{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How many games are Pro Player playing\n",
    "\n",
    "This is the data for an article written for a Gwent team, [Team Bandit Gang](https://teambanditgang.com/climbing-pro-ladder-grind-vs-skill/), here we look at the number of games pro players play each season. We'll also have a look if you can grind your way to the top or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 1;\n",
       "                var nbb_unformatted_code = \"%load_ext nb_black\\n\\nimport pandas as pd\\nimport numpy as np\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\";\n",
       "                var nbb_formatted_code = \"%load_ext nb_black\\n\\nimport pandas as pd\\nimport numpy as np\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext nb_black\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"df = pd.read_excel(\\\"./output/player_stats.xlsx\\\").drop(columns=[\\\"Unnamed: 0\\\"])\";\n",
       "                var nbb_formatted_code = \"df = pd.read_excel(\\\"./output/player_stats.xlsx\\\").drop(columns=[\\\"Unnamed: 0\\\"])\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_excel(\"./output/player_stats.xlsx\").drop(columns=[\"Unnamed: 0\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>group</th>\n",
       "      <th>matches_total</th>\n",
       "      <th>matches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M2_01 Wolf 2020</td>\n",
       "      <td>Position 0200 to 0001</td>\n",
       "      <td>88400</td>\n",
       "      <td>442.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2_02 Love 2020</td>\n",
       "      <td>Position 0200 to 0001</td>\n",
       "      <td>91993</td>\n",
       "      <td>459.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M2_03 Bear 2020</td>\n",
       "      <td>Position 0200 to 0001</td>\n",
       "      <td>103503</td>\n",
       "      <td>517.515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M2_04 Elf 2020</td>\n",
       "      <td>Position 0200 to 0001</td>\n",
       "      <td>122769</td>\n",
       "      <td>613.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M2_05 Viper 2020</td>\n",
       "      <td>Position 0200 to 0001</td>\n",
       "      <td>97914</td>\n",
       "      <td>489.570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>M3_09 Dryad 2021</td>\n",
       "      <td>Position 2800 to 2601</td>\n",
       "      <td>54602</td>\n",
       "      <td>273.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>M3_10 Cat 2021</td>\n",
       "      <td>Position 2800 to 2601</td>\n",
       "      <td>47779</td>\n",
       "      <td>238.895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>M3_11 Mahakam 2021</td>\n",
       "      <td>Position 2800 to 2601</td>\n",
       "      <td>59041</td>\n",
       "      <td>295.205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>M3_12 Wild Hunt 2021</td>\n",
       "      <td>Position 2800 to 2601</td>\n",
       "      <td>58357</td>\n",
       "      <td>291.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>M4_01 Wolf 2022</td>\n",
       "      <td>Position 2800 to 2601</td>\n",
       "      <td>55041</td>\n",
       "      <td>275.205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   season                  group  matches_total  matches\n",
       "0         M2_01 Wolf 2020  Position 0200 to 0001          88400  442.000\n",
       "1         M2_02 Love 2020  Position 0200 to 0001          91993  459.965\n",
       "2         M2_03 Bear 2020  Position 0200 to 0001         103503  517.515\n",
       "3          M2_04 Elf 2020  Position 0200 to 0001         122769  613.845\n",
       "4        M2_05 Viper 2020  Position 0200 to 0001          97914  489.570\n",
       "..                    ...                    ...            ...      ...\n",
       "345      M3_09 Dryad 2021  Position 2800 to 2601          54602  273.010\n",
       "346        M3_10 Cat 2021  Position 2800 to 2601          47779  238.895\n",
       "347    M3_11 Mahakam 2021  Position 2800 to 2601          59041  295.205\n",
       "348  M3_12 Wild Hunt 2021  Position 2800 to 2601          58357  291.785\n",
       "349       M4_01 Wolf 2022  Position 2800 to 2601          55041  275.205\n",
       "\n",
       "[350 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"from functools import reduce\\n\\nbin_size = 200\\nbins = []\\n\\nfor i in range(0, 2800, bin_size):\\n    bin_df = (\\n        df[\\n            (i < pd.to_numeric(df[\\\"rank\\\"]))\\n            & (pd.to_numeric(df[\\\"rank\\\"]) <= i + bin_size)\\n        ]\\n        .groupby([\\\"season\\\"])\\n        .agg(num_matches=pd.NamedAgg(\\\"matches\\\", \\\"sum\\\"),)\\n        .reset_index()\\n        .rename(\\n            columns={\\\"num_matches\\\": \\\"Position %04d to %04d\\\" % (i + bin_size, i + 1)}\\n        )\\n    )\\n    bins.append(bin_df)\\n\\nmerged_df = reduce(\\n    lambda left, right: pd.merge(left, right, on=\\\"season\\\", how=\\\"inner\\\"), bins\\n)\\n\\nmelted_df = merged_df.melt(\\n    id_vars=[\\\"season\\\"], var_name=\\\"group\\\", value_name=\\\"matches_total\\\"\\n)\\nmelted_df[\\\"matches\\\"] = melted_df[\\\"matches_total\\\"] / bin_size\\nmelted_df\";\n",
       "                var nbb_formatted_code = \"from functools import reduce\\n\\nbin_size = 200\\nbins = []\\n\\nfor i in range(0, 2800, bin_size):\\n    bin_df = (\\n        df[\\n            (i < pd.to_numeric(df[\\\"rank\\\"]))\\n            & (pd.to_numeric(df[\\\"rank\\\"]) <= i + bin_size)\\n        ]\\n        .groupby([\\\"season\\\"])\\n        .agg(num_matches=pd.NamedAgg(\\\"matches\\\", \\\"sum\\\"),)\\n        .reset_index()\\n        .rename(\\n            columns={\\\"num_matches\\\": \\\"Position %04d to %04d\\\" % (i + bin_size, i + 1)}\\n        )\\n    )\\n    bins.append(bin_df)\\n\\nmerged_df = reduce(\\n    lambda left, right: pd.merge(left, right, on=\\\"season\\\", how=\\\"inner\\\"), bins\\n)\\n\\nmelted_df = merged_df.melt(\\n    id_vars=[\\\"season\\\"], var_name=\\\"group\\\", value_name=\\\"matches_total\\\"\\n)\\nmelted_df[\\\"matches\\\"] = melted_df[\\\"matches_total\\\"] / bin_size\\nmelted_df\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from functools import reduce\n",
    "\n",
    "bin_size = 200\n",
    "bins = []\n",
    "\n",
    "for i in range(0, 2800, bin_size):\n",
    "    bin_df = (\n",
    "        df[\n",
    "            (i < pd.to_numeric(df[\"rank\"]))\n",
    "            & (pd.to_numeric(df[\"rank\"]) <= i + bin_size)\n",
    "        ]\n",
    "        .groupby([\"season\"])\n",
    "        .agg(num_matches=pd.NamedAgg(\"matches\", \"sum\"),)\n",
    "        .reset_index()\n",
    "        .rename(\n",
    "            columns={\"num_matches\": \"Position %04d to %04d\" % (i + bin_size, i + 1)}\n",
    "        )\n",
    "    )\n",
    "    bins.append(bin_df)\n",
    "\n",
    "merged_df = reduce(\n",
    "    lambda left, right: pd.merge(left, right, on=\"season\", how=\"inner\"), bins\n",
    ")\n",
    "\n",
    "melted_df = merged_df.melt(\n",
    "    id_vars=[\"season\"], var_name=\"group\", value_name=\"matches_total\"\n",
    ")\n",
    "melted_df[\"matches\"] = melted_df[\"matches_total\"] / bin_size\n",
    "melted_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"from matplotlib.ticker import MultipleLocator\\n\\nml = MultipleLocator(20)\\n\\nplt.rcParams[\\\"figure.figsize\\\"] = [15, 10]\\nsns.lineplot(data=melted_df, x=\\\"group\\\", y=\\\"matches\\\", hue=\\\"season\\\").invert_xaxis()\\nplt.xticks(rotation=90)\\nplt.axes().yaxis.set_minor_locator(ml)\\nplt.grid(linestyle=\\\"dashed\\\", alpha=0.5, which=\\\"both\\\")\\nplt.title(\\\"Average number of matches played per position\\\")\\nplt.xlabel(\\\"\\\")\\nplt.ylabel(\\\"Average number of matches\\\")\\nplt.tight_layout()\\nplt.savefig(\\\"./images/avg_matches_per_player.png\\\")\\nplt.savefig(\\\"./images/avg_matches_per_player.svg\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"from matplotlib.ticker import MultipleLocator\\n\\nml = MultipleLocator(20)\\n\\nplt.rcParams[\\\"figure.figsize\\\"] = [15, 10]\\nsns.lineplot(data=melted_df, x=\\\"group\\\", y=\\\"matches\\\", hue=\\\"season\\\").invert_xaxis()\\nplt.xticks(rotation=90)\\nplt.axes().yaxis.set_minor_locator(ml)\\nplt.grid(linestyle=\\\"dashed\\\", alpha=0.5, which=\\\"both\\\")\\nplt.title(\\\"Average number of matches played per position\\\")\\nplt.xlabel(\\\"\\\")\\nplt.ylabel(\\\"Average number of matches\\\")\\nplt.tight_layout()\\nplt.savefig(\\\"./images/avg_matches_per_player.png\\\")\\nplt.savefig(\\\"./images/avg_matches_per_player.svg\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MultipleLocator\n",
    "\n",
    "ml = MultipleLocator(20)\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [15, 10]\n",
    "sns.lineplot(data=melted_df, x=\"group\", y=\"matches\", hue=\"season\").invert_xaxis()\n",
    "plt.xticks(rotation=90)\n",
    "plt.axes().yaxis.set_minor_locator(ml)\n",
    "plt.grid(linestyle=\"dashed\", alpha=0.5, which=\"both\")\n",
    "plt.title(\"Average number of matches played per position\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Average number of matches\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./images/avg_matches_per_player.png\")\n",
    "plt.savefig(\"./images/avg_matches_per_player.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So clearly to get in the higher ranks you have to play more games. This echoes something I've heard multiple Pro Players say in their streams; that the current ladder system rewards the quantity over efficiency. For individual players it also gives an impression how efficient they were climbing compared to others around them, and if they would theoretically be able to climb further given time to play more games.\n",
    "\n",
    "I've reached Pro Rank each season since the season of the Viper though during the season of the Viper and Dryad I didn't get enough games in to reach a position on the leader boards. So let's have a look at my personal stats !\n",
    "\n",
    "  * Season of Magic: Position 1138 after 254 matches\n",
    "  * Season of the Griffin: Position 1167 after 260 matches\n",
    "  * Season of the Draconid: Position 1255 after 214 matches\n",
    "\n",
    "So during the Season of Magic I landed smack on the line indicating that was probably as good as it would get that season. Though during the season of the Griffin the number of games I played was a hundered games below the average number of games played by others reaching a similar position on the ladder. Similarly, during the Season of the Draconid with 214 matches played, to get into the 1400 - 1200 bin this is about 50 games played less than others in the same bin. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"ridgeline_df = df[df.season == \\\"M3_10 Cat 2021\\\"]\\n\\n\\ndef parse_rank(rank):\\n    i = (rank - 1) // 200\\n    return \\\"Position %04d to %04d\\\" % ((i + 1) * 200, i * 200 + 1)\\n\\n\\nridgeline_df[\\\"group\\\"] = ridgeline_df[\\\"rank\\\"].apply(parse_rank)\\nridgeline_df = ridgeline_df[ridgeline_df[\\\"group\\\"] != \\\"Position 3000 to 2801\\\"]\";\n",
       "                var nbb_formatted_code = \"ridgeline_df = df[df.season == \\\"M3_10 Cat 2021\\\"]\\n\\n\\ndef parse_rank(rank):\\n    i = (rank - 1) // 200\\n    return \\\"Position %04d to %04d\\\" % ((i + 1) * 200, i * 200 + 1)\\n\\n\\nridgeline_df[\\\"group\\\"] = ridgeline_df[\\\"rank\\\"].apply(parse_rank)\\nridgeline_df = ridgeline_df[ridgeline_df[\\\"group\\\"] != \\\"Position 3000 to 2801\\\"]\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ridgeline_df = df[df.season == \"M3_10 Cat 2021\"]\n",
    "\n",
    "\n",
    "def parse_rank(rank):\n",
    "    i = (rank - 1) // 200\n",
    "    return \"Position %04d to %04d\" % ((i + 1) * 200, i * 200 + 1)\n",
    "\n",
    "\n",
    "ridgeline_df[\"group\"] = ridgeline_df[\"rank\"].apply(parse_rank)\n",
    "ridgeline_df = ridgeline_df[ridgeline_df[\"group\"] != \"Position 3000 to 2801\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x504 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"sns.set_theme(style=\\\"white\\\", rc={\\\"axes.facecolor\\\": (0, 0, 0, 0)})\\n\\n\\n# Initialize the FacetGrid object\\npal = sns.cubehelix_palette(14, rot=-0.25, light=0.7)\\ng = sns.FacetGrid(\\n    ridgeline_df, row=\\\"group\\\", hue=\\\"group\\\", aspect=15, height=0.5, palette=pal\\n)\\n\\n# Draw the densities in a few steps\\ng.map(\\n    sns.kdeplot,\\n    \\\"matches\\\",\\n    bw_adjust=0.5,\\n    clip_on=False,\\n    fill=True,\\n    alpha=1,\\n    linewidth=1.5,\\n)\\ng.map(sns.kdeplot, \\\"matches\\\", clip_on=False, color=\\\"w\\\", lw=2, bw_adjust=0.5)\\ng.map(plt.axhline, y=0, lw=2, clip_on=False)\\n\\n\\n# Define and use a simple function to label the plot in axes coordinates\\ndef label(x, color, label):\\n    ax = plt.gca()\\n    ax.text(\\n        0.72,\\n        0.2,\\n        label,\\n        fontweight=\\\"bold\\\",\\n        color=\\\"black\\\",\\n        ha=\\\"left\\\",\\n        va=\\\"center\\\",\\n        transform=ax.transAxes,\\n    )\\n\\n\\ng.map(label, \\\"matches\\\")\\n\\n# Set the subplots to overlap\\ng.fig.subplots_adjust(hspace=-0.25)\\n\\n# Remove axes details that don't play well with overlap\\ng.set_titles(\\\"\\\")\\ng.set(yticks=[])\\ng.despine(bottom=True, left=True)\\nplt.savefig(\\\"./images/ridgeplot.png\\\")\\nplt.savefig(\\\"./images/ridgeplot.svg\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"sns.set_theme(style=\\\"white\\\", rc={\\\"axes.facecolor\\\": (0, 0, 0, 0)})\\n\\n\\n# Initialize the FacetGrid object\\npal = sns.cubehelix_palette(14, rot=-0.25, light=0.7)\\ng = sns.FacetGrid(\\n    ridgeline_df, row=\\\"group\\\", hue=\\\"group\\\", aspect=15, height=0.5, palette=pal\\n)\\n\\n# Draw the densities in a few steps\\ng.map(\\n    sns.kdeplot,\\n    \\\"matches\\\",\\n    bw_adjust=0.5,\\n    clip_on=False,\\n    fill=True,\\n    alpha=1,\\n    linewidth=1.5,\\n)\\ng.map(sns.kdeplot, \\\"matches\\\", clip_on=False, color=\\\"w\\\", lw=2, bw_adjust=0.5)\\ng.map(plt.axhline, y=0, lw=2, clip_on=False)\\n\\n\\n# Define and use a simple function to label the plot in axes coordinates\\ndef label(x, color, label):\\n    ax = plt.gca()\\n    ax.text(\\n        0.72,\\n        0.2,\\n        label,\\n        fontweight=\\\"bold\\\",\\n        color=\\\"black\\\",\\n        ha=\\\"left\\\",\\n        va=\\\"center\\\",\\n        transform=ax.transAxes,\\n    )\\n\\n\\ng.map(label, \\\"matches\\\")\\n\\n# Set the subplots to overlap\\ng.fig.subplots_adjust(hspace=-0.25)\\n\\n# Remove axes details that don't play well with overlap\\ng.set_titles(\\\"\\\")\\ng.set(yticks=[])\\ng.despine(bottom=True, left=True)\\nplt.savefig(\\\"./images/ridgeplot.png\\\")\\nplt.savefig(\\\"./images/ridgeplot.svg\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"white\", rc={\"axes.facecolor\": (0, 0, 0, 0)})\n",
    "\n",
    "\n",
    "# Initialize the FacetGrid object\n",
    "pal = sns.cubehelix_palette(14, rot=-0.25, light=0.7)\n",
    "g = sns.FacetGrid(\n",
    "    ridgeline_df, row=\"group\", hue=\"group\", aspect=15, height=0.5, palette=pal\n",
    ")\n",
    "\n",
    "# Draw the densities in a few steps\n",
    "g.map(\n",
    "    sns.kdeplot,\n",
    "    \"matches\",\n",
    "    bw_adjust=0.5,\n",
    "    clip_on=False,\n",
    "    fill=True,\n",
    "    alpha=1,\n",
    "    linewidth=1.5,\n",
    ")\n",
    "g.map(sns.kdeplot, \"matches\", clip_on=False, color=\"w\", lw=2, bw_adjust=0.5)\n",
    "g.map(plt.axhline, y=0, lw=2, clip_on=False)\n",
    "\n",
    "\n",
    "# Define and use a simple function to label the plot in axes coordinates\n",
    "def label(x, color, label):\n",
    "    ax = plt.gca()\n",
    "    ax.text(\n",
    "        0.72,\n",
    "        0.2,\n",
    "        label,\n",
    "        fontweight=\"bold\",\n",
    "        color=\"black\",\n",
    "        ha=\"left\",\n",
    "        va=\"center\",\n",
    "        transform=ax.transAxes,\n",
    "    )\n",
    "\n",
    "\n",
    "g.map(label, \"matches\")\n",
    "\n",
    "# Set the subplots to overlap\n",
    "g.fig.subplots_adjust(hspace=-0.25)\n",
    "\n",
    "# Remove axes details that don't play well with overlap\n",
    "g.set_titles(\"\")\n",
    "g.set(yticks=[])\n",
    "g.despine(bottom=True, left=True)\n",
    "plt.savefig(\"./images/ridgeplot.png\")\n",
    "plt.savefig(\"./images/ridgeplot.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Efficiency in different groups\n",
    "\n",
    "Similarly we can look at the average efficiency (essentially the amount of MMR gained for each match played) in different bins. This is more distorted as players that were not in the top 500 the previous season have to climb back to Pro Rank, games which do no yield you any MMR in Pro Ladder. Though the trends should still hold  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>group</th>\n",
       "      <th>mean_efficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M2_01 Wolf 2020</td>\n",
       "      <td>Position 0200 to 0001</td>\n",
       "      <td>1.091091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2_02 Love 2020</td>\n",
       "      <td>Position 0200 to 0001</td>\n",
       "      <td>1.214749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M2_03 Bear 2020</td>\n",
       "      <td>Position 0200 to 0001</td>\n",
       "      <td>1.235145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M2_04 Elf 2020</td>\n",
       "      <td>Position 0200 to 0001</td>\n",
       "      <td>1.154645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M2_05 Viper 2020</td>\n",
       "      <td>Position 0200 to 0001</td>\n",
       "      <td>1.350191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>M3_09 Dryad 2021</td>\n",
       "      <td>Position 2800 to 2601</td>\n",
       "      <td>0.423185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>M3_10 Cat 2021</td>\n",
       "      <td>Position 2800 to 2601</td>\n",
       "      <td>0.097331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>M3_11 Mahakam 2021</td>\n",
       "      <td>Position 2800 to 2601</td>\n",
       "      <td>0.537774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>M3_12 Wild Hunt 2021</td>\n",
       "      <td>Position 2800 to 2601</td>\n",
       "      <td>0.582473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>M4_01 Wolf 2022</td>\n",
       "      <td>Position 2800 to 2601</td>\n",
       "      <td>0.235512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   season                  group  mean_efficiency\n",
       "0         M2_01 Wolf 2020  Position 0200 to 0001         1.091091\n",
       "1         M2_02 Love 2020  Position 0200 to 0001         1.214749\n",
       "2         M2_03 Bear 2020  Position 0200 to 0001         1.235145\n",
       "3          M2_04 Elf 2020  Position 0200 to 0001         1.154645\n",
       "4        M2_05 Viper 2020  Position 0200 to 0001         1.350191\n",
       "..                    ...                    ...              ...\n",
       "345      M3_09 Dryad 2021  Position 2800 to 2601         0.423185\n",
       "346        M3_10 Cat 2021  Position 2800 to 2601         0.097331\n",
       "347    M3_11 Mahakam 2021  Position 2800 to 2601         0.537774\n",
       "348  M3_12 Wild Hunt 2021  Position 2800 to 2601         0.582473\n",
       "349       M4_01 Wolf 2022  Position 2800 to 2601         0.235512\n",
       "\n",
       "[350 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"from functools import reduce\\n\\nbin_size = 200\\nbins = []\\n\\nfor i in range(0, 2800, bin_size):\\n    bin_df = (\\n        df[\\n            (i < pd.to_numeric(df[\\\"rank\\\"]))\\n            & (pd.to_numeric(df[\\\"rank\\\"]) <= i + bin_size)\\n            # & (df[\\\"previous_top500\\\"] == \\\"yes\\\")\\n            & (df[\\\"mmr\\\"] >= 9600)\\n        ]\\n        .groupby([\\\"season\\\"])\\n        .agg(mean_efficiency=pd.NamedAgg(\\\"efficiency\\\", \\\"mean\\\"),)\\n        .reset_index()\\n        .rename(\\n            columns={\\\"mean_efficiency\\\": \\\"Position %04d to %04d\\\" % (i + bin_size, i + 1)}\\n        )\\n    )\\n    bins.append(bin_df)\\n\\nmerged_df = reduce(\\n    lambda left, right: pd.merge(left, right, on=\\\"season\\\", how=\\\"outer\\\"), bins\\n)\\n\\nmelted_df = merged_df.melt(\\n    id_vars=[\\\"season\\\"], var_name=\\\"group\\\", value_name=\\\"mean_efficiency\\\"\\n).fillna(0)\\n\\nmelted_df\";\n",
       "                var nbb_formatted_code = \"from functools import reduce\\n\\nbin_size = 200\\nbins = []\\n\\nfor i in range(0, 2800, bin_size):\\n    bin_df = (\\n        df[\\n            (i < pd.to_numeric(df[\\\"rank\\\"]))\\n            & (pd.to_numeric(df[\\\"rank\\\"]) <= i + bin_size)\\n            # & (df[\\\"previous_top500\\\"] == \\\"yes\\\")\\n            & (df[\\\"mmr\\\"] >= 9600)\\n        ]\\n        .groupby([\\\"season\\\"])\\n        .agg(mean_efficiency=pd.NamedAgg(\\\"efficiency\\\", \\\"mean\\\"),)\\n        .reset_index()\\n        .rename(\\n            columns={\\\"mean_efficiency\\\": \\\"Position %04d to %04d\\\" % (i + bin_size, i + 1)}\\n        )\\n    )\\n    bins.append(bin_df)\\n\\nmerged_df = reduce(\\n    lambda left, right: pd.merge(left, right, on=\\\"season\\\", how=\\\"outer\\\"), bins\\n)\\n\\nmelted_df = merged_df.melt(\\n    id_vars=[\\\"season\\\"], var_name=\\\"group\\\", value_name=\\\"mean_efficiency\\\"\\n).fillna(0)\\n\\nmelted_df\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from functools import reduce\n",
    "\n",
    "bin_size = 200\n",
    "bins = []\n",
    "\n",
    "for i in range(0, 2800, bin_size):\n",
    "    bin_df = (\n",
    "        df[\n",
    "            (i < pd.to_numeric(df[\"rank\"]))\n",
    "            & (pd.to_numeric(df[\"rank\"]) <= i + bin_size)\n",
    "            # & (df[\"previous_top500\"] == \"yes\")\n",
    "            & (df[\"mmr\"] >= 9600)\n",
    "        ]\n",
    "        .groupby([\"season\"])\n",
    "        .agg(mean_efficiency=pd.NamedAgg(\"efficiency\", \"mean\"),)\n",
    "        .reset_index()\n",
    "        .rename(\n",
    "            columns={\"mean_efficiency\": \"Position %04d to %04d\" % (i + bin_size, i + 1)}\n",
    "        )\n",
    "    )\n",
    "    bins.append(bin_df)\n",
    "\n",
    "merged_df = reduce(\n",
    "    lambda left, right: pd.merge(left, right, on=\"season\", how=\"outer\"), bins\n",
    ")\n",
    "\n",
    "melted_df = merged_df.melt(\n",
    "    id_vars=[\"season\"], var_name=\"group\", value_name=\"mean_efficiency\"\n",
    ").fillna(0)\n",
    "\n",
    "melted_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"from matplotlib.ticker import MultipleLocator\\n\\nml = MultipleLocator(0.1)\\nsns.set_theme(style=\\\"white\\\")\\nplt.rcParams[\\\"figure.figsize\\\"] = [15, 10]\\nsns.lineplot(\\n    data=melted_df, x=\\\"group\\\", y=\\\"mean_efficiency\\\", hue=\\\"season\\\"\\n).invert_xaxis()\\nplt.xticks(rotation=90)\\nplt.axes().yaxis.set_minor_locator(ml)\\nplt.grid(linestyle=\\\"dashed\\\", alpha=0.5, which=\\\"both\\\")\\nplt.tight_layout()\\nplt.savefig(\\\"./images/avg_efficiency.png\\\")\\nplt.savefig(\\\"./images/avg_efficiency.svg\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"from matplotlib.ticker import MultipleLocator\\n\\nml = MultipleLocator(0.1)\\nsns.set_theme(style=\\\"white\\\")\\nplt.rcParams[\\\"figure.figsize\\\"] = [15, 10]\\nsns.lineplot(\\n    data=melted_df, x=\\\"group\\\", y=\\\"mean_efficiency\\\", hue=\\\"season\\\"\\n).invert_xaxis()\\nplt.xticks(rotation=90)\\nplt.axes().yaxis.set_minor_locator(ml)\\nplt.grid(linestyle=\\\"dashed\\\", alpha=0.5, which=\\\"both\\\")\\nplt.tight_layout()\\nplt.savefig(\\\"./images/avg_efficiency.png\\\")\\nplt.savefig(\\\"./images/avg_efficiency.svg\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MultipleLocator\n",
    "\n",
    "ml = MultipleLocator(0.1)\n",
    "sns.set_theme(style=\"white\")\n",
    "plt.rcParams[\"figure.figsize\"] = [15, 10]\n",
    "sns.lineplot(\n",
    "    data=melted_df, x=\"group\", y=\"mean_efficiency\", hue=\"season\"\n",
    ").invert_xaxis()\n",
    "plt.xticks(rotation=90)\n",
    "plt.axes().yaxis.set_minor_locator(ml)\n",
    "plt.grid(linestyle=\"dashed\", alpha=0.5, which=\"both\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./images/avg_efficiency.png\")\n",
    "plt.savefig(\"./images/avg_efficiency.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"matches_boxplot_df = df[pd.to_numeric(df[\\\"rank\\\"]) <= 200]\\nsns.boxplot(data=matches_boxplot_df, x=\\\"season\\\", y=\\\"matches\\\")\\nplt.xticks(rotation=90)\\nplt.tight_layout()\\nplt.title(\\\"Matches played by Top 200\\\")\\nplt.xlabel(\\\"Season\\\")\\nplt.ylabel(\\\"Matched played\\\")\\nplt.savefig(\\\"./images/top200_games_boxplot.png\\\")\\nplt.savefig(\\\"./images/top200_games_boxplot.svg\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"matches_boxplot_df = df[pd.to_numeric(df[\\\"rank\\\"]) <= 200]\\nsns.boxplot(data=matches_boxplot_df, x=\\\"season\\\", y=\\\"matches\\\")\\nplt.xticks(rotation=90)\\nplt.tight_layout()\\nplt.title(\\\"Matches played by Top 200\\\")\\nplt.xlabel(\\\"Season\\\")\\nplt.ylabel(\\\"Matched played\\\")\\nplt.savefig(\\\"./images/top200_games_boxplot.png\\\")\\nplt.savefig(\\\"./images/top200_games_boxplot.svg\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matches_boxplot_df = df[pd.to_numeric(df[\"rank\"]) <= 200]\n",
    "sns.boxplot(data=matches_boxplot_df, x=\"season\", y=\"matches\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.title(\"Matches played by Top 200\")\n",
    "plt.xlabel(\"Season\")\n",
    "plt.ylabel(\"Matched played\")\n",
    "plt.savefig(\"./images/top200_games_boxplot.png\")\n",
    "plt.savefig(\"./images/top200_games_boxplot.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"matches_boxplot_df = df[\\n    (pd.to_numeric(df[\\\"rank\\\"]) > 1000) & (pd.to_numeric(df[\\\"rank\\\"]) <= 1200)\\n]\\nsns.boxplot(data=matches_boxplot_df, x=\\\"season\\\", y=\\\"matches\\\")\\nplt.xticks(rotation=90)\\nplt.tight_layout()\\nplt.savefig(\\\"./images/top1200-1000_games_boxplot.png\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"matches_boxplot_df = df[\\n    (pd.to_numeric(df[\\\"rank\\\"]) > 1000) & (pd.to_numeric(df[\\\"rank\\\"]) <= 1200)\\n]\\nsns.boxplot(data=matches_boxplot_df, x=\\\"season\\\", y=\\\"matches\\\")\\nplt.xticks(rotation=90)\\nplt.tight_layout()\\nplt.savefig(\\\"./images/top1200-1000_games_boxplot.png\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matches_boxplot_df = df[\n",
    "    (pd.to_numeric(df[\"rank\"]) > 1000) & (pd.to_numeric(df[\"rank\"]) <= 1200)\n",
    "]\n",
    "sns.boxplot(data=matches_boxplot_df, x=\"season\", y=\"matches\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./images/top1200-1000_games_boxplot.png\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}