Football_shots_XPGoal_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import and options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:03:59.747446Z",
     "start_time": "2019-02-27T11:03:57.835641Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "%matplotlib inline\n",
    "import json\n",
    "# import Image\n",
    "\n",
    "from pandas.io.json import json_normalize\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Arc, Rectangle, ConnectionPatch\n",
    "from matplotlib.offsetbox import  OffsetImage\n",
    "from matplotlib.patches import Ellipse\n",
    "from matplotlib.colors import ListedColormap\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "from numpy import ones,vstack\n",
    "from numpy.linalg import lstsq\n",
    "import plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "import squarify\n",
    "from functools import reduce\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "py.tools.set_credentials_file(username='theon', api_key='Urx3lyY3vpy94j2TJ4s4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:04:04.634176Z",
     "start_time": "2019-02-27T11:04:04.631314Z"
    }
   },
   "outputs": [],
   "source": [
    "# PITCH PARAMS\n",
    "pitch_length = 105\n",
    "pitch_width = 68\n",
    "\n",
    "pitch_y = [0, 13.85, 24.85, 26.5, 30.35, 37.65, 41.5, 43.15, 54.15, 68]\n",
    "pitch_x = [70, 88.5, 99.5, 99.5 + 5.5/2, 105]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:04:05.922767Z",
     "start_time": "2019-02-27T11:04:05.913085Z"
    }
   },
   "outputs": [],
   "source": [
    "LEAGUES = {22: \"Germany_Bundesliga\",\n",
    "           23: \"Spain_Liga\",\n",
    "           24: \"France_Ligue1\",\n",
    "           8: \"England_PL\",\n",
    "           21: \"Italy_SerieA\",\n",
    "           5: \"UEFA_Champions\",\n",
    "           4: \"World_Cup\",\n",
    "           6: \"UEFA_Europa_League\",\n",
    "           3: \"UEFA_Euro\"}\n",
    "\n",
    "SEASONS = {\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:04:06.624306Z",
     "start_time": "2019-02-27T11:04:06.615489Z"
    }
   },
   "outputs": [],
   "source": [
    "RESULT_SHOT = {\n",
    "    \"blue\": \"saved\",\n",
    "    \"yellow\": \"goal\",\n",
    "    \"darkgrey\": \"blocked\",\n",
    "    \"red\": \"off_target\"\n",
    "}\n",
    "    \n",
    "COLOR_SHOT = {\n",
    "    \"saved\": [\"#F06543\", 0.5],\n",
    "    \"goal\": [\"#5EFC8D\",  1],\n",
    "    \"blocked\": [\"#93BEDF\", 0.25],\n",
    "    \"off_target\": [\"#639A88\", 0.25]\n",
    "}\n",
    "\n",
    "COLOR_ZONE = {\n",
    "    1: \"#628AA1\",\n",
    "    2: \"#7098AE\",\n",
    "    3: \"#7FA5BB\",\n",
    "    4: \"#8EB2C8\",\n",
    "    5: \"#A0C1D4\",\n",
    "    6: \"#B2CFDF\",\n",
    "    7: \"#C5DBE8\",\n",
    "    8: \"#FFFFFF\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-24T06:09:53.756204Z",
     "start_time": "2019-08-24T06:09:53.748871Z"
    }
   },
   "outputs": [],
   "source": [
    "COLOR_ZONE = {\n",
    "    1: \"#628AA1\",\n",
    "    2: \"#7098AE\",\n",
    "    3: \"#7FA5BB\",\n",
    "    4: \"#8EB2C8\",\n",
    "    5: \"#A0C1D4\",\n",
    "    6: \"#B2CFDF\",\n",
    "    7: \"#C5DBE8\",\n",
    "    8: \"#FFFFFF\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:04:07.610707Z",
     "start_time": "2019-02-27T11:04:07.605149Z"
    }
   },
   "outputs": [],
   "source": [
    "MAJOR_LEAGUES = ['England_PL', 'Italy_SerieA', 'Spain_Liga', 'Germany_Bundesliga',\n",
    "       'France_Ligue1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read saved DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:05:40.434796Z",
     "start_time": "2019-02-27T11:05:38.923050Z"
    }
   },
   "outputs": [],
   "source": [
    "df= pd.read_csv(\"database_2702_new_xp_goal_new_zone.csv\")\n",
    "df.drop(df.columns[0], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:05:40.464311Z",
     "start_time": "2019-02-27T11:05:40.436062Z"
    }
   },
   "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>team_away</th>\n",
       "      <th>team_id_away</th>\n",
       "      <th>fixture_id</th>\n",
       "      <th>team_home</th>\n",
       "      <th>team_id_home</th>\n",
       "      <th>link</th>\n",
       "      <th>minute</th>\n",
       "      <th>part</th>\n",
       "      <th>result</th>\n",
       "      <th>score</th>\n",
       "      <th>...</th>\n",
       "      <th>league</th>\n",
       "      <th>result_processed</th>\n",
       "      <th>pos_start</th>\n",
       "      <th>pos_end</th>\n",
       "      <th>pos_delta</th>\n",
       "      <th>is_goal</th>\n",
       "      <th>zone</th>\n",
       "      <th>xp_goal</th>\n",
       "      <th>zone_v1</th>\n",
       "      <th>xp_goal_v1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Las Palmas</td>\n",
       "      <td>407</td>\n",
       "      <td>820042</td>\n",
       "      <td>Málaga</td>\n",
       "      <td>182</td>\n",
       "      <td>https://www.fourfourtwo.com/statszone/23-2015/...</td>\n",
       "      <td>9</td>\n",
       "      <td>foot_left</td>\n",
       "      <td>red</td>\n",
       "      <td>4 - 1</td>\n",
       "      <td>...</td>\n",
       "      <td>Spain_Liga</td>\n",
       "      <td>off_target</td>\n",
       "      <td>[89.83114864864868, 45.97485714285709]</td>\n",
       "      <td>[105, 43.33499651567939]</td>\n",
       "      <td>[15.16885135 -2.63986063]</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0.070853</td>\n",
       "      <td>6</td>\n",
       "      <td>0.075933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Granada CF</td>\n",
       "      <td>5683</td>\n",
       "      <td>456532</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>179</td>\n",
       "      <td>https://www.fourfourtwo.com/statszone/23-2012/...</td>\n",
       "      <td>8</td>\n",
       "      <td>foot_left</td>\n",
       "      <td>red</td>\n",
       "      <td>3 - 0</td>\n",
       "      <td>...</td>\n",
       "      <td>Spain_Liga</td>\n",
       "      <td>off_target</td>\n",
       "      <td>[103.3670945945946, 39.40328919860623]</td>\n",
       "      <td>[104.79590540540542, 37.27455052264804]</td>\n",
       "      <td>[ 1.42881081 -2.12873868]</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>0.316138</td>\n",
       "      <td>3</td>\n",
       "      <td>0.313038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Granada CF</td>\n",
       "      <td>5683</td>\n",
       "      <td>456532</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>179</td>\n",
       "      <td>https://www.fourfourtwo.com/statszone/23-2012/...</td>\n",
       "      <td>6</td>\n",
       "      <td>foot_left</td>\n",
       "      <td>blue</td>\n",
       "      <td>3 - 0</td>\n",
       "      <td>...</td>\n",
       "      <td>Spain_Liga</td>\n",
       "      <td>saved</td>\n",
       "      <td>[74.7782432432433, 39.257254355400654]</td>\n",
       "      <td>[103.81155405405407, 31.506174216027844]</td>\n",
       "      <td>[29.03331081 -7.75108014]</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.023529</td>\n",
       "      <td>7</td>\n",
       "      <td>0.034478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Granada CF</td>\n",
       "      <td>5683</td>\n",
       "      <td>456532</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>179</td>\n",
       "      <td>https://www.fourfourtwo.com/statszone/23-2012/...</td>\n",
       "      <td>2</td>\n",
       "      <td>foot_left</td>\n",
       "      <td>red</td>\n",
       "      <td>3 - 0</td>\n",
       "      <td>...</td>\n",
       "      <td>Spain_Liga</td>\n",
       "      <td>off_target</td>\n",
       "      <td>[90.7646621621622, 48.38443205574907]</td>\n",
       "      <td>[105, 38.80791637630658]</td>\n",
       "      <td>[14.23533784 -9.57651568]</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0.027652</td>\n",
       "      <td>6</td>\n",
       "      <td>0.075933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Troyes</td>\n",
       "      <td>154</td>\n",
       "      <td>438489</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>427</td>\n",
       "      <td>https://www.fourfourtwo.com/statszone/24-2012/...</td>\n",
       "      <td>46</td>\n",
       "      <td>foot_left</td>\n",
       "      <td>darkgrey</td>\n",
       "      <td>2 - 2</td>\n",
       "      <td>...</td>\n",
       "      <td>France_Ligue1</td>\n",
       "      <td>blocked</td>\n",
       "      <td>[82.59641891891896, 22.901351916376285]</td>\n",
       "      <td>[88.75864864864869, 26.39495470383273]</td>\n",
       "      <td>[6.16222973 3.49360279]</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0.028911</td>\n",
       "      <td>7</td>\n",
       "      <td>0.034478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    team_away  team_id_away  fixture_id team_home  team_id_home  \\\n",
       "0  Las Palmas           407      820042    Málaga           182   \n",
       "1  Granada CF          5683      456532   Sevilla           179   \n",
       "2  Granada CF          5683      456532   Sevilla           179   \n",
       "3  Granada CF          5683      456532   Sevilla           179   \n",
       "4      Troyes           154      438489  Toulouse           427   \n",
       "\n",
       "                                                link  minute       part  \\\n",
       "0  https://www.fourfourtwo.com/statszone/23-2015/...       9  foot_left   \n",
       "1  https://www.fourfourtwo.com/statszone/23-2012/...       8  foot_left   \n",
       "2  https://www.fourfourtwo.com/statszone/23-2012/...       6  foot_left   \n",
       "3  https://www.fourfourtwo.com/statszone/23-2012/...       2  foot_left   \n",
       "4  https://www.fourfourtwo.com/statszone/24-2012/...      46  foot_left   \n",
       "\n",
       "     result  score    ...             league  result_processed  \\\n",
       "0       red  4 - 1    ...         Spain_Liga        off_target   \n",
       "1       red  3 - 0    ...         Spain_Liga        off_target   \n",
       "2      blue  3 - 0    ...         Spain_Liga             saved   \n",
       "3       red  3 - 0    ...         Spain_Liga        off_target   \n",
       "4  darkgrey  2 - 2    ...      France_Ligue1           blocked   \n",
       "\n",
       "                                 pos_start  \\\n",
       "0   [89.83114864864868, 45.97485714285709]   \n",
       "1   [103.3670945945946, 39.40328919860623]   \n",
       "2   [74.7782432432433, 39.257254355400654]   \n",
       "3    [90.7646621621622, 48.38443205574907]   \n",
       "4  [82.59641891891896, 22.901351916376285]   \n",
       "\n",
       "                                    pos_end                  pos_delta  \\\n",
       "0                  [105, 43.33499651567939]  [15.16885135 -2.63986063]   \n",
       "1   [104.79590540540542, 37.27455052264804]  [ 1.42881081 -2.12873868]   \n",
       "2  [103.81155405405407, 31.506174216027844]  [29.03331081 -7.75108014]   \n",
       "3                  [105, 38.80791637630658]  [14.23533784 -9.57651568]   \n",
       "4    [88.75864864864869, 26.39495470383273]    [6.16222973 3.49360279]   \n",
       "\n",
       "  is_goal  zone   xp_goal zone_v1 xp_goal_v1  \n",
       "0       0    56  0.070853       6   0.075933  \n",
       "1       0    91  0.316138       3   0.313038  \n",
       "2       0    19  0.023529       7   0.034478  \n",
       "3       0    57  0.027652       6   0.075933  \n",
       "4       0    28  0.028911       7   0.034478  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform coordinates for representations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:05:46.776247Z",
     "start_time": "2019-02-27T11:05:46.763811Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Line Solution to transform X coordinates from scrapped datasets to Y coordinates for representation | \n",
      " y = -0.07020905923344939x + 59.134843205574846\n",
      "============================\n",
      "Line Solution to transform Y coordinates from scrapped datasets to x coordinates for representation | \n",
      " y = -0.07432432432432425x + 116.5945945945946\n"
     ]
    }
   ],
   "source": [
    "# Correspondence between scrapped data (top angles of pitch boxes at 71 and 650 on X coordinates to Y coordina\n",
    "\n",
    "\n",
    "points_x = [(71,54.15),(645,13.85)]\n",
    "x_coords, y_coords = zip(*points_x)\n",
    "A = vstack([x_coords,ones(len(x_coords))]).T\n",
    "m_x_transform, c_x_transform = lstsq(A, y_coords, rcond=None)[0]\n",
    "print(\"Line Solution to transform X coordinates from scrapped datasets to Y coordinates for representation | \\n y = {m}x + {c}\".format(m=m_x_transform,c=c_x_transform))\n",
    "\n",
    "print(\"============================\")\n",
    "\n",
    "# Correspondence between scrapped data (top angles of lines at 150 and 650 on Y coordinates to X coordinates)\n",
    "\n",
    "points_y = [(156,105),(378,88.5)]\n",
    "x_coords, y_coords = zip(*points_y)\n",
    "A = vstack([x_coords,ones(len(x_coords))]).T\n",
    "m_y_transform, c_y_transform = lstsq(A, y_coords, rcond=None)[0]\n",
    "print(\"Line Solution to transform Y coordinates from scrapped datasets to x coordinates for representation | \\n y = {m}x + {c}\".format(m=m_y_transform,c=c_y_transform))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:05:47.278704Z",
     "start_time": "2019-02-27T11:05:47.272148Z"
    }
   },
   "outputs": [],
   "source": [
    "def transform_to_X(y):\n",
    "    return m_y_transform * y + c_y_transform\n",
    "\n",
    "def transform_to_Y(x):\n",
    "    return m_x_transform * x + c_x_transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:05:48.462820Z",
     "start_time": "2019-02-27T11:05:48.447421Z"
    }
   },
   "outputs": [],
   "source": [
    "def pos_start_transform(array):\n",
    "    return list(map(lambda elem: [max(0.01, transform_to_X(elem[1])), max(0.01, min(pitch_width, transform_to_Y(elem[0])))], array))\n",
    "\n",
    "def pos_end_transform(array):\n",
    "    l = list(map(lambda elem: [\n",
    "        min(transform_to_X(elem[1]), 105),\n",
    "        min(pitch_y[5], max(pitch_y[4], transform_to_Y(elem[0]))) if elem[2] == \"goal\" or elem[2] == \"saved\" else min(pitch_width, max(0.01, transform_to_Y(elem[0])))\n",
    "    ], array))\n",
    "    return l\n",
    "\n",
    "def pos_delta(array):\n",
    "    return list(map(lambda elem: np.subtract(elem[1], elem[0]), array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:05:53.004345Z",
     "start_time": "2019-02-27T11:05:50.421365Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"pos_start\"] = pos_start_transform(df[[\"x1\", \"y1\"]].values)\n",
    "df[\"pos_end\"] = pos_end_transform(df[[\"x2\", \"y2\", \"result_processed\"]].values)\n",
    "df[\"pos_delta\"] = pos_delta(df[[\"pos_start\", \"pos_end\"]].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data visualization | Macro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map number of shots collected in the 5 major European leagues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T18:26:23.720826Z",
     "start_time": "2019-02-25T18:26:23.716794Z"
    }
   },
   "outputs": [],
   "source": [
    "POSITION_COUNTRY = {\n",
    "    \"Italy_SerieA\": [41.992834, 13.884544],\n",
    "    \"France_Ligue1\": [46.398719, 4.233663],\n",
    "    \"England_PL\": [53.068819, -1.646930],\n",
    "    \"Germany_Bundesliga\": [50.771006, 10.553582],\n",
    "    \"Spain_Liga\": [39.981012, -4.358449]\n",
    "}"
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  {
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   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T18:26:24.537757Z",
     "start_time": "2019-02-25T18:26:24.322597Z"
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    "df_temp = df.groupby([\"league\", \"season_year\", \"fixture_id\"]).count().iloc[:, 0:1].reset_index()\n",
    "df_temp.columns = [\"league\", \"season_year\", \"fixture_id\", \"total_shots\"]\n",
    "df_agg = df_temp.groupby([\"league\", \"season_year\"]).agg({\"fixture_id\": \"count\", \"total_shots\": \"sum\"}).reset_index()\n",
    "df_agg.columns = [\"league\", \"season_year\", \"total_games\", \"total_shots\"]\n",
    "df_agg[\"shots_per_game\"] = df_agg[\"total_shots\"] / df_agg[\"total_games\"]"
   ]
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  {
   "cell_type": "code",
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       "                league  season_year  total_shots\n",
       "0           England_PL         2010        10896\n",
       "1           England_PL         2011        10661\n",
       "2           England_PL         2012        10390\n",
       "3           England_PL         2013        10108\n",
       "4           England_PL         2014         9742\n",
       "5           England_PL         2015         9644\n",
       "6           England_PL         2016         9569\n",
       "7        France_Ligue1         2012         8941\n",
       "8        France_Ligue1         2013         8844\n",
       "9        France_Ligue1         2014         8575\n",
       "10       France_Ligue1         2015         8617\n",
       "11  Germany_Bundesliga         2012         7665\n",
       "12  Germany_Bundesliga         2013         8241\n",
       "13  Germany_Bundesliga         2014         7884\n",
       "14  Germany_Bundesliga         2015         7749\n",
       "15  Germany_Bundesliga         2016         7511\n",
       "16        Italy_SerieA         2012         9855\n",
       "17        Italy_SerieA         2013        10044\n",
       "18        Italy_SerieA         2014         9875\n",
       "19        Italy_SerieA         2015         9665\n",
       "20        Italy_SerieA         2016         9939\n",
       "21          Spain_Liga         2012         9788\n",
       "22          Spain_Liga         2013         9483\n",
       "23          Spain_Liga         2014         8879\n",
       "24          Spain_Liga         2015         8894\n",
       "25          Spain_Liga         2016         8953\n",
       "26      UEFA_Champions         2010         3416\n",
       "27      UEFA_Champions         2011         3426\n",
       "28      UEFA_Champions         2012         3273\n",
       "29      UEFA_Champions         2013         3165\n",
       "30      UEFA_Champions         2014         3147\n",
       "31      UEFA_Champions         2015         3368\n",
       "32      UEFA_Champions         2016         3031\n",
       "33           UEFA_Euro         2011          867\n",
       "34           UEFA_Euro         2015         1347\n",
       "35  UEFA_Europa_League         2012         5278\n",
       "36           World_Cup         2013         1888"
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     "execution_count": 281,
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    "df_for_map_season = df_agg.groupby([\"league\", \"season_year\"]).agg({\"total_shots\": \"sum\"}).reset_index()"
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     "start_time": "2019-02-21T15:40:48.638585Z"
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   "source": [
    "df_for_map = df_agg.groupby(\"league\").agg({\"total_shots\": \"sum\"}).iloc[0:5, :].reset_index()"
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  {
   "cell_type": "code",
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            circle_marker_547014acad4e48249ff489ed102d1047.bindPopup(popup_f86d1d87345e41ec8c719b21ed54a45e)
            ;

            
        
    
            var circle_marker_597244ff99a54d7d89619df317a24a1b = L.circleMarker(
                [39.981012, -4.358449],
                {
  "bubblingMouseEvents": true,
  "color": "#71ddc0",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#71ddc0",
  "fillOpacity": 0.2,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 32.55716966706054,
  "stroke": true,
  "weight": 3
}
                )
                .addTo(map_8012c64e3efc4537b88fd35c86664f1e);
            
    
            var popup_3a79613f5f9a48108025be3c3a4592ae = L.popup({maxWidth: '300'
            
            });

            
                var html_4d0dfa9af91e4feeac5625ba82e5d4b7 = $(`<div id="html_4d0dfa9af91e4feeac5625ba82e5d4b7" style="width: 100.0%; height: 100.0%;">Spain | # collected shots | 46,840</div>`)[0];
                popup_3a79613f5f9a48108025be3c3a4592ae.setContent(html_4d0dfa9af91e4feeac5625ba82e5d4b7);
            

            circle_marker_597244ff99a54d7d89619df317a24a1b.bindPopup(popup_3a79613f5f9a48108025be3c3a4592ae)
            ;

            
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fd14845bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# tmp = df_msg_incl_bs_wo_coord.join(pos_train)\n",
    "# tmp = tmp[[\"lat\", \"lng\"]]\n",
    "# tmp.drop_duplicates(inplace=True)\n",
    "\n",
    "# t0 = dt.datetime.now()\n",
    "import folium\n",
    "\n",
    "\n",
    "# Barycentre Coordinates\n",
    "barycentre = [46.398719, 4.233663]\n",
    "\n",
    "# Sample (0.33% over 1.5 million) \n",
    "data = df_for_map.values\n",
    "\n",
    "# Build map \n",
    "map_bases = folium.Map(location=barycentre, zoom_start=3.70, \n",
    "tiles='cartodbpositron', width=640, height=480)\n",
    "\n",
    "# Plot coordinates using comprehension list\n",
    "[folium.CircleMarker(data[i][2],\n",
    "                     color='#71ddc0',\n",
    "                     fill_color='#71ddc0',\n",
    "                     popup=data[i][0].split(\"_\")[0] + \" | # collected shots | {:,}\".format(data[i][1]),\n",
    "                     radius=data[i][1]*170/df_for_map[\"total_shots\"].sum()).add_to(map_bases)\n",
    " for i in range(len(data))]\n",
    "\n",
    "\n",
    "# Display map in Jupyter\n",
    "display(map_bases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T15:43:24.987207Z",
     "start_time": "2019-02-21T15:43:24.967216Z"
    }
   },
   "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>league</th>\n",
       "      <th>total_shots</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>England_PL</td>\n",
       "      <td>71010</td>\n",
       "      <td>[53.068819, -1.64693]</td>\n",
       "      <td>53.068819</td>\n",
       "      <td>-1.646930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France_Ligue1</td>\n",
       "      <td>34977</td>\n",
       "      <td>[46.398719, 4.233663]</td>\n",
       "      <td>46.398719</td>\n",
       "      <td>4.233663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany_Bundesliga</td>\n",
       "      <td>39050</td>\n",
       "      <td>[50.771006, 10.553582]</td>\n",
       "      <td>50.771006</td>\n",
       "      <td>10.553582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Italy_SerieA</td>\n",
       "      <td>49378</td>\n",
       "      <td>[41.992834, 13.884544]</td>\n",
       "      <td>41.992834</td>\n",
       "      <td>13.884544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Spain_Liga</td>\n",
       "      <td>45997</td>\n",
       "      <td>[39.981012, -4.358449]</td>\n",
       "      <td>39.981012</td>\n",
       "      <td>-4.358449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               league  total_shots             coordinates        lat  \\\n",
       "0          England_PL        71010   [53.068819, -1.64693]  53.068819   \n",
       "1       France_Ligue1        34977   [46.398719, 4.233663]  46.398719   \n",
       "2  Germany_Bundesliga        39050  [50.771006, 10.553582]  50.771006   \n",
       "3        Italy_SerieA        49378  [41.992834, 13.884544]  41.992834   \n",
       "4          Spain_Liga        45997  [39.981012, -4.358449]  39.981012   \n",
       "\n",
       "         lon  \n",
       "0  -1.646930  \n",
       "1   4.233663  \n",
       "2  10.553582  \n",
       "3  13.884544  \n",
       "4  -4.358449  "
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_for_map[\"lat\"] = df_for_map[\"coordinates\"].apply(lambda x: x[0])\n",
    "df_for_map[\"lon\"] = df_for_map[\"coordinates\"].apply(lambda x: x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assess evolution of average number of shots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-15T22:35:15.359203Z",
     "start_time": "2019-02-15T22:35:15.332728Z"
    }
   },
   "outputs": [],
   "source": [
    "test = df_agg[df_agg[\"league\"].isin(MAJOR_LEAGUES)].pivot_table(index=\"season_year\", values=\"shots_per_game\", columns=\"league\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-15T23:17:39.966761Z",
     "start_time": "2019-02-15T23:17:36.353814Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~theon/74.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "import cufflinks as cf\n",
    "import pandas as pd\n",
    "\n",
    "# cf.set_config_file(offline=True, world_readable=True, theme='ggplot')\n",
    "\n",
    "# df = cf.datagen.lines()\n",
    "# df.head()\n",
    "\n",
    "# Create a simple dataframe..\n",
    "\n",
    "test.iplot(kind='scatter', filename='test')\n",
    "\n",
    "colors = ['blue', 'orange', 'green','red', 'purple'] # Individual Line Color\n",
    "dashes = ['solid', 'solid', 'dashdot', 'dash', 'solid'] # Individual Line Style\n",
    "widths = [2, 4, 6, 1, 4] # Individual Line Width\n",
    "\n",
    "layout = {\n",
    "    \"title\": 'Average numbers of shots per game by season',\n",
    "    \"hovermode\": 'closest',\n",
    "    \"xaxis\": dict(\n",
    "        title='Season (Year)'\n",
    "    ),\n",
    "    \"yaxis\": dict(\n",
    "        title='Average number of shots taken per game'\n",
    "    )\n",
    "}\n",
    "\n",
    "\n",
    "test.iplot(kind='scatter', mode='lines', colors=colors, dash=dashes, layout=layout, filename='line-style-and-color')\n",
    "\n",
    "# plot_url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show distribution of shots by part of the body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-15T23:51:25.507469Z",
     "start_time": "2019-02-15T23:51:25.502436Z"
    }
   },
   "outputs": [],
   "source": [
    "pal = sns.cubehelix_palette(4, start=2.5, rot=0, dark=0.4, light=.93, reverse=True)\n",
    "pal_hex = pal.as_hex() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-16T00:15:48.474988Z",
     "start_time": "2019-02-16T00:15:48.419598Z"
    },
    "collapsed": true
   },
   "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>part</th>\n",
       "      <th>total_shots</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>head</td>\n",
       "      <td>42642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>left_foot</td>\n",
       "      <td>78291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>other_parts</td>\n",
       "      <td>955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>right_foot</td>\n",
       "      <td>130526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>unknown</td>\n",
       "      <td>24977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          part  total_shots\n",
       "0         head        42642\n",
       "1    left_foot        78291\n",
       "2  other_parts          955\n",
       "3   right_foot       130526\n",
       "4      unknown        24977"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_part_league = df.groupby([\"league\", \"part\"]).agg({\"shot_by\": \"count\"}).reset_index()\n",
    "df_agg_part_league.columns = [\"league\", \"part\", \"total_shots\"]\n",
    "df_agg_part = df_agg_part_league.groupby(\"part\").agg({\"total_shots\": \"sum\"}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-16T00:15:59.947770Z",
     "start_time": "2019-02-16T00:15:58.504439Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~theon/76.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = df_agg_part.part.values\n",
    "values = df_agg_part.total_shots.values\n",
    "colors = pal_hex + [\"#a2a7ad\"]\n",
    "\n",
    "trace = go.Pie(labels=labels, values=values,\n",
    "               hoverinfo='label+percent', textinfo='value', \n",
    "               textfont=dict(size=13),\n",
    "               marker=dict(colors=colors, \n",
    "                           line=dict(color='#000000', width=0.5)))\n",
    "\n",
    "layout = {\n",
    "    \"title\": 'Repartition of collected shots data by parts used',\n",
    "}\n",
    "\n",
    "fig = go.Figure(data=[trace], layout=layout)\n",
    "py.iplot(fig, filename='pie_chart_part')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-16T00:19:16.264427Z",
     "start_time": "2019-02-16T00:19:16.260606Z"
    }
   },
   "outputs": [],
   "source": [
    "part = [\"right_foot\", \"left_foot\", \"head\", \"other_parts\", \"unknown\"]\n",
    "COLOR_SHOTS = dict(zip(part, pal_hex + [\"#a2a7ad\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-16T00:19:29.011439Z",
     "start_time": "2019-02-16T00:19:25.473032Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~theon/12.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [go.Bar(x=df_agg_part_league[df_agg_part_league[\"part\"] == part].league,\n",
    "               y=df_agg_part_league[df_agg_part_league[\"part\"] == part].total_shots,\n",
    "               name=part,\n",
    "                  marker=dict(color=COLOR_SHOTS[part])) \n",
    "          for part in part]\n",
    "layout = go.Layout(\n",
    "    barmode='stack',\n",
    "    title=\"Number of shots collected by league\"\n",
    ")\n",
    "\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.plotly.iplot(fig, filename='stacked-bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data visualization | Pitch level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define pitch visualization functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:12:31.611089Z",
     "start_time": "2019-02-27T11:12:31.601606Z"
    }
   },
   "outputs": [],
   "source": [
    "def draw_shots(shots, arrow=False):\n",
    "    for index, shot in shots.iterrows():\n",
    "        start_x = shot[\"pos_start\"][0]\n",
    "        start_y = shot[\"pos_start\"][1]\n",
    "        delta_x = shot[\"pos_delta\"][0]\n",
    "        delta_y = shot[\"pos_delta\"][1]\n",
    "        c = COLOR_SHOT[shot[\"result_processed\"]][0]\n",
    "        a = COLOR_SHOT[shot[\"result_processed\"]][1]\n",
    "        \n",
    "        if arrow:\n",
    "            plt.arrow(shot[\"pos_start\"][0], shot[\"pos_start\"][1], shot[\"pos_delta\"][0], shot[\"pos_delta\"][1],\n",
    "                  head_width=1,\n",
    "                  head_length=1,\n",
    "                  color=c,\n",
    "                  alpha=a,\n",
    "                  length_includes_head=True)\n",
    "        else:\n",
    "            plt.scatter(shot[\"pos_start\"][0], shot[\"pos_start\"][1],\n",
    "                        c = c,\n",
    "                        label = shot[\"result_processed\"],\n",
    "                        s=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:12:31.916814Z",
     "start_time": "2019-02-27T11:12:31.895482Z"
    }
   },
   "outputs": [],
   "source": [
    "x_size = 105.0\n",
    "y_size = 68.0\n",
    "\n",
    "from matplotlib.patches import Arc\n",
    "\n",
    "def create_pitch(ax, lines_color=\"white\"):\n",
    "    #set up field    \n",
    "    \n",
    "    ax.xaxis.set_visible(False)\n",
    "    ax.yaxis.set_visible(False)\n",
    "\n",
    "    plt.xlim([0,x_size])\n",
    "    plt.ylim([0,y_size])\n",
    "\n",
    "    box_height = ((40.3)/y_size)\n",
    "    box_width = (16.5/x_size)\n",
    "\n",
    "    keeper_zone_height = ((18.27)/y_size)\n",
    "    keeper_zone_width = (5.5/x_size)\n",
    "\n",
    "    r1 = plt.Rectangle((0, 0), 1, 1,\n",
    "                       edgecolor=lines_color, facecolor=\"none\", alpha=1, transform=ax.transAxes) #pitch\n",
    "\n",
    "    r2 = plt.Line2D([0.5, 0.5], [1, 0],\n",
    "                    c=lines_color, transform=ax.transAxes) #half-way line\n",
    "\n",
    "    r3 = plt.Rectangle((0, (1-box_height)/2), box_width, box_height,\n",
    "                       ec=lines_color, fc='none', transform=ax.transAxes) #penalty area\n",
    "\n",
    "    r4 = plt.Rectangle((1-box_width, (1-box_height)/2), box_width, box_height,\n",
    "                       ec=lines_color, fc='none', transform=ax.transAxes) #penalty area\n",
    "\n",
    "    r5 = Ellipse((0.5, 0.5), 9.15*2/x_size, 9.15*2/y_size,\n",
    "                                    ec=lines_color, fc='none', transform=ax.transAxes) #middle circle\n",
    "\n",
    "    r6 = plt.Rectangle((1-keeper_zone_width, (1-keeper_zone_height)/2), keeper_zone_width, keeper_zone_height,\n",
    "                       ec=lines_color, fc='none', transform=ax.transAxes) #keeper zone\n",
    "\n",
    "    r7 = plt.Rectangle((0, (1-keeper_zone_height)/2), keeper_zone_width, keeper_zone_height,\n",
    "                       ec=lines_color, fc='none', transform=ax.transAxes) #keeper zone\n",
    "\n",
    "    r8 = plt.scatter(52.5, 34, s=15, color=lines_color) \n",
    "    \n",
    "    r9 = plt.scatter(11, 34, s=15, color=lines_color) \n",
    "    \n",
    "    r10 = plt.scatter(105-11, 34, s=15, color=lines_color)\n",
    "    \n",
    "#     arc_1 = plt.Arc((9.7,40),height=16.2,width=16.2,angle=0,theta1=310,theta2=50,color=\"black\")\n",
    "#     arc_2 = plt.Arc((110.3,40),height=16.2,width=16.2,angle=0,theta1=130,theta2=230,color=\"black\")\n",
    "    \n",
    "    ax.add_patch(Arc((11.3,68/2),height=16.2,width=16.2,angle=0,theta1=310,theta2=50,color=lines_color))\n",
    "    ax.add_patch(Arc((105-11.3,68/2),height=16.2,width=16.2,angle=0,theta1=130,theta2=230,color=lines_color))\n",
    "        \n",
    "    fig.lines.extend([r1, r2, r3, r4, r5, r6, r7, r8, r9, r10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:12:32.585183Z",
     "start_time": "2019-02-27T11:12:32.580655Z"
    }
   },
   "outputs": [],
   "source": [
    "def draw_shots_by_zone(shots, arrow=False):\n",
    "    for index, shot in shots.iterrows():\n",
    "        start_x = shot[\"pos_start\"][0]\n",
    "        start_y = shot[\"pos_start\"][1]\n",
    "        delta_x = shot[\"pos_delta\"][0]\n",
    "        delta_y = shot[\"pos_delta\"][1]\n",
    "        c = COLOR_ZONE[shot[\"zone\"]]\n",
    "        \n",
    "        if arrow:\n",
    "            plt.arrow(shot[\"pos_start\"][0], shot[\"pos_start\"][1], shot[\"pos_delta\"][0], shot[\"pos_delta\"][1],\n",
    "                  head_width=1,\n",
    "                  head_length=1,\n",
    "                  color=c,\n",
    "                  length_includes_head=True)\n",
    "        else:\n",
    "            plt.scatter(shot[\"pos_start\"][0], shot[\"pos_start\"][1],\n",
    "                        c = c,\n",
    "                        label = shot[\"result_processed\"],\n",
    "                        s=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-24T22:49:16.743342Z",
     "start_time": "2019-02-24T22:49:16.088782Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1260x816 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_x = [pos[0] for pos in df[\"pos_start\"].iloc[0:50]]\n",
    "pos_y = [pos[1] for pos in df[\"pos_start\"].iloc[0:50]]\n",
    "\n",
    "# Colours options \n",
    "pitch_color = '#4E7E4F'\n",
    "line_color = 'white'\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(x_size/6, y_size/6))\n",
    "fig.patch.set_facecolor(pitch_color)\n",
    "ax = fig.add_subplot(1, 1, 1, facecolor=pitch_color)\n",
    "\n",
    "#shades: give us the heat map we desire\n",
    "# n_levels: draw more lines, the larger n, the more bluerry it loos\n",
    "pal = sns.cubehelix_palette(25, start=2, rot=0, dark=0.4, light=.93, reverse=True, as_cmap=True)\n",
    "# sns.kdeplot(x, y, cmap=pal);\n",
    "sns.kdeplot(pos_x, pos_y, shade = \"True\", cmap=pal, n_levels = 30)\n",
    "# draw_shots(df_all.iloc[0:5000], arrow=False)\n",
    "plt.xlim([0,x_size])\n",
    "plt.ylim([0,y_size])\n",
    "plt.axis(\"off\")\n",
    "create_pitch(ax, lines_color=line_color)\n",
    "# plt.title(\"Distribution heatmap of shot positions on a sample of 50,000 shots\", color=\"white\", size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize example of distribution on 5k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-24T22:58:28.342935Z",
     "start_time": "2019-02-24T22:58:24.408987Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1260x816 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_x = [pos[0] for pos in df[\"pos_start\"].sample(n=5000, random_state=1)]\n",
    "pos_y = [pos[1] for pos in df[\"pos_start\"].sample(n=5000, random_state=1)]\n",
    "\n",
    "# Colours options \n",
    "pitch_color = '#4E7E4F'\n",
    "line_color = 'white'\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(x_size/6, y_size/6))\n",
    "fig.patch.set_facecolor(pitch_color)\n",
    "ax = fig.add_subplot(1, 1, 1, facecolor=pitch_color)\n",
    "\n",
    "#shades: give us the heat map we desire\n",
    "# n_levels: draw more lines, the larger n, the more bluerry it loos\n",
    "pal = sns.cubehelix_palette(25, start=2, rot=0, dark=0.4, light=.93, reverse=True, as_cmap=True)\n",
    "# sns.kdeplot(x, y, cmap=pal);\n",
    "sns.kdeplot(pos_x, pos_y, shade = \"True\", cmap=pal, n_levels = 30)\n",
    "# draw_shots(df_all.iloc[0:5000], arrow=False)\n",
    "plt.xlim([0,x_size])\n",
    "plt.ylim([0,y_size])\n",
    "plt.axis(\"off\")\n",
    "create_pitch(ax, lines_color=line_color)\n",
    "# plt.title(\"Distribution heatmap of shot positions on a sample of 50,000 shots\", color=\"white\", size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-24T20:15:30.778729Z",
     "start_time": "2019-02-24T20:14:51.391589Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1260x816 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_x = [pos[0] for pos in df[df[\"is_goal\"]==1][\"pos_start\"]]\n",
    "pos_y = [pos[1] for pos in df[df[\"is_goal\"]==1][\"pos_start\"]]\n",
    "\n",
    "# Colours options \n",
    "pitch_color = '#4E7E4F'\n",
    "line_color = 'white'\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(x_size/6, y_size/6))\n",
    "fig.patch.set_facecolor(pitch_color)\n",
    "ax = fig.add_subplot(1, 1, 1, facecolor=pitch_color)\n",
    "\n",
    "#shades: give us the heat map we desire\n",
    "# n_levels: draw more lines, the larger n, the more bluerry it loos\n",
    "pal = sns.cubehelix_palette(25, start=2, rot=0, dark=0.4, light=.93, reverse=True, as_cmap=True)\n",
    "# sns.kdeplot(x, y, cmap=pal);\n",
    "sns.kdeplot(pos_x, pos_y, shade = \"True\", cmap=pal, n_levels = 30)\n",
    "# draw_shots(df_all.iloc[0:5000], arrow=False)\n",
    "plt.xlim([0,x_size])\n",
    "plt.ylim([0,y_size])\n",
    "plt.axis(\"off\")\n",
    "create_pitch(ax, lines_color=line_color)\n",
    "# plt.title(\"Distribution heatmap of shot positions on a sample of 50,000 shots\", color=\"white\", size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T22:54:27.787403Z",
     "start_time": "2019-02-25T22:54:27.409003Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"check_start_0\"] = df[\"pos_start\"].apply(lambda x: x[0])\n",
    "df[\"check_start_1\"] = df[\"pos_start\"].apply(lambda x: x[1])\n",
    "df[\"check_end_0\"] = df[\"pos_end\"].apply(lambda x: x[0])\n",
    "df[\"check_end_1\"] = df[\"pos_end\"].apply(lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-24T21:01:35.015486Z",
     "start_time": "2019-02-24T21:01:34.822690Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1260x816 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of Germany shots against Brazil after adjusting x and y \n",
    "\n",
    "fig = plt.figure(figsize=(x_size/6, y_size/6))\n",
    "fig.patch.set_facecolor('#4E7E4F')\n",
    "ax = fig.add_subplot(1, 1, 1, facecolor='#4E7E4F')\n",
    "\n",
    "#shades: give us the heat map we desire\n",
    "# n_levels: draw more lines, the larger n, the more bluerry it loos\n",
    "pal = sns.cubehelix_palette(25, start=2, rot=0, dark=0.4, light=.93, reverse=True, as_cmap=True)\n",
    "# sns.kdeplot(x, y, cmap=pal);\n",
    "draw_shots(df[(df[\"fixture_id\"]==731827)&(df[\"shot_by\"]==\"away\")], arrow=True)\n",
    "plt.xlim([0,x_size])\n",
    "plt.ylim([0,y_size])\n",
    "plt.axis(\"off\")\n",
    "create_pitch(ax)\n",
    "\n",
    "# Add legend of shots data\n",
    "\n",
    "custom_lines = [Line2D([0], [0], color=COLOR_SHOT[\"goal\"][0], lw=2),\n",
    "                Line2D([0], [0], color=COLOR_SHOT[\"saved\"][0], lw=2),\n",
    "                Line2D([0], [0], color=COLOR_SHOT[\"blocked\"][0], lw=2),\n",
    "                Line2D([0], [0], color=COLOR_SHOT[\"off_target\"][0], lw=2)\n",
    "               ]\n",
    "\n",
    "ax.legend(custom_lines, ['Goal', 'Saved', 'Blocked', 'Off-target'])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matrice Shots per Goals / Total shots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://plot.ly/python/multiple-transforms/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T17:35:24.855027Z",
     "start_time": "2019-02-21T17:35:24.848282Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_columns_reviewed(side):\n",
    "    if side == \"away\":\n",
    "        columns_reviewed = ['team', 'team_id', 'fixture_id', 'link', 'minute',\n",
    "       'part', 'result', 'score', 'shot_by', 'x1', 'x2', 'y1', 'y2',\n",
    "       'match_date_y', 'season_year', 'league', 'result_processed',\n",
    "       'pos_start', 'pos_end', 'pos_delta', 'is_goal', 'zone', 'xp_goal']\n",
    "    else:\n",
    "        columns_reviewed = ['fixture_id', 'team', 'team_id', 'link', 'minute',\n",
    "       'part', 'result', 'score', 'shot_by', 'x1', 'x2', 'y1', 'y2',\n",
    "       'match_date_y', 'season_year', 'league', 'result_processed',\n",
    "       'pos_start', 'pos_end', 'pos_delta', 'is_goal', 'zone', 'xp_goal']\n",
    "    return columns_reviewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T17:35:27.439505Z",
     "start_time": "2019-02-21T17:35:27.427987Z"
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   "source": [
    "def process_df(df, side):\n",
    "    side_against = \"away\" if side == \"home\" else \"home\"\n",
    "    df_processed = df[df.columns.drop(list(df.filter(regex=side_against)))]\n",
    "    df_processed = df_processed[df_processed[\"shot_by\"]==side]\n",
    "    df_processed.columns = get_columns_reviewed(side)\n",
    "    return df_processed"
   ]
  },
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   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T17:35:28.533595Z",
     "start_time": "2019-02-21T17:35:28.338493Z"
    }
   },
   "outputs": [],
   "source": [
    "df_home = process_df(df, \"home\")\n",
    "df_away = process_df(df, \"away\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T17:35:30.782649Z",
     "start_time": "2019-02-21T17:35:30.642860Z"
    }
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   "outputs": [],
   "source": [
    "df_team_view = df_away.append(df_home)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T17:35:35.592449Z",
     "start_time": "2019-02-21T17:35:35.292920Z"
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   },
   "outputs": [],
   "source": [
    "df_macro_stat = df_team_view[[\"team\", \"fixture_id\", \"league\", \"season_year\", \"shot_by\", \"is_goal\"]]\\\n",
    ".groupby([\"league\", \"team\", \"season_year\", \"fixture_id\"])\\\n",
    ".agg({\"shot_by\": \"count\", \"is_goal\": \"sum\"})\\\n",
    ".reset_index()"
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  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T17:35:36.391873Z",
     "start_time": "2019-02-21T17:35:36.330336Z"
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   "outputs": [],
   "source": [
    "df_team_stat = df_macro_stat.groupby([\"league\", \"team\"])\\\n",
    ".agg({\"fixture_id\": \"count\", \"shot_by\": [\"sum\", \"mean\"], \"is_goal\": \"sum\"})\\\n",
    ".reset_index()"
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   "cell_type": "code",
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   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T17:35:37.383341Z",
     "start_time": "2019-02-21T17:35:37.370877Z"
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   },
   "outputs": [],
   "source": [
    "df_team_stat.columns = ['_'.join(col) for col in df_team_stat.columns.values]\n",
    "df_team_stat[\"goal_per_shot\"] = df_team_stat[\"is_goal_sum\"] / df_team_stat[\"shot_by_sum\"]"
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>league_</th>\n",
       "      <th>team_</th>\n",
       "      <th>fixture_id_count</th>\n",
       "      <th>shot_by_sum</th>\n",
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       "      <td>Arsenal</td>\n",
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       "      <td>447</td>\n",
       "      <td>0.109344</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>England_PL</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England_PL</td>\n",
       "      <td>Blackburn Rovers</td>\n",
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       "      league_             team_  fixture_id_count  shot_by_sum  shot_by_mean  \\\n",
       "0  England_PL           Arsenal               266         4088     15.368421   \n",
       "1  England_PL       Aston Villa               228         2579     11.311404   \n",
       "2  England_PL   Birmingham City                38          396     10.421053   \n",
       "3  England_PL  Blackburn Rovers                75          891     11.880000   \n",
       "4  England_PL         Blackpool                38          521     13.710526   \n",
       "\n",
       "   is_goal_sum  goal_per_shot  \n",
       "0          447       0.109344  \n",
       "1          207       0.080264  \n",
       "2           35       0.088384  \n",
       "3           79       0.088664  \n",
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    "df_team_stat_light = df_team_stat[df_team_stat[\"league_\"].isin(MAJOR_LEAGUES)]\n",
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11.788235294117648, 13.094117647058823, 17.647058823529413, 12.676470588235293, 13.452941176470588, 9.147058823529411, 11.905882352941177, 11.529411764705882, 9.808823529411764, 12.764705882352942, 12.882352941176471, 13.264705882352942, 9.911764705882353, 12.29585798816568, 10.676470588235293, 11.196078431372548, 13.372549019607844, 12.272058823529411, 13.676470588235293], \"y\": [0.09812667261373774, 0.09283819628647215, 0.10124069478908189, 0.1111111111111111, 0.11004784688995216, 0.1209471766848816, 0.12005856515373353, 0.06930693069306931, 0.09580838323353294, 0.07906558849955077, 0.13533333333333333, 0.06148491879350348, 0.10275470048097946, 0.10610932475884244, 0.08547430830039526, 0.09948979591836735, 0.10719640179910045, 0.1359447004608295, 0.091324200913242, 0.06430155210643015, 0.08902077151335312, 0.10923965351299326, 0.06336088154269973, 0.09281961471103327, 0.10117302052785923, 0.09406830437387657, 0.10408602150537634], \"text\": [\"1. FC K\\u00f6ln\", \"1. FC N\\u00fcrnberg\", \"1. FSV Mainz 05\", \"1899 Hoffenheim\", \"Bayer 04 Leverkusen\", \"Borussia Dortmund\", \"Borussia M\\u00f6nchengladbach\", \"Eintracht Braunschweig\", \"Eintracht Frankfurt\", \"FC Augsburg\", \"FC Bayern M\\u00fcnchen\", \"FC Ingolstadt 04\", \"FC Schalke 04\", \"Fortuna D\\u00fcsseldorf\", \"Hamburger SV\", \"Hannover 96\", \"Hertha BSC\", \"RB Leipzig\", \"SC Freiburg\", \"SC Paderborn 07\", \"SV Darmstadt 98\", \"SV Werder Bremen\", \"SpVgg Greuther F\\u00fcrth\", \"Sport-Club Freiburg\", \"TSG 1899 Hoffenheim\", \"VfB Stuttgart\", \"VfL Wolfsburg\"], \"mode\": \"markers\", \"name\": \"Germany_Bundesliga\"}, {\"type\": \"scatter\", \"x\": [9.881578947368421, 10.18421052631579, 9.539473684210526, 12.151315789473685, 10.68421052631579, 12.06578947368421, 10.491228070175438, 11.421052631578947, 10.324561403508772, 10.552631578947368, 12.730263157894736, 11.861842105263158, 13.256578947368421, 13.592105263157896, 11.631578947368421, 12.131578947368421, 11.111842105263158, 12.31578947368421, 10.964912280701755, 10.263157894736842, 13.835526315789474, 10.927631578947368, 10.592105263157896, 10.631578947368421, 11.960526315789474, 11.269736842105264, 10.894736842105264, 11.302631578947368], \"y\": [0.09454061251664447, 0.09302325581395349, 0.1006896551724138, 0.08770979967514889, 0.07142857142857142, 0.08724100327153762, 0.09531772575250837, 0.07603686635944701, 0.09090909090909091, 0.06733167082294264, 0.08372093023255814, 0.0920687742651137, 0.11315136476426799, 0.09777347531461762, 0.05656108597285068, 0.10918293564714389, 0.10301953818827708, 0.07692307692307693, 0.0704, 0.10576923076923077, 0.1388492629576795, 0.08910295003010235, 0.10559006211180125, 0.08663366336633663, 0.1034103410341034, 0.09632224168126094, 0.0785024154589372, 0.08847497089639116], \"text\": [\"Ajaccio\", \"Angers\", \"Bastia\", \"Bordeaux\", \"Brest\", \"Caen\", \"Evian Thonon Gaillard\", \"GFC Ajaccio\", \"Guingamp\", \"Lens\", \"Lille\", \"Lorient\", \"Lyon\", \"Marseille\", \"Metz\", \"Monaco\", \"Montpellier\", \"Nancy\", \"Nantes\", \"Nice\", \"Paris Saint-Germain\", \"Reims\", \"Rennes\", \"Sochaux\", \"St Etienne\", \"Toulouse\", \"Troyes\", \"Valenciennes\"], \"mode\": \"markers\", \"name\": \"France_Ligue1\"}], {\"title\": \"Attacking performance per team\", \"hovermode\": \"closest\", \"xaxis\": {\"title\": \"# shots taken per game\", \"autorange\": true}, \"yaxis\": {\"title\": \"1 / # shots per goal (=goal per shot)\", \"autorange\": true}, \"shapes\": [{\"type\": \"line\", \"x0\": 12.18027522978486, \"y0\": 0.05127249357326478, \"x1\": 12.18027522978486, \"y1\": 0.17256393001345896, \"line\": {\"color\": \"#a2a7ad\", \"width\": 1.5, \"dash\": \"solid\"}}, {\"type\": \"line\", \"x0\": 9.005, \"y0\": 0.09189771633208056, \"x1\": 18.162894736842105, \"y1\": 0.09189771633208056, \"line\": {\"color\": \"#a2a7ad\", \"width\": 1.5, \"dash\": \"solid\"}}]}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"246b9641-d9f6-4fae-a4b0-12a089511d32\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.offline as off\n",
    "\n",
    "off.init_notebook_mode(connected=True)\n",
    "\n",
    "data = []\n",
    "\n",
    "for league in MAJOR_LEAGUES:\n",
    "    trace_tmp = dict(\n",
    "        type = 'scatter',\n",
    "        x = df_team_stat_light[df_team_stat_light[\"league_\"]==league][\"shot_by_mean\"].values,\n",
    "        y = df_team_stat_light[df_team_stat_light[\"league_\"]==league][\"goal_per_shot\"].values,\n",
    "        text= df_team_stat_light[df_team_stat_light[\"league_\"]==league][\"team_\"],\n",
    "        mode = 'markers',\n",
    "        name=league\n",
    "    )\n",
    "    data.append(trace_tmp)\n",
    "\n",
    "\n",
    "\n",
    "layout = {\n",
    "    \"title\": 'Attacking performance per team',\n",
    "    \"hovermode\": 'closest',\n",
    "    \"xaxis\": dict(\n",
    "        title='# shots taken per game',\n",
    "        autorange=True\n",
    "    ),\n",
    "    \"yaxis\": dict(\n",
    "        title='1 / # shots per goal (=goal per shot)',\n",
    "        autorange=True\n",
    "    ),\n",
    "    'shapes': [\n",
    "        # Line Vertical\n",
    "        {\n",
    "            'type': 'line',\n",
    "            'x0': df_team_stat_light[\"shot_by_mean\"].mean(),\n",
    "            'y0': df_team_stat_light[\"goal_per_shot\"].min() + 0.005,\n",
    "            'x1': df_team_stat_light[\"shot_by_mean\"].mean(),\n",
    "            'y1': df_team_stat_light[\"goal_per_shot\"].max() + 0.005,\n",
    "            'line': {\n",
    "                'color': '#a2a7ad',\n",
    "                'width': 1.5,\n",
    "                'dash': 'solid',\n",
    "            },\n",
    "        },\n",
    "        # Line Horizontal\n",
    "        {\n",
    "            'type': 'line',\n",
    "            'x0': df_team_stat_light[\"shot_by_mean\"].min() + 0.005,\n",
    "            'y0': df_team_stat_light[\"goal_per_shot\"].mean(),\n",
    "            'x1': df_team_stat_light[\"shot_by_mean\"].max() + 0.005,\n",
    "            'y1': df_team_stat_light[\"goal_per_shot\"].mean(),\n",
    "            'line': {\n",
    "                'color': '#a2a7ad',\n",
    "                'width': 1.5,\n",
    "                'dash': 'solid',\n",
    "            },\n",
    "        },\n",
    "    ]\n",
    "}\n",
    "\n",
    "fig = {\n",
    "    'data': data,\n",
    "    'layout': layout,\n",
    "}\n",
    "\n",
    "off.iplot(fig, validate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XP Goal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining zone - Grid test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:12:53.866038Z",
     "start_time": "2019-02-27T11:12:53.861177Z"
    }
   },
   "outputs": [],
   "source": [
    "zone_x = [0, 72, 77.5, 83.0, 88.5, 94.0, 99.5, 102.25, 105]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:12:54.272024Z",
     "start_time": "2019-02-27T11:12:54.268656Z"
    }
   },
   "outputs": [],
   "source": [
    "zone_y = [0, 13.85 - 5.5, 13.85, 31.25-5.5*2, 31.25-5.5, 31.25, 31.25+5.5, 31.25+5.5*2, 31.25+5.5*3, pitch_y[-2], pitch_y[-2]+5.5, pitch_y[-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:57:02.008833Z",
     "start_time": "2019-02-27T11:57:02.001776Z"
    }
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "zones_table = pd.DataFrame(list(itertools.product(zone_x, zone_y)))\n",
    "zones_table.columns = [\"x\", \"y\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:57:02.381382Z",
     "start_time": "2019-02-27T11:57:02.359464Z"
    }
   },
   "outputs": [],
   "source": [
    "zones_table[\"zone\"] = list(range(1, zones_table.shape[0]+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T12:23:40.390656Z",
     "start_time": "2019-02-27T12:23:40.372367Z"
    }
   },
   "outputs": [],
   "source": [
    "zones_table.loc[(zones_table[\"y\"]<zone_y[1]), \"zone\"] = 200\n",
    "zones_table.loc[(zones_table[\"y\"]>=zone_y[-2]), \"zone\"] = 201\n",
    "# zones_table.loc[(zones_table[\"y\"]>=zone_y[-2]), \"zone\"] = 202\n",
    "zones_table.loc[(zones_table[\"y\"]<=zone_y[4])&(zones_table[\"test[(test[\"check_start_0\"] > zone_x[-3])&(test[\"check_start_1\"] > zone_y[1])&(test[\"check_start_1\"] < zone_y[3])][\"pos_start\"]x\"]>=zone_x[-2])&(zones_table[\"y\"]>=zone_y[1]), \"zone\"] = 203\n",
    "zones_table.loc[zones_table[\"x\"]<zone_x[1], \"zone\"] = 210"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T13:00:30.107537Z",
     "start_time": "2019-02-27T13:00:30.102965Z"
    }
   },
   "outputs": [],
   "source": [
    "def zones_lookup(row):\n",
    "    tmp_1 = zones_table[zones_table[\"x\"]<row[\"pos_start\"][0]]\n",
    "    tmp_2 = tmp_1.iloc[tmp_1['x'].iloc[(tmp_1['x']-row[\"pos_start\"][0]).abs().argsort()[:12]].index]\n",
    "    tmp_3 = tmp_2[tmp_2[\"y\"]<row[\"pos_start\"][1]]\n",
    "    zone = tmp_3[\"zone\"].iloc[(tmp_3[\"y\"]-row[\"pos_start\"][1]).abs().argsort()[:1]].values[0]\n",
    "    return zone\n",
    "\n",
    "def zones_consolidation(row):\n",
    "    if row[\"pos_start\"][0]<zone_x[1]:\n",
    "        return 200\n",
    "    elif (row[\"pos_start\"][1]>zone_y[-2]):\n",
    "        return 201\n",
    "    elif  (row[\"pos_start\"][1]<zone_y[1]):\n",
    "        return 202\n",
    "    elif (row[\"pos_start\"][0]>zone_x[-3])&(row[\"pos_start\"][1]>zone_y[1])&(row[\"pos_start\"][1]<zone_y[3]):\n",
    "        return 203\n",
    "    elif (row[\"pos_start\"][0]>zone_x[-3])&(row[\"pos_start\"][1]>zone_y[-4])&(row[\"pos_start\"][1]<zone_y[-2]):\n",
    "        return 204\n",
    "    else:\n",
    "        return row[\"zone\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T13:00:36.339317Z",
     "start_time": "2019-02-27T13:00:36.172621Z"
    }
   },
   "outputs": [],
   "source": [
    "test = df.copy().iloc[0:15000]\n",
    "test[\"check_start_0\"] = test[\"pos_start\"].apply(lambda x: x[0])\n",
    "test[\"check_start_1\"] = test[\"pos_start\"].apply(lambda x: x[1])\n",
    "test[\"check_end_0\"] = test[\"pos_end\"].apply(lambda x: x[0])\n",
    "test[\"check_end_1\"] = test[\"pos_end\"].apply(lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T13:01:05.061634Z",
     "start_time": "2019-02-27T13:00:37.126042Z"
    }
   },
   "outputs": [],
   "source": [
    "test[\"zone\"] = test.apply(zones_lookup, axis=1)\n",
    "test[\"zone\"] = test.apply(zones_consolidation, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T13:02:30.314626Z",
     "start_time": "2019-02-27T13:02:30.296421Z"
    }
   },
   "outputs": [],
   "source": [
    "COLOR_ZONE = dict(zip(list(range(1,71)), sns.cubehelix_palette(71, start=.5, rot=-.75, reverse=True)))\n",
    "COLOR_ZONE = dict(zip(list(range(1,71)), sns.cubehelix_palette(71, start=15, rot=10, reverse=True)))\n",
    "COLOR_ZONE = dict(zip(list(range(1,zones_table.shape[0])), sns.cubehelix_palette(zones_table.shape[0], start=100, dark=0.5, rot=19199119191919, reverse=True)))\n",
    "COLOR_ZONE = dict(zip(list(range(1,zones_table.shape[0])), sns.diverging_palette(160, 24, n=110, sep=1)))\n",
    "COLOR_ZONE = dict(zip(list(range(1,zones_table.shape[0])), (sns.cubehelix_palette(4, start=.4, rot=-.75, reverse=True) + sns.cubehelix_palette(4, start=.4, rot=-.75, reverse=False)) * 20))\n",
    "COLOR_ZONE = dict(zip(list(range(1,zones_table.shape[0])), ([\"yellow\", \"orange\", \"purple\", \"blue\"] + [\"orange\", \"blue\", \"yellow\", \"black\"]) * 20))\n",
    "\n",
    "# sns.cubehelix_palette(4, start=.4, rot=-.75, reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T13:05:00.500281Z",
     "start_time": "2019-02-27T13:05:00.495152Z"
    }
   },
   "outputs": [],
   "source": [
    "COLOR_ZONE[200] = 'red'\n",
    "COLOR_ZONE[201] = 'purple'\n",
    "COLOR_ZONE[202] = \"purple\"\n",
    "COLOR_ZONE[203] = \"black\"\n",
    "COLOR_ZONE[204] = \"white\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-26T21:05:16.576126Z",
     "start_time": "2019-02-26T21:05:16.571588Z"
    }
   },
   "outputs": [],
   "source": [
    "def zones_lookup_individual(pos):\n",
    "    print(pos)\n",
    "    tmp_1 = zones_table[zones_table[\"x\"]<pos[0]]\n",
    "    print(tmp_1)\n",
    "    tmp_2 = tmp_1.iloc[tmp_1['x'].iloc[(tmp_1['x']-pos[0]).abs().argsort()[:12]].index]\n",
    "    print(tmp_2)\n",
    "    tmp_3 = tmp_2[tmp_2[\"y\"]<pos[1]]\n",
    "    print(tmp_3)\n",
    "    zone = tmp_3[\"zone\"].iloc[(tmp_3[\"y\"]-pos[1]).abs().argsort()[:1]].values[0]\n",
    "    return zone"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drawing zones representation for medium article"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:13:05.811886Z",
     "start_time": "2019-02-27T11:13:05.807914Z"
    }
   },
   "outputs": [],
   "source": [
    "zone_y_viz = zone_y[1:-1]\n",
    "zone_x_viz = zone_x[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:14:56.417530Z",
     "start_time": "2019-02-27T11:14:56.404227Z"
    }
   },
   "outputs": [],
   "source": [
    "colors = sns.cubehelix_palette(4, start=.4, rot=-.75, reverse=True)\n",
    "colors = sns.diverging_palette(220, 20, n=4)\n",
    "colors = sns.cubehelix_palette(8, start=.5, rot=-.75)[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:31:49.871751Z",
     "start_time": "2019-02-27T11:31:49.863874Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[72, 77.5, 83.0, 88.5, 94.0, 99.5, 102.25, 105]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_x_viz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T11:48:42.113019Z",
     "start_time": "2019-02-27T11:48:41.871236Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uuuCY6L3B2nHzrb/PngQAzc7l6wCUnZqaQpx79lGx7jprxpBjLxHkrdiECdPiqCEXxQ6DNosfnvRfUVVVlT0JAJqVKAegrLRps0pccuFx0WaVVeJHP7na68fLwPwF78SJJ/8sNtqoZ5zxk+9GoeDpCwDlw6MaAGVj1VXbxBWXnhSL3nk3hp59Qyxb1pA9iWayePGS+OGpV0W3rp3ivLO/H4VCIXsSADQLUQ5AWWjbtk1cdskJMevt+rjgwluiWCxmT6KZLV26LH58xnWx6qpt4tyzjhTmAJQFUQ5Aq/dBkB8fb8+eGxf/9NfR2NiUPYmV5P33G+KMs34Z7duvGuecdYRL2QFo9TySAdCqFQrVcdEFx0Rd3YK46BJBXgmWLWuI08+8Ljp2aBc/Oe072XMA4HMR5QC0aiedcFBUV1fFhZfcJsgryLJlDXHGWddHn97rxGHf3j17DgB8ZqIcgFZr/313iq236h9nnXtjFIuN2XNoYe+9tyxOO/3aOGC/nWPwjptnzwGAz0SUA9AqfXGbjePw7+4Zp/3k2njnnSXZc0hSVzc/fnzGtfHjHx0Wffv2zJ4DACtMlAPQ6vTq1T3OOfOIOPOcG2LGW3Oy55Bs/IRpcdkVd8VPLzouart1zp4DACtElAPQqnTu3DEuu/iEuOaX98Uroydmz6FEPDPs5XjgwWHx04uPj7Zt22TPAYDlJsoBaDUKheq48Pyj48/PvBSPPf589hxKzO13PhZTpsyMoad/N3sKACw3UQ5Aq3HoIbtHQ0MxbrjpwewplKhLLrsjeq7XPfbYffvsKQCwXEQ5AK3CRhuuGwcd+JW48JLboqnJR5/x8d5/vyEuuPCWOP64A6P7Wl2z5wDAfyTKASh5q6xSE2efeWRcfe3vYs6c+dlzKHGTJs+Iu+95Moae/r2oqqrKngMAn0qUA1Dyvn/k3vHm9Lfj8SdeyJ5CK3HXb/8YbdqsEgfuv3P2FAD4VKIcgJK26cA+sftu28ell92ZPYVWpLGxKS646NY4/Lt7Rs/11sqeAwCfSJQDULLatWsbZ51xeFx+xV0xf8E72XNoZaZPnx2/uvXhOGvoEVEoeMoDQGnyCAVAyTr6+/vG6DETY/hzo7Kn0Eo98OCwWLx4SRx80K7ZUwDgY4lyAErSeuuuGV/76rZx9bW/y55CK9bU1BSXX/mb+Pa3dovVO3fMngMA/0aUA1CSjhmyX/z2nidigcvW+ZymT58dT/7pxfjed7+RPQUA/o0oB6DkDNykd2y88QZxz71/yp5Cmbjt14/G1766bayzzhrZUwDgI0Q5ACXn+GMPjJt+9VAsW/Z+9hTKxLz5i+Kee5+KY76/X/YUAPgIUQ5ASRn8pc2jQ4dVfSY5ze7u/30qNh3YJzYesEH2FAD4kCgHoGQUCoU49pj949rrfheNjU3ZcygzS5cui5tueThOOO6A7CkA8CFRDkDJ2HOPQfH223PjL399LXsKZeqxx0dGp9U6xA6DNsueAgARIcoBKBFVVVVx8H/tGrf++pHsKZSxxsamuO32P/jccgBKhigHoCRss/WAWLr0/Xhl9MTsKZS5p4e9HD3XWzN6914newoAiHIASsOB++8c993/dPYMKkCxWIwHHx4eB+6/c/YUABDlAORbu0dtbLJJ73jiqRezp1AhHvr9s/GVnbeK1Tq2z54CQIUT5QCkO2D/L8ejfxgZS5cuy55ChZg7d2E8//yrsec3dsieAkCFE+UApFp11Tbx9d0Gxf0PPpM9hQpz731Px/77fTmqq6uypwBQwUQ5AKl223XbGP3qxJg1qz57ChXmtbFvxMIFi2O7bQdmTwGggolyAFLt+Y0vxQMPDcueQYW6/6FhLmEHIJUoByBNbbfOsd66a8bfXhqXPYUK9exzo2LrLQdEmzarZE8BoEKJcgDSfGmHL8TzL7waxWIxewoVauHCxTF+wtTYZusB2VMAqFCiHIA0g3fcPIY/Oyp7BhVu+LOjYvCOm2fPoEw1FJuyJzSrgnooSY7Liiul+6wmewAAlalDh3ax6cA+cebZN2RPocI9+9yo+N539ojq6qpobCyvgGrN+vQoj6epk2Y2ZE9oVuVyXP6pXI6P47LiSuk+K6GfDwBQSbbfdmC8MnpivLtkafYUKtyst+fGnDnzY9OBfbKnAFCBRDkAKT64dP3/smdAREQMf84l7ADkEOUAtLhVVqmJbb+4STw3cnT2FIiIf76ufIvsGQBUIFEOQIvru9F6MXNmXcyduzB7CkRExMRJ02PVtm1izTW7ZE8BoMKIcgBaXP/+68fYcVOyZ8BHjBs/NQb0Xz97BgAVRpQD0OIG9O8lyik5Y8dNif79emXPAKDCiHIAWlz/fuvHuPFTs2fAR4wb50w5AC1PlAPQotq3axs9uneLSZPfyp4CHzF2/JTo39+ZcgBaligHoEX17dszJk2eEcViMXsKfMS8eYvi3XeXxjrrrJE9BYAKIsoBaFEDvMkbJWzsuCmxsUvYAWhBohyAFtW/Xy+vJ6dkjRs3xevKAWhRohyAFrXeemvFlKkzs2fAx5oydWasu+6a2TMAqCCiHIAWVVu7esyZMz97Bnysurr5UVu7evYMACqIKAegxRQK1bF6544xb97C7CnwsebULRDlALQoUQ5Ai+nSpVPMm78oisXG7CnwsebNWxidO3WIQsFTJABahkccAFrMGrWdo67OpeuUrmKxMeYveCe6dOmUPQWACiHKAWgxa9R2ibr6Bdkz4FPV1c2PNWo7Z88AoEKIcgBaTK0z5bQCdfVeVw5AyxHlALSY2trVRTklr65uftR2E+UAtAxRDkCLWW219rFw0bvZM+BTLVy4ODp37pA9A4AKIcoBaDE1hUI0NBSzZ8CnamgoRqFQyJ4BQIUQ5a1Yly6rxXVXnxpPPnZVXHf1qdGly2rZkwA+VUGUN6suXVaLvhutF1/YbEOPA82oWCz6SDSgVei0avs4fddvxmk7HR3f2XL/6NCmXfYkPgOPOK3YhecfHQM36RMdOrSLgZv0iQvPPzp7EsCnKhSqo1gU5c3lwvOPjg4d2kWhUPA40IyKxUZRDrQKx+/4jeizRo9oW9Mm1u3cPQ4Y+PXsSXwGHnFasY02XC9qaj64vK6mphAbbbhe8iKAT1ddXRXRlL2ifGy04XpRVVUVER4HmlNjY1NUV3uKBJS+nl3XiJrqD3qgUF2I7qutkbyIz8IjTiv2+sQ3P7wMtKGhGK9PfDN5EcCnayg2fhDmNIvXJ74ZTU0f/JTD40DzKRSqvcwCaBWmzZ0TDY0f/P+q2FiMWYvmJC/isxDlrdjQs2+IMX+fFIsXL4kxf58UQ8++IXsSwKcqFosfXuHD5zf07Bti8eIlUSwWPQ40o5qaQhSLjdkzAP6ja599NCbNmRlLG5bF9AWz4r4xj2VP4jOoyR7AZzdv3qI47sTLs2cALLcPXqsrypvLvHmLYsLrH5wdP+EHVySvKR/OlAOtxcL33o2Ln7w3+vSQda2ZM+UAtJhly96PVVdtkz0DPlXbtm1i2bKG7BkAVAhRDkCLqa9fEF27dsqeAZ+qW7fOUV+/IHsGABVClAPQYurqFkRt7erZM+BT1XZbPebUzcueAUCFEOUAtJg5dfOjtpsop7TV1naOujpnygFoGaIcgBZTVz8/ams7Z8+AT1Vbu3rMqZufPQOACiHKAWgxLl+n1LVv1zaqIuLdd9/LngJAhRDlALSYxYuXRHVVVbRv1zZ7Cnys2trVo86bvAHQgkQ5AC1qTt18Z8spWbW1q0edS9cBaEGiHIAWNWfOvOjevVv2DPhY3dfqGnPmeOd1AFqOKAegRb3++pvRr2/P7Bnwsfr17RkTJr6ZPQOACiLKAWhRY8dPjf79emXPgI/Vv//6MXbs1OwZAFQQUQ5Aixo7bkoM6L9+9gz4N4VCIfr0XicmTBDlALQcUQ5Ai5oxY0506NAuuqy+WvYU+IgN1u8Rb8+eG+8uWZo9BYAKIsoBaFFNTU0xbvzU6N/fJeyUlgH9149x45wlB6BliXIAWty48VO8rpyS079/rxg7bkr2DAAqjCgHoMWNHTfV68opOQP6ry/KAWhxohyAFvfa2Ddik016R3V1VfYUiIiI9u3aRs/11orXJ07PngJAhRHlALS42bPnRX39gthk497ZUyAiIrbddmC8MnpiLFv2fvYUACqMKAcgxfBnR8XgHTfPngERETF4x81j+HOjsmcAUIFEOQAphj83KnbacYvsGRA1NYXYftuB8dyIV7KnAFCBRDkAKSZMmBY1q9TEBuv3yJ5Chdti874xbdqsqK9fkD0FgAokygFI8+yz/xeDnS0nmUvXWZkKZfZsu6HYlD2Bj+G4rLhSus9qsgcAULmGPzcqjjv6gPj1HX/InkKFqqqqih132DxO+uHPsqfwLybNbMie0Cz69Civp9vlclz+qVyOj+Oy4krpPiuzn90B0JqMemVirL12bayxxurZU6hQ/fv1jHeXvBfT3nw7ewoAFUqUA5CmWCzG8OdGxe5f2y57ChVq969tF08/81L2DAAqmCgHINV9DzwT++375SiU2wsvKXnt27WN3b62XTz48PDsKQBUMM+AAEg1YcK0mD17buwwaLPsKVSYr399UPztpXExZ8787CkAVDBRDkC6++5/Or55wFeyZ1BBqqqq4sD9do7f3f/n7CkAVDhRDkC6p4e9HD17do/eG6ydPYUKsc3WA2LZ++/HqFdez54CQIUT5QCka2goxkMPD48D9t85ewoV4sD9d47f3fd09gwAEOUAlIYHHx4eu3xl61itY/vsKZS5tXvUxsBN+sQTT72YPQUARDkApWHu3IXx/AtjYu+9dsyeQpk76Ju7xKOPjYilS5dlTwEAUQ5A6bjt9kfjkIN3dbaclaZHj26x267bxm/veTJ7CgBEhCgHoIRMnTorhj07Kg479OvZUyhTQ47aN+69788xd+7C7CkAEBGiHIASc/Otv4+9vrFDdF+ra/YUykz/fr1iyy36OUsOQEkR5QCUlPr6BfG7+5+OIUftmz2FMnP8sQfGLbf+PpYsWZo9BQA+JMoBKDm/vfuJ2HrrAdF3o/Wyp1Amtt9uYHTr1ike+cOI7CkA8BGiHICS8+6SpXHrbY/E8ccemD2FMlBdXRXHH3NAXHf9/VEsNmbPAYCPEOUAlKSHH3ku1lyzSwzabtPsKbRye33jS7Fw0eJ4bsQr2VMA4N+IcgBKUrFYjMt/9pv40anf9hFpfGbd1+oaQ76/b1xx5W+zpwDAxxLlAJSsl14eF8OfHRX/ffLB2VNohaqqqmLo6d+Lu+95MiZNnpE9BwA+ligHoKRdd/39MWDABvHlnbbMnkIrc+D+O0ebNqvEXb/9Y/YUAPhEohyAkrZ06bK44MJb4pSTvxVduqyWPYdWoud6a8Xh390zLrjo1mhsbMqeAwCfSJQDUPL+/tob8cijI+InPzosewqtQKFQHWcNPSJ+devDMX367Ow5APCpRDkArcLNt/0+1lqrW+yx+/bZUyhxhx6yeyxevCQeeHBY9hQA+I9EOQCtQkNDMc6/8JY4/rgDo1ev7tlzKFEDB/aOgw78Slz0019HU5PL1gEofaIcgFZj8uQZcd0v74vLLj4hOnXqkD2HEtO9e7e46IJj44KLbovZs+dlzwGA5SLKAWhVHn1sZDwz/OW4+H+OjZqaQvYcSkT79qvG5ZecEHfc+Vi88Jcx2XMAYLmJcgBanV/e8EAsWvRunHbKodlTKAGFQnWcf+73Y9To1+Pe+/6cPQcAVogoB6DVaWpqivP+5+bou9F6ccjBX8ueQ7ITjjswagqFuPKqe7KnAMAKE+UAtEpLliyNH51+TRx04C4x+EubZ88hyb57D47tvjgwzjznhigWi9lzAGCFiXIAWq05c+bH6WdeFz8+7bDo369X9hxa2LbbbBxHHr5XnPqTq+Odd5ZkzwGAz0SUA9CqjR03NS659Pa4/KcnRt++PbPn0EK23mpAnH3WkTH07Btixow52XMA4DMT5QC0es8+90pcesWdccVPT4y+G62XPYeVbKst+8d55xwVQ8+6Pka/OjF7DgB8LqIcgLIw/NlRcfmVv4krLj3JGfMyttWW/eP8c78fQ8+6IUa98nr2HAD43EQ6txksAAAOCklEQVQ5AGVj2PD/i8t+dldcedlJsenAPtlzaGY7DNoszj/nqH8E+YTsOQDQLGqyBwBAcxr+7Kh4771lccmFx8Z5/3NLvPjX17In0Qx2/eoX46Tjvxmn/vjqGDtuavYcAGg2zpQDUHZe/OtrcfqZ18c5Zx4Ru+26bfYcPqeDvrlLHH/MAfGD/75SkANQdkQ5AGVp9KsT48STfxZHHr5XnHT8N6NQ8JDX2rRt2ybOHnpE7LHb9nHMCZfG5Dfeyp4EAM3OMxQAytbkN96KI4dcFOuv3yN+fsXJsXrnjtmTWE7d1+oa1197WlRVV8XRx18as2bVZ08CgJVClANQ1ha9826c+uOrY8zfJ8fNN57hndlbga227Bc3XX96PP7HF+K8C26OpUuXZU8CgJXGG70BUPYaG5vihpsejAkTpsWVl/8grr7m3nj8iReyZ/ExDj7oq3HIt3aLcy/4Vbz08vjsOQCw0olyACrG08NejqnTZsXFFx4bAwasH9ddf7+zsCVitY7t49T/PiR69uweQ465OGa9PTd7EgC0CJevA1BRJr/xVhw15OLo1KlD3HHr2bHlFv2yJ1W8nQZvEXf++pxYsHBxHH3cTwU5ABXFmXIAKs6id96N8y64OQZtv2mcNfTweOGFMXHNL++LxYuXZE+rKF27dopTTv5W9N5gnTjr3Jti9KsTsycBQItzphyAijXy+Vfj0O+eF41NTXHnr8+JHQZtlj2pYnx99+3jjlvPjmlvvh3fPfJ8QQ5AxXKmHICKtnjxkrjsirtii837xumnfSe+9tUvxs9/cU/Mm78oe1pZ6r5W1zjt1EOja5dO8cMf/SImTJiWPQkAUjlTDgAR8X+jJsRhh58fs+fMi7tuPy++9509on27ttmzykanTh3iuGMOiFt/dWaMeuX1OPLoiwU5AIQoB4APLV26LK795X0x5LhLolevHvG/v70wDj7oq9GmzSrZ01qt9u1XjSMP3yvuueuCaN++bRx2+Plx+52PRbFYzJ4GACXB5esA8C+mT58d511wc/TuvU58/4i94+CDdo3bbn80HvnDiGhoEJPLo23bNnHg/jvHIQfvGs//ZUwcOeSieGtmXfYsACg5ohwAPsHkyTPi9DN/GQMGrB9HH7VvfPuQ3eLmW38fT/3pr+L8E7Rt2yb2/MYO8Z1Dvx5jxkyK40+6IqZMnZk9CwBKligHgP9g7NgpcfIpP4/Nv9A3jjx8zzj+mAPikT+MiIcfeTZmzqzPnlcS1u/VI/bdZ3Dstuu2MXrMpPjRT67xmnGIiIZiU9QUqrJnNJtCdUSxMXsF/8pxWXGldJ+JcgBYTqNemRAnnvyz6NWze+yz145x8w1DY9z4KfHgQ8NjxPOjo1gqj+4tpE2bmvjyTlvGvnvvFOuss0Y88uhzcfhR/xOz3p6bPY0y0KdHeTxNnTSzIXtCsyqX4/JP5XJ8HJcVV0r3WeksAYBWYuq0WfGLa++N6296ML7y5a3iWwd/LU754bfi94+OiEf+MCJmzSrvs+e9enaPvfb8Unx99+3j9dffjHvufSqeGzHam7cBwGcgygHgM1q27P14/IkX4vEnXojeG6wd++w9OG65cWjMqZsXI0e+GiOeHx2vjX0jGhubsqd+LoVCIb6w2Yaxw6BNY9D2m0X79qvG4398PoYcc0nMeGtO9jwAaNVEOQA0g8lvvBVXXnV3XHX1PbHxxhvEDttvFqedemh069Y5XnhhTIx4fnS8+NfX4p13lmRPXS6rd+4Y2203MHbYfrPYZpsBMX367BgxcnSce/6vYsLrb0ZTU+v+QQMAlApRDgDNqLGxKcaMmRxjxkyOG256MNZas2sM2n7T2GP3QXH6ad+JSZNnxLjxU2PChGkxfsK0mDJ1Vvpl36usUhO9N1g7+vXtGX379owB/daP9dZbK/760tgY+fyrceUv7o65cxembgSAciXKAWAlenv23HjgoWHxwEPDom3bNjFwkw2i70Y9Y5ttNo5Dv717rLVm13hjylsxfsK0mDBhWkycND3m1M2PuXMXNvvHrrVpUxNdu3SKNdfsGhtuuG7069sz+m3UM3r27B7TZ8z+cMOTT70Yr42dEu+/Xx5vgAQApUyUA0ALWbp0Wbz08vh46eXxH36tXbu2sWGfDwJ54Ca9Y+89d4za2s7RpUuneOedJVE/d0HU1y+IuvoPQr2+fmG8997SaCg2RrFYjC5dOkVExO67bRc1NYUoFArRvl3b6Nq1U9R26xzdunWOrl07R223ztGuXduon7sw6urmx6RJ02Ps2Cnx4MPDY9KkGbFs2ftZdwsAVLQVjvKRw29cGTta3MyZddkTACCWLFkar46ZFK+OmfSRr1dXV0Xnzh2jW9cPwvqfgb12j27Rps0qUSgUolCojs6dO0Q0RWyz1YAo/iPU31u6LOrrF8SkyTNi7tyFUVf/QdgvXLjYa8EBKGl17yyI2w79YfaMFrXCUT5o8JCVsQMA+P80NjbFvHmLYt68RTFx0vRPvN01V50SEREXXHRrS00DgJXm1AdvaZHv0xKfU37WLicu1+2qV/IOAAAA4BOIcgAAAEgiygEAACCJKAcAAIAkohwAAACSiHIAAABIIsoBAAAgiSgHAACAJKIcAAAAkohyAAAASCLKAQAAIIkoBwAAgCSiHAAAAJKIcgAAAEgiygEAACCJKAcAAIAkohwAAACSiHIAAABIIsoBAAAgiSgHAACAJKIcAAAAkohyAAAASCLKAQAAIIkoBwAAgCSiHAAAAJKIcgAAAEgiygEAACCJKAcAAIAkohwAAACSiHIAAABIIsoBAAAgiSgHAACAJKIcAAAAkohyAAAASCLKAQAAIIkoBwAAgCSiHAAAAJKIcgAAAEgiygEAACCJKAcAAIAkNdkDAACgXBWqI4qN2SuaT0OxKWoKVdkz+BfldFzq3yxGYwv8nZkztbjyv8kuy3czUQ4AQMmZNLMhe0Kz6NOjvJ5ul8tx+adyOT7ldFxaIshLjcvXAQAAIIkoBwAAgCSiHAAAAJKIcgAAAEgiygEAACCJKAcAAIAkohwAAACSiHIAAABIIsoBAAAgiSgHAACAJKIcAAAAkohyAAAASCLKAQAAIIkoBwAAgCSiHAAAAJKIcgAAAEgiygEAACCJKAcAAIAkohwAAACSiHIAAABIIsoBAAAgiSgHAACAJKIcAAAAkohyAAAASCLKAQAAIIkoBwAAgCSiHAAAAJKIcgAAAEgiygEAACCJKAcAAIAkohwAAACSiHIAAABIIsoBAAAgiSgHAACAJKIcAAAAkohyAAAASCLKAQAAIIkoBwAAgCSiHAAAAJKIcgAAAEgiygEAACCJKAcAAIAkohwAAACSiHIAAABIIsoBAAAgiSgHAACAJKIcAAAAkohyAAAASCLKAQAAIIkoBwAAgCSiHAAAAJKIcgAAAEgiygEAACCJKAcAAIAkohwAAACSiHIAAABIUrOiv2Hk8BtXxg6IiIiZM+uyJwAANJuGYlPUFKqyZzSbQnVEsTF7RfOYt2RhnLXLidkzYMWjfNDgIStjBwAAfKhPjxV+mlqSJs1syJ7QrMrluERE/PC+m7MnNJtyOi4vvLA0e0KzeXToqct1O5evAwAAQBJRDgAAAElEOQAAACQR5QAAAJBElAMAAEASUQ4AAABJRDkAAAAkEeUAAACQRJQDAABAElEOAAAASUQ5AAAAJBHlAAAAkESUAwAAQBJRDgAAAElEOQAAACQR5QAAAJBElAMAAEASUQ4AAABJRDkAAAAkEeUAAACQRJQDAABAElEOAAAASUQ5AAAAJBHlAAAAkESUAwAAQBJRDgAAAElEOQAAACQR5QAAAJBElAMAAEASUQ4AAABJRDkAAAAkEeUAAACQRJQDAABAElEOAAAASUQ5AAAAJBHlAAAAkESUAwAAQBJRDgAAAElEOQAAACQR5QAAAJBElAMAAEASUQ4AAABJRDkAAAAkEeUAAACQRJQDAABAElEOAAAASUQ5AAAAJBHlAAAAkKQmewAAAPyrP91Rnz2BjzElewAfa0r2AD4XZ8oBAAAgiSgHAACAJKIcAAAAkohyAAAASCLKAQAAIIkoBwAAgCSiHAAAAJKIcgAAAEgiygEAACCJKAcAAIAkohwAAACSiHIAAABIIsoBAAAgiSgHAACAJKIcAAAAkohyAAAASCLKAQAAIIkoBwAAgCSiHAAAAJKIcgAAAEgiygEAACCJKAcAAIAkohwAAACSiHIAAABIIsoBAAAgiSgHAACAJKIcAAAAkohyAAAASCLKAQAAIIkoBwAAgCSiHAAAAJKIcgAAAEgiygEAACCJKAcAAIAkohwAAACSiHIAAABIIsoBAAAgiSgHAACAJKIcAAAAkohyAAAASCLKAQAAIIkoBwAAgCSiHAAAAJKIcgAAAEgiygEAACCJKAcAAIAkohwAAACSiHIAAABIIsoBAAAgiSgHAACAJKIcAAAAkohyAAAASCLKAQAAIIkoBwAAgCSiHAAAAJKIcgAAAEgiygEAACCJKAcAAIAkohwAAACSiHIAAABIIsoBAAAgiSgHAACAJKIcAAAAkohyAAAASCLKAQAAIIkoBwAAgCSiHAAAAJKIcgAAAEgiygEAACCJKAcAAIAkohwAAACSiHIAAABIIsoBAAAgiSgHAACAJDUrcuMpU6fEyOE3rqwtAMBn5PEZAErLlKlTlut2VXufvU/Typ0CAAAAfByXrwMAAEASUQ4AAABJRDkAAAAkEeUAAACQRJQDAABAElEOAAAASUQ5AAAAJBHlAAAAkESUAwAAQJL/Bx+YHWtT91Y1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1260x816 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatch\n",
    "import random\n",
    "fig = plt.figure(figsize=(x_size/6, y_size/6))\n",
    "fig.patch.set_facecolor('#4E7E4F')\n",
    "ax = fig.add_subplot(1, 1, 1, facecolor='#4E7E4F')\n",
    "\n",
    "id_color = 0\n",
    "\n",
    "for idx, x in enumerate(zone_x):\n",
    "    for idy, y in enumerate(zone_y):\n",
    "        if (idx < len(zone_x_viz)-1) and (idy < len(zone_y_viz)-1):\n",
    "            ax.add_artist(mpatch.Rectangle((zone_x_viz[idx], zone_y_viz[idy]), zone_x_viz[idx+1]-zone_x_viz[idx], zone_y_viz[idy+1]-zone_y_viz[idy], color=colors[id_color]))\n",
    "            if id_color < len(colors)-1:\n",
    "                id_color +=1\n",
    "            else:\n",
    "                id_color = 0\n",
    "ax.add_artist(mpatch.Rectangle((zone_x_viz[-3], zone_y_viz[0]), zone_x_viz[-1]-zone_x_viz[-3], zone_y_viz[3]-zone_y_viz[1], color=sns.cubehelix_palette(8, start=.5, rot=-.75)[4]))\n",
    "ax.add_artist(mpatch.Rectangle((zone_x_viz[-3], zone_y_viz[7]), zone_x_viz[-1]-zone_x_viz[-3], zone_y_viz[3]-zone_y_viz[1], color=sns.cubehelix_palette(8, start=.5, rot=-.75)[4]))\n",
    "ax.add_artist(mpatch.Rectangle((zone_x_viz[0], zone_y_viz[-1]), zone_x_viz[-1]-zone_x_viz[0], 68-zone_y_viz[-1], color=sns.cubehelix_palette(8, start=.5, rot=-.75)[5]))\n",
    "ax.add_artist(mpatch.Rectangle((zone_x_viz[0], 0), zone_x_viz[-1]-zone_x_viz[0], zone_y_viz[0], color=sns.cubehelix_palette(8, start=.5, rot=-.75)[5]))\n",
    "ax.add_artist(mpatch.Rectangle((0, 0), zone_x_viz[0], 68, color=sns.cubehelix_palette(8, start=.5, rot=-.75)[6]))\n",
    "create_pitch(ax)\n",
    "# plt.title(\"Delimitation of pitch zones (8)\", color=\"white\", fontsize=15)\n",
    "plt.savefig('grid_graph.png')\n",
    "# Image.open('zone_graph.png').save('zone_graph.jpg','JPEG')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining zone and Calculate XP - Grid - DF Total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def zones_lookup(row):\n",
    "    if row[\"pos_start\"][0]<zone_x[1]:\n",
    "        return 200\n",
    "    elif (row[\"pos_start\"][1]>zone_y[-2]):\n",
    "        return 201\n",
    "    elif  (row[\"pos_start\"][1]<zone_y[1]):\n",
    "        return 202\n",
    "    elif (row[\"pos_start\"][0]>zone_x[-3])&(row[\"pos_start\"][1]>zone_y[1])&(row[\"pos_start\"][1]<zone_y[3]):\n",
    "        return 203\n",
    "    elif (row[\"pos_start\"][0]>zone_x[-3])&(row[\"pos_start\"][1]>zone_y[-4])&(row[\"pos_start\"][1]<zone_y[-2]):\n",
    "        return 204\n",
    "    else:\n",
    "        return row[\"zone\"]\n",
    "    tmp_1 = zones_table[zones_table[\"x\"]<row[\"pos_start\"][0]]\n",
    "    tmp_2 = tmp_1.iloc[tmp_1['x'].iloc[(tmp_1['x']-row[\"pos_start\"][0]).abs().argsort()[:12]].index]\n",
    "    tmp_3 = tmp_2[tmp_2[\"y\"]<row[\"pos_start\"][1]]\n",
    "    zone = tmp_3[\"zone\"].iloc[(tmp_3[\"y\"]-row[\"pos_start\"][1]).abs().argsort()[:1]].values[0]\n",
    "    return zone\n",
    "\n",
    "def zones_consolidation(row):\n",
    "    if row[\"pos_start\"][0]<zone_x[1]:\n",
    "        return 200\n",
    "    elif (row[\"pos_start\"][1]>zone_y[-2]):\n",
    "        return 201\n",
    "    elif  (row[\"pos_start\"][1]<zone_y[1]):\n",
    "        return 202\n",
    "    elif (row[\"pos_start\"][0]>zone_x[-3])&(row[\"pos_start\"][1]>zone_y[1])&(row[\"pos_start\"][1]<zone_y[3]):\n",
    "        return 203\n",
    "    elif (row[\"pos_start\"][0]>zone_x[-3])&(row[\"pos_start\"][1]>zone_y[-4])&(row[\"pos_start\"][1]<zone_y[-2]):\n",
    "        return 204\n",
    "    else:\n",
    "        return row[\"zone\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T14:49:20.558055Z",
     "start_time": "2019-02-27T14:48:18.628162Z"
    }
   },
   "outputs": [],
   "source": [
    "# df[\"zone\"] = df.apply(zones_lookup, axis=1)\n",
    "df[\"zone\"] = df.apply(zones_consolidation, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T14:52:57.702721Z",
     "start_time": "2019-02-27T14:52:57.011087Z"
    }
   },
   "outputs": [],
   "source": [
    "df_xp = df[(df[\"is_goal\"] == 1)&(df[\"part\"] != \"unknown\")&(df[\"part\"] != \"other_parts\")].groupby([\"zone\", \"part\"]).count() / df[df[\"part\"]!=\"unknown\"].groupby([\"zone\",\"part\"]).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T14:52:59.057328Z",
     "start_time": "2019-02-27T14:52:59.037493Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_xp = pd.DataFrame(df_xp[\"result\"]).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T14:53:02.602635Z",
     "start_time": "2019-02-27T14:53:02.584977Z"
    }
   },
   "outputs": [],
   "source": [
    "df_xp = df_xp.set_index([\"zone\", \"part\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T14:53:06.819173Z",
     "start_time": "2019-02-27T14:53:06.805088Z"
    }
   },
   "outputs": [],
   "source": [
    "def xp_calc(row):\n",
    "#     print(row[\"zone\"])\n",
    "#     print(row[\"part\"])\n",
    "    try:\n",
    "        return df_xp.loc[(row[\"zone\"], row[\"part\"]), :][0]\n",
    "    except KeyError:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T14:56:03.713711Z",
     "start_time": "2019-02-27T14:53:12.193928Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"xp_goal\"] = df.apply(xp_calc, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T07:34:21.713461Z",
     "start_time": "2019-02-27T07:34:21.477501Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1260x816 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatch\n",
    "\n",
    "fig = plt.figure(figsize=(x_size/6, y_size/6))\n",
    "fig.patch.set_facecolor('#4E7E4F')\n",
    "ax = fig.add_subplot(1, 1, 1, facecolor='#4E7E4F')\n",
    "\n",
    "rectangles = {'1' : mpatch.Rectangle((pitch_x[3],pitch_y[4]), pitch_x[4]-pitch_x[3], pitch_y[5]-pitch_y[4], color=COLOR_ZONE[1]),\n",
    "              '2' : mpatch.Rectangle((pitch_x[2],pitch_y[4]), pitch_x[3]-pitch_x[2], pitch_y[5]-pitch_y[4], color=COLOR_ZONE[2]),\n",
    "              '3_R' : mpatch.Rectangle((pitch_x[2],pitch_y[3]), pitch_x[-1]-pitch_x[2], pitch_y[4]-pitch_y[3], color=COLOR_ZONE[3]),\n",
    "              '3_L' : mpatch.Rectangle((pitch_x[2],pitch_y[5]), pitch_x[-1]-pitch_x[2], pitch_y[6]-pitch_y[5], color=COLOR_ZONE[3]),\n",
    "              '4' : mpatch.Rectangle((pitch_x[1],pitch_y[3]), pitch_x[2]-pitch_x[1], pitch_y[6]-pitch_y[3], color=COLOR_ZONE[4]),\n",
    "              '5_R' : mpatch.Rectangle((pitch_x[2],pitch_y[1]), pitch_x[-1]-pitch_x[2], pitch_y[3]-pitch_y[1], color=COLOR_ZONE[5]),\n",
    "              '5_L' : mpatch.Rectangle((pitch_x[2],pitch_y[6]), pitch_x[-1]-pitch_x[2], pitch_y[8]-pitch_y[6], color=COLOR_ZONE[5]),\n",
    "              '6_R' : mpatch.Rectangle((pitch_x[1],pitch_y[1]), pitch_x[2]-pitch_x[1], pitch_y[8]-pitch_y[6], color=COLOR_ZONE[6]),\n",
    "              '6_L' : mpatch.Rectangle((pitch_x[1],pitch_y[6]), pitch_x[2]-pitch_x[1], pitch_y[8]-pitch_y[6], color=COLOR_ZONE[6]),\n",
    "              '7' : mpatch.Rectangle((pitch_x[0],pitch_y[1]), pitch_x[1]-pitch_x[0], pitch_y[-2]-pitch_y[1], color=COLOR_ZONE[7]),\n",
    "              '8_R' : mpatch.Rectangle((pitch_x[0],0), pitch_x[-1]-pitch_x[0], pitch_y[1], color=COLOR_ZONE[8]),\n",
    "              '8_L' : mpatch.Rectangle((pitch_x[0],pitch_y[-2]), pitch_x[-1]-pitch_x[0], pitch_y[1], color=COLOR_ZONE[8]),\n",
    "              '9' : mpatch.Rectangle((0,0), pitch_x[0], pitch_y[-1], color=COLOR_ZONE[9])\n",
    "             }\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "\n",
    "for r in rectangles:\n",
    "    ax.add_artist(rectangles[r])\n",
    "    rx, ry = rectangles[r].get_xy()\n",
    "    cx = rx + rectangles[r].get_width()/2.0\n",
    "    cy = ry + rectangles[r].get_height()/2.0\n",
    "\n",
    "    ax.annotate(r, (cx, cy), color='w', weight='bold', \n",
    "                fontsize=15, ha='center', va='center')\n",
    "# ax.set_xlim((0, 15))\n",
    "# ax.set_ylim((0, 15))\n",
    "# ax.set_aspect('equal')\n",
    "create_pitch(ax)\n",
    "plt.title(\"Delimitation of pitch zones (8)\", **hfont, color=\"white\", fontsize=15)\n",
    "plt.savefig('zone_graph.png')\n",
    "# Image.open('zone_graph.png').save('zone_graph.jpg','JPEG')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team performace analysis based on XP goals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading df_team_long dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T19:04:17.271261Z",
     "start_time": "2019-02-21T19:04:17.223806Z"
    }
   },
   "outputs": [],
   "source": [
    "df_team_long = pd.read_csv(\"df_team_long.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating df_team_long dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:06:09.679639Z",
     "start_time": "2019-02-27T15:06:09.670035Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[89.83114864864868, 45.97485714285709]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"pos_start\"].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:06:14.923679Z",
     "start_time": "2019-02-27T15:06:14.806617Z"
    }
   },
   "outputs": [],
   "source": [
    "df_temp = df.sort_values([\"fixture_id\", \"minute\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:06:18.777832Z",
     "start_time": "2019-02-27T15:06:18.772607Z"
    }
   },
   "outputs": [],
   "source": [
    "def is_goal_side(row, side):\n",
    "    result = 1 if row[\"result_processed\"] == \"goal\" and row[\"shot_by\"] == side else 0\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:48:32.785800Z",
     "start_time": "2019-02-27T15:48:32.397089Z"
    }
   },
   "outputs": [],
   "source": [
    "df_temp[\"score_home\"] = df_temp[\"score\"].apply(lambda x: int(x.split(\" - \")[0]))\n",
    "df_temp[\"score_away\"] = df_temp[\"score\"].apply(lambda x: int(x.split(\" - \")[1]))\n",
    "\n",
    "# Define columns to record goals for and against away team\n",
    "\n",
    "df_temp[\"goal_for_home\"] = df_temp[\"score_home\"]\n",
    "df_temp[\"goal_against_home\"] = df_temp[\"score_away\"]\n",
    "\n",
    "# Define columns to record goals for and against away team\n",
    "\n",
    "df_temp[\"goal_for_away\"] = df_temp[\"score_away\"]\n",
    "df_temp[\"goal_against_away\"] = df_temp[\"score_home\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:48:33.625183Z",
     "start_time": "2019-02-27T15:48:33.450806Z"
    }
   },
   "outputs": [],
   "source": [
    "df_score = df_temp[[\"fixture_id\", \"match_date\", \"league\", \"season_year\", \"team_away\", \"team_home\", \"score_away\", \"score_home\", \"goal_for_home\", \"goal_against_home\", \"goal_for_away\", \"goal_against_away\"]].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:48:36.884242Z",
     "start_time": "2019-02-27T15:48:36.838671Z"
    }
   },
   "outputs": [],
   "source": [
    "df_score_xp = df_temp.groupby([\"fixture_id\", \"shot_by\"]).agg({\"xp_goal\": \"sum\"}).reset_index().pivot_table(columns=\"shot_by\", values=\"xp_goal\", index=\"fixture_id\").reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:48:38.798209Z",
     "start_time": "2019-02-27T15:48:38.766806Z"
    }
   },
   "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>fixture_id</th>\n",
       "      <th>match_date</th>\n",
       "      <th>league</th>\n",
       "      <th>season_year</th>\n",
       "      <th>team_away</th>\n",
       "      <th>team_home</th>\n",
       "      <th>score_away</th>\n",
       "      <th>score_home</th>\n",
       "      <th>goal_for_home</th>\n",
       "      <th>goal_against_home</th>\n",
       "      <th>goal_for_away</th>\n",
       "      <th>goal_against_away</th>\n",
       "      <th>xp_away</th>\n",
       "      <th>xp_home</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>321657</td>\n",
       "      <td>2010-08-14 15:00:00</td>\n",
       "      <td>England_PL</td>\n",
       "      <td>2010</td>\n",
       "      <td>West Ham United</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.809138</td>\n",
       "      <td>3.389169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>321658</td>\n",
       "      <td>2010-08-14 15:00:00</td>\n",
       "      <td>England_PL</td>\n",
       "      <td>2010</td>\n",
       "      <td>Everton</td>\n",
       "      <td>Blackburn Rovers</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.505867</td>\n",
       "      <td>0.587266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>321659</td>\n",
       "      <td>2010-08-14 15:00:00</td>\n",
       "      <td>England_PL</td>\n",
       "      <td>2010</td>\n",
       "      <td>Blackpool</td>\n",
       "      <td>Wigan Athletic</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.918771</td>\n",
       "      <td>1.191305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>321660</td>\n",
       "      <td>2010-08-14 15:00:00</td>\n",
       "      <td>England_PL</td>\n",
       "      <td>2010</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>Bolton Wanderers</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.366111</td>\n",
       "      <td>0.571520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321661</td>\n",
       "      <td>2010-08-14 17:30:00</td>\n",
       "      <td>England_PL</td>\n",
       "      <td>2010</td>\n",
       "      <td>West Bromwich Albion</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.304474</td>\n",
       "      <td>3.507508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixture_id           match_date      league  season_year  \\\n",
       "0      321657  2010-08-14 15:00:00  England_PL         2010   \n",
       "1      321658  2010-08-14 15:00:00  England_PL         2010   \n",
       "2      321659  2010-08-14 15:00:00  England_PL         2010   \n",
       "3      321660  2010-08-14 15:00:00  England_PL         2010   \n",
       "4      321661  2010-08-14 17:30:00  England_PL         2010   \n",
       "\n",
       "              team_away         team_home  score_away  score_home  \\\n",
       "0       West Ham United       Aston Villa           0           3   \n",
       "1               Everton  Blackburn Rovers           0           1   \n",
       "2             Blackpool    Wigan Athletic           4           0   \n",
       "3                Fulham  Bolton Wanderers           0           0   \n",
       "4  West Bromwich Albion           Chelsea           0           6   \n",
       "\n",
       "   goal_for_home  goal_against_home  goal_for_away  goal_against_away  \\\n",
       "0              3                  0              0                  3   \n",
       "1              1                  0              0                  1   \n",
       "2              0                  4              4                  0   \n",
       "3              0                  0              0                  0   \n",
       "4              6                  0              0                  6   \n",
       "\n",
       "    xp_away   xp_home  \n",
       "0  0.809138  3.389169  \n",
       "1  1.505867  0.587266  \n",
       "2  1.918771  1.191305  \n",
       "3  1.366111  0.571520  \n",
       "4  0.304474  3.507508  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_team = pd.concat([df_score.set_index(\"fixture_id\"), df_score_xp.set_index(\"fixture_id\")], axis=1).reset_index()\n",
    "df_team.rename(columns={'away':'xp_away',\n",
    "                    'home':'xp_home'}, inplace=True)\n",
    "df_team.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:50:48.836964Z",
     "start_time": "2019-02-27T15:50:48.831744Z"
    }
   },
   "outputs": [],
   "source": [
    "def calculate_points(team_for, team_against):\n",
    "    # Calculate the number of points gained from a game for team_for\n",
    "    for score in zip(team_for, team_against):\n",
    "        if score[0] < score[1]:\n",
    "            yield 0\n",
    "        elif score[0] == score[1]:\n",
    "            yield 1\n",
    "        else:\n",
    "            yield 3\n",
    "\n",
    "def diff_scoring(team_for, team_against):\n",
    "    return np.subtract(team_for, team_against)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:51:54.528101Z",
     "start_time": "2019-02-27T15:51:54.479684Z"
    }
   },
   "outputs": [],
   "source": [
    "df_team[\"point_away\"] = list(calculate_points(df_team[\"score_away\"].values, df_team[\"score_home\"].values))\n",
    "df_team[\"point_home\"] = list(calculate_points(df_team[\"score_home\"].values, df_team[\"score_away\"].values))\n",
    "\n",
    "df_team[\"xpoint_away\"] = list(calculate_points(df_team[\"xp_away\"].values, df_team[\"xp_home\"].values))\n",
    "df_team[\"xpoint_home\"] = list(calculate_points(df_team[\"xp_home\"].values, df_team[\"xp_away\"].values))\n",
    "\n",
    "df_team[\"deltaGoal_away\"] = diff_scoring(df_team[\"score_away\"].values, df_team[\"score_home\"].values)\n",
    "df_team[\"deltaGoal_home\"] = diff_scoring(df_team[\"score_home\"].values, df_team[\"score_away\"].values)\n",
    "\n",
    "df_team[\"deltaXpGoal_away\"] = diff_scoring(df_team[\"xp_away\"].values, df_team[\"xp_home\"].values)\n",
    "df_team[\"deltaXpGoal_home\"] = diff_scoring(df_team[\"xp_home\"].values, df_team[\"xp_away\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:54:39.932730Z",
     "start_time": "2019-02-27T15:54:38.707920Z"
    }
   },
   "outputs": [],
   "source": [
    "df_team_long = pd.wide_to_long(df_team, stubnames=['team', 'score', 'goal_for', 'goal_against', 'xp', 'point', 'xpoint', 'deltaGoal', 'deltaXpGoal'], i=['league', 'season_year', 'fixture_id', 'match_date'], j='side',\n",
    "                        sep='_', suffix='\\D+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:55:12.244571Z",
     "start_time": "2019-02-27T15:55:12.229792Z"
    }
   },
   "outputs": [],
   "source": [
    "df_team_long = df_team_long.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:55:13.007045Z",
     "start_time": "2019-02-27T15:55:12.988490Z"
    }
   },
   "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>league</th>\n",
       "      <th>season_year</th>\n",
       "      <th>fixture_id</th>\n",
       "      <th>match_date</th>\n",
       "      <th>side</th>\n",
       "      <th>team</th>\n",
       "      <th>score</th>\n",
       "      <th>goal_for</th>\n",
       "      <th>goal_against</th>\n",
       "      <th>xp</th>\n",
       "      <th>point</th>\n",
       "      <th>xpoint</th>\n",
       "      <th>deltaGoal</th>\n",
       "      <th>deltaXpGoal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>England_PL</td>\n",
       "      <td>2010</td>\n",
       "      <td>321657</td>\n",
       "      <td>2010-08-14 15:00:00</td>\n",
       "      <td>away</td>\n",
       "      <td>West Ham United</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.809138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "      <td>-2.580031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>England_PL</td>\n",
       "      <td>2010</td>\n",
       "      <td>321657</td>\n",
       "      <td>2010-08-14 15:00:00</td>\n",
       "      <td>home</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3.389169</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2.580031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>England_PL</td>\n",
       "      <td>2010</td>\n",
       "      <td>321658</td>\n",
       "      <td>2010-08-14 15:00:00</td>\n",
       "      <td>away</td>\n",
       "      <td>Everton</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.505867</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.918601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England_PL</td>\n",
       "      <td>2010</td>\n",
       "      <td>321658</td>\n",
       "      <td>2010-08-14 15:00:00</td>\n",
       "      <td>home</td>\n",
       "      <td>Blackburn Rovers</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.587266</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.918601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>England_PL</td>\n",
       "      <td>2010</td>\n",
       "      <td>321659</td>\n",
       "      <td>2010-08-14 15:00:00</td>\n",
       "      <td>away</td>\n",
       "      <td>Blackpool</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.918771</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.727467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       league  season_year  fixture_id           match_date  side  \\\n",
       "0  England_PL         2010      321657  2010-08-14 15:00:00  away   \n",
       "1  England_PL         2010      321657  2010-08-14 15:00:00  home   \n",
       "2  England_PL         2010      321658  2010-08-14 15:00:00  away   \n",
       "3  England_PL         2010      321658  2010-08-14 15:00:00  home   \n",
       "4  England_PL         2010      321659  2010-08-14 15:00:00  away   \n",
       "\n",
       "               team  score  goal_for  goal_against        xp  point  xpoint  \\\n",
       "0   West Ham United      0         0             3  0.809138      0       0   \n",
       "1       Aston Villa      3         3             0  3.389169      3       3   \n",
       "2           Everton      0         0             1  1.505867      0       3   \n",
       "3  Blackburn Rovers      1         1             0  0.587266      3       0   \n",
       "4         Blackpool      4         4             0  1.918771      3       3   \n",
       "\n",
       "   deltaGoal  deltaXpGoal  \n",
       "0         -3    -2.580031  \n",
       "1          3     2.580031  \n",
       "2         -1     0.918601  \n",
       "3          1    -0.918601  \n",
       "4          4     0.727467  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_team_long.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:55:55.188186Z",
     "start_time": "2019-02-27T15:55:55.163993Z"
    }
   },
   "outputs": [],
   "source": [
    "df_team_long[\"cumPoint\"] = df_team_long.groupby([\"league\", \"season_year\", \"team\"])[\"point\"].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:55:56.172874Z",
     "start_time": "2019-02-27T15:55:56.153379Z"
    }
   },
   "outputs": [],
   "source": [
    "df_team_long[\"gameday\"] = df_team_long.groupby([\"league\", \"season_year\", \"team\"])[\"point\"].cumcount() + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:55:56.930817Z",
     "start_time": "2019-02-27T15:55:56.900737Z"
    }
   },
   "outputs": [],
   "source": [
    "df_team_long[\"cumGoalDiff\"] = df_team_long.groupby([\"league\", \"season_year\", \"team\"])[\"deltaGoal\"].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:55:57.502128Z",
     "start_time": "2019-02-27T15:55:57.484155Z"
    }
   },
   "outputs": [],
   "source": [
    "df_team_long[\"cumAttack\"] = df_team_long.groupby([\"league\", \"season_year\", \"team\"])[\"goal_for\"].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T15:55:58.100919Z",
     "start_time": "2019-02-27T15:55:58.082405Z"
    }
   },
   "outputs": [],
   "source": [
    "df_team_long[\"cumDefense\"] = df_team_long.groupby([\"league\", \"season_year\", \"team\"])[\"goal_against\"].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T16:07:39.365860Z",
     "start_time": "2019-02-27T16:07:39.330811Z"
    }
   },
   "outputs": [],
   "source": [
    "df_team_long[\"ranking\"] = df_team_long.sort_values([\"cumGoalDiff\", \"cumAttack\", \"cumDefense\", \"team\"], ascending=[False, False, True, True]).groupby([\"league\", \"season_year\", \"gameday\"])[\"cumPoint\"].rank(method=\"first\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T16:11:18.377869Z",
     "start_time": "2019-02-27T16:11:18.371615Z"
    }
   },
   "outputs": [],
   "source": [
    "df_team_long[\"ranking\"] = df_team_long[\"ranking\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T16:18:50.177936Z",
     "start_time": "2019-02-27T16:18:50.155774Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def show(team, league, season):\n",
    "    \n",
    "    max_league_position = df_team_long[(df_team_long[\"season_year\"]==season)\\\n",
    "                       &(df_team_long[\"league\"]==league)\\\n",
    "                       ][\"ranking\"].max()\n",
    "    \n",
    "    temp = df_team_long[(df_team_long[\"season_year\"]==season)\\\n",
    "                        &(df_team_long[\"team\"]==team)\\\n",
    "                       &(df_team_long[\"league\"]==league)\\\n",
    "                       ]\n",
    "    viz = pd.DataFrame({\"rolling_xp\": temp['deltaXpGoal'].rolling(window=5).mean(),\n",
    "                    \"rolling_actual\": temp['deltaGoal'].rolling(window=5).mean(),\n",
    "                        \"ranking\": temp[\"ranking\"],\n",
    "                   \"game_number\": list(range(1, temp.shape[0]+1))})\n",
    "    # Create a trace\n",
    "    trace_xp = go.Scatter(\n",
    "        x = viz[\"game_number\"],\n",
    "        y = viz[\"rolling_xp\"],\n",
    "#         mode = 'lines+markers',\n",
    "        mode=\"lines\",\n",
    "        name = 'XP Goals Difference | Rolling average'\n",
    "    )\n",
    "\n",
    "    trace_act = go.Scatter(\n",
    "        x = viz[\"game_number\"],\n",
    "        y = viz[\"rolling_actual\"],\n",
    "#         mode = 'lines+markers',\n",
    "        mode=\"lines\",\n",
    "        name = 'Actual Goals Difference | Rolling average'\n",
    "    )\n",
    "    \n",
    "    trace_ranking = go.Scatter(\n",
    "        x = viz[\"game_number\"],\n",
    "        y = viz[\"ranking\"],\n",
    "        mode = 'lines+markers',\n",
    "        line= {'dash': 'dash', \"width\":0.5},\n",
    "        name = 'Ranking',\n",
    "        yaxis='y2',\n",
    "        marker = dict(\n",
    "        size = 5,\n",
    "            symbol=\"pentagon-open\",\n",
    "        color = '#7a7d82')\n",
    "    )\n",
    "\n",
    "    layout = {\n",
    "        \"title\": 'Comparison of 5-game Rolling Average of Expected Goals Difference vs. Actual Goals Difference \\n {} | {}-{}'.format(team,str(season),str(season+1)),\n",
    "        \"hovermode\": 'closest',\n",
    "        \"xaxis\": dict(\n",
    "            title='Game',\n",
    "            showline=False\n",
    "        ),\n",
    "        \"yaxis\": dict(\n",
    "            title='5-game Rolling Average Goal Difference'\n",
    "        ),\n",
    "        \"yaxis2\": dict(\n",
    "#             autorange=\"reversed\",\n",
    "            dtick=1,\n",
    "            range=[max_league_position, 0.01],\n",
    "            title='League Ranking',\n",
    "            overlaying='y',\n",
    "            showgrid=False,\n",
    "            showline=False,\n",
    "            side='right')\n",
    "    }\n",
    "\n",
    "\n",
    "    data = [trace_xp, trace_act, trace_ranking]\n",
    "    fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "\n",
    "    return py.plotly.iplot(fig, filename='{}_{}_{}'.format(team,str(season),str(season+1)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2019-02-27T16:18:06.866910Z"
    },
    "code_folding": [
     2
    ]
   },
   "outputs": [],
   "source": [
    "import plotly.offline as off\n",
    "\n",
    "def off_show(team, league, season):\n",
    "    off.init_notebook_mode(connected=True)\n",
    "    max_league_position = df_team_long[(df_team_long[\"season_year\"]==season)\\\n",
    "                       &(df_team_long[\"league\"]==league)\\\n",
    "                       ][\"ranking\"].max()\n",
    "    \n",
    "                                              \n",
    "    temp = df_team_long[(df_team_long[\"season_year\"]==season)\\\n",
    "                        &(df_team_long[\"team\"]==team)\\\n",
    "                       &(df_team_long[\"league\"]==league)\\\n",
    "                       ]\n",
    "    viz = pd.DataFrame({\"rolling_xp\": temp['deltaXpGoal'].rolling(window=5).mean(),\n",
    "                    \"rolling_actual\": temp['deltaGoal'].rolling(window=5).mean(),\n",
    "                        \"ranking\": temp[\"ranking\"],\n",
    "                   \"game_number\": list(range(1, temp.shape[0]+1))})\n",
    "    # Create a trace\n",
    "    trace_xp = go.Scatter(\n",
    "        x = viz[\"game_number\"],\n",
    "        y = viz[\"rolling_xp\"],\n",
    "#         mode = 'lines+markers',\n",
    "        mode=\"lines\",\n",
    "        name = 'XP Goals Difference | Rolling average'\n",
    "    )\n",
    "\n",
    "    trace_act = go.Scatter(\n",
    "        x = viz[\"game_number\"],\n",
    "        y = viz[\"rolling_actual\"],\n",
    "#         mode = 'lines+markers',\n",
    "        mode=\"lines\",\n",
    "        name = 'Actual Goals Difference | Rolling average'\n",
    "    )\n",
    "    \n",
    "    trace_ranking = go.Scatter(\n",
    "        x = viz[\"game_number\"],\n",
    "        y = viz[\"ranking\"],\n",
    "        mode = 'lines+markers',\n",
    "        line= {'dash': 'dash', \"width\":0.5},\n",
    "        name = 'Ranking',\n",
    "        yaxis='y2',\n",
    "        marker = dict(\n",
    "        size = 5,\n",
    "            symbol=\"pentagon-open\",\n",
    "        color = '#7a7d82')\n",
    "    )\n",
    "\n",
    "    layout = {\n",
    "        \"title\": 'Comparison of 5-game Rolling Average of Expected Goals Difference vs. Actual Goals Difference \\n {} | {}-{}'.format(team,str(season),str(season+1)),\n",
    "        \"hovermode\": 'closest',\n",
    "        \"xaxis\": dict(\n",
    "            title='Game',\n",
    "            showline=False\n",
    "        ),\n",
    "        \"yaxis\": dict(\n",
    "            title='5-game Rolling Average Goal Difference'\n",
    "        ),\n",
    "        \"yaxis2\": dict(\n",
    "#             autorange=\"reversed\",\n",
    "            dtick=1,\n",
    "            range=[max_league_position, 0.01],\n",
    "            title='League Ranking',\n",
    "            overlaying='y',\n",
    "            showgrid=False,\n",
    "            showline=False,\n",
    "            side='right')\n",
    "    }\n",
    "\n",
    "\n",
    "    data = [trace_xp, trace_act, trace_ranking]\n",
    "    fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "\n",
    "    return off.iplot(fig, filename='{}_{}_{}'.format(team,str(season),str(season+1)))\n"
   ]
  },
  {
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   "source": [
    "team = \"FC Bayern München\"\n",
    "league = \"Germany_Bundesliga\"\n",
    "season = 2012\n",
    "\n",
    "off_show(team, league, season)"
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