",
    "DATA_FNAME = 'data/llm/events_test.txt'\n",
    "K = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Organizing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(DATA_FNAME, sep=' ', header=None, dtype=str)\n",
    "df = df.fillna('<NaN>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer_map, detokenizer_map = get_tokenizer_map(df)\n",
    "restrictions, offset_restrictions = get_restrictions(K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiLayerBinaryClassifier(\n",
       "  (model): Sequential(\n",
       "    (0): Linear(in_features=18, out_features=256, bias=True)\n",
       "    (1): ReLU()\n",
       "    (2): Linear(in_features=256, out_features=256, bias=True)\n",
       "    (3): ReLU()\n",
       "    (4): Linear(in_features=256, out_features=139, bias=True)\n",
       "    (5): Sigmoid()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = torch.load(f'models/llm/full_{MODEL_NAME}.pth')\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_error(df, loc, k=1, metrics_type='regression'):\n",
    "    restrictions, offset_restrictions = get_restrictions(k)\n",
    "\n",
    "    _df = df.copy()\n",
    "    _df['target'] = _df[loc]\n",
    "    for j in range(loc, k*11+7):\n",
    "        _df[j] = '<NaN>'\n",
    "    _df = _df[(_df['target'] != '<NaN>') & (_df['target'] != '<GAME_OVER>')]\n",
    "\n",
    "    df_train_11 = _df.copy()\n",
    "\n",
    "    for j in range(0, k*11+7):\n",
    "        _df[j] = _df[j].map(tokenizer_map)\n",
    "\n",
    "    X_train_11 = _df.drop('target', axis=1).astype(float).values\n",
    "\n",
    "    pred_11 = model(torch.tensor(X_train_11, dtype=torch.float32).to(DEVICE))\n",
    "    pred_11 = torch.argmax(pred_11[:, restrictions[loc]], dim=1) + offset_restrictions[loc]\n",
    "    df_train_11['pred'] = pred_11.cpu().detach().numpy()\n",
    "    df_train_11['target'] = df_train_11['target'].map(tokenizer_map)\n",
    "    \n",
    "    if metrics_type == 'regression':\n",
    "        return {'MAE': round(mean_absolute_error(df_train_11['target'], df_train_11['pred']), 5), 'R2': round(r2_score(df_train_11['target'], df_train_11['pred']), 5)}\n",
    "    elif metrics_type == 'classification':\n",
    "        if len(restrictions[loc]) == 2:\n",
    "            return {'ACC': round(accuracy_score(df_train_11['target'], df_train_11['pred']), 5), 'F1': round(f1_score(df_train_11['target'], df_train_11['pred']), 5)}\n",
    "        else:\n",
    "            return {'ACC': round(accuracy_score(df_train_11['target'], df_train_11['pred']), 5), 'F1': round(f1_score(df_train_11['target'], df_train_11['pred'], average='weighted'), 5)}\n",
    "    else:\n",
    "        raise Exception('Unknown metrics type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Event Type</th>\n",
       "      <th>Is Goal</th>\n",
       "      <th>Is Accurate</th>\n",
       "      <th>Is Home Team</th>\n",
       "      <th>Time Elapsed</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACC</th>\n",
       "      <td>0.57294</td>\n",
       "      <td>0.99842</td>\n",
       "      <td>0.82469</td>\n",
       "      <td>0.91534</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.51770</td>\n",
       "      <td>0.67636</td>\n",
       "      <td>0.87030</td>\n",
       "      <td>0.91505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.74213</td>\n",
       "      <td>7.35026</td>\n",
       "      <td>12.81247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.39220</td>\n",
       "      <td>0.77132</td>\n",
       "      <td>0.50249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Event Type  Is Goal  Is Accurate  Is Home Team  Time Elapsed        x  \\\n",
       "ACC     0.57294  0.99842      0.82469       0.91534           NaN      NaN   \n",
       "F1      0.51770  0.67636      0.87030       0.91505           NaN      NaN   \n",
       "MAE         NaN      NaN          NaN           NaN       1.74213  7.35026   \n",
       "R2          NaN      NaN          NaN           NaN       0.39220  0.77132   \n",
       "\n",
       "            y  \n",
       "ACC       NaN  \n",
       "F1        NaN  \n",
       "MAE  12.81247  \n",
       "R2    0.50249  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\n",
    "    'Event Type': calculate_error(df, 11, k=K, metrics_type='classification'),\n",
    "    'Is Goal': calculate_error(df, 12, k=K, metrics_type='classification'),\n",
    "    'Is Accurate': calculate_error(df, 13, k=K, metrics_type='classification'),\n",
    "    'Is Home Team': calculate_error(df, 14, k=K, metrics_type='classification'),\n",
    "    'Time Elapsed': calculate_error(df, 15, k=K, metrics_type='regression'),\n",
    "    'x': calculate_error(df, 16, k=K, metrics_type='regression'),\n",
    "    'y': calculate_error(df, 17, k=K, metrics_type='regression'),\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# xG Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1512x979.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_state = ['simple_pass', '0', '1', '1', '1', '20', '47', '75', '50', '0', '0']\n",
    "draw_xg_map(base_state, model=model, k=K)\n",
    "plt.plot(75, 50, marker='p', color='lightgreen', markersize=15, markeredgecolor='black', markeredgewidth=2)\n",
    "plt.savefig(f'figures/paper_sigkdd/{MODEL_NAME}_xg_v1.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1512x979.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_state = ['cross', '0', '1', '1', '1', '20', '40', '90', '80', '0', '0']\n",
    "draw_xg_map(base_state, model=model, k=K)\n",
    "plt.plot(90, 80, marker='p', color='lightgreen', markersize=15, markeredgecolor='black', markeredgewidth=2)\n",
    "plt.savefig(f'figures/paper_sigkdd/{MODEL_NAME}_xg_v2.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Short-term Scoring Probability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>912059</th>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "      <td>58</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912060</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912061</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>4</td>\n",
       "      <td>75</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0   1   2   3   4   5   6    7   8   9   10\n",
       "912059  120   0   1   0   2  46  58  100   0   0   2\n",
       "912060  101   0   1   0   2  47   0   90  10   0   2\n",
       "912061  101   0   1   0   2  47   4   75  22   0   2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_df = df.loc[912059:912068].copy()\n",
    "for i in range(11, 18):\n",
    "    forecast_df[i] = '<NaN>'\n",
    "for i in range(0, 18):\n",
    "    forecast_df[i] = forecast_df[i].map(tokenizer_map)\n",
    "forecast_df[[i for i in range(11)]].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>912059</th>\n",
       "      <th>912060</th>\n",
       "      <th>912061</th>\n",
       "      <th>912062</th>\n",
       "      <th>912063</th>\n",
       "      <th>912064</th>\n",
       "      <th>912065</th>\n",
       "      <th>912066</th>\n",
       "      <th>912067</th>\n",
       "      <th>912068</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.0</th>\n",
       "      <th>2.0</th>\n",
       "      <td>870.0</td>\n",
       "      <td>887.0</td>\n",
       "      <td>892.0</td>\n",
       "      <td>883.0</td>\n",
       "      <td>896.0</td>\n",
       "      <td>875.0</td>\n",
       "      <td>886.0</td>\n",
       "      <td>671.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>69.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>930.0</td>\n",
       "      <td>938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <td>49.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         912059  912060  912061  912062  912063  912064  912065  912066  \\\n",
       "9   10                                                                    \n",
       "0.0 2.0   870.0   887.0   892.0   883.0   896.0   875.0   886.0   671.0   \n",
       "    3.0    69.0    72.0    57.0    67.0    63.0    69.0    78.0   265.0   \n",
       "1.0 2.0    49.0    37.0    44.0    42.0    38.0    51.0    30.0    35.0   \n",
       "\n",
       "         912067  912068  \n",
       "9   10                   \n",
       "0.0 2.0     0.0     0.0  \n",
       "    3.0   930.0   938.0  \n",
       "1.0 2.0     0.0     0.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_original = simulate_from_dataframe(forecast_df, model, model_k=K, max_events=2000, n_sims_per_row=1000)\n",
    "df_original = pd.concat([df_original[df_original.index == i][[9, 10]].value_counts().rename(i) for i in forecast_df.index], axis=1).fillna(0)\n",
    "df_original.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Artificial 0-0 Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>912059</th>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
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       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>912060</th>\n",
       "      <td>101</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912061</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>4</td>\n",
       "      <td>75</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0   1   2   3   4   5   6    7   8   9   10\n",
       "912059  120   0   1   0   2  46  58  100   0   0   0\n",
       "912060  101   0   1   0   2  47   0   90  10   0   0\n",
       "912061  101   0   1   0   2  47   4   75  22   0   0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_df = df.loc[912059:912068].copy()\n",
    "for i in range(11, 18):\n",
    "    forecast_df[i] = '<NaN>'\n",
    "for i in range(0, 18):\n",
    "    forecast_df[i] = forecast_df[i].map(tokenizer_map)\n",
    "forecast_df.loc[forecast_df[10] == 2, 10] = 0\n",
    "forecast_df.loc[forecast_df[10] == 3, 10] = 1\n",
    "forecast_df[[i for i in range(11)]].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>912059</th>\n",
       "      <th>912060</th>\n",
       "      <th>912061</th>\n",
       "      <th>912062</th>\n",
       "      <th>912063</th>\n",
       "      <th>912064</th>\n",
       "      <th>912065</th>\n",
       "      <th>912066</th>\n",
       "      <th>912067</th>\n",
       "      <th>912068</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.0</th>\n",
       "      <th>0.0</th>\n",
       "      <td>875.0</td>\n",
       "      <td>890.0</td>\n",
       "      <td>888.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>890.0</td>\n",
       "      <td>872.0</td>\n",
       "      <td>883.0</td>\n",
       "      <td>708.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>67.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>927.0</td>\n",
       "      <td>933.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <th>0.0</th>\n",
       "      <td>46.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         912059  912060  912061  912062  912063  912064  912065  912066  \\\n",
       "9   10                                                                    \n",
       "0.0 0.0   875.0   890.0   888.0   879.0   890.0   872.0   883.0   708.0   \n",
       "    1.0    67.0    70.0    65.0    70.0    65.0    77.0    69.0   237.0   \n",
       "1.0 0.0    46.0    36.0    40.0    45.0    44.0    45.0    41.0    32.0   \n",
       "\n",
       "         912067  912068  \n",
       "9   10                   \n",
       "0.0 0.0     0.0     0.0  \n",
       "    1.0   927.0   933.0  \n",
       "1.0 0.0     0.0     0.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_artificial_score = simulate_from_dataframe(forecast_df, model, model_k=K, max_events=2000, n_sims_per_row=1000)\n",
    "df_artificial_score = pd.concat([df_artificial_score[df_artificial_score.index == i][[9, 10]].value_counts().rename(i) for i in forecast_df.index], axis=1).fillna(0)\n",
    "df_artificial_score.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Artificial Time remaining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>912059</th>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912060</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912061</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>75</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0   1   2   3   4   5   6    7   8   9   10\n",
       "912059  120   0   1   0   2   0  58  100   0   0   2\n",
       "912060  101   0   1   0   2   0   0   90  10   0   2\n",
       "912061  101   0   1   0   2   0   4   75  22   0   2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_df = df.loc[912059:912068].copy()\n",
    "for i in range(11, 18):\n",
    "    forecast_df[i] = '<NaN>'\n",
    "for i in range(0, 18):\n",
    "    forecast_df[i] = forecast_df[i].map(tokenizer_map)\n",
    "forecast_df[[5]] = 0\n",
    "forecast_df[[i for i in range(11)]].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 10.7 s\n",
      "Wall time: 9.95 s\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>912059</th>\n",
       "      <th>912060</th>\n",
       "      <th>912061</th>\n",
       "      <th>912062</th>\n",
       "      <th>912063</th>\n",
       "      <th>912064</th>\n",
       "      <th>912065</th>\n",
       "      <th>912066</th>\n",
       "      <th>912067</th>\n",
       "      <th>912068</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <td>123.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.0</th>\n",
       "      <th>3.0</th>\n",
       "      <td>107.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>89.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         912059  912060  912061  912062  912063  912064  912065  912066  \\\n",
       "9   10                                                                    \n",
       "2.0 3.0   123.0   108.0   103.0   120.0   116.0   116.0   100.0    90.0   \n",
       "1.0 3.0   107.0   111.0   108.0   110.0   116.0   128.0   115.0   116.0   \n",
       "    4.0    89.0    77.0    88.0    87.0    89.0    78.0    77.0   102.0   \n",
       "\n",
       "         912067  912068  \n",
       "9   10                   \n",
       "2.0 3.0    71.0    80.0  \n",
       "1.0 3.0    56.0    81.0  \n",
       "    4.0   117.0   121.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df_artificial_time = simulate_from_dataframe(forecast_df, model, model_k=K, max_events=2000, n_sims_per_row=1000)\n",
    "df_artificial_time = pd.concat([df_artificial_time[df_artificial_time.index == i][[9, 10]].value_counts().rename(i) for i in forecast_df.index], axis=1).fillna(0)\n",
    "df_artificial_time.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Artificial Score and Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>912059</th>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912060</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912061</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>75</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0   1   2   3   4   5   6    7   8   9   10\n",
       "912059  120   0   1   0   2   0  58  100   0   0   0\n",
       "912060  101   0   1   0   2   0   0   90  10   0   0\n",
       "912061  101   0   1   0   2   0   4   75  22   0   0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_df = df.loc[912059:912068].copy()\n",
    "for i in range(11, 18):\n",
    "    forecast_df[i] = '<NaN>'\n",
    "for i in range(0, 18):\n",
    "    forecast_df[i] = forecast_df[i].map(tokenizer_map)\n",
    "forecast_df[[5]] = 0\n",
    "forecast_df.loc[forecast_df[10] == 2, 10] = 0\n",
    "forecast_df.loc[forecast_df[10] == 3, 10] = 1\n",
    "forecast_df[[i for i in range(11)]].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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></th>\n",
       "      <th>912059</th>\n",
       "      <th>912060</th>\n",
       "      <th>912061</th>\n",
       "      <th>912062</th>\n",
       "      <th>912063</th>\n",
       "      <th>912064</th>\n",
       "      <th>912065</th>\n",
       "      <th>912066</th>\n",
       "      <th>912067</th>\n",
       "      <th>912068</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <th>1.0</th>\n",
       "      <td>113.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2.0</th>\n",
       "      <th>1.0</th>\n",
       "      <td>102.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>86.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         912059  912060  912061  912062  912063  912064  912065  912066  \\\n",
       "9   10                                                                    \n",
       "1.0 1.0   113.0   135.0   113.0   116.0   128.0   111.0   108.0   103.0   \n",
       "2.0 1.0   102.0    95.0   106.0    92.0   114.0    96.0    97.0   105.0   \n",
       "    0.0    86.0    67.0    78.0    76.0    71.0    64.0    64.0    49.0   \n",
       "\n",
       "         912067  912068  \n",
       "9   10                   \n",
       "1.0 1.0    92.0    96.0  \n",
       "2.0 1.0    74.0    64.0  \n",
       "    0.0     0.0     0.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_artificial_score_time = simulate_from_dataframe(forecast_df, model, model_k=K, max_events=2000, n_sims_per_row=1000)\n",
    "df_artificial_score_time = pd.concat([df_artificial_score_time[df_artificial_score_time.index == i][[9, 10]].value_counts().rename(i) for i in forecast_df.index], axis=1).fillna(0)\n",
    "df_artificial_score_time.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Realistic performance on the number of goals scored per match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "912059    2.904\n",
       "912060    2.956\n",
       "912061    3.049\n",
       "912062    3.035\n",
       "912063    2.932\n",
       "912064    3.022\n",
       "912065    2.935\n",
       "912066    3.107\n",
       "912067    3.988\n",
       "912068    3.894\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_df = df_artificial_score_time.copy()\n",
    "_df['goals'] = _df.index.map(lambda x: x[0] + x[1])\n",
    "_df['new_goals'] = _df.index.map(lambda x: x[0] + x[1])\n",
    "for col in _df.columns[:-2]:\n",
    "    _df[col] = _df[col] * _df['new_goals'] / _df[col].sum()\n",
    "_df[_df.columns[:-2]].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probabilities within scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_probability_away_win(df):\n",
    "    _df = df.copy()\n",
    "    _df['wins'] = _df.index.map(lambda x: x[0] < x[1])\n",
    "    _df['draws'] = _df.index.map(lambda x: x[0] == x[1])\n",
    "\n",
    "    points = []\n",
    "    for col in _df.columns[:-2]:\n",
    "        points.append(((_df[col] * _df['wins']).sum() * 3 + (_df[col] * _df['draws']).sum()) / _df[col].sum())\n",
    "    \n",
    "    return points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (6, 3)\n",
    "plt.plot(calculate_probability_away_win(df_original), label='Original Situation')\n",
    "plt.plot(calculate_probability_away_win(df_artificial_score), label='Artificial Score')\n",
    "plt.plot(calculate_probability_away_win(df_artificial_time), label='Artificial Time')\n",
    "plt.plot(calculate_probability_away_win(df_artificial_score_time), label='Artificial Score and Time')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating full game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>910375</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>49</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910376</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>38</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910377</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0   1   2   3   4   5   6   7   8   9   10\n",
       "910375  101   0   1   0   1   0   5  49  50   0   0\n",
       "910376  101   0   1   0   1   0   6  38  57   0   0\n",
       "910377  101   0   1   0   1   0   9  28  35   0   0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_df = df.loc[910375:912068].copy()\n",
    "for i in range(11, 18):\n",
    "    forecast_df[i] = '<NaN>'\n",
    "for i in range(0, 18):\n",
    "    forecast_df[i] = forecast_df[i].map(tokenizer_map)\n",
    "forecast_df[[i for i in range(11)]].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1min 24s\n",
      "Wall time: 1min 25s\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>910375</th>\n",
       "      <th>910376</th>\n",
       "      <th>910377</th>\n",
       "      <th>910378</th>\n",
       "      <th>910379</th>\n",
       "      <th>910380</th>\n",
       "      <th>910381</th>\n",
       "      <th>910382</th>\n",
       "      <th>910383</th>\n",
       "      <th>910384</th>\n",
       "      <th>...</th>\n",
       "      <th>912059</th>\n",
       "      <th>912060</th>\n",
       "      <th>912061</th>\n",
       "      <th>912062</th>\n",
       "      <th>912063</th>\n",
       "      <th>912064</th>\n",
       "      <th>912065</th>\n",
       "      <th>912066</th>\n",
       "      <th>912067</th>\n",
       "      <th>912068</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <th>2.0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <th>2.0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 1694 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         910375  910376  910377  910378  910379  910380  910381  910382  \\\n",
       "9   10                                                                    \n",
       "2.0 3.0     8.0     5.0     4.0     3.0     9.0     5.0     5.0     5.0   \n",
       "3.0 2.0     7.0     8.0     4.0     5.0     8.0     4.0     9.0     3.0   \n",
       "2.0 2.0     7.0     8.0    10.0     6.0     5.0     2.0     3.0     9.0   \n",
       "\n",
       "         910383  910384  ...  912059  912060  912061  912062  912063  912064  \\\n",
       "9   10                   ...                                                   \n",
       "2.0 3.0     6.0     7.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "3.0 2.0     6.0     7.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2.0 2.0     7.0    11.0  ...     0.0     0.0     0.0     1.0     0.0     0.0   \n",
       "\n",
       "         912065  912066  912067  912068  \n",
       "9   10                                   \n",
       "2.0 3.0     0.0     0.0     0.0     0.0  \n",
       "3.0 2.0     0.0     0.0     0.0     0.0  \n",
       "2.0 2.0     0.0     0.0     0.0     0.0  \n",
       "\n",
       "[3 rows x 1694 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df_full = simulate_from_dataframe(forecast_df, model, model_k=K, max_events=2000, n_sims_per_row=100)\n",
    "df_full = pd.concat([df_full[df_full.index == i][[9, 10]].value_counts().rename(i) for i in forecast_df.index], axis=1).fillna(0)\n",
    "df_full.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What if analysis - betting scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>911239</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911240</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911241</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0   1   2   3   4   5   6   7   8   9   10\n",
       "911239  101   0   1   1   2   0   1  51  48   0   0\n",
       "911240  101   0   1   1   2   0   3  45  50   0   0\n",
       "911241  101   0   1   1   2   0   6  30  31   0   0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_df = df.loc[910375:912067].copy()\n",
    "for i in range(11, 18):\n",
    "    forecast_df[i] = '<NaN>'\n",
    "for i in range(0, 18):\n",
    "    forecast_df[i] = forecast_df[i].map(tokenizer_map)\n",
    "forecast_df = forecast_df[(forecast_df[4] == 2)]\n",
    "forecast_df[[i for i in range(11)]].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>911239</th>\n",
       "      <th>911240</th>\n",
       "      <th>911241</th>\n",
       "      <th>911242</th>\n",
       "      <th>911243</th>\n",
       "      <th>911244</th>\n",
       "      <th>911245</th>\n",
       "      <th>911246</th>\n",
       "      <th>911247</th>\n",
       "      <th>911248</th>\n",
       "      <th>...</th>\n",
       "      <th>912058</th>\n",
       "      <th>912059</th>\n",
       "      <th>912060</th>\n",
       "      <th>912061</th>\n",
       "      <th>912062</th>\n",
       "      <th>912063</th>\n",
       "      <th>912064</th>\n",
       "      <th>912065</th>\n",
       "      <th>912066</th>\n",
       "      <th>912067</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2.0</th>\n",
       "      <th>1.0</th>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <th>1.0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 829 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         911239  911240  911241  911242  911243  911244  911245  911246  \\\n",
       "9   10                                                                    \n",
       "2.0 1.0    12.0    12.0     9.0     6.0     8.0    12.0    10.0    15.0   \n",
       "    0.0     9.0     6.0     5.0     4.0     9.0    11.0     6.0     6.0   \n",
       "3.0 1.0     8.0     5.0     5.0     6.0     3.0     5.0     5.0     9.0   \n",
       "\n",
       "         911247  911248  ...  912058  912059  912060  912061  912062  912063  \\\n",
       "9   10                   ...                                                   \n",
       "2.0 1.0    15.0    12.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "    0.0     9.0     5.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "3.0 1.0     6.0     7.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "         912064  912065  912066  912067  \n",
       "9   10                                   \n",
       "2.0 1.0     0.0     0.0     0.0     0.0  \n",
       "    0.0     0.0     0.0     0.0     0.0  \n",
       "3.0 1.0     0.0     0.0     0.0     0.0  \n",
       "\n",
       "[3 rows x 829 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wia = simulate_from_dataframe(forecast_df, model, model_k=K, max_events=2000, n_sims_per_row=100)\n",
    "df_wia = pd.concat([df_wia[df_wia.index == i][[9, 10]].value_counts().rename(i) for i in forecast_df.index], axis=1).fillna(0)\n",
    "df_wia.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_event_to_end_probabilities(df):\n",
    "    _df = df.copy()\n",
    "    _df['h_wins'] = _df.index.map(lambda x: x[0] > x[1])\n",
    "    _df['draws'] = _df.index.map(lambda x: x[0] == x[1])\n",
    "    _df['a_wins'] = _df.index.map(lambda x: x[0] < x[1])\n",
    "    _df['over_2_5'] = _df.index.map(lambda x: (x[0] + x[1]) > 2.5)\n",
    "    _df['under_2_5'] = _df.index.map(lambda x: (x[0] + x[1]) <= 2.5)\n",
    "\n",
    "    h_wins = []\n",
    "    draws = []\n",
    "    a_wins = []\n",
    "    over_2_5 = []\n",
    "    under_2_5 = []\n",
    "    for col in _df.columns[:-2]:\n",
    "        h_wins.append((_df[col] * _df['h_wins']).sum() / _df[col].sum())\n",
    "        draws.append((_df[col] * _df['draws']).sum() / _df[col].sum())\n",
    "        a_wins.append((_df[col] * _df['a_wins']).sum() / _df[col].sum())\n",
    "        over_2_5.append((_df[col] * _df['over_2_5']).sum() / _df[col].sum())\n",
    "        under_2_5.append((_df[col] * _df['under_2_5']).sum() / _df[col].sum())\n",
    "    \n",
    "    return h_wins, draws, a_wins, over_2_5, under_2_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 406 ms\n",
      "Wall time: 401 ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "plt.rcParams['figure.figsize'] = (6, 3)\n",
    "h_wins, draws, a_wins, _, _ = calculate_event_to_end_probabilities(df_wia)\n",
    "plt.plot(pd.Series(h_wins).rolling(10, min_periods=1).mean()[:-1], label='Home')\n",
    "plt.plot(pd.Series(draws).rolling(10, min_periods=1).mean()[:-1], label='Draw')\n",
    "plt.plot(pd.Series(a_wins).rolling(10, min_periods=1).mean()[:-1], label='Away')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (6, 3)\n",
    "h_wins, draws, a_wins, _, _ = calculate_event_to_end_probabilities(df_wia)\n",
    "plt.plot(pd.Series(h_wins).rolling(10, min_periods=1).mean()[:-1] - pd.Series(h_wins).rolling(10, min_periods=1).mean().rolling(10, min_periods=1).mean()[:-1], label='Home')\n",
    "#plt.plot(pd.Series(draws).rolling(10, min_periods=1).mean()[:-1] - pd.Series(draws).rolling(10, min_periods=1).mean().rolling(10, min_periods=1).mean()[:-1], label='Draw')\n",
    "plt.plot(pd.Series(a_wins).rolling(10, min_periods=1).mean()[:-1] - pd.Series(a_wins).rolling(10, min_periods=1).mean().rolling(10, min_periods=1).mean()[:-1], label='Away')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (6, 3)\n",
    "_, _, _, over, under = calculate_event_to_end_probabilities(df_wia)\n",
    "plt.plot(pd.Series(over).rolling(5, min_periods=1).mean(), label='Over 2.5')\n",
    "plt.plot(pd.Series(under).rolling(5, min_periods=1).mean(), label='Under 2.5')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Short term scoring chance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>911239</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911240</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911241</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0   1   2   3   4   5   6   7   8   9   10\n",
       "911239  101   0   1   1   2   0   1  51  48   0   0\n",
       "911240  101   0   1   1   2   0   3  45  50   0   0\n",
       "911241  101   0   1   1   2   0   6  30  31   0   0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_df = df.loc[910375:912067].copy()\n",
    "for i in range(11, 18):\n",
    "    forecast_df[i] = '<NaN>'\n",
    "for i in range(0, 18):\n",
    "    forecast_df[i] = forecast_df[i].map(tokenizer_map)\n",
    "forecast_df = forecast_df[(forecast_df[4] == 2)]\n",
    "forecast_df[[i for i in range(11)]].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_SIMS = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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></th>\n",
       "      <th>911239</th>\n",
       "      <th>911240</th>\n",
       "      <th>911241</th>\n",
       "      <th>911242</th>\n",
       "      <th>911243</th>\n",
       "      <th>911244</th>\n",
       "      <th>911245</th>\n",
       "      <th>911246</th>\n",
       "      <th>911247</th>\n",
       "      <th>911248</th>\n",
       "      <th>...</th>\n",
       "      <th>912058</th>\n",
       "      <th>912059</th>\n",
       "      <th>912060</th>\n",
       "      <th>912061</th>\n",
       "      <th>912062</th>\n",
       "      <th>912063</th>\n",
       "      <th>912064</th>\n",
       "      <th>912065</th>\n",
       "      <th>912066</th>\n",
       "      <th>912067</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <th>0.0</th>\n",
       "      <td>975.0</td>\n",
       "      <td>975.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>980.0</td>\n",
       "      <td>972.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>984.0</td>\n",
       "      <td>972.0</td>\n",
       "      <td>974.0</td>\n",
       "      <td>969.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <th>0.0</th>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 829 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         911239  911240  911241  911242  911243  911244  911245  911246  \\\n",
       "9   10                                                                    \n",
       "0.0 0.0   975.0   975.0   977.0   980.0   972.0   978.0   984.0   972.0   \n",
       "1.0 0.0    16.0    14.0    12.0    14.0    10.0    13.0    11.0    23.0   \n",
       "0.0 1.0     9.0    10.0    11.0     6.0    18.0     9.0     5.0     5.0   \n",
       "\n",
       "         911247  911248  ...  912058  912059  912060  912061  912062  912063  \\\n",
       "9   10                   ...                                                   \n",
       "0.0 0.0   974.0   969.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "1.0 0.0     9.0    14.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "0.0 1.0    17.0    16.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "         912064  912065  912066  912067  \n",
       "9   10                                   \n",
       "0.0 0.0     0.0     0.0     0.0     0.0  \n",
       "1.0 0.0     0.0     0.0     0.0     0.0  \n",
       "0.0 1.0     0.0     0.0     0.0     0.0  \n",
       "\n",
       "[3 rows x 829 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_la10 = simulate_from_dataframe(forecast_df, model, model_k=K, max_events=10, n_sims_per_row=N_SIMS)\n",
    "df_la10 = pd.concat([df_la10[df_la10.index == i][[9, 10]].value_counts().rename(i) for i in forecast_df.index], axis=1).fillna(0)\n",
    "df_la10.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "momentum_h_scoring = []\n",
    "momentum_a_scoring = []\n",
    "for i in forecast_df.index:\n",
    "    current_h_score = forecast_df.loc[i][9]\n",
    "    current_a_score = forecast_df.loc[i][10]\n",
    "\n",
    "    momentum_h_scoring.append(df_la10[i].loc[current_h_score+1, current_a_score])\n",
    "    momentum_a_scoring.append(df_la10[i].loc[current_h_score, current_a_score+1])\n",
    "    #print(i, current_h_score, current_a_score, df_la10[i].loc[current_h_score, current_a_score], , df_la10[i].loc[current_h_score, current_a_score+1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1728x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (12, 3)\n",
    "plt.plot(np.array(momentum_h_scoring))\n",
    "plt.plot(-np.array(momentum_a_scoring))\n",
    "plt.ylim([-200, 200]);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}