scl_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "af4b6fbc-44cb-4af5-b8f0-a5ab594abc44",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# TODO!!!\n",
    "## Currently does not pull playoffs -- Only have playoffs for first two seasons?\n",
    "## Some seasons are missing week information, I did not try to figure it out in these cases and just entered the matches in the order they appeared\n",
    "## Track history so you can graph players over time\n",
    "## Graphing should exclude 0 elo seasons\n",
    "## Record Peak Elo\n",
    "\n",
    "# Make case insensitive!!!\n",
    "# Add win rates, maybe even faction specific"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff6e20d1-81e6-4cdd-a5f9-fe5d9b8bb7dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "170f0752-93e3-4beb-b0b6-5f5bd0809d62",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import os.path\n",
    "\n",
    "from google.auth.transport.requests import Request\n",
    "from google.oauth2.credentials import Credentials\n",
    "from google_auth_oauthlib.flow import InstalledAppFlow\n",
    "from googleapiclient.discovery import build\n",
    "from googleapiclient.errors import HttpError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "38190535-d371-426d-89d4-45bc20f1f5c6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from collections import defaultdict\n",
    "from time import sleep\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aaf95961-6fa1-40b2-a9ee-f859f112d7e9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# If modifying these scopes, delete the file token.json.\n",
    "SCOPES = ['https://www.googleapis.com/auth/spreadsheets.readonly']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30e0de4f-7b39-476b-b4bb-34a4d4d4bff8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "early_data_sheet = \"1fd-dMfTfOwcOkNwIDbjMhQgTLZjS1COTPWi3f96Bols\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b1230eb4-da19-4615-afa3-7b87ee795112",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SheetsService():\n",
    "    def __init__(self):\n",
    "        self.service = self.build_service()\n",
    "        self.sheet = self.service.spreadsheets()\n",
    "\n",
    "\n",
    "    def build_service(self):\n",
    "        \"\"\"Shows basic usage of the Sheets API.\n",
    "        Prints values from a sample spreadsheet.\n",
    "        \"\"\"\n",
    "        creds = None\n",
    "        # The file token.json stores the user's access and refresh tokens, and is\n",
    "        # created automatically when the authorization flow completes for the first\n",
    "        # time.\n",
    "        if os.path.exists('token.json'):\n",
    "            creds = Credentials.from_authorized_user_file('token.json', SCOPES)\n",
    "        # If there are no (valid) credentials available, let the user log in.\n",
    "        if not creds or not creds.valid:\n",
    "            if creds and creds.expired and creds.refresh_token:\n",
    "                creds.refresh(Request())\n",
    "            else:\n",
    "                flow = InstalledAppFlow.from_client_secrets_file(\n",
    "                    'credentials.json', SCOPES)\n",
    "                creds = flow.run_local_server(port=0)\n",
    "            # Save the credentials for the next run\n",
    "            with open('token.json', 'w') as token:\n",
    "                token.write(creds.to_json())\n",
    "\n",
    "        try:\n",
    "            service = build('sheets', 'v4', credentials=creds)\n",
    "            return service\n",
    "\n",
    "        except HttpError as err:\n",
    "            print(err)\n",
    "        \n",
    "        \n",
    "    def get_sheet_as_df(self, sheet_id, cell_range, has_header = False, columns = None):\n",
    "        # Call the Sheets API\n",
    "        result = self.sheet.values().get(spreadsheetId=sheet_id,\n",
    "                                    range=cell_range).execute()\n",
    "        values = result.get('values', [])\n",
    "\n",
    "        if not values:\n",
    "            print('No data found.')\n",
    "            \n",
    "        if len(values) < 10:\n",
    "            return \"SKIP\"\n",
    "        \n",
    "        if has_header:\n",
    "            return pd.DataFrame(data = values[1:], columns = values[0])\n",
    "        else:\n",
    "            if columns != None:\n",
    "                return pd.DataFrame(data = values, columns = columns)\n",
    "            else:\n",
    "                return pd.DataFrame(data = values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f0c868b2-f5d2-4db8-9617-c475169da863",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "service = SheetsService()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d08285f6-f6a4-4d0e-ae01-775ad7cbb180",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sheet_ser = service.service.spreadsheets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a7969f5f-01d8-4f1f-a1c1-24b7fe1e50eb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Example match sheet title: S1W1-FRX-RANDO-2\n",
    "# A - SEASON\n",
    "# B- WEEK\n",
    "# C - GAME\n",
    "# D - GAME LENGTH\n",
    "# E - TEAM\n",
    "# F - FACTION\n",
    "# G - RESULT\n",
    "# H - PILOT\n",
    "# I - SCORE\n",
    "# J - KILLS\n",
    "# K - DEATHS\n",
    "# L - ASSISTS\n",
    "# M - AI KILLS\n",
    "# N - CAP SHIP DMG\n",
    "\n",
    "# Create known aliases map\n",
    "# Build player - elo board. Teams do not matter\n",
    "# Can build team elo board as well, MAYBE MAYBE MAYBE or insert teams as players (maybe have a player/team column) -- will require some name continuity research\n",
    "\n",
    "# Go through each season \n",
    "# Go through each week of that season\n",
    "# Go through each match of that week\n",
    "# For each match extract all pilots\n",
    "# If pilot does not exist, create an entry with default elo (1500)\n",
    "# Retrieve each pilot elo and divide into teams. \n",
    "# Calculate avg elo for each team.\n",
    "# Adjust elo for each pilot based on W or L\n",
    "# Adjust elo for each team based on W or L\n",
    "# At end of each season, save elo snapshot as separate dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "db976ef9-1c2b-4792-a437-ef2b21d42b65",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class EloBuilder():\n",
    "    def __init__(self):\n",
    "        self.season = 1\n",
    "        self.num_seasons = 8\n",
    "        self.processed_games = 0\n",
    "        self.skipped_games = 0\n",
    "        self.elo_board = self.initialize_elo()\n",
    "        print(\"Elo initialized\")\n",
    "        self.sheets = SheetsService()\n",
    "        print(\"sheets initialized\")\n",
    "        # self.sheets_service = SheetsService()\n",
    "        # self.sheets = self.sheets_service.sheet\n",
    "        self.player_aliases = self.make_aliases()\n",
    "        print(\"aliases made\")\n",
    "        self.season_sheets = {\n",
    "            1: [\"1fd-dMfTfOwcOkNwIDbjMhQgTLZjS1COTPWi3f96Bols\"],\n",
    "            2: [\"1fd-dMfTfOwcOkNwIDbjMhQgTLZjS1COTPWi3f96Bols\"],\n",
    "            3: [\"1fd-dMfTfOwcOkNwIDbjMhQgTLZjS1COTPWi3f96Bols\"],\n",
    "            4: [\"1elPKPM53ghLvY3FBJcFjWPUizyaWwZZsfyleboNwsbE\", \"187ceE1WiFRF4PidFwO4tIbetEJKSX3qeWm0rm3h89DY\", \"1I_5UvfCPgmczgrpKJdvsKMj8GlpqOnSe7l4DQJAE4iE\"],\n",
    "            5: [\"19V9fh1BTFFRpyeHdrjJWP37get5T5RNeKCQTFyXVAFM\", \"10Jz9XpC2mmiVu3BADMHBgZf46qkIxhvCb7Ew83NAZjY\", \"1lmkI3mXDiE-aOUsWscSxJOOj3Q0t63sj7xD9t4qjaCw\"],\n",
    "            6: [\"1g9hhnHwMSJQggbyA6UzHPM7eh9mEm9WPW5XWBgD_8F4\", \"19E0J_uq_hm9qjG7DPamMFg5eNFW0qZl39Fe4ifCP8bg\", \"1ov8vuxItAeeya7qSp7GLiIZYFZ6ny7kDIY0ZbZNDwPM\"],\n",
    "            7: [\"1VSP1i2hUfhoXfvwGlKiEevkrmtKKASSMfeb-Z7R_FXE\"],\n",
    "            8: [\"1tQ6Fvg_jVRny3weLGcrV0vadI9geUe9-_zgSt8HJpLY\"]\n",
    "        }\n",
    "        self.season_weeks = {\n",
    "            1: 7,\n",
    "            2: 6,\n",
    "            3: 3,\n",
    "            4: 0,\n",
    "            5: 0,\n",
    "            6: 0,\n",
    "            7: 0,\n",
    "            8: 0\n",
    "        }\n",
    "        self.division_order = [\"\", \"Elite\", \"Challenger\", \"Open\"]\n",
    "        self.season_snapshots = defaultdict(pd.DataFrame)\n",
    "        \n",
    "    def initialize_elo(self):\n",
    "        cols = [\"player\", \"score\", \"games_played\", \"first_season\", \"latest_season\"]\n",
    "        df = pd.DataFrame(columns = cols)\n",
    "        df.set_index(\"player\", inplace = True)\n",
    "        return df\n",
    "    \n",
    "    def add_new_player(self, player):\n",
    "        player_row = [1500, 0, self.season, self.season]\n",
    "        self.elo_board.loc[player] = player_row\n",
    "    \n",
    "    def make_aliases(self):\n",
    "        sheet = \"1niMELgIKCzxrBOP0BLJdAKDcvtXRA3Zty3sNPbiVorI\"\n",
    "        result = self.sheets.sheet.values().get(spreadsheetId=sheet,\n",
    "                                    range=\"NameFixes!A1:Z\").execute()\n",
    "        values = result.get('values', [])\n",
    "        # data = self.sheets_service.get_sheet_as_df(sheet, \"NameFixes!A1:Z\")\n",
    "        aliases = defaultdict(list)\n",
    "        for name in values:\n",
    "            aliases[name[0]] = set([n for n in name[1:] if len(n) > 1])\n",
    "        return aliases\n",
    "    \n",
    "    def match_alias(self, name):\n",
    "        for k,v in self.player_aliases.items():\n",
    "            if name in v:\n",
    "                return k  \n",
    "        return name\n",
    "    \n",
    "    ##\n",
    "    ### Board construction\n",
    "    ##\n",
    "    \n",
    "    def build_elo_board(self):\n",
    "        for season in range(1,self.num_seasons+1):\n",
    "            self.process_season()\n",
    "            self.season_snapshots[self.season] = self.elo_board.copy()\n",
    "            print(f\"--- Processed season: {self.season} ---\")\n",
    "            self.season += 1\n",
    "        pass\n",
    "    \n",
    "    def process_season(self):\n",
    "        # retrieves all match sheet titles for a season\n",
    "        if self.season <= 3:\n",
    "            current_sheet = self.season_sheets[self.season][0]\n",
    "            meta = self.sheets.sheet.get(spreadsheetId=current_sheet).execute()\n",
    "            sh = meta.get('sheets', '')\n",
    "            game_titles = [i['properties']['title'] for i in sh if i['properties']['title'][:3] == f\"S{self.season}W\"]\n",
    "            # Currently omits playoffs, can add later\n",
    "            for week in range(1,self.season_weeks[self.season]+1):\n",
    "                week_games = [i for i in game_titles if i[:4] == f\"S{self.season}W{week}\"]\n",
    "                week_games.sort()\n",
    "                self.process_week_jareen(week_games, current_sheet)\n",
    "                print(f\"Processed S{self.season}W{week}\")\n",
    "                \n",
    "        elif self.season <= self.num_seasons:\n",
    "            current_sheets = self.season_sheets[self.season]\n",
    "            columns = [\"GAME_ID\",\"PILOT\",\"TEAM\",\"POINTS\",\"KILLS\",\"DEATHS\",\"ASSISTS\",\"AI KILLS\",\"CAP SHIIP DMG\",\"FACTION\",\"RESULT\"]\n",
    "            \n",
    "            for idx, division in enumerate(current_sheets):\n",
    "                div = idx\n",
    "                if self.season <= 6:\n",
    "                    div += 1\n",
    "                data = self.sheets.get_sheet_as_df(division, 'Data!A1:K', columns = columns)\n",
    "                data[\"true_name\"] = data.PILOT.apply(self.match_alias)\n",
    "                game_ids = data.GAME_ID.unique()\n",
    "                \n",
    "                for game in game_ids:\n",
    "                    self.processed_games += 1\n",
    "                    try:\n",
    "                        self.process_game(data[data.GAME_ID == game])\n",
    "                    except Exception as e:\n",
    "                        self.skipped_games += 1\n",
    "                        print(f\"Skipped {game} -- S{self.season}{self.division_order[idx]}\")\n",
    "                        print(e)\n",
    "                print(f\"Processed S{self.season}{self.division_order[idx]}\")\n",
    "\n",
    "        else:\n",
    "            print(\"Ran out of seasons\")\n",
    "            #figure out scheme for later season, has Elite/Challenger/Open for seasons 4-6\n",
    "            pass\n",
    "    \n",
    "    def process_week_jareen(self, games, sheet):\n",
    "        # processes all match sheets for a week of a season\n",
    "        for game in games:\n",
    "            self.processed_games += 1\n",
    "            cells = f\"{game}!A1:N\"\n",
    "            try:\n",
    "                data = self.sheets.get_sheet_as_df(sheet, cells, has_header = True)\n",
    "                if type(data) != str:\n",
    "                    data[\"true_name\"] = data.PILOT.apply(self.match_alias)\n",
    "                    self.process_game(data)\n",
    "                else: \n",
    "                    print(f\"Skipped {game} -- < 10 players\")\n",
    "                    self.skipped_games += 1\n",
    "            except Exception as e:\n",
    "                print(f\"Skipped {game} -- Other failure\")\n",
    "                print(e)\n",
    "                break\n",
    "                self.skipped_games += 1\n",
    "        sleep(60)\n",
    "    \n",
    "    def process_game(self, game):\n",
    "        # processes match result data from a game\n",
    "        team_w = game[(game.RESULT == \"W\") | (game.RESULT == \"Win\")]\n",
    "        team_l = game[(game.RESULT == \"L\") | (game.RESULT == \"Loss\")]\n",
    "        if team_w.shape[0] != 0:\n",
    "            # game_names = {i: match_aliases(i) for i in game.PILOT}\n",
    "            for name in game.true_name:\n",
    "                if name not in self.elo_board.index:\n",
    "                    self.add_new_player(name)\n",
    "            winner_team_elo = self.calculate_team_elo(team_w)\n",
    "            loser_team_elo = self.calculate_team_elo(team_l)\n",
    "            for name in team_w.true_name:\n",
    "                self.update_player_elo(name, \"w\", team_w, loser_team_elo)\n",
    "            for name in team_l.true_name:\n",
    "                self.update_player_elo(name, \"l\", team_l, winner_team_elo)\n",
    "                \n",
    "    ##\n",
    "    ### Elo update calculations\n",
    "    ##\n",
    "        \n",
    "    def calculate_team_elo(self, team):\n",
    "        total = 0\n",
    "        for player in team.true_name:\n",
    "            total += self.elo_board.loc[player, \"score\"]\n",
    "        return round(total/len(team), 0)\n",
    "    \n",
    "    def update_player_elo(self, player, result, team, opp_elo):\n",
    "        # p_elo = self.elo_board.loc[player].score\n",
    "        p_elo = self.weighted_player_elo(player, team)\n",
    "        if result == 'w':\n",
    "            win_chance = 1.0 / (1 + 10**((opp_elo - p_elo)/400))\n",
    "            delta = round(32 * (1 - win_chance), 0)\n",
    "            self.elo_board.loc[player, \"score\"] += delta\n",
    "        else:\n",
    "            win_chance = 1.0 / (1 + 10**((p_elo - opp_elo)/400))\n",
    "            delta = round(32 * (1 - win_chance), 0)\n",
    "            self.elo_board.loc[player, \"score\"] -= delta\n",
    "        \n",
    "        self.elo_board.loc[player, \"games_played\"] += 1\n",
    "        self.elo_board.loc[player, \"latest_season\"] = self.season\n",
    "        \n",
    "    def weighted_player_elo(self, player, team):\n",
    "        p_elo = self.elo_board.loc[player].score\n",
    "        team_elo = self.calculate_team_elo(team[team.PILOT != player])\n",
    "        wt = (p_elo + team_elo) / 2\n",
    "        return round(wt, 0)\n",
    "    \n",
    "    ##\n",
    "    ### Graphing stuff below here\n",
    "    ##\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d7116f15-1145-4044-944f-c2c928b9538c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elo initialized\n",
      "sheets initialized\n",
      "aliases made\n"
     ]
    }
   ],
   "source": [
    "elo = EloBuilder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "17b04d5b-8dc2-4cab-b7ec-b02c0015da57",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipped S1W1-NPC-1 -- < 10 players\n",
      "Skipped S1W1-NPC-2 -- < 10 players\n",
      "Processed S1W1\n",
      "Skipped S1W2-NPC-1 -- < 10 players\n",
      "Skipped S1W2-NPC-2 -- < 10 players\n",
      "Processed S1W2\n",
      "Processed S1W3\n",
      "Processed S1W4\n",
      "Processed S1W5\n",
      "Processed S1W6\n",
      "Processed S1W7\n",
      "--- Processed season: 1 ---\n",
      "Skipped S2W1-SKL-BAY-1 -- Other failure\n",
      "14 columns passed, passed data had 7 columns\n",
      "Processed S2W1\n",
      "Skipped S2W2-GHST-SKL-1 -- Other failure\n",
      "14 columns passed, passed data had 7 columns\n",
      "Processed S2W2\n",
      "Processed S2W3\n",
      "Skipped S2W4-F-SPICY-1 -- Other failure\n",
      "14 columns passed, passed data had 7 columns\n",
      "Processed S2W4\n",
      "Processed S2W5\n",
      "Processed S2W6\n",
      "--- Processed season: 2 ---\n",
      "Skipped S3W1-TSQ-1 -- < 10 players\n",
      "Skipped S3W1-TSQ-2 -- < 10 players\n",
      "Processed S3W1\n",
      "Processed S3W2\n",
      "Skipped S3W3-NOVA-IN-1 -- Other failure\n",
      "14 columns passed, passed data had 3 columns\n",
      "Processed S3W3\n",
      "--- Processed season: 3 ---\n",
      "Processed S4\n",
      "Processed S4Elite\n",
      "Processed S4Challenger\n",
      "--- Processed season: 4 ---\n",
      "Processed S5\n",
      "Processed S5Elite\n",
      "Processed S5Challenger\n",
      "--- Processed season: 5 ---\n",
      "Processed S6\n",
      "Processed S6Elite\n",
      "Processed S6Challenger\n",
      "--- Processed season: 6 ---\n",
      "Processed S7\n",
      "--- Processed season: 7 ---\n",
      "Processed S8\n",
      "--- Processed season: 8 ---\n"
     ]
    }
   ],
   "source": [
    "elo.build_elo_board()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bab68428-ce16-44ff-b99e-e6dac8196276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(528, 4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elo.elo_board.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "6cd3884c-9bce-42a0-9f36-f169010ba334",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1500.2386363636363"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elo.elo_board.score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bd957b3a-c43d-4a17-8e99-835e6493c41f",
   "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>score</th>\n",
       "      <th>games_played</th>\n",
       "      <th>first_season</th>\n",
       "      <th>latest_season</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jishiiqua</th>\n",
       "      <td>1802</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hod</th>\n",
       "      <td>1796</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Esoda</th>\n",
       "      <td>1786</td>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matticus_Rex</th>\n",
       "      <td>1761</td>\n",
       "      <td>71</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dead_Weight</th>\n",
       "      <td>1755</td>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>8</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>six8ten</th>\n",
       "      <td>1318</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evil Pepe</th>\n",
       "      <td>1301</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Billy_KeyStrokes</th>\n",
       "      <td>1287</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HudsonA08</th>\n",
       "      <td>1285</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BawangByte</th>\n",
       "      <td>1263</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>528 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  score  games_played  first_season  latest_season\n",
       "player                                                            \n",
       "Jishiiqua          1802            36             4              6\n",
       "Hod                1796            82             1              8\n",
       "Esoda              1786            87             2              8\n",
       "Matticus_Rex       1761            71             2              8\n",
       "Dead_Weight        1755            61             3              8\n",
       "...                 ...           ...           ...            ...\n",
       "six8ten            1318            32             6              7\n",
       "Evil Pepe          1301            31             5              7\n",
       "Billy_KeyStrokes   1287            16             3              8\n",
       "HudsonA08          1285            30             3              7\n",
       "BawangByte         1263            39             3              7\n",
       "\n",
       "[528 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elo.elo_board.sort_values('score', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d10149ef-6811-4d25-b471-284c5d833b40",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_player_history(elo, player):\n",
    "    matched_player = elo.match_alias(player)\n",
    "    return [elo.season_snapshots[s+1].loc[matched_player].score if matched_player in elo.season_snapshots[s+1].index else 0 for s in range(elo.num_seasons)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "5d0c9471-9320-46d0-ad9d-24240f3a9121",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "45a04b8a-5a13-47b1-b10a-6743f57faf99",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fca95565c0>]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(get_player_history(elo, \"Dead_Weight\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "5f035072-8596-423b-804b-c4ad2ea28c70",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fcad8791e0>]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(get_player_history(elo, \"Hod\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "ce09f1a2-d4cd-4af4-b623-69cacf7d069a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fcaddb5fc0>]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OnYucnBzs3bsX9957L6qqqrB3796hlkoiCW+WxykmIiJKJIMOM83Nzbj77rtRVVWF3Nzcc17jdDrx+OOPY+rUqVHHfT4ftm3bhoqKCsyYMQMFBQVYs2YNHA4H9u/fDwDYs2cPNBoNnnzySeTn52P27Nm48847sXnz5sG3jkR1Zr0Mn2QiIqLEMegw88UXX0Cj0eDNN9/EpEmT+p0XBAGPPvoo/u3f/q1fmKmvr0d3dzdKSkoix8xmM8aPH49Dhw4BAGprazF16lSo1Wf2MJk2bRoaGxtx6tSpwZZLIgmFBDQ2c2SGiIgSz6B3PSstLe23DuZsO3bsQEtLCzZu3IhNmzZFnXM4HACA7OzsqONZWVmRcw6HA2PHju13HgCampowfPjwwZYMAFDHYI2HSqWM+jmVnWjpgtcXhE6jQo4tHUqlQlbtPx+594Hc2w+wD9h+ebcfSIw+EHUL1/r6evzud7/Dzp07odVq+513u90A0O+cTqdDR0cHAMDj8ZzzPAB4vd4h1aVUKpCZaRrSZwfCbDbE7N6J4m9ftQIALv/OMFit0U8yyaH9FyP3PpB7+wH2Adsv7/YD0vaBaGHG6/Vi8eLFuOeee1BQUHDOa/R6PYDetTPhX4c/azAYIteEFwOffR4AjEbjkGoLhQS4XD1D+uyFqFRKmM0GuFxuBIMh0e+fSP7+VQsA4DsjTGhv7wYgr/afj9z7QO7tB9gHbL+82w/Erg/MZsOAR3tECzOfffYZvvzyS/zud7/D+vXrAQB+vx+BQABFRUX4/e9/H5lecjqdyMnJiXzW6XRi3LhxAAC73Q6n0xl17/DXNpttyPUFArH7SxYMhmJ6/0Tw9Ynexb9jbOn92iqH9l+M3PtA7u0H2Adsv7zbD0jbB6KFmYkTJ0aeSAp75ZVXsH//frzyyiuw2WxQKpVIS0tDTU1NJMy4XC7U1dWhrKwMAFBcXIzdu3cjGAxCpVIBAA4ePIi8vDxYrVaxyqVBCARDOOYML/7lk0xERJRYRAszer0eY8aMiTqWkZEBtVoddbysrAxVVVWwWCwYNWoUVq1aBbvdjpkzZwIAZs+ejS1btmDp0qUoLy/H559/jh07dmD58uVilUqDdLylC4GgAJNejRHDOC9MRESJRdQFwANRUVGBQCCAyspKeDweFBcXY+vWrdBoNAAAq9WKLVu2YOXKlZg1axZGjBiBJUuWYNasWfEulfqc/XJJhUIhcTVERETRFIIgCFIXEWvBYAhtbd2i31etViIzs3dBbCrPlW57+zDe/78m3PwvY/CT6/Mjx+XS/guRex/Ivf0A+4Dtl3f7gdj1gcViGvACYPk+GE8D1uDo2/nXzs3yiIgo8TDM0AV5fUGcPNU7qpU3kmGGiIgSD8MMXdDR5k4IApCZrsOwNJ3U5RAREfXDMEMXFH65ZK6dj2QTEVFiYpihCzrzpmxOMRERUWJimKELamzim7KJiCixMczQeXW5/XCe7n05aC53/iUiogTFMEPn1dj3SHZWpgEmvUbiaoiIiM6NYYbOq4FTTERElAQYZui8GsOLf/kkExERJTCGGTqvb8KPZXNkhoiIEhjDDJ1Te6cXHV0+KBTAGBtHZoiIKHExzNA5hfeXGTXcBJ1WJXE1RERE58cwQ+fEzfKIiChZMMzQOTUyzBARUZJgmKF+BEFAo4OPZRMRUXJgmKF+nKfd6PYEoFYpMWqESepyiIiILohhhvoJr5fJsaVBreJfESIiSmz8TkX9RF4uaecUExERJT6GGeqnIbJZHveXISKixMcwQ1GCoRCONnPxLxERJQ+GGYpy8lQPfP4Q9FoV7Faj1OUQERFdFMMMRYlMMdnToVQoJK6GiIjo4hhmKEojXy5JRERJhmGGojT0Pcl0GcMMERElCYYZivAHgjje0gWATzIREVHyYJihiG+dXQiGBKQbNbCa9VKXQ0RENCAMMxQR2Swv2wwFF/8SEVGSYJihiLOfZCIiIkoWDDMUEQ4z3CyPiIiSySWFmU2bNuGOO+6IOvbf//3fmD17NoqKilBaWorf/va38Hg8kfNerxfLly9HSUkJioqK8NBDD6GtrS3qHh9++CF+8pOfYNKkSbjxxhvx9ttvX0qZNABubwCO1h4ADDNERJRchhxmdu7cibVr10Ydq62txX333Ycf/OAHeOONN/DEE0/gnXfewfLlyyPXLFu2DO+//z7WrVuHl156Cd988w0qKioi57/++mssWLAA06dPx759+3DrrbdiyZIl+PDDD4daKg3AUUcnBABWsw5mk1bqcoiIiAZMPdgPNDc344knnkBNTQ1yc3Ojzu3evRvXXnst7r77bgBAbm4uHnzwQVRWVmL58uVob29HdXU1Nm7ciClTpgAAVq9ejRtvvBGffPIJioqK8NJLL2HcuHF48MEHAQD5+fmoq6vDli1bUFJSconNpfNp4GZ5RESUpAYdZr744gtoNBq8+eabWL9+PU6cOBE5d9ddd0GpjB7sUSqV8Pv96OrqwscffwwAmDZtWuR8Xl4ebDYbDh06hKKiItTW1uKGG26Iuse0adOwcuVKCIIw5Kds1GrxlwepVMqon5NZY9/LJfNHZQy4r1Kp/UMl9z6Qe/sB9gHbL+/2A4nRB4MOM6WlpSgtLT3nufHjx0d97ff7sWPHDlx99dWwWCxobm5GZmYmdDpd1HVZWVlwOBwAAIfDAbvd3u+82+1Ge3s7LBbLYEuGUqlAZqZp0J8bKLPZELN7x8tRR2+YmTg2a9B9lQrtv1Ry7wO5tx9gH7D98m4/IG0fDDrMDFQgEMCSJUvw5ZdfYufOnQAAt9sNrbb/egydTgev1wsA8Hg8/a4Jf+3z+YZUSygkwOXqGdJnL0SlUsJsNsDlciMYDIl+/3hxdfvgbHcDAIanadHe3j2gz6VK+y+F3PtA7u0H2Adsv7zbD8SuD8xmw4BHe2ISZrq6uvDAAw/go48+wu9+9ztMnDgRAKDX688ZSLxeLwyG3kSn0+n6XRP+OnzNUAQCsftLFgyGYnr/WPvq+GkAQLbVCK1aOei2JHv7xSD3PpB7+wH2Adsv7/YD0vaB6GHG6XRi/vz5OHHiBLZu3Yri4uLIObvdjtOnT8Pn80WNvjidTthsNgBAdnY2nE5nv3sajUakp3Mzt1gIv1wy187Fv0RElHxEXa3T0dGBn//852hra8POnTujggwAXHPNNQiFQpGFwADQ0NCA5ubmyLVTpkzBRx99FPW5gwcPYvLkyf0WF5M4zmyWx7BIRETJR9R08PTTT+PYsWNYtWoVLBYLWlpaIj+CwSBsNhtuuukmVFZWoqamBp9//jkWLVqEqVOnorCwEABwxx134PPPP0dVVRW+/vprbNu2DX/+859RXl4uZqnURxAENHLnXyIiSmKiTTMFg0G888478Pv9+PnPf97v/F/+8heMHj0aK1aswFNPPYX77rsPAHD99dejsrIyct0VV1yBDRs2YNWqVXjppZcwevRorFq1invMxEibywtXjx8qpQI5tjSpyyEiIho0hSAIgtRFxFowGEJb28Ce0BkMtVqJzEwT2tu7k3bhV229Exuq/44cWxqWzZ06qM+mQvsvldz7QO7tB9gHbL+82w/Erg8sFtOAn2biIhSZa3BwiomIiJIbw4zMNZxkmCEiouTGMCNjIUHA0ebwY9l8komIiJITw4yMNbf1wO0NQqtWYtSI2L3ugYiIKJYYZmQsvL9Mji0dKu7hQ0RESYrfwWQsvPMv18sQEVEyY5iRsUbu/EtERCmAYUamAsEQvnV2AeDIDBERJTeGGZk60dINfyAEo06NrMyhv42ciIhIagwzMhXeLC83Ox0KhULiaoiIiIaOYUam+HJJIiJKFQwzMhV+kinXzjBDRETJjWFGhrz+IE609L54k08yERFRsmOYkaFvmzsREgRkmLTITNdJXQ4REdElYZiRobM3y+PiXyIiSnYMMzIUXvybyykmIiJKAQwzMtTAJ5mIiCiFMMzITI/Hj+Z2NwCGGSIiSg0MMzLT4OhdLzNimB5pBo3E1RAREV06hhmZ4WZ5RESUahhmZIab5RERUaphmJGZM4t/+SQTERGlBoYZGTnd5UV7pxcKBTDGzjBDRESpgWFGRsKjMiOtJui1aomrISIiEgfDjIxE1stwiomIiFIIw4yM8EkmIiJKRQwzMiEIAnf+JSKilMQwIxMtHR50ewJQKRUYPSJN6nKIiIhEwzAjE+EpphxbGjRq/rETEVHquKTvaps2bcIdd9wRdezw4cMoKytDYWEhSktL8fLLL0edD4VCeOGFFzB9+nQUFhZi/vz5OHbs2KDuQYPXEHlTNqeYiIgotQw5zOzcuRNr166NOtbe3o65c+ciJycHe/fuxb333ouqqirs3bs3cs2GDRuwa9curFixArt370YoFEJ5eTl8Pt+A70GDF36SKY87/xIRUYoZ9GYjzc3NeOKJJ1BTU4Pc3Nyoc3v27IFGo8GTTz4JtVqN/Px8HD16FJs3b8bs2bPh8/mwbds2LF68GDNmzAAArFmzBtOnT8f+/ftx8803X/QeNHihkICjfS+Y5M6/RESUagY9MvPFF19Ao9HgzTffxKRJk6LO1dbWYurUqVCrz2SkadOmobGxEadOnUJ9fT26u7tRUlISOW82mzF+/HgcOnRoQPegwWtq7YbXH4ROo0K21SR1OURERKIa9MhMaWkpSktLz3nO4XBg7NixUceysrIAAE1NTXA4HACA7OzsfteEz13sHsOHDx9syQAAdQwWvapUyqifE9XR5i4AvZvlabUq0e6bLO2PJbn3gdzbD7AP2H55tx9IjD4QdU97j8cDrVYbdUyn0wEAvF4v3G43AJzzmo6OjgHdYyiUSgUyM2M3ImE2G2J2bzGcaOsBAFyZZ41JPyR6++NB7n0g9/YD7AO2X97tB6TtA1HDjF6vjyzkDQsHEKPRCL1eDwDw+XyRX4evMRgMA7rHUIRCAlyuniF99kJUKiXMZgNcLjeCwZDo9xdLfUMbAGCkxYD29m7R7pss7Y8lufeB3NsPsA/Yfnm3H4hdH5jNhgGP9ogaZux2O5xOZ9Sx8Nc2mw2BQCByLCcnJ+qacePGDegeQxUIxO4vWTAYiun9L4U/EMK3zb2Lf3Ns6TGpM5HbHy9y7wO5tx9gH7D98m4/IG0fiDrBVVxcjI8//hjBYDBy7ODBg8jLy4PVakVBQQHS0tJQU1MTOe9yuVBXV4fi4uIB3YMG53hLF4IhAWkGDUZk6C/+ASIioiQjapiZPXs2urq6sHTpUnz11VfYt28fduzYgQULFgDoXStTVlaGqqoq/OUvf0F9fT0efPBB2O12zJw5c0D3oMGJbJZnT4dCoZC4GiIiIvGJOs1ktVqxZcsWrFy5ErNmzcKIESOwZMkSzJo1K3JNRUUFAoEAKisr4fF4UFxcjK1bt0Kj0Qz4HjRw3PmXiIhSnUIQBEHqImItGAyhrU28ha9harUSmZkmtLd3J+xc6eNbanDiVDcqZk9E4RVDe6z9fJKh/bEm9z6Qe/sB9gHbL+/2A7HrA4vFNOAFwPJ9MF4GPL4ATrb2hjju/EtERKmKYSaFHXV0QhCAzHQdMtJ0UpdDREQUEwwzKSzyckmulyEiohTGMJPCwot/OcVERESpjGEmhfFJJiIikgOGmRTV2ePDqQ4PACDPzpEZIiJKXQwzKarR0btexpZpgFGvkbgaIiKi2GGYSVFn1stwiomIiFIbw0yKaux7konrZYiIKNUxzKQgQRD4JBMREckGw0wKau/0oqPbB6VCgRwbwwwREaU2hpkUFN4sb9QIE3QalcTVEBERxRbDTApqdHCKiYiI5INhJgVxszwiIpIThpkUExKEM+9ksjPMEBFR6mOYSTHOdjfc3gA0aiVGjTBJXQ4REVHMMcykmPAUU05WGtQq/vESEVHq43e7FMP1MkREJDcMMykmvPMvn2QiIiK5YJhJIcFQCN82h8MMR2aIiEgeGGZSyImWbvgCIRh0KtgsRqnLISIiiguGmRTS6OgdlRljS4dSoZC4GiIiovhgmEkhkZdLjuQUExERyQfDTAqJhBlulkdERDLCMJMifP4gTrR0A+DiXyIikheGmRTxrbMLwZAAs1EDi1kndTlERERxwzCTIs7eLE/Bxb9ERCQjDDMpojG8XoZTTEREJDMMMymigTv/EhGRTIkeZgKBAJ5//nl873vfQ1FREebMmYNPP/00cv7w4cMoKytDYWEhSktL8fLLL0d9PhQK4YUXXsD06dNRWFiI+fPn49ixY2KXmVJ6PAE42noA8J1MREQkP6KHmRdffBGvv/46VqxYgerqauTl5aG8vBxOpxPt7e2YO3cucnJysHfvXtx7772oqqrC3r17I5/fsGEDdu3ahRUrVmD37t0IhUIoLy+Hz+cTu9SUcdTRO8VkNethNmolroaIiCi+RA8zBw4cwM0334zrrrsOY8aMwaOPPorOzk58+umn2LNnDzQaDZ588knk5+dj9uzZuPPOO7F582YAgM/nw7Zt21BRUYEZM2agoKAAa9asgcPhwP79+8UuNWU0ODjFRERE8iV6mLFarfif//kfHD9+HMFgEK+99hq0Wi0KCgpQW1uLqVOnQq1WR66fNm0aGhsbcerUKdTX16O7uxslJSWR82azGePHj8ehQ4fELjVlNHDxLxERyZj64pcMztKlS/HLX/4S3//+96FSqaBUKrFu3Trk5OTA4XBg7NixUddnZWUBAJqamuBwOAAA2dnZ/a4JnxsqtVr8tc4qlTLqZ6k09i3+vXx0RkzaeT6J0n4pyb0P5N5+gH3A9su7/UBi9IHoYearr75Ceno61q9fD5vNhtdffx2LFy/Gq6++Co/HA602ek2HTte7wZvX64Xb7QaAc17T0dEx5JqUSgUyM01D/vzFmM2GmN37Yk53etHq8kChAAqvtMOo18S9Binbnyjk3gdybz/APmD75d1+QNo+EDXMNDU14aGHHsKOHTswZcoUAMCECRPw1VdfYd26ddDr9f0W8nq9XgCA0WiEXq8H0Lt2Jvzr8DUGw9A7KRQS4HL1DPnz56NSKWE2G+ByuREMhkS//0B8+uUpAEC21QSv2wevO34LpROh/VKTex/Ivf0A+4Dtl3f7gdj1gdlsGPBoj6hh5rPPPoPf78eECROijk+aNAl//etfMXLkSDidzqhz4a9tNhsCgUDkWE5OTtQ148aNu6TaAoHY/SULBkMxvf+FfHX8NAAg154uWQ1Stj9RyL0P5N5+gH3A9su7/YC0fSDqBJfdbgcA/OMf/4g6fuTIEeTm5qK4uBgff/wxgsFg5NzBgweRl5cHq9WKgoICpKWloaamJnLe5XKhrq4OxcXFYpaaMs5slsfFv0REJE+ihpmJEyfimmuuwSOPPIKDBw+isbERa9euxYcffohf/OIXmD17Nrq6urB06VJ89dVX2LdvH3bs2IEFCxYA6F0rU1ZWhqqqKvzlL39BfX09HnzwQdjtdsycOVPMUlOCIAhnvZOJj2UTEZE8iTrNpFQq8eKLL2Lt2rX41a9+hY6ODowdOxY7duzApEmTAABbtmzBypUrMWvWLIwYMQJLlizBrFmzIveoqKhAIBBAZWUlPB4PiouLsXXrVmg08V/YmuhaOzzocvuhUiqQk5UmdTlERESSUAiCIEhdRKwFgyG0tXWLfl+1WonMTBPa27slmSc8VO/Ei9V/xxhbOp6YG/9pOKnbnwjk3gdybz/APmD75d1+IHZ9YLGYBrwAWL4PxqeAM5vlcYqJiIjki2EmiTVG1stw8S8REckXw0ySCgkCGh18komIiIhhJkk5Wnvg8QWhVSsxcrhR6nKIiIgkwzCTpMLrZcbY06FS8o+RiIjki98Fk1QjN8sjIiICwDCTtL7hZnlEREQAGGaSUiAYwjEnR2aIiIgAhpmkdLylC4GgAJNejaxhfO08ERHJG8NMEgq/XDLXng6FQiFxNURERNJimElCDdwsj4iIKIJhJgk1Rl5jwDBDRETEMJNkvL4gTpzqfWkmwwwRERHDTNI52twJQQAy0rTITNdJXQ4REZHkGGaSTGSKyc5RGSIiIoBhJuk0hF8uOZJhhoiICGCYSToNkcW/3PmXiIgIYJhJKl1uP5ztbgBALqeZiIiIADDMJJVGR++oTNYwA9IMGomrISIiSgwMM0kksvMvp5iIiIgiGGaSCDfLIyIi6o9hJok0MMwQERH1wzCTJNo7vTjd5YNCAYyxcZqJiIgojGEmSYSnmEYON0GnVUlcDRERUeJgmEkSDQ7u/EtERHQuDDNJIvwkEzfLIyIiisYwkwQEQYhMM+Vy8S8REVEUhpkk0HLajW5PAGqVAt/JSpO6HCIiooTCMJMEvukblflOVjrUKv6RERERnS0m3xmrq6vxwx/+EBMmTMBNN92EP/3pT5Fzx48fx4IFCzB58mRcd911WLt2LYLBYNTnd+7cie9///uYOHEibr/9dtTV1cWizKTRyPUyRERE5yV6mPnDH/6ApUuXYs6cOXj77bdx8803Y9GiRfjkk0/g9/sxb948AMDu3buxbNky/Nd//RfWr18f+fwbb7yBZ599Fr/85S+xb98+jB49GnPnzkVbW5vYpSYNbpZHRER0fmoxbyYIAp5//nn87Gc/w5w5cwAA99xzD2pra/HRRx/hxIkTOHnyJPbs2YOMjAyMHTsWra2tePbZZ3H33XdDq9Vi48aNKCsrw49//GMAwFNPPYUbbrgBr7/+OhYsWCBmuUkhGArhaHP4nUwMM0RERP9M1JGZhoYGnDhxAj/60Y+ijm/duhULFixAbW0trrrqKmRkZETOTZs2DV1dXTh8+DBaW1vR2NiIkpKSyHm1Wo0pU6bg0KFDYpaaNJpO9cDnD0GnVSHbYpS6HCIiooQj6shMQ0MDAKCnpwfz5s1DXV0dRo8ejXvuuQelpaVwOByw2+1Rn8nKygIANDU1Qa3uLSc7O7vfNfX19ZdUm1ot/vIgVd9iXFUMF+Uedfatl7GnQ5tgO//Go/2JTu59IPf2A+wDtl/e7QcSow9EDTNdXV0AgEceeQT33XcfFi9ejHfffRcLFy7E9u3b4fF4YDZHT5XodDoAgNfrhdvtBgBotdp+13i93iHXpVQqkJlpGvLnL8ZsNsTs3idbe/vkysuGx7QNlyKW7U8Wcu8DubcfYB+w/fJuPyBtH4gaZjQaDQBg3rx5mDVrFgDgyiuvRF1dHbZv3w69Xg+fzxf1mXBIMRqN0Ov1AHDOawyGoXdSKCTA5eoZ8ufPR6VSwmw2wOVyIxgMiX5/ADjc2AoAGGkxoL29Oya/x1DFo/2JTu59IPf2A+wDtl/e7Qdi1wdms2HAoz2ihhmbzQYAGDt2bNTxyy+/HP/7v/+LqVOn4siRI1HnnE5n5LPh6SWn04n8/Pyoa8L3HqpAIHZ/yYLBUEzu7w+EcKy5d7QrJystpm24FLFqfzKRex/Ivf0A+4Dtl3f7AWn7QNQJrquuugomkwmfffZZ1PEjR44gJycHxcXFqKuri0xHAcDBgwdhMplQUFAAq9WKvLw81NTURM4HAgHU1taiuLhYzFKTwjFnF4IhAWkGDYZn6KUuh4iIKCGJGmb0ej3Ky8uxfv16/PGPf8S3336LF198ER988AHmzp2LG264ASNGjMADDzyA+vp6HDhwAKtXr8Zdd90VWSdz1113Yfv27XjjjTfw1Vdf4bHHHoPH48Ett9wiZqlJ4ez9ZRQKhcTVEBERJSZRp5kAYOHChTAYDFizZg2am5uRn5+PdevW4dprrwUAbNmyBcuXL8dtt92GjIwM3H777Vi4cGHk87fddhs6Ozuxdu1anD59GldffTW2b98Oi8UidqkJ70yY4c6/RERE56MQBEGQuohYCwZDaGsTf/GsWq1EZqYJ7e3dMZknXPr7g2hq7UHFLRNRePlw0e9/qWLd/mQg9z6Qe/sB9gHbL+/2A7HrA4vFNOAFwPJ9MD7Bub0BOFp7n8DiawyIiIjOj2EmQR11dEIAYDHrkGHSXvR6IiIiuWKYSVANjr71MnaOyhAREV0Iw0yCamgKv1ySi3+JiIguhGEmQTWe9Vg2ERERnR/DTAJy9fhwqsMDAMi1c2SGiIjoQhhmElBj3xSTzWKEUa+RuBoiIqLExjCTgBq5WR4REdGAMcwkoMjOv3ySiYiI6KIYZhKMIAhnwsxIhhkiIqKLYZhJMG0uL1w9fqiUCuRkpUldDhERUcJjmEkw4VGZUcNN0GpUEldDRESU+BhmEkx4599c7i9DREQ0IAwzCSb8WDafZCIiIhoYhpkEEhIENDq48y8REdFgMMwkkOa2Hri9QWjUSowcbpK6HCIioqTAMJNAwlNMObY0qFX8oyEiIhoIfsdMINwsj4iIaPAYZhJIA9fLEBERDRrDTIIIBEP4trkLAJDLJ5mIiIgGjGEmQZxo6YY/EIJBp4bNYpS6HCIioqTBMJMgIpvl2dOhVCgkroaIiCh5MMwkiMYmrpchIiIaCoaZBNHAnX+JiIiGhGEmAXj9QZxo6QbAkRkiIqLBYphJAMeauxASBJhNWmSm66Quh4iIKKkwzCSAM5vlpUPBxb9ERESDwjCTALhZHhER0dAxzCSA8OLfXIYZIiKiQWOYkViPx4/mth4AfJKJiIhoKGIaZhoaGlBUVIR9+/ZFjh0+fBhlZWUoLCxEaWkpXn755ajPhEIhvPDCC5g+fToKCwsxf/58HDt2LJZlSqrB0TsqMzxDj3SjVuJqiIiIkk/Mwozf78fixYvR09MTOdbe3o65c+ciJycHe/fuxb333ouqqirs3bs3cs2GDRuwa9curFixArt370YoFEJ5eTl8Pl+sSpVUeLM8TjERERENTczCzLp165CWlhZ1bM+ePdBoNHjyySeRn5+P2bNn484778TmzZsBAD6fD9u2bUNFRQVmzJiBgoICrFmzBg6HA/v3749VqZIKr5e5jGGGiIhoSNSxuOmhQ4fw2muvobq6GjNmzIgcr62txdSpU6FWn/ltp02bhk2bNuHUqVM4efIkuru7UVJSEjlvNpsxfvx4HDp0CDfffPOQa1Krxc9tKpUy6uehCI/MXD46IyY1xpIY7U92cu8DubcfYB+w/fJuP5AYfSB6mHG5XFiyZAkqKyuRnZ0ddc7hcGDs2LFRx7KysgAATU1NcDgcANDvc1lZWZFzQ6FUKpCZaRry5y/GbDYM6XPtLg/aOr1QKIBJBTYY9RqRK4uPobY/lci9D+TefoB9wPbLu/2AtH0gephZtmwZioqK8KMf/ajfOY/HA602epGrTte7463X64Xb7QaAc17T0dEx5JpCIQEuV8/FLxwklUoJs9kAl8uNYDA06M9/cqQFADDSaoLX7YPXnVzrgi61/alA7n0g9/YD7AO2X97tB2LXB2azYcCjPaKGmerqatTW1uKtt94653m9Xt9vIa/X6wUAGI1G6PV6AL1rZ8K/Dl9jMFxa4gsEYveXLBgMDen+Xx3vDWi59vSY1hdrQ21/KpF7H8i9/QD7gO2Xd/sBaftA1DCzd+9etLa2Rq2TAYAnnngC77zzDux2O5xOZ9S58Nc2mw2BQCByLCcnJ+qacePGiVlqQgjv/MsnmYiIiIZO1DBTVVUFj8cTdWzmzJmoqKjAj3/8Y/zhD3/A7t27EQwGoVKpAAAHDx5EXl4erFYr0tPTkZaWhpqamkiYcblcqKurQ1lZmZilSk4QBDT2PcnE1xgQERENnahhxmaznfO41WqFzWbD7NmzsWXLFixduhTl5eX4/PPPsWPHDixfvhxA71qZsrIyVFVVwWKxYNSoUVi1ahXsdjtmzpwpZqmSO9XhQZfbD5VSge9kpV38A0RERHROMXk0+3ysViu2bNmClStXYtasWRgxYgSWLFmCWbNmRa6pqKhAIBBAZWUlPB4PiouLsXXrVmg0yfmkz/mE35Q9OisNmiR7JJuIiCiRxDzM/OMf/4j6euLEiXjttdfOe71KpcLDDz+Mhx9+ONalSSocZjjFREREdGk4JCCR8M6/eXa+XJKIiOhSMMxIIBQScLTvBZN5IzkyQ0REdCkYZiTQ1NoNrz8InUaFkdbY7UxMREQkBwwzEghPMY2xpUGpVEhcDRERUXJjmJEAN8sjIiISD8OMBBr5JBMREZFoGGbiLBAM4ZizCwCQl80nmYiIiC4Vw0ycHXN2IRAUYNKrMWIYXxlPRER0qRhm4iw8xZSbbYZCwcW/REREl4phJs4im+VxiomIiEgUDDNxFnmNgZ2Lf4mIiMTAMBNHHl8AJ1u7AfCxbCIiIrEwzMTRUUcnBAEYlqZFZrpO6nKIiIhSAsNMHJ1ZL8NRGSIiIrEwzMRRo4Ob5REREYmNYSaOGrjzLxERkegYZuKky+1Hy2kPACCXj2UTERGJhmEmTsKb5WVlGmDSaySuhoiIKHUwzMQJp5iIiIhig2EmTiJPMtk5xURERCQmhpk4EAQhMjLDzfKIiIjExTATB+2dXnR0+6BQAGNsHJkhIiISE8NMHISnmEYNN0GnVUlcDRERUWphmImD8GZ5nGIiIiISH8NMHITXy1zGMENERCQ6hpkYEwQBjXwnExERUcwwzMSYs92NHm8AapUSo0aYpC6HiIgo5TDMxFh4iinHlga1it1NREQkNn53jbEzm+VxiomIiCgWRA8zp0+fxq9//Wtcf/31mDx5Mn7605+itrY2cv7DDz/ET37yE0yaNAk33ngj3n777ajPe71eLF++HCUlJSgqKsJDDz2EtrY2scuMm4bIk0zcX4aIiCgWRA8zixYtwieffILVq1dj7969uPLKKzFv3jx88803+Prrr7FgwQJMnz4d+/btw6233oolS5bgww8/jHx+2bJleP/997Fu3Tq89NJL+Oabb1BRUSF2mXERDIXwrYOLf4mIiGJJLebNjh49ig8++AC7du3CNddcAwB4/PHH8f/+3//DW2+9hdbWVowbNw4PPvggACA/Px91dXXYsmULSkpK0NzcjOrqamzcuBFTpkwBAKxevRo33ngjPvnkExQVFYlZbsydPNUDXyAEvVYFu9UodTlEREQpSdSRmczMTGzevBkTJkyIHFMoFFAoFHC5XKitrUVJSUnUZ6ZNm4aPP/4YgiDg448/jhwLy8vLg81mw6FDh8QsNS4i72Oyp0OpUEhcDRERUWoSdWTGbDbju9/9btSxd999F0ePHsVjjz2GN954A3a7Pep8VlYW3G432tvb0dzcjMzMTOh0un7XOByOS6pNrRZ/rbOq7+kk1XmeUjraN8V02aiMmPz+UrtY++VA7n0g9/YD7AO2X97tBxKjD0QNM//sb3/7G371q19h5syZmDFjBjweD7RabdQ14a99Ph/cbne/8wCg0+ng9XqHXIdSqUBmZuz2eDGbDec8ftTZBQCYcMWImP7+Ujtf++VE7n0g9/YD7AO2X97tB6Ttg5iFmQMHDmDx4sWYPHkyqqqqAPSGEp/PF3Vd+GuDwQC9Xt/vPND7hJPBMPROCoUEuFw9Q/78+ahUSpjNBrhcbgSDoahzvkAQR/ummbLMOrS3d4v++0vtQu2XC7n3gdzbD7AP2H55tx+IXR+YzYYBj/bEJMy8+uqrWLlyJW688Ub89re/jYy2ZGdnw+l0Rl3rdDphNBqRnp4Ou92O06dPw+fzRY3QOJ1O2Gy2S6opEIjdX7JgMNTv/g0nXAiGBKQbNRhm0sb095faudovN3LvA7m3H2AfsP3ybj8gbR+IPsG1a9curFixAnPmzMHq1aujQsmUKVPw0UcfRV1/8OBBTJ48GUqlEtdccw1CoVBkITAANDQ0oLm5GcXFxWKXGlPhxb952WYouPiXiIgoZkQNMw0NDXjqqafwgx/8AAsWLMCpU6fQ0tKClpYWdHZ24o477sDnn3+OqqoqfP3119i2bRv+/Oc/o7y8HABgs9lw0003obKyEjU1Nfj888+xaNEiTJ06FYWFhWKWGnPhnX9z7dwsj4iIKJZEnWZ699134ff78d577+G9996LOjdr1iw888wz2LBhA1atWoWXXnoJo0ePxqpVq6Ie116xYgWeeuop3HfffQCA66+/HpWVlWKWGReNjjMjM0RERBQ7CkEQBKmLiLVgMIS2NvEX4KrVSmRmmtDe3h01T+j2BnDfmr9CALD2/utgNvV/QisVnK/9ciL3PpB7+wH2Adsv7/YDsesDi8U04AXA8n0wPoYaHZ0QAFjNupQNMkRERImCYSYGGsM7/3KKiYiIKOYYZmLgmyaulyEiIooXhpkYCI/M5PFJJiIiophjmBGZq9uHVlfvqxfG2DkyQ0REFGsMMyILb5Zntxhh1Mf01VdEREQEhhnRNXC9DBERUVwxzIis0dG7829eNtfLEBERxQPDjIgEQeDIDBERUZwxzIio1eVBZ48fKqUCObY0qcshIiKSBYYZETX2vVxy1AgTNGqVxNUQERHJA8OMiDjFREREFH8MMyJimCEiIoo/hhmRhAQh8iRTLnf+JSIiihuGGZE4Wnvg8QWhVSsxaoRJ6nKIiIhkg2FGJOEpphxbOlRKdisREVG88LuuSMJPMuVyszwiIqK4YpgRSYODi3+JiIikwDAjgkAwhG+buwAAlzHMEBERxRXDjAiOO7sQCIZg1KmRlWmQuhwiIiJZYZgRwTcne6eYcrPToVAoJK6GiIhIXhhmRMDN8oiIiKTDMCOCyMiMnWGGiIgo3hhmLpHHG8CJlm4AQB4fyyYiIoo7hplL9PWJDoQEARkmLTLTdVKXQ0REJDsMM5foy2OnAfSul+HiXyIiovhjmLlEXx5rB8Cdf4mIiKTCMHOJzh6ZISIiovhjmLkE3W4/mk71Lv7NtXNkhoiISAoJGWZCoRBeeOEFTJ8+HYWFhZg/fz6OHTsmdVn9hPeXyRpmQLpRK3E1RERE8pSQYWbDhg3YtWsXVqxYgd27dyMUCqG8vBw+n0/q0qKE95fJG8kpJiIiIqkkXJjx+XzYtm0bKioqMGPGDBQUFGDNmjVwOBzYv3+/1OVFCY/MXMYwQ0REJJmECzP19fXo7u5GSUlJ5JjZbMb48eNx6NAhCSvrLzIyw8W/REREklFLXcA/czgcAIDs7Oyo41lZWZFzQ6FWi5vbOrq8aO/0QqkA8kdnQK1KuFwYc6q+Nqtk2PYwufeB3NsPsA/Yfnm3H0iMPki4MON2uwEAWm30glqdToeOjo4h3VOpVCAz03TJtZ3NYNIhy2JEQU4msobL+0kms9kgdQmSk3sfyL39APuA7Zd3+wFp+yDhwoxerwfQu3Ym/GsA8Hq9MBiG1lGhkACXq0eU+s62+r5/RUaGES6XG8FgSPT7JzqVSgmz2SDb9gPsA7m3H2AfsP3ybj8Quz4wmw0DHu1JuDATnl5yOp3IycmJHHc6nRg3btyQ7xsIiP+XLDx1FQyGYnL/ZCH39gPsA7m3H2AfsP3ybj8gbR8k3CRfQUEB0tLSUFNTEznmcrlQV1eH4uJiCSsjIiKiRJRwIzNarRZlZWWoqqqCxWLBqFGjsGrVKtjtdsycOVPq8oiIiCjBJFyYAYCKigoEAgFUVlbC4/GguLgYW7duhUajkbo0IiIiSjAJGWZUKhUefvhhPPzww1KXQkRERAku4dbMEBEREQ0GwwwRERElNYYZIiIiSmoMM0RERJTUGGaIiIgoqTHMEBERUVJjmCEiIqKkxjBDRERESY1hhoiIiJKaQhAEQeoiYk0QBIRCsWmmSqWU7WvfAbYfYB/Ivf0A+4Dtl3f7gdj0gVKpgEKhGNC1sggzRERElLo4zURERERJjWGGiIiIkhrDDBERESU1hhkiIiJKagwzRERElNQYZoiIiCipMcwQERFRUmOYISIioqTGMENERERJjWGGiIiIkhrDDBERESU1hhkiIiJKagwzRERElNQYZoYgFArhhRdewPTp01FYWIj58+fj2LFjUpclmU2bNuGOO+6Quoy4On36NH7961/j+uuvx+TJk/HTn/4UtbW1UpcVV62trXj44Ycxbdo0FBUV4Re/+AW+/vprqcuSRENDA4qKirBv3z6pS4mb5uZmjBs3rt8POfUBAFRXV+OHP/whJkyYgJtuugl/+tOfpC4pLmpqas755z9u3Dh8//vfj3s96rj/jilgw4YN2LVrF5555hnY7XasWrUK5eXleOutt6DVaqUuL6527tyJtWvXYsqUKVKXEleLFi1CS0sLVq9eDavVildeeQXz5s3DG2+8gcsuu0zq8uLi3nvvRSgUwubNm2EymfD888/jzjvvxP79+2EwGKQuL278fj8WL16Mnp4eqUuJq/r6euh0Ohw4cAAKhSJyPD09XcKq4usPf/gDli5disceewzTp0/H22+/jUWLFsFut6OoqEjq8mKqqKgI77//ftSxTz/9FPfffz8WLlwY93o4MjNIPp8P27ZtQ0VFBWbMmIGCggKsWbMGDocD+/fvl7q8uGlubsbdd9+Nqqoq5ObmSl1OXB09ehQffPABli1bhilTpiAvLw+PP/44srKy8NZbb0ldXlx0dHRg1KhR+M1vfoOJEyciPz8fCxcuhNPpxJdffil1eXG1bt06pKWlSV1G3B05cgS5ubnIysrCiBEjIj/0er3UpcWFIAh4/vnn8bOf/Qxz5sxBTk4O7rnnHvzLv/wLPvroI6nLizmtVhv1524ymfD0009j1qxZmD17dtzrYZgZpPr6enR3d6OkpCRyzGw2Y/z48Th06JCElcXXF198AY1GgzfffBOTJk2Supy4yszMxObNmzFhwoTIMYVCAYVCAZfLJWFl8ZORkYHnnnsOY8eOBQC0tbVhx44dsNvtuPzyyyWuLn4OHTqE1157Dc8884zUpcTdP/7xD+Tn50tdhmQaGhpw4sQJ/OhHP4o6vnXrVixYsECiqqSzceNGuN1uPPLII5L8/pxmGiSHwwEAyM7OjjqelZUVOScHpaWlKC0tlboMSZjNZnz3u9+NOvbuu+/i6NGjeOyxxySqSjqPP/449uzZA61WixdffBFGo1HqkuLC5XJhyZIlqKys7PfvgRwcOXIEmZmZmDNnDhoaGjBmzBjcc889uP7666UuLS4aGhoAAD09PZg3bx7q6uowevRo3HPPPbL7tzH8PzMPPfQQhg0bJkkNHJkZJLfbDQD91sbodDp4vV4pSiKJ/e1vf8OvfvUrzJw5EzNmzJC6nLj7+c9/jr179+Lmm2/Gvffeiy+++ELqkuJi2bJlKCoq6vd/5nIQCATwzTffoKOjA/fffz82b96MwsJC/OIXv8CHH34odXlx0dXVBQB45JFHcPPNN2Pbtm3413/9VyxcuFA2fRC2a9cupKen4z/+4z8kq4EjM4MUng/2+XxRc8Ner1dWix6p14EDB7B48WJMnjwZVVVVUpcjifC00sqVK/HZZ5/h1VdfxdNPPy1xVbFVXV2N2tpa2ayR+mdqtRo1NTVQqVSRfwevvvpqfPnll9i6dWvUNHyq0mg0AIB58+Zh1qxZAIArr7wSdXV12L59uyz6IKy6uhr//u//Lul6KY7MDFJ4ONnpdEYddzqdsNlsUpREEnn11Vdx//3343vf+x42btwInU4ndUlx09bWhrfffhuBQCByTKlU4vLLL+/330Yq2rt3L1pbWzFjxgwUFRVFnlx54oknUF5eLnF18WEymfp987riiivQ3NwsUUXxFf73PrxuLOzyyy/H8ePHpShJEvX19Th27JjkI5QMM4NUUFCAtLQ01NTURI65XC7U1dWhuLhYwsoonnbt2oUVK1Zgzpw5WL16teweyT916hQWLVoUNZzu9/tRV1cni0WhVVVVeOedd1BdXR35AQAVFRVYuXKltMXFwZdffonJkydH/TsIAH//+99lswD8qquugslkwmeffRZ1/MiRI8jJyZGoqvirra2F1WpFQUGBpHVwmmmQtFotysrKUFVVBYvFglGjRmHVqlWw2+2YOXOm1OVRHDQ0NOCpp57CD37wAyxYsACnTp2KnNPr9bLYZ2Ps2LG4/vrr8Zvf/Aa/+c1vkJGRgU2bNsHlcuHOO++UuryYO98orNVqlcUIbX5+Pi677DI8+eSTWL58OTIzM7Fnzx58+umn2Lt3r9TlxYVer0d5eTnWr18Pm82GiRMn4u2338YHH3yAHTt2SF1e3NTV1WHcuHFSl8EwMxQVFRUIBAKorKyEx+NBcXExtm7dGplDpdT27rvvwu/347333sN7770XdW7WrFmyeUx39erVeO655/Dggw+is7MTU6ZMwc6dOzFy5EipS6MYUyqV2LhxI5577jk88MADcLlcGD9+PLZv395v2iWVLVy4EAaDAWvWrEFzczPy8/Oxbt06XHvttVKXFjctLS2SPcF0NoUgCILURRARERENFdfMEBERUVJjmCEiIqKkxjBDRERESY1hhoiIiJIawwwRERElNYYZIiIiSmoMM0RERJTUGGaIiIgoqTHMEBERUVJjmCEiIqKkxjBDRERESe3/A1PGBJPhoIcNAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(get_player_history(elo, \"Eledran\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b60ab193-c5de-403e-b430-73e878d4aa9e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "07f527d5-f7a7-43b6-a8be-0d7f3a819d0b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1543, 1511, 1613, 1610, 1592, 1657, 1661]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_player_history(elo, \"Eckpired\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a1aea3f5-733d-4c00-99e6-ab12953e6871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1534, 1584, 1584, 1584, 1584, 1584, 1584, 1584]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_player_history(elo, \"dstrik\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c5257eef-efd9-43cf-b2bc-82171b139dd1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1516, 1516, 1516, 1516, 1516, 1516, 1516, 1516]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_player_history(elo, \"DStrik\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "c65daaad-1f6c-41a6-a276-1e45496c21ca",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 1534, 1539, 1548, 1473, 1456]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_player_history(elo, \"SuperKev\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f533ac1-a0b1-4f15-9547-d33085128b54",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "f79032c6-ac33-435e-94bd-e9f7722b9e74",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "elo = EloBuilder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e93a1eef-1f96-4983-b954-e96149b36111",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}