2023RPChange.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tbapy\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "tba = tbapy.TBA(\"gl4GXuoqG8anLUrLo356LIeeQZk15cfSoXF72YT3mYkI38cCoAmReoCSSF4XWccQ\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_old_rp(match, alliance):\n",
    "    if match['comp_level'] != 'qm':\n",
    "        return None\n",
    "    rps = 0\n",
    "    if match[\"score_breakdown\"][alliance][\"linkPoints\"]/5 >= (4 if match[\"score_breakdown\"][\"red\"][\"coopertitionCriteriaMet\"] and match[\"score_breakdown\"][\"blue\"][\"coopertitionCriteriaMet\"] else 5):\n",
    "        rps += 1\n",
    "    if match[\"score_breakdown\"][alliance][\"activationBonusAchieved\"]:\n",
    "        rps += 1\n",
    "    if match[\"winning_alliance\"] == alliance:\n",
    "        rps += 2\n",
    "    elif match[\"winning_alliance\"] == \"\":\n",
    "        rps += 1\n",
    "    return rps\n",
    "\n",
    "def get_new_rp(match, alliance):\n",
    "    if match['comp_level'] != 'qm':\n",
    "        return None\n",
    "    rps = 0\n",
    "    if match[\"score_breakdown\"][alliance][\"linkPoints\"]/5 >= (5 if match[\"score_breakdown\"][\"red\"][\"coopertitionCriteriaMet\"] and match[\"score_breakdown\"][\"blue\"][\"coopertitionCriteriaMet\"] else 6):\n",
    "        rps += 1\n",
    "    if match[\"score_breakdown\"][alliance][\"activationBonusAchieved\"]:\n",
    "        rps += 1\n",
    "    if match[\"winning_alliance\"] == alliance:\n",
    "        rps += 2\n",
    "    elif match[\"winning_alliance\"] == \"\":\n",
    "        rps += 1\n",
    "    return rps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FIRST Chesapeake District Championship\n",
      "Peachtree District Championship\n",
      "FIRST Indiana State Championship\n",
      "FIRST Israel District Championship\n",
      "FIRST in Michigan State Championship presented by DTE Foundation\n",
      "FIRST in Michigan State Championship presented by DTE Foundation - DTE ENERGY FOUNDATION Division\n",
      "FIRST in Michigan State Championship presented by DTE Foundation - FORD Division\n",
      "FIRST in Michigan State Championship presented by DTE Foundation - APTIV Division\n",
      "FIRST in Michigan State Championship presented by DTE Foundation - CONSUMERS ENERGY Division\n",
      "FIRST Mid-Atlantic District Championship\n",
      "FIRST North Carolina District State Championship\n",
      "New England FIRST District Championship\n",
      "New England FIRST District Championship - MEIR Division\n",
      "New England FIRST District Championship - WILSON Division\n",
      "FIRST Ontario Provincial Championship\n",
      "FIRST Ontario Provincial Championship - TECHNOLOGY Division\n",
      "FIRST Ontario Provincial Championship - SCIENCE Division\n",
      "Pacific Northwest FIRST District Championship\n",
      "FIRST In Texas District Championship presented by Phillips 66\n",
      "FIRST In Texas District Championship presented by Phillips 66 - APOLLO Division\n",
      "FIRST In Texas District Championship presented by Phillips 66 - MERCURY Division\n"
     ]
    }
   ],
   "source": [
    "events = tba.events(2023, simple=True)  # Get all events for 2023\n",
    "events = filter(lambda x: x[\"event_type\"] in [2,5], events) # Only DCMP events\n",
    "\n",
    "quals = []\n",
    "elims = []\n",
    "rank_change = []\n",
    "\n",
    "for event in events:\n",
    "    print(event[\"name\"])\n",
    "    \n",
    "    old_rps = {}\n",
    "    new_rps = {}\n",
    "\n",
    "    matches = tba.event_matches(event[\"key\"])  # Get all matches for event\n",
    "    for match in matches:\n",
    "        if match[\"comp_level\"] == \"qm\": # Qualification match\n",
    "            # Add game piece count to quals list\n",
    "            quals.append(match[\"score_breakdown\"][\"red\"][\"teleopGamePieceCount\"])\n",
    "            quals.append(match[\"score_breakdown\"][\"blue\"][\"teleopGamePieceCount\"])\n",
    "\n",
    "            # Calculate old and new RPs & avg match score (1st tiebreaker)\n",
    "            for team in match[\"alliances\"][\"red\"][\"team_keys\"]:\n",
    "                if team not in old_rps:\n",
    "                    old_rps[team] = [0,0]\n",
    "                    new_rps[team] = [0,0]\n",
    "                old_rps[team][0] += get_old_rp(match, \"red\")\n",
    "                if get_old_rp(match, \"red\") != match[\"score_breakdown\"][\"red\"][\"rp\"]:\n",
    "                    print(get_old_rp(match, \"red\"))\n",
    "                    print(match[\"score_breakdown\"][\"red\"][\"rp\"])\n",
    "                    print(match)\n",
    "                old_rps[team][1] += match[\"alliances\"][\"red\"][\"score\"]\n",
    "                new_rps[team][0] += get_new_rp(match, \"red\")\n",
    "                new_rps[team][1] += match[\"alliances\"][\"red\"][\"score\"]\n",
    "            for team in match[\"alliances\"][\"blue\"][\"team_keys\"]:\n",
    "                if team not in old_rps:\n",
    "                    old_rps[team] = [0,0]\n",
    "                    new_rps[team] = [0,0]\n",
    "                old_rps[team][0] += get_old_rp(match, \"blue\")\n",
    "                old_rps[team][1] += match[\"alliances\"][\"blue\"][\"score\"]\n",
    "                new_rps[team][0] += get_new_rp(match, \"blue\")\n",
    "                new_rps[team][1] += match[\"alliances\"][\"blue\"][\"score\"]\n",
    "        \n",
    "        else:   # Elimination match\n",
    "            # Add game piece count to elims list\n",
    "            elims.append(match[\"score_breakdown\"][\"red\"][\"teleopGamePieceCount\"])\n",
    "            elims.append(match[\"score_breakdown\"][\"blue\"][\"teleopGamePieceCount\"])\n",
    "    # Sort teams by RPs and calculate rank change\n",
    "    old_rps = dict(zip([team for team,rps in sorted(old_rps.items(), key=lambda x: x[1], reverse=True)], range(1, len(old_rps)+1)))\n",
    "    new_rps = dict(zip([team for team,rps in sorted(new_rps.items(), key=lambda x: x[1], reverse=True)], range(1, len(new_rps)+1)))\n",
    "    for team, old_rank in old_rps.items():\n",
    "        rank_change.append(new_rps[team] - old_rank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot game piece count histograms\n",
    "plt.figure()\n",
    "bins = range(0,27)\n",
    "plt.hist([quals, elims], bins=bins, label=[\"quals\", \"elims\"])\n",
    "plt.title(\"DCMP Game Pieces Per Match\")\n",
    "plt.xlabel(\"Game Piece Count\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Plot rank change histogram\n",
    "plt.figure()\n",
    "plt.hist(rank_change, bins=range(-10,10))\n",
    "plt.title(\"DCMP Rank Change\")\n",
    "plt.xlabel(\"Rank Change\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archimedes Division\n",
      "Curie Division\n",
      "Daly Division\n",
      "Galileo Division\n",
      "Hopper Division\n",
      "Johnson Division\n",
      "Milstein Division\n",
      "Newton Division\n"
     ]
    }
   ],
   "source": [
    "events = tba.events(2023, simple=True)  # Get all events for 2023\n",
    "events = filter(lambda x: x[\"event_type\"]==3, events) # Only CMP divisions\n",
    "\n",
    "quals = []\n",
    "elims = []\n",
    "rank_change = []\n",
    "\n",
    "for event in events:\n",
    "    print(event[\"name\"])\n",
    "    \n",
    "    old_rps = {}\n",
    "    new_rps = {}\n",
    "\n",
    "    matches = tba.event_matches(event[\"key\"])  # Get all matches for event\n",
    "    for match in matches:\n",
    "        if match[\"comp_level\"] == \"qm\": # Qualification match\n",
    "            # Add game piece count to quals list\n",
    "            quals.append(match[\"score_breakdown\"][\"red\"][\"teleopGamePieceCount\"]+match[\"score_breakdown\"][\"red\"][\"extraGamePieceCount\"])\n",
    "            quals.append(match[\"score_breakdown\"][\"blue\"][\"teleopGamePieceCount\"]+match[\"score_breakdown\"][\"blue\"][\"extraGamePieceCount\"])\n",
    "\n",
    "            # Calculate old and new RPs & avg match score (1st tiebreaker)\n",
    "            for team in match[\"alliances\"][\"red\"][\"team_keys\"]:\n",
    "                if team not in old_rps:\n",
    "                    old_rps[team] = [0,0]\n",
    "                    new_rps[team] = [0,0]\n",
    "                old_rps[team][0] += get_old_rp(match, \"red\")\n",
    "                if get_new_rp(match, \"red\") != match[\"score_breakdown\"][\"red\"][\"rp\"]:\n",
    "                    print(get_old_rp(match, \"red\"))\n",
    "                    print(match[\"score_breakdown\"][\"red\"][\"rp\"])\n",
    "                    print(match)\n",
    "                old_rps[team][1] += match[\"alliances\"][\"red\"][\"score\"]\n",
    "                new_rps[team][0] += get_new_rp(match, \"red\")\n",
    "                new_rps[team][1] += match[\"alliances\"][\"red\"][\"score\"]\n",
    "            for team in match[\"alliances\"][\"blue\"][\"team_keys\"]:\n",
    "                if team not in old_rps:\n",
    "                    old_rps[team] = [0,0]\n",
    "                    new_rps[team] = [0,0]\n",
    "                old_rps[team][0] += get_old_rp(match, \"blue\")\n",
    "                old_rps[team][1] += match[\"alliances\"][\"blue\"][\"score\"]\n",
    "                new_rps[team][0] += get_new_rp(match, \"blue\")\n",
    "                new_rps[team][1] += match[\"alliances\"][\"blue\"][\"score\"]\n",
    "        \n",
    "        else:   # Elimination match\n",
    "            # Add game piece count to elims list\n",
    "            elims.append(match[\"score_breakdown\"][\"red\"][\"teleopGamePieceCount\"]+match[\"score_breakdown\"][\"red\"][\"extraGamePieceCount\"])\n",
    "            elims.append(match[\"score_breakdown\"][\"blue\"][\"teleopGamePieceCount\"]+match[\"score_breakdown\"][\"blue\"][\"extraGamePieceCount\"])\n",
    "    # Sort teams by RPs and calculate rank change\n",
    "    old_rps = dict(zip([team for team,rps in sorted(old_rps.items(), key=lambda x: x[1], reverse=True)], range(1, len(old_rps)+1)))\n",
    "    new_rps = dict(zip([team for team,rps in sorted(new_rps.items(), key=lambda x: x[1], reverse=True)], range(1, len(new_rps)+1)))\n",
    "    for team, old_rank in old_rps.items():\n",
    "        rank_change.append(new_rps[team] - old_rank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot game piece count histograms\n",
    "plt.figure(figsize=(12,8))\n",
    "bins = range(0,35)\n",
    "plt.hist([quals, elims], bins=bins, label=[\"quals\", \"elims\"])\n",
    "plt.title(\"CMP Game Pieces Per Match\")\n",
    "plt.xlabel(\"Game Piece Count\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Plot rank change histogram\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.hist(rank_change, bins=range(-20,20))\n",
    "plt.xticks(range(-20,21,5))\n",
    "plt.title(\"CMP Rank Change\")\n",
    "plt.xlabel(\"Rank Change\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statbotics\n",
    "\n",
    "sb = statbotics.Statbotics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "epas = {}\n",
    "for team in old_rps.keys():\n",
    "    epas[team] = sb.get_team_year(int(team[3:]), 2023, fields=[\"epa_end\"])[\"epa_end\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.scatter(epas.values(), [new_rps[team] for team in epas.keys()], label=\"with TU21\")\n",
    "plt.scatter(epas.values(), [old_rps[team] for team in epas.keys()], label=\"w/o TU21\")\n",
    "plt.legend()\n",
    "plt.title(\"Rank vs EPA\")\n",
    "plt.xlabel(\"EPA\")\n",
    "plt.ylabel(\"Rank\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.bar(epas.values(), [new_rps[team]-old_rps[team] for team in epas.keys()])\n",
    "plt.title(\"Rank Change vs EPA\")\n",
    "plt.xlabel(\"EPA\")\n",
    "plt.ylabel(\"Rank Change\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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",
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