roster_management_clean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "slotcodes = {\n",
    "    0 : 'QB', 1 : 'QB',\n",
    "    2 : 'RB', 3 : 'RB',\n",
    "    4 : 'WR', 5 : 'WR',\n",
    "    6 : 'TE', 7 : 'TE',\n",
    "    16: 'D/ST',\n",
    "    17: 'K',\n",
    "    20: 'Bench',\n",
    "    21: 'IR',\n",
    "    23: 'Flex'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "swid = \"your swid\"\n",
    "espn = \"your espn cookie\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_matchups(league_id, season, week, swid='', espn=''):\n",
    "    ''' \n",
    "    Pull full JSON of matchup data from ESPN API for a particular week.\n",
    "    '''\n",
    "    \n",
    "    url = 'https://fantasy.espn.com/apis/v3/games/ffl/seasons/' + \\\n",
    "      str(season) + '/segments/0/leagues/' + str(league_id)\n",
    "\n",
    "    r = requests.get(url + '?view=mMatchup&view=mMatchupScore',\n",
    "                     params={'scoringPeriodId': week, 'matchupPeriodId': week},\n",
    "                     cookies={\"SWID\": swid, \"espn_s2\": espn})\n",
    "    return r.json()\n",
    "\n",
    "def get_slates(json):\n",
    "    '''\n",
    "    Constructs week team slates with slotted position, \n",
    "    position, and points (actual and ESPN projected),\n",
    "    given full matchup info (`get_matchups`)\n",
    "    '''\n",
    "    \n",
    "    slates = {}\n",
    "\n",
    "    for team in d['teams']:\n",
    "        slate = []\n",
    "        for p in team['roster']['entries']:\n",
    "            # get name\n",
    "            name  = p['playerPoolEntry']['player']['fullName']\n",
    "\n",
    "            # get actual lineup slot\n",
    "            slotid = p['lineupSlotId']\n",
    "            slot = slotcodes[slotid]\n",
    "\n",
    "            # get projected and actual scores\n",
    "            act, proj = 0, 0\n",
    "            for stat in p['playerPoolEntry']['player']['stats']:\n",
    "                if stat['scoringPeriodId'] != week:\n",
    "                    continue\n",
    "                if stat['statSourceId'] == 0:\n",
    "                    act = stat['appliedTotal']\n",
    "                elif stat['statSourceId'] == 1:\n",
    "                    proj = stat['appliedTotal']\n",
    "                else:\n",
    "                    print('Error')\n",
    "\n",
    "            # get type of player\n",
    "            pos = 'Unk'\n",
    "            ess = p['playerPoolEntry']['player']['eligibleSlots']\n",
    "            if 0 in ess: pos = 'QB'\n",
    "            elif 2 in ess: pos = 'RB'\n",
    "            elif 4 in ess: pos = 'WR'\n",
    "            elif 6 in ess: pos = 'TE'\n",
    "            elif 16 in ess: pos = 'D/ST'\n",
    "            elif 17 in ess: pos = 'K'\n",
    "\n",
    "            slate.append([name, slotid, slot, pos, act, proj])\n",
    "\n",
    "        slate = pd.DataFrame(slate, columns=['Name', 'SlotID', 'Slot', 'Pos', 'Actual', 'Proj'])\n",
    "        slates[team['id']] = slate\n",
    "\n",
    "    return slates\n",
    "\n",
    "def compute_pts(slates, posns, struc):\n",
    "    '''\n",
    "    Given slates (`get_slates`), compute total roster pts:\n",
    "    actual, optimal, and using ESPN projections\n",
    "    \n",
    "    Parameters\n",
    "    --------------\n",
    "    slates : `dict` of `DataFrames`\n",
    "        (from `get_slates`)\n",
    "    posns : `list`\n",
    "        roster positions, e.g. ['QB','RB', 'WR', 'TE']\n",
    "    struc : `list`\n",
    "        slots per position, e.g. [1,2,2,1]\n",
    "        \n",
    "    * This is not flexible enough to handle \"weird\" leagues\n",
    "    like 6 Flex slots with constraints on # total RB/WR\n",
    "    \n",
    "    Returns\n",
    "    --------------\n",
    "    `dict` of `dict`s with actual, ESPN, optimal points\n",
    "    '''\n",
    "    \n",
    "    data = {}\n",
    "    for tmid, slate in slates.items():\n",
    "        pts = {'opts': 0, 'epts': 0, 'apts': 0}\n",
    "\n",
    "        # ACTUAL STARTERS\n",
    "        pts['apts'] = slate.query('Slot not in [\"Bench\", \"IR\"]').filter(['Actual']).sum().values[0]\n",
    "\n",
    "        # OPTIMAL and ESPNPROJ STARTERS\n",
    "        for method, cat in [('Actual', 'opts'), ('Proj', 'epts')]:\n",
    "            actflex = -100  # actual pts scored by flex\n",
    "            proflex = -100  # \"proj\" pts scored by flex\n",
    "            for pos, num in zip(posns, struc):\n",
    "                # actual points, sorted by either actual or proj outcome\n",
    "                t = slate.query('Pos == @pos').sort_values(by=method, ascending=False).filter(['Actual']).values[:,0]\n",
    "\n",
    "                # projected points, sorted by either actual or proj outcome\n",
    "                t2 = slate.query('Pos == @pos').sort_values(by=method, ascending=False).filter(['Proj']).values[:,0]\n",
    "\n",
    "                # sum up points\n",
    "                pts[cat] += t[:num].sum()\n",
    "\n",
    "                # set the next best as flex\n",
    "                if pos in ['RB', 'WR', 'TE'] and len(t) > num:\n",
    "                    fn = t[num] if method=='Actual' else t2[num]\n",
    "                    if fn > proflex:\n",
    "                        actflex = t[num]\n",
    "                        proflex = fn\n",
    "\n",
    "            pts[cat] += actflex\n",
    "        \n",
    "        data[tmid] = pts\n",
    "        \n",
    "    return data\n",
    "\n",
    "def get_teamnames(league_id, season, week, swid='', espn=''):\n",
    "    url = 'https://fantasy.espn.com/apis/v3/games/ffl/seasons/' + \\\n",
    "      str(season) + '/segments/0/leagues/' + str(league_id)\n",
    "    \n",
    "    r = requests.get(url + '?view=mTeam',\n",
    "                  params={'scoringPeriodId': week},\n",
    "                  cookies={\"SWID\": swid, \"espn_s2\": espn})\n",
    "    d = r.json()\n",
    "    \n",
    "    tm_names = {tm['id']: tm['location'].strip() + ' ' + tm['nickname'].strip() \\\n",
    "                for tm in d['teams']}\n",
    "    \n",
    "    return tm_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_week(d, data, week, tm_names, nummatchups=5, \n",
    "              minx=70, maxx=200, legend=4):\n",
    "    fig, ax = plt.subplots(1,1, figsize=(12,8))\n",
    "\n",
    "    # hardcoded plot adjustments\n",
    "    dif, offset = 5, 2\n",
    "    \n",
    "    # for y-axis tick labels\n",
    "    tmlist, tmticks, tmbold = [], [], []\n",
    "\n",
    "    cury = 0\n",
    "    for g in d['schedule'][:nummatchups]:\n",
    "        aid, anm = -1, ''\n",
    "        hid, hnm = -1, ''\n",
    "        try:\n",
    "            aid = g['away']['teamId']\n",
    "            anm = tm_names[aid]\n",
    "            hid = g['home']['teamId']\n",
    "            hnm = tm_names[hid]\n",
    "        except:\n",
    "            continue\n",
    "\n",
    "        tmlist.append(anm)\n",
    "        tmlist.append(hnm)\n",
    "\n",
    "        if data[aid]['apts'] > data[hid]['apts']:\n",
    "            tmbold.extend([1,0])\n",
    "        else:\n",
    "            tmbold.extend([0,1])\n",
    "\n",
    "        for pts in [data[aid], data[hid]]:\n",
    "            h = 1 if (pts['opts']-offset) > pts['apts'] else 0\n",
    "            tmticks.append(cury)\n",
    "            ax.plot([minx, maxx], [cury, cury], 'k--', linewidth=1, alpha=0.1)\n",
    "            ax.plot([pts['apts'], pts['opts']-offset*h], [cury, cury], 'k-')\n",
    "            ax.scatter(pts['epts'], cury, c='w', s=200, marker='o', edgecolor='g')\n",
    "            ax.scatter(pts['apts'], cury, c='k', s=100)\n",
    "\n",
    "            # if optimal==actual, need to put blue inside black\n",
    "            if pts['opts'] == pts['apts']:\n",
    "                ax.scatter(pts['opts'], cury, c='w', s=25)\n",
    "                ax.scatter(pts['opts'], cury, c='b', s=25, alpha=0.2)\n",
    "            else:\n",
    "                ax.scatter(pts['opts'], cury, c='b', s=100, alpha=0.2)\n",
    "\n",
    "            cury += dif\n",
    "\n",
    "        cury += 2*dif\n",
    "\n",
    "    # setting y-axis\n",
    "    ax.set(yticks=tmticks,\n",
    "           yticklabels=tmlist)\n",
    "    for k, tick in enumerate(ax.yaxis.get_major_ticks()):\n",
    "        if tmbold[k] == 1:\n",
    "            tick.label1.set_fontweight('bold')\n",
    "\n",
    "    # legend stuff\n",
    "    ax.scatter([],[], c='k', s=100, label='Actual')\n",
    "    ax.scatter([],[], c='w', s=200, marker='o', edgecolor='g', label='ESPN')\n",
    "    ax.scatter([],[], c='b', s=100, alpha=0.2, label='Best Possible')\n",
    "    ax.legend(loc=legend, borderaxespad=2, borderpad=1, labelspacing=1.5, \n",
    "              shadow=True, fontsize=12)\n",
    "\n",
    "    ax.set(title='Week %d' % week)\n",
    "\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## current season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "league_id = 123456\n",
    "\n",
    "season = 2019\n",
    "week = 1\n",
    "posns = ['QB', 'RB', 'WR', 'Flex', 'TE', 'D/ST', 'K']\n",
    "struc = [1,2,3,1,1,1,1]\n",
    "\n",
    "d      = get_matchups(league_id, season, week, swid=swid, espn=espn)\n",
    "slates = get_slates(d)\n",
    "wdata  = compute_pts(slates, posns, struc)\n",
    "tms    = get_teamnames(league_id, season, week, swid=swid, espn=espn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# override for blog\n",
    "tms = {\n",
    "    1: 'His Excellency the Commissioner',\n",
    "    2: 'Beat Navy',\n",
    "    3: 'The Gus Bus',\n",
    "    4: 'Turn Your Head and Goff',\n",
    "    5: 'That B* Team 2 Married',\n",
    "    9: 'a^2 + b^2 = c^hawks',\n",
    "    12: 'the mustard',\n",
    "    13: 'Master of the Sword',\n",
    "    14: 'La Flama Blanca',\n",
    "    15: 'Glitter Fairies'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plot_week(d, wdata, week, tms, nummatchups=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## last season (multiple weeks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Week: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 "
     ]
    }
   ],
   "source": [
    "league_id = 123456\n",
    "\n",
    "season = 2018\n",
    "posns = ['QB', 'RB', 'WR', 'Flex', 'TE', 'D/ST', 'K']\n",
    "struc = [1,2,3,1,1,1,1]\n",
    "\n",
    "data = {}\n",
    "slates = {}\n",
    "ds = {}\n",
    "print('Week:', end=' ')\n",
    "for week in range(1,15):\n",
    "    print(week, end=' ')\n",
    "    d      = get_matchups(league_id, season, week, swid=swid, espn=espn)\n",
    "    wslate = get_slates(d)\n",
    "    wdata  = compute_pts(wslate, posns, struc)\n",
    "    data[week] = wdata\n",
    "    slates[week] = wslate\n",
    "    ds[week] = d\n",
    "tm_names = get_teamnames(league_id, season, 1, swid=swid, espn=espn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# override for blog\n",
    "tm_names = {\n",
    "    1: 'Goodell\\'s HAMMER',\n",
    "    2: 'Beat Navy',\n",
    "    3: 'The Gus Bus',\n",
    "    4: 'Turn Your Head and Goff',\n",
    "    5: 'That B* Team 2 Married',\n",
    "    9: 'a^2 + b^2 = c^hawks',\n",
    "    12: 'the mustard',\n",
    "    13: 'Master of the Sword',\n",
    "    14: 'La Flama Blanca',\n",
    "    15: 'Glitter Fairies'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "wks = range(1,15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-13.942857142857143"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get league average diff\n",
    "adiffs = []\n",
    "for wk in wks:\n",
    "    for tmid, pts in data[wk].items():\n",
    "        adiffs.append(pts['apts']-pts['opts'])\n",
    "avgdiffs = sum(adiffs) / len(adiffs)\n",
    "avgdiffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126.27142857142857"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get league average optimal points\n",
    "allopts = []\n",
    "for wk in wks:\n",
    "    for tmid, pts in data[wk].items():\n",
    "        allopts.append(pts['opts'])\n",
    "avgopts = sum(allopts) / len(allopts)\n",
    "avgopts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmid = 14\n",
    "tmnm = tm_names[tmid]\n",
    "nummatchups = 5\n",
    "wks = range(1,15)\n",
    "\n",
    "fig, ax = plt.subplots(1,1, figsize=(9,8))\n",
    "\n",
    "# figure out wins\n",
    "# this is hacky\n",
    "wins = [0 for _ in wks]\n",
    "for g in ds[1]['schedule']:\n",
    "    wk = int(list(g['away']['pointsByScoringPeriod'].keys())[0])\n",
    "    if wk > 14:\n",
    "        continue\n",
    "        \n",
    "    aid, hid = g['away']['teamId'], g['home']['teamId']\n",
    "    apt, hpt = g['away']['totalPoints'], g['home']['totalPoints']\n",
    "    if aid == tmid:\n",
    "        if apt > hpt:\n",
    "            wins[wk-1] = 1\n",
    "    elif hid == tmid:\n",
    "        if hpt > apt:\n",
    "            wins[wk-1] = 1\n",
    "\n",
    "apts = [data[wk][tmid]['apts'] for wk in wks]\n",
    "epts = [data[wk][tmid]['epts'] for wk in wks]\n",
    "opts = [data[wk][tmid]['opts'] for wk in wks]\n",
    "diff = [a-o for o,a in zip(opts,apts)]\n",
    "\n",
    "clist = sns.color_palette('Blues', len(wks))\n",
    "\n",
    "minx, maxx = min(diff)-10, 5\n",
    "miny, maxy = min(opts)-10, max(opts)+20\n",
    "\n",
    "ax.plot([0,0], [miny+5, maxy-10], 'k-', alpha=0.2)\n",
    "ax.plot([avgdiffs, avgdiffs], [miny+5, maxy-10], 'k--', alpha=0.2)\n",
    "ax.plot([minx+10, -2], [avgopts, avgopts], 'k--', alpha=0.2)\n",
    "ax.text(minx+15, avgopts-4, 'LEAGUE AVERAGES', {'color': 'black', 'fontstyle': 'italic'},\n",
    "        alpha=0.5)\n",
    "\n",
    "for wk in wks:\n",
    "    ax.scatter(diff[wk-1], opts[wk-1], \n",
    "               marker='o' if wins[wk-1]==1 else 'x',\n",
    "               s=200, c=clist[wk-1], edgecolors='k',\n",
    "               label=wk)\n",
    "    \n",
    "ax.legend(bbox_to_anchor=(1.15, 0.8), frameon=False,\n",
    "          title='Week')\n",
    "\n",
    "# this is really hacky\n",
    "ax.arrow(maxx-30,maxy-5, -(maxx-20),0,\n",
    "         head_width=5, head_length=2, facecolor='k', edgecolor='k', alpha=0.2)\n",
    "ax.text(maxx-27,maxy-8, 'better starters', {'color': 'black', 'fontstyle': 'italic'}, alpha=0.5)\n",
    "ax.arrow(minx+5,miny+30, 0, (maxy-miny) / 2,\n",
    "         head_width=2, head_length=4, facecolor='k', edgecolor='k', alpha=0.2)\n",
    "ax.text(minx+5.5,miny+70, 'better roster', {'color': 'black', 'fontstyle': 'italic'},\n",
    "        rotation=90, alpha=0.5)\n",
    "\n",
    "ax.set(xlim=[minx,maxx], ylim=[miny,maxy],\n",
    "       xticks=range(int(minx/10)*10-10,9,10),\n",
    "       xlabel='Actual points back from Best Possible',\n",
    "       ylabel='Best Possible points',\n",
    "       title='%s (%d)' % (tmnm, season))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = []\n",
    "for tmid, tmname in tm_names.items():\n",
    "    opts  = [data[wk][tmid]['opts'] for wk in wks]\n",
    "    diffs = [data[wk][tmid]['apts']-data[wk][tmid]['opts'] for wk in wks]\n",
    "    opts = sum(opts) / len(opts)\n",
    "    diffs = sum(diffs) / len(diffs)\n",
    "    z.append({'id': tmid, 'name': tmname, 'opt': opts, 'diff': diffs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(9,8))\n",
    "\n",
    "playoffs = [1, 9, 13, 14]\n",
    "\n",
    "clist = sns.color_palette('deep', len(tm_names))\n",
    "\n",
    "minx, maxx = min([zz['diff'] for zz in z])-10, 5\n",
    "miny, maxy = min([zz['opt'] for zz in z])-10, max([zz['opt'] for zz in z])+20\n",
    "\n",
    "ax.plot([0,0], [miny+5, maxy-10], 'k-', alpha=0.2)\n",
    "ax.plot([avgdiffs, avgdiffs], [miny+5, maxy-10], 'k--', alpha=0.2)\n",
    "ax.plot([minx+10, -2], [avgopts, avgopts], 'k--', alpha=0.2)\n",
    "ax.text(maxx-15, avgopts-1.5, 'LEAGUE AVERAGES', {'color': 'black', 'fontstyle': 'italic'},\n",
    "        alpha=0.5)\n",
    "\n",
    "for i, zz in enumerate(z):\n",
    "    ax.scatter(zz['diff'], zz['opt'], \n",
    "               marker='o' if zz['id'] in playoffs else 'x',\n",
    "               s=200, c=clist[i], edgecolors='k',\n",
    "               label=zz['name'])\n",
    "    \n",
    "ax.legend(bbox_to_anchor=(1.05, 0.8), frameon=False)\n",
    "\n",
    "# this is really hacky\n",
    "ax.arrow(maxx-30,maxy-5, -(maxx-20),0,\n",
    "         head_width=2.5, head_length=2, facecolor='k', edgecolor='k', alpha=0.2)\n",
    "ax.text(maxx-25,maxy-7, 'better starters', {'color': 'black', 'fontstyle': 'italic'}, alpha=0.5)\n",
    "ax.arrow(minx+5,miny+10, 0, (maxy-miny) / 2,\n",
    "         head_width=1.5, head_length=3, facecolor='k', edgecolor='k', alpha=0.2)\n",
    "ax.text(minx+5.5,miny+30, 'better roster', {'color': 'black', 'fontstyle': 'italic'},\n",
    "        rotation=90, alpha=0.5)\n",
    "\n",
    "ax.set(xlim=[minx,maxx], ylim=[miny,maxy],\n",
    "       xticks=range(int(minx/10)*10-10,9,10),\n",
    "       xlabel='Actual points back from Best Possible',\n",
    "       ylabel='Best Possible points',\n",
    "       title='%d Season' % season)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}