{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "8a5944ea", "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "def set_dark_mode():\n", " plt.style.use('dark_background')\n", " plt.rcParams['axes.facecolor'] = '#121212'\n", " plt.rcParams['figure.facecolor'] = '#121212'\n", " plt.rcParams['savefig.facecolor'] = '#121212'\n", " plt.rcParams['axes.edgecolor'] = 'white'\n", " plt.rcParams['axes.labelcolor'] = 'white'\n", " plt.rcParams['xtick.color'] = 'white'\n", " plt.rcParams['ytick.color'] = 'white'\n", " plt.rcParams['text.color'] = 'white'\n", " plt.rcParams['legend.edgecolor'] = 'white'\n", " plt.rcParams['lines.linewidth'] = 2.0" ] }, { "cell_type": "code", "execution_count": 2, "id": "e6834cc6", "metadata": {}, "outputs": [], "source": [ "from dataclasses import dataclass\n", "from typing import List, Dict\n", "import random\n", "from collections import Counter\n", "\n", "@dataclass(frozen=True)\n", "class Match:\n", " home: str\n", " away: str\n", "\n", "def generate_calendar(teams: List[str], seed: int = 42, shuffle_rounds: bool = True) -> List[List[Match]]:\n", " \"\"\"\n", " Circle method (even N):\n", " - First half: N-1 rounds; in each round i, pair arr[j] vs arr[-1-j].\n", " - Rotate all except the first team: arr = [arr[0], arr[-1], arr[1], ..., arr[-2]]\n", " - Second half: mirror (swap home/away) of first half.\n", " Guarantees: one match/team/round; 19 home + 19 away per team.\n", " \"\"\"\n", " assert len(teams) % 2 == 0, \"Number of teams must be even.\"\n", " rng = random.Random(seed)\n", " arr = teams[:]\n", " rng.shuffle(arr)\n", " n = len(arr)\n", " half = n // 2\n", "\n", " rounds_first_half: List[List[Match]] = []\n", " for r in range(n - 1):\n", " # Pair fronts with backs\n", " round_pairs = []\n", " for j in range(half):\n", " a = arr[j]\n", " b = arr[-1 - j]\n", " # Alternate home/away by round and by pair index to help balance\n", " if (r + j) % 2 == 0:\n", " round_pairs.append(Match(home=a, away=b))\n", " else:\n", " round_pairs.append(Match(home=b, away=a))\n", " rounds_first_half.append(round_pairs)\n", "\n", " # Rotate all but the first item: [A, B, C, ..., Y, Z] -> [A, Z, B, C, ..., Y]\n", " if n > 2:\n", " arr = [arr[0]] + [arr[-1]] + arr[1:-1]\n", "\n", " # Mirror for second half (swap home/away)\n", " rounds_second_half = [[Match(home=m.away, away=m.home) for m in rnd] for rnd in rounds_first_half]\n", "\n", " # Optionally shuffle within halves to randomize matchday order (keeps validity)\n", " if shuffle_rounds:\n", " rng.shuffle(rounds_first_half)\n", " rng.shuffle(rounds_second_half)\n", "\n", " season = rounds_first_half + rounds_second_half\n", " _validate_calendar(season, teams)\n", " return season\n", "\n", "def _validate_calendar(season: List[List[Match]], teams: List[str]) -> None:\n", " n = len(teams)\n", " assert len(season) == 2*(n-1), f\"Expected {2*(n-1)} rounds, got {len(season)}.\"\n", " # Each round: every team appears once\n", " teamset = set(teams)\n", " for i, rnd in enumerate(season, 1):\n", " seen = set()\n", " for m in rnd:\n", " assert m.home in teamset and m.away in teamset and m.home != m.away\n", " assert m.home not in seen and m.away not in seen, f\"Team plays twice in round {i}\"\n", " seen.add(m.home); seen.add(m.away)\n", " assert len(seen) == n, f\"Missing teams in round {i}\"\n", "\n", " # Home/away exactly n-1 each; each ordered pair exactly once\n", " home_counts = Counter()\n", " away_counts = Counter()\n", " pair_counts = Counter()\n", " for rnd in season:\n", " for m in rnd:\n", " home_counts[m.home] += 1\n", " away_counts[m.away] += 1\n", " pair_counts[(m.home, m.away)] += 1\n", "\n", " for t in teams:\n", " assert home_counts[t] == (n-1), f\"{t} home games: {home_counts[t]} != {n-1}\"\n", " assert away_counts[t] == (n-1), f\"{t} away games: {away_counts[t]} != {n-1}\"\n", "\n", " for a in teams:\n", " for b in teams:\n", " if a == b: continue\n", " assert pair_counts[(a,b)] == 1, f\"Pair {a} vs {b} appears {pair_counts[(a,b)]} times\"" ] }, { "cell_type": "code", "execution_count": 3, "id": "77945800", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rounds: 38; Matches total: 380 (should be 38 & 380)\n", "\n", "Matchday 1\n", "Team12 vs Team08\n", "Team19 vs Team06\n", "Team17 vs Team03\n", "Team02 vs Team13\n", "Team11 vs Team07\n", "Team18 vs Team05\n", "Team15 vs Team09\n", "Team20 vs Team10\n", "Team04 vs Team16\n", "Team14 vs Team01\n", "\n", "Matchday 2\n", "Team08 vs Team19\n", "Team12 vs Team03\n", "Team02 vs Team06\n", "Team17 vs Team07\n", "Team18 vs Team13\n", "Team11 vs Team09\n", "Team20 vs Team05\n", "Team15 vs Team16\n", "Team14 vs Team10\n", "Team04 vs Team01\n" ] } ], "source": [ "teams = [f\"Team{i:02d}\" for i in range(1, 21)]\n", "season = generate_calendar(teams, seed = 2,shuffle_rounds=True)\n", "print(f\"Rounds: {len(season)}; Matches total: {sum(len(r) for r in season)} (should be 38 & 380)\")\n", "for md in range(2):\n", " print(f\"\\nMatchday {md+1}\")\n", " for m in season[md]:\n", " print(f\"{m.home} vs {m.away}\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "a68b3e47", "metadata": {}, "outputs": [], "source": [ "from collections import defaultdict\n", "from typing import Tuple\n", "\n", "@dataclass\n", "class TeamRow:\n", " pts: int = 0\n", " gf: int = 0\n", " ga: int = 0\n", " gd: int = 0\n", " w: int = 0\n", " d: int = 0\n", " l: int = 0\n", "\n", "\n", "# ----------------------------\n", "# Strengths helpers\n", "# ----------------------------\n", "def make_tiered_strengths(teams: List[str]) -> Dict[str, float]:\n", " \"\"\"\n", " Simple, reproducible strengths:\n", " - Strong top 6, solid next 6, average next 4, weaker bottom 4.\n", " Values are on a free scale; 0 = league-average.\n", " \"\"\"\n", " # Customize to taste\n", " tiers = (\n", " +0.55, +0.45, +0.35, +0.30, +0.25, +0.20, # top 6\n", " +0.10, +0.08, +0.06, +0.04, +0.02, 0.00, # next 6\n", " -0.02, -0.04, -0.06, # next 4\n", " -0.20, -0.30, -0.45, -0.55 # bottom 4\n", " )\n", " tiers = tiers if len(tiers) >= len(teams) else np.linspace(0.6, -0.6, len(teams))\n", " strengths = {t: float(tiers[i]) for i, t in enumerate(teams)}\n", " return strengths\n", "\n", "# ----------------------------\n", "# Poisson match model\n", "# ----------------------------\n", "def _poisson(rng: random.Random, lam: float) -> int:\n", " # Knuth's algorithm (fine for this use)\n", " L = np.exp(-lam)\n", " k, p = 0, 1.0\n", " while p > L:\n", " k += 1\n", " p *= rng.random()\n", " return k - 1\n", "\n", "def simulate_match(home: str, away: str, strengths: Dict[str, float],\n", " base_rate: float = 1.35, home_adv: float = 0.30,\n", " rng: random.Random = None) -> Tuple[int, int, int, int]:\n", " \"\"\"\n", " Returns (home_pts, away_pts, gh, ga)\n", " \"\"\"\n", " if rng is None:\n", " rng = random.Random()\n", " s_h = strengths[home]\n", " s_a = strengths[away]\n", " xg_h = base_rate * np.exp(home_adv + (s_h - s_a))\n", " xg_a = base_rate * np.exp(0.0 + (s_a - s_h))\n", " gh = _poisson(rng, xg_h)\n", " ga = _poisson(rng, xg_a)\n", "\n", " if gh > ga:\n", " return 3, 0, gh, ga\n", " elif ga > gh:\n", " return 0, 3, gh, ga\n", " else:\n", " return 1, 1, gh, ga\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "ed61058b", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from typing import List, Dict, Tuple\n", "from collections import defaultdict\n", "\n", "# ---------- Run a season AND keep detailed logs ----------\n", "def simulate_season_with_logs(\n", " teams: List[str],\n", " season: List[List[Match]],\n", " strengths: Dict[str, float],\n", " base_rate: float = 1.35,\n", " home_adv: float = 0.30,\n", " seed: int = 7\n", ") -> Tuple[Dict[str, TeamRow], List[dict]]:\n", " \"\"\"\n", " Returns:\n", " - table: final TeamRow per team\n", " - logs: list of dicts, one per match, including matchday & per-team stats\n", " \"\"\"\n", " rng = random.Random(seed)\n", " table: Dict[str, TeamRow] = {t: TeamRow() for t in teams}\n", " logs: List[dict] = []\n", "\n", " for md, rnd in enumerate(season, 1):\n", " for m in rnd:\n", " p_h, p_a, gh, ga = simulate_match(m.home, m.away, strengths, base_rate, home_adv, rng)\n", "\n", " # update table\n", " table[m.home].pts += p_h\n", " table[m.away].pts += p_a\n", " table[m.home].gf += gh; table[m.home].ga += ga\n", " table[m.away].gf += ga; table[m.away].ga += gh\n", " if p_h == 3:\n", " table[m.home].w += 1; table[m.away].l += 1\n", " elif p_a == 3:\n", " table[m.away].w += 1; table[m.home].l += 1\n", " else:\n", " table[m.home].d += 1; table[m.away].d += 1\n", "\n", " # match-level logs (both perspectives)\n", " logs.append({\n", " \"matchday\": md, \"home\": m.home, \"away\": m.away,\n", " \"gh\": gh, \"ga\": ga,\n", " \"pts_home\": p_h, \"pts_away\": p_a\n", " })\n", "\n", " for t in teams:\n", " table[t].gd = table[t].gf - table[t].ga\n", "\n", " return table, logs\n", "\n", "# ---------- Build tidy dataframes ----------\n", "def build_match_df(logs: List[dict]) -> pd.DataFrame:\n", " \"\"\"\n", " One row per match with basic info and result labels.\n", " \"\"\"\n", " df = pd.DataFrame(logs).sort_values([\"matchday\", \"home\"])\n", " def result(gh, ga):\n", " if gh > ga: return \"H\"\n", " if ga > gh: return \"A\"\n", " return \"D\"\n", " df[\"result\"] = np.where(df[\"gh\"] > df[\"ga\"], \"H\", np.where(df[\"ga\"] > df[\"gh\"], \"A\", \"D\"))\n", " return df\n", "\n", "def build_team_timeseries_df(teams: List[str], match_df: pd.DataFrame) -> pd.DataFrame:\n", " \"\"\"\n", " Panel time series for forecasting.\n", " Returns columns:\n", " unique_id (team), ds (matchday 1..38), y (cumulative points),\n", " pts (points gained that round), gf, ga, gd (cumulative),\n", " w,d,l (cumulative), opponent, ha ('H'/'A'), goals_for, goals_against, result\n", " \"\"\"\n", " rows = []\n", " # Expand to team perspective\n", " for _, r in match_df.iterrows():\n", " # Home row\n", " rows.append({\n", " \"matchday\": r.matchday, \"team\": r.home, \"opponent\": r.away, \"ha\": \"H\",\n", " \"goals_for\": r.gh, \"goals_against\": r.ga,\n", " \"pts\": 3 if r.gh > r.ga else (1 if r.gh == r.ga else 0),\n", " \"result\": \"W\" if r.gh > r.ga else (\"D\" if r.gh == r.ga else \"L\")\n", " })\n", " # Away row\n", " rows.append({\n", " \"matchday\": r.matchday, \"team\": r.away, \"opponent\": r.home, \"ha\": \"A\",\n", " \"goals_for\": r.ga, \"goals_against\": r.gh,\n", " \"pts\": 3 if r.ga > r.gh else (1 if r.ga == r.gh else 0),\n", " \"result\": \"W\" if r.ga > r.gh else (\"D\" if r.ga == r.gh else \"L\")\n", " })\n", "\n", " td = pd.DataFrame(rows).sort_values([\"team\", \"matchday\"])\n", " # Cumulative aggregates per team\n", " td[\"cum_pts\"] = td.groupby(\"team\")[\"pts\"].cumsum()\n", " td[\"cum_gf\"] = td.groupby(\"team\")[\"goals_for\"].cumsum()\n", " td[\"cum_ga\"] = td.groupby(\"team\")[\"goals_against\"].cumsum()\n", " td[\"cum_gd\"] = td[\"cum_gf\"] - td[\"cum_ga\"]\n", "\n", " # Cumulative W/D/L (nice features if needed)\n", " td[\"w1\"] = (td[\"result\"] == \"W\").astype(int)\n", " td[\"d1\"] = (td[\"result\"] == \"D\").astype(int)\n", " td[\"l1\"] = (td[\"result\"] == \"L\").astype(int)\n", " td[\"cum_w\"] = td.groupby(\"team\")[\"w1\"].cumsum()\n", " td[\"cum_d\"] = td.groupby(\"team\")[\"d1\"].cumsum()\n", " td[\"cum_l\"] = td.groupby(\"team\")[\"l1\"].cumsum()\n", " td.drop(columns=[\"w1\",\"d1\",\"l1\"], inplace=True)\n", "\n", " # StatsForecast/TimeGPT-ready view\n", " ts = td.rename(columns={\n", " \"team\": \"unique_id\",\n", " \"matchday\": \"ds\",\n", " \"cum_pts\": \"y\"\n", " })[[\n", " \"unique_id\", \"ds\", \"y\", # <-- required for many Nixtla pipelines\n", " \"pts\", \"opponent\", \"ha\",\n", " \"goals_for\", \"goals_against\", \"result\",\n", " \"cum_gf\", \"cum_ga\", \"cum_gd\", \"cum_w\", \"cum_d\", \"cum_l\"\n", " ]]\n", " ts[\"ds\"] = ts[\"ds\"].astype(int)\n", " return ts, td\n", "\n", "def build_standings_by_round(td: pd.DataFrame) -> pd.DataFrame:\n", " \"\"\"\n", " Returns a tidy standings table for every matchday:\n", " columns: matchday, pos, team, pts, gd, gf\n", " \"\"\"\n", " # pick cumulative metrics at each round\n", " snap = td[[\"matchday\", \"team\", \"cum_pts\", \"cum_gd\", \"cum_gf\"]].copy()\n", " snap = snap.rename(columns={\"cum_pts\":\"pts\", \"cum_gd\":\"gd\", \"cum_gf\":\"gf\"})\n", " # rank within each round\n", " snap[\"pos\"] = snap.groupby(\"matchday\") \\\n", " .apply(lambda g: g.sort_values([\"pts\",\"gd\",\"gf\"], ascending=False)\n", " .assign(pos=lambda x: np.arange(1, len(x)+1))) \\\n", " .reset_index(level=0, drop=True)[\"pos\"]\n", " return snap.sort_values([\"matchday\", \"pos\"]).reset_index(drop=True)\n", "\n", "# ---------- One-shot convenience wrapper ----------\n", "def prepare_forecasting_data(\n", " teams: List[str],\n", " season: List[List[Match]],\n", " strengths: Dict[str, float],\n", " base_rate: float = 1.35,\n", " home_adv: float = 0.30,\n", " seed: int = 7,\n", " cutoff_matchday: int = None\n", "):\n", " \"\"\"\n", " Simulate a season and return:\n", " - matches_df: one row per match with scores and results\n", " - ts_df: timeseries dataframe ready for forecasting (columns: unique_id, ds, y, ...)\n", " - standings_df: positions for every team at every matchday\n", " If cutoff_matchday is provided (e.g., 20), trims the data to 1..cutoff_matchday (for training)\n", " and returns 'h' = remaining matchdays (38 - cutoff_matchday) as the forecast horizon.\n", " \"\"\"\n", " final_table, logs = simulate_season_with_logs(\n", " teams, season, strengths, base_rate=base_rate, home_adv=home_adv, seed=seed\n", " )\n", " matches_df = build_match_df(logs)\n", " ts_df, team_detail_df = build_team_timeseries_df(teams, matches_df)\n", " standings_df = build_standings_by_round(team_detail_df)\n", "\n", " h = None\n", " if cutoff_matchday is not None:\n", " ts_df = ts_df[ts_df[\"ds\"] <= cutoff_matchday].copy()\n", " standings_df = standings_df[standings_df[\"matchday\"] <= cutoff_matchday].copy()\n", " h = 38 - int(cutoff_matchday)\n", "\n", " return {\n", " \"matches_df\": matches_df, # match results (one row per match)\n", " \"ts_df\": ts_df, # timeseries for forecasting: unique_id, ds, y (cumulative points)\n", " \"standings_df\": standings_df, # team rankings at each matchday\n", " \"h\": h # forecast horizon (remaining matchdays) if cutoff was used\n", " }\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "1ce85cdd", "metadata": {}, "outputs": [], "source": [ "teams = [f\"Team{i:02d}\" for i in range(1, 21)]\n", "season = generate_calendar(teams, seed=2025, shuffle_rounds=True)\n", "strengths = make_tiered_strengths(teams)\n", "\n", "# 1) Full season → dataframes for plots + forecasting\n", "full_season_results = prepare_forecasting_data(teams, season, strengths, seed=777)\n", "matches_df = full_season_results[\"matches_df\"]\n", "full_season_ts = full_season_results[\"ts_df\"] # (unique_id, ds, y) ready for StatsForecast/TimeGPT\n", "standings_df = full_season_results[\"standings_df\"]\n", "\n", "# 2) Train on first 20 matchdays, forecast remaining 18\n", "train_data = prepare_forecasting_data(teams, season, strengths, seed=777, cutoff_matchday=35)\n", "train_ts = train_data[\"ts_df\"] # ds ∈ [1..20]\n", "forecast_horizon = train_data[\"h\"] # 18 matchdays remaining\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "208ccb8b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "| | unique_id | ds | y | pts | opponent | ha | goals_for | goals_against | result | cum_gf | cum_ga | cum_gd | cum_w | cum_d | cum_l |\n", "|----:|:------------|-----:|----:|------:|:-----------|:-----|------------:|----------------:|:---------|---------:|---------:|---------:|--------:|--------:|--------:|\n", "| 0 | Team01 | 1 | 3 | 3 | Team20 | H | 6 | 0 | W | 6 | 0 | 6 | 1 | 0 | 0 |\n", "| 20 | Team01 | 2 | 6 | 3 | Team09 | H | 4 | 1 | W | 10 | 1 | 9 | 2 | 0 | 0 |\n", "| 40 | Team01 | 3 | 9 | 3 | Team11 | H | 5 | 1 | W | 15 | 2 | 13 | 3 | 0 | 0 |\n", "| 60 | Team01 | 4 | 10 | 1 | Team07 | H | 0 | 0 | D | 15 | 2 | 13 | 3 | 1 | 0 |\n", "| 80 | Team01 | 5 | 13 | 3 | Team12 | H | 5 | 3 | W | 20 | 5 | 15 | 4 | 1 | 0 |\n", "| 117 | Team01 | 6 | 16 | 3 | Team19 | A | 7 | 1 | W | 27 | 6 | 21 | 5 | 1 | 0 |\n", "| 120 | Team01 | 7 | 16 | 0 | Team10 | H | 1 | 2 | L | 28 | 8 | 20 | 5 | 1 | 1 |\n", "| 140 | Team01 | 8 | 19 | 3 | Team14 | H | 3 | 1 | W | 31 | 9 | 22 | 6 | 1 | 1 |\n", "| 160 | Team01 | 9 | 22 | 3 | Team04 | H | 5 | 2 | W | 36 | 11 | 25 | 7 | 1 | 1 |\n", "| 180 | Team01 | 10 | 25 | 3 | Team05 | H | 2 | 1 | W | 38 | 12 | 26 | 8 | 1 | 1 |\n", "| 200 | Team01 | 11 | 28 | 3 | Team13 | H | 4 | 1 | W | 42 | 13 | 29 | 9 | 1 | 1 |\n", "| 220 | Team01 | 12 | 31 | 3 | Team06 | H | 6 | 0 | W | 48 | 13 | 35 | 10 | 1 | 1 |\n", "| 240 | Team01 | 13 | 34 | 3 | Team17 | H | 6 | 0 | W | 54 | 13 | 41 | 11 | 1 | 1 |\n", "| 273 | Team01 | 14 | 37 | 3 | Team16 | A | 5 | 0 | W | 59 | 13 | 46 | 12 | 1 | 1 |\n", "| 280 | Team01 | 15 | 40 | 3 | Team03 | H | 3 | 1 | W | 62 | 14 | 48 | 13 | 1 | 1 |\n", "| 315 | Team01 | 16 | 43 | 3 | Team18 | A | 4 | 1 | W | 66 | 15 | 51 | 14 | 1 | 1 |\n", "| 320 | Team01 | 17 | 46 | 3 | Team02 | H | 2 | 0 | W | 68 | 15 | 53 | 15 | 1 | 1 |\n", "| 347 | Team01 | 18 | 47 | 1 | Team08 | A | 0 | 0 | D | 68 | 15 | 53 | 15 | 2 | 1 |\n", "| 373 | Team01 | 19 | 50 | 3 | Team15 | A | 3 | 0 | W | 71 | 15 | 56 | 16 | 2 | 1 |\n", "| 380 | Team01 | 20 | 53 | 3 | Team08 | H | 2 | 1 | W | 73 | 16 | 57 | 17 | 2 | 1 |\n", "| 415 | Team01 | 21 | 56 | 3 | Team12 | A | 1 | 0 | W | 74 | 16 | 58 | 18 | 2 | 1 |\n", "| 439 | Team01 | 22 | 59 | 3 | Team20 | A | 5 | 1 | W | 79 | 17 | 62 | 19 | 2 | 1 |\n", "| 447 | Team01 | 23 | 62 | 3 | Team06 | A | 2 | 1 | W | 81 | 18 | 63 | 20 | 2 | 1 |\n", "| 467 | Team01 | 24 | 65 | 3 | Team09 | A | 2 | 0 | W | 83 | 18 | 65 | 21 | 2 | 1 |\n", "| 485 | Team01 | 25 | 65 | 0 | Team05 | A | 1 | 2 | L | 84 | 20 | 64 | 21 | 2 | 2 |\n", "| 513 | Team01 | 26 | 66 | 1 | Team13 | A | 0 | 0 | D | 84 | 20 | 64 | 21 | 3 | 2 |\n", "| 520 | Team01 | 27 | 69 | 3 | Team19 | H | 5 | 0 | W | 89 | 20 | 69 | 22 | 3 | 2 |\n", "| 541 | Team01 | 28 | 69 | 0 | Team02 | A | 0 | 1 | L | 89 | 21 | 68 | 22 | 3 | 3 |\n", "| 577 | Team01 | 29 | 72 | 3 | Team17 | A | 2 | 0 | W | 91 | 21 | 70 | 23 | 3 | 3 |\n", "| 589 | Team01 | 30 | 75 | 3 | Team07 | A | 2 | 1 | W | 93 | 22 | 71 | 24 | 3 | 3 |\n", "| 613 | Team01 | 31 | 78 | 3 | Team11 | A | 5 | 2 | W | 98 | 24 | 74 | 25 | 3 | 3 |\n", "| 620 | Team01 | 32 | 81 | 3 | Team18 | H | 5 | 0 | W | 103 | 24 | 79 | 26 | 3 | 3 |\n", "| 643 | Team01 | 33 | 81 | 0 | Team03 | A | 1 | 3 | L | 104 | 27 | 77 | 26 | 3 | 4 |\n", "| 669 | Team01 | 34 | 84 | 3 | Team10 | A | 1 | 0 | W | 105 | 27 | 78 | 27 | 3 | 4 |\n", "| 680 | Team01 | 35 | 87 | 3 | Team15 | H | 3 | 1 | W | 108 | 28 | 80 | 28 | 3 | 4 |\n", "| 3 | Team02 | 1 | 0 | 0 | Team04 | A | 1 | 2 | L | 1 | 2 | -1 | 0 | 0 | 1 |\n", "| 22 | Team02 | 2 | 3 | 3 | Team19 | H | 8 | 1 | W | 9 | 3 | 6 | 1 | 0 | 1 |\n", "| 42 | Team02 | 3 | 4 | 1 | Team10 | H | 1 | 1 | D | 10 | 4 | 6 | 1 | 1 | 1 |\n", "| 62 | Team02 | 4 | 5 | 1 | Team03 | H | 2 | 2 | D | 12 | 6 | 6 | 1 | 2 | 1 |\n", "| 89 | Team02 | 5 | 6 | 1 | Team13 | A | 1 | 1 | D | 13 | 7 | 6 | 1 | 3 | 1 |\n", "| 100 | Team02 | 6 | 9 | 3 | Team05 | H | 3 | 1 | W | 16 | 8 | 8 | 2 | 3 | 1 |\n", "| 137 | Team02 | 7 | 12 | 3 | Team16 | A | 3 | 1 | W | 19 | 9 | 10 | 3 | 3 | 1 |\n", "| 142 | Team02 | 8 | 15 | 3 | Team17 | H | 6 | 0 | W | 25 | 9 | 16 | 4 | 3 | 1 |\n", "| 162 | Team02 | 9 | 18 | 3 | Team06 | H | 4 | 2 | W | 29 | 11 | 18 | 5 | 3 | 1 |\n", "| 199 | Team02 | 10 | 21 | 3 | Team20 | A | 4 | 0 | W | 33 | 11 | 22 | 6 | 3 | 1 |\n", "| 205 | Team02 | 11 | 21 | 0 | Team07 | A | 1 | 3 | L | 34 | 14 | 20 | 6 | 3 | 2 |\n", "| 233 | Team02 | 12 | 24 | 3 | Team15 | A | 3 | 0 | W | 37 | 14 | 23 | 7 | 3 | 2 |\n", "| 257 | Team02 | 13 | 27 | 3 | Team18 | A | 8 | 0 | W | 45 | 14 | 31 | 8 | 3 | 2 |\n", "| 260 | Team02 | 14 | 30 | 3 | Team12 | H | 1 | 0 | W | 46 | 14 | 32 | 9 | 3 | 2 |\n", "| 287 | Team02 | 15 | 30 | 0 | Team08 | A | 0 | 1 | L | 46 | 15 | 31 | 9 | 3 | 3 |\n", "| 300 | Team02 | 16 | 33 | 3 | Team09 | H | 2 | 0 | W | 48 | 15 | 33 | 10 | 3 | 3 |\n", "| 321 | Team02 | 17 | 33 | 0 | Team01 | A | 0 | 2 | L | 48 | 17 | 31 | 10 | 3 | 4 |\n", "| 340 | Team02 | 18 | 36 | 3 | Team14 | H | 5 | 2 | W | 53 | 19 | 34 | 11 | 3 | 4 |\n", "| 360 | Team02 | 19 | 39 | 3 | Team11 | H | 2 | 1 | W | 55 | 20 | 35 | 12 | 3 | 4 |\n", "| 395 | Team02 | 20 | 42 | 3 | Team14 | A | 5 | 2 | W | 60 | 22 | 38 | 13 | 3 | 4 |\n", "| 400 | Team02 | 21 | 45 | 3 | Team13 | H | 5 | 0 | W | 65 | 22 | 43 | 14 | 3 | 4 |\n", "| 420 | Team02 | 22 | 48 | 3 | Team04 | H | 4 | 0 | W | 69 | 22 | 47 | 15 | 3 | 4 |\n", "| 440 | Team02 | 23 | 51 | 3 | Team15 | H | 2 | 1 | W | 71 | 23 | 48 | 16 | 3 | 4 |\n", "| 479 | Team02 | 24 | 54 | 3 | Team19 | A | 4 | 0 | W | 75 | 23 | 52 | 17 | 3 | 4 |\n", "| 480 | Team02 | 25 | 57 | 3 | Team20 | H | 4 | 0 | W | 79 | 23 | 56 | 18 | 3 | 4 |\n", "| 500 | Team02 | 26 | 60 | 3 | Team07 | H | 5 | 1 | W | 84 | 24 | 60 | 19 | 3 | 4 |\n", "| 527 | Team02 | 27 | 61 | 1 | Team05 | A | 0 | 0 | D | 84 | 24 | 60 | 19 | 4 | 4 |\n", "| 540 | Team02 | 28 | 64 | 3 | Team01 | H | 1 | 0 | W | 85 | 24 | 61 | 20 | 4 | 4 |\n", "| 560 | Team02 | 29 | 67 | 3 | Team18 | H | 6 | 0 | W | 91 | 24 | 67 | 21 | 4 | 4 |\n", "| 581 | Team02 | 30 | 68 | 1 | Team03 | A | 0 | 0 | D | 91 | 24 | 67 | 21 | 5 | 4 |\n", "| 611 | Team02 | 31 | 71 | 3 | Team10 | A | 3 | 0 | W | 94 | 24 | 70 | 22 | 5 | 4 |\n", "| 631 | Team02 | 32 | 72 | 1 | Team09 | A | 2 | 2 | D | 96 | 26 | 70 | 22 | 6 | 4 |\n", "| 640 | Team02 | 33 | 75 | 3 | Team08 | H | 3 | 0 | W | 99 | 26 | 73 | 23 | 6 | 4 |\n", "| 660 | Team02 | 34 | 78 | 3 | Team16 | H | 4 | 0 | W | 103 | 26 | 77 | 24 | 6 | 4 |\n", "| 691 | Team02 | 35 | 81 | 3 | Team11 | A | 4 | 1 | W | 107 | 27 | 80 | 25 | 6 | 4 |\n", "| 11 | Team03 | 1 | 3 | 3 | Team12 | A | 2 | 1 | W | 2 | 1 | 1 | 1 | 0 | 0 |\n", "| 33 | Team03 | 2 | 4 | 1 | Team15 | A | 1 | 1 | D | 3 | 2 | 1 | 1 | 1 | 0 |\n", "| 47 | Team03 | 3 | 5 | 1 | Team08 | A | 1 | 1 | D | 4 | 3 | 1 | 1 | 2 | 0 |\n", "| 63 | Team03 | 4 | 6 | 1 | Team02 | A | 2 | 2 | D | 6 | 5 | 1 | 1 | 3 | 0 |\n", "| 95 | Team03 | 5 | 9 | 3 | Team18 | A | 2 | 1 | W | 8 | 6 | 2 | 2 | 3 | 0 |\n", "| 105 | Team03 | 6 | 12 | 3 | Team10 | A | 5 | 0 | W | 13 | 6 | 7 | 3 | 3 | 0 |\n", "| 133 | Team03 | 7 | 13 | 1 | Team14 | A | 2 | 2 | D | 15 | 8 | 7 | 3 | 4 | 0 |\n", "| 159 | Team03 | 8 | 16 | 3 | Team20 | A | 2 | 0 | W | 17 | 8 | 9 | 4 | 4 | 0 |\n", "| 169 | Team03 | 9 | 19 | 3 | Team13 | A | 4 | 1 | W | 21 | 9 | 12 | 5 | 4 | 0 |\n", "| 195 | Team03 | 10 | 22 | 3 | Team16 | A | 5 | 1 | W | 26 | 10 | 16 | 6 | 4 | 0 |\n", "| 207 | Team03 | 11 | 23 | 1 | Team09 | A | 2 | 2 | D | 28 | 12 | 16 | 6 | 5 | 0 |\n", "| 225 | Team03 | 12 | 23 | 0 | Team07 | A | 1 | 2 | L | 29 | 14 | 15 | 6 | 5 | 1 |\n", "| 243 | Team03 | 13 | 23 | 0 | Team04 | A | 2 | 3 | L | 31 | 17 | 14 | 6 | 5 | 2 |\n", "| 275 | Team03 | 14 | 26 | 3 | Team17 | A | 1 | 0 | W | 32 | 17 | 15 | 7 | 5 | 2 |\n", "| 281 | Team03 | 15 | 26 | 0 | Team01 | A | 1 | 3 | L | 33 | 20 | 13 | 7 | 5 | 3 |\n", "| 303 | Team03 | 16 | 26 | 0 | Team06 | A | 0 | 2 | L | 33 | 22 | 11 | 7 | 5 | 4 |\n", "| 329 | Team03 | 17 | 29 | 3 | Team11 | A | 1 | 0 | W | 34 | 22 | 12 | 8 | 5 | 4 |\n", "| 342 | Team03 | 18 | 30 | 1 | Team05 | H | 1 | 1 | D | 35 | 23 | 12 | 8 | 6 | 4 |\n", "| 362 | Team03 | 19 | 33 | 3 | Team19 | H | 8 | 1 | W | 43 | 24 | 19 | 9 | 6 | 4 |\n", "| 385 | Team03 | 20 | 36 | 3 | Team05 | A | 1 | 0 | W | 44 | 24 | 20 | 10 | 6 | 4 |\n", "| 402 | Team03 | 21 | 39 | 3 | Team18 | H | 7 | 1 | W | 51 | 25 | 26 | 11 | 6 | 4 |\n", "| 422 | Team03 | 22 | 42 | 3 | Team12 | H | 2 | 0 | W | 53 | 25 | 28 | 12 | 6 | 4 |\n", "| 442 | Team03 | 23 | 45 | 3 | Team07 | H | 1 | 0 | W | 54 | 25 | 29 | 13 | 6 | 4 |\n", "| 460 | Team03 | 24 | 48 | 3 | Team15 | H | 4 | 1 | W | 58 | 26 | 32 | 14 | 6 | 4 |\n", "| 482 | Team03 | 25 | 51 | 3 | Team16 | H | 2 | 0 | W | 60 | 26 | 34 | 15 | 6 | 4 |\n", "| 502 | Team03 | 26 | 54 | 3 | Team09 | H | 3 | 0 | W | 63 | 26 | 37 | 16 | 6 | 4 |\n", "| 522 | Team03 | 27 | 57 | 3 | Team10 | H | 4 | 1 | W | 67 | 27 | 40 | 17 | 6 | 4 |\n", "| 542 | Team03 | 28 | 60 | 3 | Team11 | H | 5 | 0 | W | 72 | 27 | 45 | 18 | 6 | 4 |\n", "| 562 | Team03 | 29 | 60 | 0 | Team04 | H | 1 | 2 | L | 73 | 29 | 44 | 18 | 6 | 5 |\n", "| 580 | Team03 | 30 | 61 | 1 | Team02 | H | 0 | 0 | D | 73 | 29 | 44 | 18 | 7 | 5 |\n", "| 600 | Team03 | 31 | 64 | 3 | Team08 | H | 5 | 0 | W | 78 | 29 | 49 | 19 | 7 | 5 |\n", "| 622 | Team03 | 32 | 67 | 3 | Team06 | H | 2 | 0 | W | 80 | 29 | 51 | 20 | 7 | 5 |\n", "| 642 | Team03 | 33 | 70 | 3 | Team01 | H | 3 | 1 | W | 83 | 30 | 53 | 21 | 7 | 5 |\n", "| 662 | Team03 | 34 | 73 | 3 | Team14 | H | 5 | 1 | W | 88 | 31 | 57 | 22 | 7 | 5 |\n", "| 699 | Team03 | 35 | 76 | 3 | Team19 | A | 5 | 1 | W | 93 | 32 | 61 | 23 | 7 | 5 |\n", "| 2 | Team04 | 1 | 3 | 3 | Team02 | H | 2 | 1 | W | 2 | 1 | 1 | 1 | 0 | 0 |\n", "| 24 | Team04 | 2 | 6 | 3 | Team14 | H | 4 | 1 | W | 6 | 2 | 4 | 2 | 0 | 0 |\n", "| 57 | Team04 | 3 | 9 | 3 | Team19 | A | 4 | 0 | W | 10 | 2 | 8 | 3 | 0 | 0 |\n", "| 73 | Team04 | 4 | 10 | 1 | Team16 | A | 1 | 1 | D | 11 | 3 | 8 | 3 | 1 | 0 |\n", "| 82 | Team04 | 5 | 13 | 3 | Team11 | H | 4 | 0 | W | 15 | 3 | 12 | 4 | 1 | 0 |\n", "| 115 | Team04 | 6 | 16 | 3 | Team18 | A | 5 | 0 | W | 20 | 3 | 17 | 5 | 1 | 0 |\n", "| 122 | Team04 | 7 | 19 | 3 | Team06 | H | 2 | 0 | W | 22 | 3 | 19 | 6 | 1 | 0 |\n", "| 145 | Team04 | 8 | 22 | 3 | Team07 | A | 2 | 1 | W | 24 | 4 | 20 | 7 | 1 | 0 |\n", "| 161 | Team04 | 9 | 22 | 0 | Team01 | A | 2 | 5 | L | 26 | 9 | 17 | 7 | 1 | 1 |\n", "| 182 | Team04 | 10 | 22 | 0 | Team09 | H | 1 | 2 | L | 27 | 11 | 16 | 7 | 1 | 2 |\n", "| 202 | Team04 | 11 | 23 | 1 | Team10 | H | 4 | 4 | D | 31 | 15 | 16 | 7 | 2 | 2 |\n", "| 222 | Team04 | 12 | 26 | 3 | Team05 | H | 5 | 0 | W | 36 | 15 | 21 | 8 | 2 | 2 |\n", "| 242 | Team04 | 13 | 29 | 3 | Team03 | H | 3 | 2 | W | 39 | 17 | 22 | 9 | 2 | 2 |\n", "| 271 | Team04 | 14 | 29 | 0 | Team15 | A | 1 | 2 | L | 40 | 19 | 21 | 9 | 2 | 3 |\n", "| 282 | Team04 | 15 | 32 | 3 | Team12 | H | 4 | 0 | W | 44 | 19 | 25 | 10 | 2 | 3 |\n", "| 305 | Team04 | 16 | 35 | 3 | Team08 | A | 3 | 0 | W | 47 | 19 | 28 | 11 | 2 | 3 |\n", "| 322 | Team04 | 17 | 38 | 3 | Team17 | H | 4 | 0 | W | 51 | 19 | 32 | 12 | 2 | 3 |\n", "| 351 | Team04 | 18 | 39 | 1 | Team13 | A | 2 | 2 | D | 53 | 21 | 32 | 12 | 3 | 3 |\n", "| 379 | Team04 | 19 | 42 | 3 | Team20 | A | 3 | 2 | W | 56 | 23 | 33 | 13 | 3 | 3 |\n", "| 382 | Team04 | 20 | 45 | 3 | Team13 | H | 2 | 1 | W | 58 | 24 | 34 | 14 | 3 | 3 |\n", "| 413 | Team04 | 21 | 48 | 3 | Team11 | A | 2 | 1 | W | 60 | 25 | 35 | 15 | 3 | 3 |\n", "| 421 | Team04 | 22 | 48 | 0 | Team02 | A | 0 | 4 | L | 60 | 29 | 31 | 15 | 3 | 4 |\n", "| 445 | Team04 | 23 | 49 | 1 | Team05 | A | 2 | 2 | D | 62 | 31 | 31 | 15 | 4 | 4 |\n", "| 475 | Team04 | 24 | 52 | 3 | Team14 | A | 1 | 0 | W | 63 | 31 | 32 | 16 | 4 | 4 |\n", "| 489 | Team04 | 25 | 55 | 3 | Team09 | A | 2 | 1 | W | 65 | 32 | 33 | 17 | 4 | 4 |\n", "| 511 | Team04 | 26 | 56 | 1 | Team10 | A | 1 | 1 | D | 66 | 33 | 33 | 17 | 5 | 4 |\n", "| 524 | Team04 | 27 | 59 | 3 | Team18 | H | 5 | 1 | W | 71 | 34 | 37 | 18 | 5 | 4 |\n", "| 557 | Team04 | 28 | 62 | 3 | Team17 | A | 3 | 1 | W | 74 | 35 | 39 | 19 | 5 | 4 |\n", "| 563 | Team04 | 29 | 65 | 3 | Team03 | A | 2 | 1 | W | 76 | 36 | 40 | 20 | 5 | 4 |\n", "| 582 | Team04 | 30 | 68 | 3 | Team16 | H | 4 | 0 | W | 80 | 36 | 44 | 21 | 5 | 4 |\n", "| 602 | Team04 | 31 | 71 | 3 | Team19 | H | 6 | 2 | W | 86 | 38 | 48 | 22 | 5 | 4 |\n", "| 624 | Team04 | 32 | 74 | 3 | Team08 | H | 4 | 1 | W | 90 | 39 | 51 | 23 | 5 | 4 |\n", "| 655 | Team04 | 33 | 77 | 3 | Team12 | A | 1 | 0 | W | 91 | 39 | 52 | 24 | 5 | 4 |\n", "| 667 | Team04 | 34 | 80 | 3 | Team06 | A | 3 | 2 | W | 94 | 41 | 53 | 25 | 5 | 4 |\n", "| 682 | Team04 | 35 | 83 | 3 | Team20 | H | 5 | 1 | W | 99 | 42 | 57 | 26 | 5 | 4 |\n", "| 19 | Team05 | 1 | 3 | 3 | Team19 | A | 3 | 0 | W | 3 | 0 | 3 | 1 | 0 | 0 |\n", "| 37 | Team05 | 2 | 6 | 3 | Team18 | A | 2 | 1 | W | 5 | 1 | 4 | 2 | 0 | 0 |\n", "| 51 | Team05 | 3 | 9 | 3 | Team15 | A | 4 | 0 | W | 9 | 1 | 8 | 3 | 0 | 0 |\n", "| 69 | Team05 | 4 | 12 | 3 | Team13 | A | 3 | 2 | W | 12 | 3 | 9 | 4 | 0 | 0 |\n", "| 93 | Team05 | 5 | 12 | 0 | Team16 | A | 0 | 1 | L | 12 | 4 | 8 | 4 | 0 | 1 |\n", "| 101 | Team05 | 6 | 12 | 0 | Team02 | A | 1 | 3 | L | 13 | 7 | 6 | 4 | 0 | 2 |\n", "| 131 | Team05 | 7 | 12 | 0 | Team11 | A | 0 | 2 | L | 13 | 9 | 4 | 4 | 0 | 3 |\n", "| 147 | Team05 | 8 | 13 | 1 | Team08 | A | 2 | 2 | D | 15 | 11 | 4 | 4 | 1 | 3 |\n", "| 179 | Team05 | 9 | 16 | 3 | Team20 | A | 5 | 0 | W | 20 | 11 | 9 | 5 | 1 | 3 |\n", "| 181 | Team05 | 10 | 16 | 0 | Team01 | A | 1 | 2 | L | 21 | 13 | 8 | 5 | 1 | 4 |\n", "| 211 | Team05 | 11 | 19 | 3 | Team12 | A | 2 | 0 | W | 23 | 13 | 10 | 6 | 1 | 4 |\n", "| 223 | Team05 | 12 | 19 | 0 | Team04 | A | 0 | 5 | L | 23 | 18 | 5 | 6 | 1 | 5 |\n", "| 251 | Team05 | 13 | 22 | 3 | Team14 | A | 6 | 1 | W | 29 | 19 | 10 | 7 | 1 | 5 |\n", "| 267 | Team05 | 14 | 25 | 3 | Team10 | A | 3 | 1 | W | 32 | 20 | 12 | 8 | 1 | 5 |\n", "| 285 | Team05 | 15 | 26 | 1 | Team07 | A | 2 | 2 | D | 34 | 22 | 12 | 8 | 2 | 5 |\n", "| 313 | Team05 | 16 | 29 | 3 | Team17 | A | 2 | 0 | W | 36 | 22 | 14 | 9 | 2 | 5 |\n", "| 327 | Team05 | 17 | 30 | 1 | Team09 | A | 0 | 0 | D | 36 | 22 | 14 | 9 | 3 | 5 |\n", "| 343 | Team05 | 18 | 31 | 1 | Team03 | A | 1 | 1 | D | 37 | 23 | 14 | 9 | 4 | 5 |\n", "| 365 | Team05 | 19 | 31 | 0 | Team06 | A | 1 | 2 | L | 38 | 25 | 13 | 9 | 4 | 6 |\n", "| 384 | Team05 | 20 | 31 | 0 | Team03 | H | 0 | 1 | L | 38 | 26 | 12 | 9 | 4 | 7 |\n", "| 404 | Team05 | 21 | 34 | 3 | Team16 | H | 1 | 0 | W | 39 | 26 | 13 | 10 | 4 | 7 |\n", "| 424 | Team05 | 22 | 37 | 3 | Team19 | H | 5 | 1 | W | 44 | 27 | 17 | 11 | 4 | 7 |\n", "| 444 | Team05 | 23 | 38 | 1 | Team04 | H | 2 | 2 | D | 46 | 29 | 17 | 11 | 5 | 7 |\n", "| 462 | Team05 | 24 | 38 | 0 | Team18 | H | 1 | 2 | L | 47 | 31 | 16 | 11 | 5 | 8 |\n", "| 484 | Team05 | 25 | 41 | 3 | Team01 | H | 2 | 1 | W | 49 | 32 | 17 | 12 | 5 | 8 |\n", "| 504 | Team05 | 26 | 44 | 3 | Team12 | H | 3 | 1 | W | 52 | 33 | 19 | 13 | 5 | 8 |\n", "| 526 | Team05 | 27 | 45 | 1 | Team02 | H | 0 | 0 | D | 52 | 33 | 19 | 13 | 6 | 8 |\n", "| 544 | Team05 | 28 | 48 | 3 | Team09 | H | 1 | 0 | W | 53 | 33 | 20 | 14 | 6 | 8 |\n", "| 564 | Team05 | 29 | 51 | 3 | Team14 | H | 3 | 1 | W | 56 | 34 | 22 | 15 | 6 | 8 |\n", "| 584 | Team05 | 30 | 54 | 3 | Team13 | H | 6 | 0 | W | 62 | 34 | 28 | 16 | 6 | 8 |\n", "| 604 | Team05 | 31 | 57 | 3 | Team15 | H | 5 | 1 | W | 67 | 35 | 32 | 17 | 6 | 8 |\n", "| 626 | Team05 | 32 | 60 | 3 | Team17 | H | 5 | 0 | W | 72 | 35 | 37 | 18 | 6 | 8 |\n", "| 644 | Team05 | 33 | 63 | 3 | Team07 | H | 4 | 0 | W | 76 | 35 | 41 | 19 | 6 | 8 |\n", "| 664 | Team05 | 34 | 66 | 3 | Team11 | H | 2 | 1 | W | 78 | 36 | 42 | 20 | 6 | 8 |\n", "| 684 | Team05 | 35 | 69 | 3 | Team06 | H | 3 | 1 | W | 81 | 37 | 44 | 21 | 6 | 8 |\n", "| 7 | Team06 | 1 | 3 | 3 | Team09 | A | 4 | 1 | W | 4 | 1 | 3 | 1 | 0 | 0 |\n", "| 29 | Team06 | 2 | 6 | 3 | Team08 | A | 2 | 0 | W | 6 | 1 | 5 | 2 | 0 | 0 |\n", "| 59 | Team06 | 3 | 9 | 3 | Team20 | A | 5 | 1 | W | 11 | 2 | 9 | 3 | 0 | 0 |\n", "| 67 | Team06 | 4 | 12 | 3 | Team10 | A | 5 | 2 | W | 16 | 4 | 12 | 4 | 0 | 0 |\n", "| 91 | Team06 | 5 | 13 | 1 | Team15 | A | 2 | 2 | D | 18 | 6 | 12 | 4 | 1 | 0 |\n", "| 113 | Team06 | 6 | 16 | 3 | Team17 | A | 3 | 1 | W | 21 | 7 | 14 | 5 | 1 | 0 |\n", "| 123 | Team06 | 7 | 16 | 0 | Team04 | A | 0 | 2 | L | 21 | 9 | 12 | 5 | 1 | 1 |\n", "| 151 | Team06 | 8 | 19 | 3 | Team13 | A | 3 | 1 | W | 24 | 10 | 14 | 6 | 1 | 1 |\n", "| 163 | Team06 | 9 | 19 | 0 | Team02 | A | 2 | 4 | L | 26 | 14 | 12 | 6 | 1 | 2 |\n", "| 197 | Team06 | 10 | 20 | 1 | Team18 | A | 1 | 1 | D | 27 | 15 | 12 | 6 | 2 | 2 |\n", "| 209 | Team06 | 11 | 23 | 3 | Team11 | A | 2 | 0 | W | 29 | 15 | 14 | 7 | 2 | 2 |\n", "| 221 | Team06 | 12 | 23 | 0 | Team01 | A | 0 | 6 | L | 29 | 21 | 8 | 7 | 2 | 3 |\n", "| 245 | Team06 | 13 | 26 | 3 | Team07 | A | 1 | 0 | W | 30 | 21 | 9 | 8 | 2 | 3 |\n", "| 262 | Team06 | 14 | 29 | 3 | Team19 | H | 3 | 0 | W | 33 | 21 | 12 | 9 | 2 | 3 |\n", "| 293 | Team06 | 15 | 32 | 3 | Team16 | A | 3 | 1 | W | 36 | 22 | 14 | 10 | 2 | 3 |\n", "| 302 | Team06 | 16 | 35 | 3 | Team03 | H | 2 | 0 | W | 38 | 22 | 16 | 11 | 2 | 3 |\n", "| 331 | Team06 | 17 | 38 | 3 | Team14 | A | 5 | 0 | W | 43 | 22 | 21 | 12 | 2 | 3 |\n", "| 344 | Team06 | 18 | 39 | 1 | Team12 | H | 0 | 0 | D | 43 | 22 | 21 | 12 | 3 | 3 |\n", "| 364 | Team06 | 19 | 42 | 3 | Team05 | H | 2 | 1 | W | 45 | 23 | 22 | 13 | 3 | 3 |\n", "| 393 | Team06 | 20 | 43 | 1 | Team12 | A | 1 | 1 | D | 46 | 24 | 22 | 13 | 4 | 3 |\n", "| 406 | Team06 | 21 | 46 | 3 | Team15 | H | 3 | 0 | W | 49 | 24 | 25 | 14 | 4 | 3 |\n", "| 426 | Team06 | 22 | 49 | 3 | Team09 | H | 4 | 2 | W | 53 | 26 | 27 | 15 | 4 | 3 |\n", "| 446 | Team06 | 23 | 49 | 0 | Team01 | H | 1 | 2 | L | 54 | 28 | 26 | 15 | 4 | 4 |\n", "| 464 | Team06 | 24 | 49 | 0 | Team08 | H | 0 | 3 | L | 54 | 31 | 23 | 15 | 4 | 5 |\n", "| 486 | Team06 | 25 | 49 | 0 | Team18 | H | 2 | 4 | L | 56 | 35 | 21 | 15 | 4 | 6 |\n", "| 506 | Team06 | 26 | 52 | 3 | Team11 | H | 2 | 1 | W | 58 | 36 | 22 | 16 | 4 | 6 |\n", "| 528 | Team06 | 27 | 55 | 3 | Team17 | H | 2 | 1 | W | 60 | 37 | 23 | 17 | 4 | 6 |\n", "| 546 | Team06 | 28 | 58 | 3 | Team14 | H | 5 | 1 | W | 65 | 38 | 27 | 18 | 4 | 6 |\n", "| 566 | Team06 | 29 | 61 | 3 | Team07 | H | 2 | 1 | W | 67 | 39 | 28 | 19 | 4 | 6 |\n", "| 586 | Team06 | 30 | 61 | 0 | Team10 | H | 0 | 1 | L | 67 | 40 | 27 | 19 | 4 | 7 |\n", "| 606 | Team06 | 31 | 64 | 3 | Team20 | H | 2 | 0 | W | 69 | 40 | 29 | 20 | 4 | 7 |\n", "| 623 | Team06 | 32 | 64 | 0 | Team03 | A | 0 | 2 | L | 69 | 42 | 27 | 20 | 4 | 8 |\n", "| 646 | Team06 | 33 | 65 | 1 | Team16 | H | 3 | 3 | D | 72 | 45 | 27 | 20 | 5 | 8 |\n", "| 666 | Team06 | 34 | 65 | 0 | Team04 | H | 2 | 3 | L | 74 | 48 | 26 | 20 | 5 | 9 |\n", "| 685 | Team06 | 35 | 65 | 0 | Team05 | A | 1 | 3 | L | 75 | 51 | 24 | 20 | 5 | 10 |\n", "| 4 | Team07 | 1 | 3 | 3 | Team13 | H | 3 | 2 | W | 3 | 2 | 1 | 1 | 0 | 0 |\n", "| 26 | Team07 | 2 | 6 | 3 | Team11 | H | 3 | 0 | W | 6 | 2 | 4 | 2 | 0 | 0 |\n", "| 44 | Team07 | 3 | 9 | 3 | Team14 | H | 1 | 0 | W | 7 | 2 | 5 | 3 | 0 | 0 |\n", "| 61 | Team07 | 4 | 10 | 1 | Team01 | A | 0 | 0 | D | 7 | 2 | 5 | 3 | 1 | 0 |\n", "| 84 | Team07 | 5 | 13 | 3 | Team09 | H | 6 | 1 | W | 13 | 3 | 10 | 4 | 1 | 0 |\n", "| 111 | Team07 | 6 | 16 | 3 | Team16 | A | 4 | 1 | W | 17 | 4 | 13 | 5 | 1 | 0 |\n", "| 124 | Team07 | 7 | 19 | 3 | Team17 | H | 3 | 1 | W | 20 | 5 | 15 | 6 | 1 | 0 |\n", "| 144 | Team07 | 8 | 19 | 0 | Team04 | H | 1 | 2 | L | 21 | 7 | 14 | 6 | 1 | 1 |\n", "| 177 | Team07 | 9 | 22 | 3 | Team19 | A | 4 | 0 | W | 25 | 7 | 18 | 7 | 1 | 1 |\n", "| 184 | Team07 | 10 | 25 | 3 | Team12 | H | 2 | 1 | W | 27 | 8 | 19 | 8 | 1 | 1 |\n", "| 204 | Team07 | 11 | 28 | 3 | Team02 | H | 3 | 1 | W | 30 | 9 | 21 | 9 | 1 | 1 |\n", "| 224 | Team07 | 12 | 31 | 3 | Team03 | H | 2 | 1 | W | 32 | 10 | 22 | 10 | 1 | 1 |\n", "| 244 | Team07 | 13 | 31 | 0 | Team06 | H | 0 | 1 | L | 32 | 11 | 21 | 10 | 1 | 2 |\n", "| 277 | Team07 | 14 | 34 | 3 | Team18 | A | 4 | 0 | W | 36 | 11 | 25 | 11 | 1 | 2 |\n", "| 284 | Team07 | 15 | 35 | 1 | Team05 | H | 2 | 2 | D | 38 | 13 | 25 | 11 | 2 | 2 |\n", "| 311 | Team07 | 16 | 38 | 3 | Team15 | A | 2 | 0 | W | 40 | 13 | 27 | 12 | 2 | 2 |\n", "| 324 | Team07 | 17 | 41 | 3 | Team10 | H | 3 | 0 | W | 43 | 13 | 30 | 13 | 2 | 2 |\n", "| 359 | Team07 | 18 | 44 | 3 | Team20 | A | 3 | 0 | W | 46 | 13 | 33 | 14 | 2 | 2 |\n", "| 367 | Team07 | 19 | 44 | 0 | Team08 | A | 0 | 2 | L | 46 | 15 | 31 | 14 | 2 | 3 |\n", "| 386 | Team07 | 20 | 47 | 3 | Team20 | H | 1 | 0 | W | 47 | 15 | 32 | 15 | 2 | 3 |\n", "| 409 | Team07 | 21 | 50 | 3 | Team09 | A | 4 | 1 | W | 51 | 16 | 35 | 16 | 2 | 3 |\n", "| 433 | Team07 | 22 | 53 | 3 | Team13 | A | 2 | 0 | W | 53 | 16 | 37 | 17 | 2 | 3 |\n", "| 443 | Team07 | 23 | 53 | 0 | Team03 | A | 0 | 1 | L | 53 | 17 | 36 | 17 | 2 | 4 |\n", "| 471 | Team07 | 24 | 56 | 3 | Team11 | A | 2 | 0 | W | 55 | 17 | 38 | 18 | 2 | 4 |\n", "| 495 | Team07 | 25 | 59 | 3 | Team12 | A | 2 | 1 | W | 57 | 18 | 39 | 19 | 2 | 4 |\n", "| 501 | Team07 | 26 | 59 | 0 | Team02 | A | 1 | 5 | L | 58 | 23 | 35 | 19 | 2 | 5 |\n", "| 530 | Team07 | 27 | 62 | 3 | Team16 | H | 3 | 1 | W | 61 | 24 | 37 | 20 | 2 | 5 |\n", "| 551 | Team07 | 28 | 62 | 0 | Team10 | A | 1 | 3 | L | 62 | 27 | 35 | 20 | 2 | 6 |\n", "| 567 | Team07 | 29 | 62 | 0 | Team06 | A | 1 | 2 | L | 63 | 29 | 34 | 20 | 2 | 7 |\n", "| 588 | Team07 | 30 | 62 | 0 | Team01 | H | 1 | 2 | L | 64 | 31 | 33 | 20 | 2 | 8 |\n", "| 617 | Team07 | 31 | 65 | 3 | Team14 | A | 1 | 0 | W | 65 | 31 | 34 | 21 | 2 | 8 |\n", "| 628 | Team07 | 32 | 68 | 3 | Team15 | H | 4 | 0 | W | 69 | 31 | 38 | 22 | 2 | 8 |\n", "| 645 | Team07 | 33 | 68 | 0 | Team05 | A | 0 | 4 | L | 69 | 35 | 34 | 22 | 2 | 9 |\n", "| 675 | Team07 | 34 | 71 | 3 | Team17 | A | 3 | 1 | W | 72 | 36 | 36 | 23 | 2 | 9 |\n", "| 686 | Team07 | 35 | 71 | 0 | Team08 | H | 0 | 1 | L | 72 | 37 | 35 | 23 | 2 | 10 |\n", "| 15 | Team08 | 1 | 1 | 1 | Team16 | A | 1 | 1 | D | 1 | 1 | 0 | 0 | 1 | 0 |\n", "| 28 | Team08 | 2 | 1 | 0 | Team06 | H | 0 | 2 | L | 1 | 3 | -2 | 0 | 1 | 1 |\n", "| 46 | Team08 | 3 | 2 | 1 | Team03 | H | 1 | 1 | D | 2 | 4 | -2 | 0 | 2 | 1 |\n", "| 64 | Team08 | 4 | 5 | 3 | Team09 | H | 2 | 1 | W | 4 | 5 | -1 | 1 | 2 | 1 |\n", "| 86 | Team08 | 5 | 8 | 3 | Team17 | H | 5 | 0 | W | 9 | 5 | 4 | 2 | 2 | 1 |\n", "| 102 | Team08 | 6 | 11 | 3 | Team11 | H | 6 | 0 | W | 15 | 5 | 10 | 3 | 2 | 1 |\n", "| 126 | Team08 | 7 | 14 | 3 | Team19 | H | 3 | 0 | W | 18 | 5 | 13 | 4 | 2 | 1 |\n", "| 146 | Team08 | 8 | 15 | 1 | Team05 | H | 2 | 2 | D | 20 | 7 | 13 | 4 | 3 | 1 |\n", "| 164 | Team08 | 9 | 15 | 0 | Team12 | H | 2 | 4 | L | 22 | 11 | 11 | 4 | 3 | 2 |\n", "| 186 | Team08 | 10 | 18 | 3 | Team10 | H | 3 | 1 | W | 25 | 12 | 13 | 5 | 3 | 2 |\n", "| 219 | Team08 | 11 | 21 | 3 | Team18 | A | 2 | 1 | W | 27 | 13 | 14 | 6 | 3 | 2 |\n", "| 226 | Team08 | 12 | 24 | 3 | Team13 | H | 2 | 0 | W | 29 | 13 | 16 | 7 | 3 | 2 |\n", "| 246 | Team08 | 13 | 25 | 1 | Team20 | H | 2 | 2 | D | 31 | 15 | 16 | 7 | 4 | 2 |\n", "| 264 | Team08 | 14 | 28 | 3 | Team14 | H | 5 | 1 | W | 36 | 16 | 20 | 8 | 4 | 2 |\n", "| 286 | Team08 | 15 | 31 | 3 | Team02 | H | 1 | 0 | W | 37 | 16 | 21 | 9 | 4 | 2 |\n", "| 304 | Team08 | 16 | 31 | 0 | Team04 | H | 0 | 3 | L | 37 | 19 | 18 | 9 | 4 | 3 |\n", "| 333 | Team08 | 17 | 34 | 3 | Team15 | A | 2 | 1 | W | 39 | 20 | 19 | 10 | 4 | 3 |\n", "| 346 | Team08 | 18 | 35 | 1 | Team01 | H | 0 | 0 | D | 39 | 20 | 19 | 10 | 5 | 3 |\n", "| 366 | Team08 | 19 | 38 | 3 | Team07 | H | 2 | 0 | W | 41 | 20 | 21 | 11 | 5 | 3 |\n", "| 381 | Team08 | 20 | 38 | 0 | Team01 | A | 1 | 2 | L | 42 | 22 | 20 | 11 | 5 | 4 |\n", "| 419 | Team08 | 21 | 41 | 3 | Team17 | A | 2 | 1 | W | 44 | 23 | 21 | 12 | 5 | 4 |\n", "| 428 | Team08 | 22 | 44 | 3 | Team16 | H | 3 | 0 | W | 47 | 23 | 24 | 13 | 5 | 4 |\n", "| 455 | Team08 | 23 | 47 | 3 | Team13 | A | 2 | 1 | W | 49 | 24 | 25 | 14 | 5 | 4 |\n", "| 465 | Team08 | 24 | 50 | 3 | Team06 | A | 3 | 0 | W | 52 | 24 | 28 | 15 | 5 | 4 |\n", "| 491 | Team08 | 25 | 50 | 0 | Team10 | A | 2 | 6 | L | 54 | 30 | 24 | 15 | 5 | 5 |\n", "| 508 | Team08 | 26 | 53 | 3 | Team18 | H | 1 | 0 | W | 55 | 30 | 25 | 16 | 5 | 5 |\n", "| 535 | Team08 | 27 | 56 | 3 | Team11 | A | 3 | 0 | W | 58 | 30 | 28 | 17 | 5 | 5 |\n", "| 548 | Team08 | 28 | 59 | 3 | Team15 | H | 3 | 0 | W | 61 | 30 | 31 | 18 | 5 | 5 |\n", "| 579 | Team08 | 29 | 62 | 3 | Team20 | A | 6 | 1 | W | 67 | 31 | 36 | 19 | 5 | 5 |\n", "| 591 | Team08 | 30 | 63 | 1 | Team09 | A | 2 | 2 | D | 69 | 33 | 36 | 19 | 6 | 5 |\n", "| 601 | Team08 | 31 | 63 | 0 | Team03 | A | 0 | 5 | L | 69 | 38 | 31 | 19 | 6 | 6 |\n", "| 625 | Team08 | 32 | 63 | 0 | Team04 | A | 1 | 4 | L | 70 | 42 | 28 | 19 | 6 | 7 |\n", "| 641 | Team08 | 33 | 63 | 0 | Team02 | A | 0 | 3 | L | 70 | 45 | 25 | 19 | 6 | 8 |\n", "| 677 | Team08 | 34 | 66 | 3 | Team19 | A | 6 | 1 | W | 76 | 46 | 30 | 20 | 6 | 8 |\n", "| 687 | Team08 | 35 | 69 | 3 | Team07 | A | 1 | 0 | W | 77 | 46 | 31 | 21 | 6 | 8 |\n", "| 6 | Team09 | 1 | 0 | 0 | Team06 | H | 1 | 4 | L | 1 | 4 | -3 | 0 | 0 | 1 |\n", "| 21 | Team09 | 2 | 0 | 0 | Team01 | A | 1 | 4 | L | 2 | 8 | -6 | 0 | 0 | 2 |\n", "| 53 | Team09 | 3 | 3 | 3 | Team16 | A | 2 | 0 | W | 4 | 8 | -4 | 1 | 0 | 2 |\n", "| 65 | Team09 | 4 | 3 | 0 | Team08 | A | 1 | 2 | L | 5 | 10 | -5 | 1 | 0 | 3 |\n", "| 85 | Team09 | 5 | 3 | 0 | Team07 | A | 1 | 6 | L | 6 | 16 | -10 | 1 | 0 | 4 |\n", "| 119 | Team09 | 6 | 6 | 3 | Team20 | A | 3 | 1 | W | 9 | 17 | -8 | 2 | 0 | 4 |\n", "| 128 | Team09 | 7 | 9 | 3 | Team12 | H | 5 | 0 | W | 14 | 17 | -3 | 3 | 0 | 4 |\n", "| 157 | Team09 | 8 | 12 | 3 | Team18 | A | 5 | 3 | W | 19 | 20 | -1 | 4 | 0 | 4 |\n", "| 171 | Team09 | 9 | 12 | 0 | Team15 | A | 0 | 1 | L | 19 | 21 | -2 | 4 | 0 | 5 |\n", "| 183 | Team09 | 10 | 15 | 3 | Team04 | A | 2 | 1 | W | 21 | 22 | -1 | 5 | 0 | 5 |\n", "| 206 | Team09 | 11 | 16 | 1 | Team03 | H | 2 | 2 | D | 23 | 24 | -1 | 5 | 1 | 5 |\n", "| 229 | Team09 | 12 | 16 | 0 | Team11 | A | 0 | 3 | L | 23 | 27 | -4 | 5 | 1 | 6 |\n", "| 259 | Team09 | 13 | 17 | 1 | Team19 | A | 1 | 1 | D | 24 | 28 | -4 | 5 | 2 | 6 |\n", "| 269 | Team09 | 14 | 17 | 0 | Team13 | A | 1 | 2 | L | 25 | 30 | -5 | 5 | 2 | 7 |\n", "| 289 | Team09 | 15 | 17 | 0 | Team14 | A | 2 | 3 | L | 27 | 33 | -6 | 5 | 2 | 8 |\n", "| 301 | Team09 | 16 | 17 | 0 | Team02 | A | 0 | 2 | L | 27 | 35 | -8 | 5 | 2 | 9 |\n", "| 326 | Team09 | 17 | 18 | 1 | Team05 | H | 0 | 0 | D | 27 | 35 | -8 | 5 | 3 | 9 |\n", "| 355 | Team09 | 18 | 19 | 1 | Team17 | A | 0 | 0 | D | 27 | 35 | -8 | 5 | 4 | 9 |\n", "| 369 | Team09 | 19 | 19 | 0 | Team10 | A | 0 | 1 | L | 27 | 36 | -9 | 5 | 4 | 10 |\n", "| 388 | Team09 | 20 | 22 | 3 | Team17 | H | 3 | 1 | W | 30 | 37 | -7 | 6 | 4 | 10 |\n", "| 408 | Team09 | 21 | 22 | 0 | Team07 | H | 1 | 4 | L | 31 | 41 | -10 | 6 | 4 | 11 |\n", "| 427 | Team09 | 22 | 22 | 0 | Team06 | A | 2 | 4 | L | 33 | 45 | -12 | 6 | 4 | 12 |\n", "| 448 | Team09 | 23 | 23 | 1 | Team11 | H | 2 | 2 | D | 35 | 47 | -12 | 6 | 5 | 12 |\n", "| 466 | Team09 | 24 | 23 | 0 | Team01 | H | 0 | 2 | L | 35 | 49 | -14 | 6 | 5 | 13 |\n", "| 488 | Team09 | 25 | 23 | 0 | Team04 | H | 1 | 2 | L | 36 | 51 | -15 | 6 | 5 | 14 |\n", "| 503 | Team09 | 26 | 23 | 0 | Team03 | A | 0 | 3 | L | 36 | 54 | -18 | 6 | 5 | 15 |\n", "| 532 | Team09 | 27 | 26 | 3 | Team20 | H | 5 | 1 | W | 41 | 55 | -14 | 7 | 5 | 15 |\n", "| 545 | Team09 | 28 | 26 | 0 | Team05 | A | 0 | 1 | L | 41 | 56 | -15 | 7 | 5 | 16 |\n", "| 568 | Team09 | 29 | 29 | 3 | Team19 | H | 3 | 1 | W | 44 | 57 | -13 | 8 | 5 | 16 |\n", "| 590 | Team09 | 30 | 30 | 1 | Team08 | H | 2 | 2 | D | 46 | 59 | -13 | 8 | 6 | 16 |\n", "| 608 | Team09 | 31 | 33 | 3 | Team16 | H | 3 | 2 | W | 49 | 61 | -12 | 9 | 6 | 16 |\n", "| 630 | Team09 | 32 | 34 | 1 | Team02 | H | 2 | 2 | D | 51 | 63 | -12 | 9 | 7 | 16 |\n", "| 648 | Team09 | 33 | 37 | 3 | Team14 | H | 3 | 0 | W | 54 | 63 | -9 | 10 | 7 | 16 |\n", "| 671 | Team09 | 34 | 38 | 1 | Team12 | A | 1 | 1 | D | 55 | 64 | -9 | 10 | 8 | 16 |\n", "| 688 | Team09 | 35 | 41 | 3 | Team10 | H | 1 | 0 | W | 56 | 64 | -8 | 11 | 8 | 16 |\n", "| 13 | Team10 | 1 | 0 | 0 | Team14 | A | 1 | 2 | L | 1 | 2 | -1 | 0 | 0 | 1 |\n", "| 31 | Team10 | 2 | 0 | 0 | Team13 | A | 1 | 3 | L | 2 | 5 | -3 | 0 | 0 | 2 |\n", "| 43 | Team10 | 3 | 1 | 1 | Team02 | A | 1 | 1 | D | 3 | 6 | -3 | 0 | 1 | 2 |\n", "| 66 | Team10 | 4 | 1 | 0 | Team06 | H | 2 | 5 | L | 5 | 11 | -6 | 0 | 1 | 3 |\n", "| 99 | Team10 | 5 | 4 | 3 | Team20 | A | 3 | 0 | W | 8 | 11 | -3 | 1 | 1 | 3 |\n", "| 104 | Team10 | 6 | 4 | 0 | Team03 | H | 0 | 5 | L | 8 | 16 | -8 | 1 | 1 | 4 |\n", "| 121 | Team10 | 7 | 7 | 3 | Team01 | A | 2 | 1 | W | 10 | 17 | -7 | 2 | 1 | 4 |\n", "| 148 | Team10 | 8 | 10 | 3 | Team19 | H | 4 | 2 | W | 14 | 19 | -5 | 3 | 1 | 4 |\n", "| 166 | Team10 | 9 | 13 | 3 | Team17 | H | 4 | 2 | W | 18 | 21 | -3 | 4 | 1 | 4 |\n", "| 187 | Team10 | 10 | 13 | 0 | Team08 | A | 1 | 3 | L | 19 | 24 | -5 | 4 | 1 | 5 |\n", "| 203 | Team10 | 11 | 14 | 1 | Team04 | A | 4 | 4 | D | 23 | 28 | -5 | 4 | 2 | 5 |\n", "| 237 | Team10 | 12 | 17 | 3 | Team18 | A | 5 | 0 | W | 28 | 28 | 0 | 5 | 2 | 5 |\n", "| 255 | Team10 | 13 | 20 | 3 | Team16 | A | 2 | 1 | W | 30 | 29 | 1 | 6 | 2 | 5 |\n", "| 266 | Team10 | 14 | 20 | 0 | Team05 | H | 1 | 3 | L | 31 | 32 | -1 | 6 | 2 | 6 |\n", "| 291 | Team10 | 15 | 20 | 0 | Team15 | A | 0 | 1 | L | 31 | 33 | -2 | 6 | 2 | 7 |\n", "| 306 | Team10 | 16 | 20 | 0 | Team12 | H | 1 | 2 | L | 32 | 35 | -3 | 6 | 2 | 8 |\n", "| 325 | Team10 | 17 | 20 | 0 | Team07 | A | 0 | 3 | L | 32 | 38 | -6 | 6 | 2 | 9 |\n", "| 348 | Team10 | 18 | 20 | 0 | Team11 | H | 1 | 3 | L | 33 | 41 | -8 | 6 | 2 | 10 |\n", "| 368 | Team10 | 19 | 23 | 3 | Team09 | H | 1 | 0 | W | 34 | 41 | -7 | 7 | 2 | 10 |\n", "| 391 | Team10 | 20 | 23 | 0 | Team11 | A | 1 | 2 | L | 35 | 43 | -8 | 7 | 2 | 11 |\n", "| 410 | Team10 | 21 | 26 | 3 | Team20 | H | 5 | 0 | W | 40 | 43 | -3 | 8 | 2 | 11 |\n", "| 430 | Team10 | 22 | 26 | 0 | Team14 | H | 1 | 2 | L | 41 | 45 | -4 | 8 | 2 | 12 |\n", "| 450 | Team10 | 23 | 29 | 3 | Team18 | H | 6 | 0 | W | 47 | 45 | 2 | 9 | 2 | 12 |\n", "| 468 | Team10 | 24 | 32 | 3 | Team13 | H | 1 | 0 | W | 48 | 45 | 3 | 10 | 2 | 12 |\n", "| 490 | Team10 | 25 | 35 | 3 | Team08 | H | 6 | 2 | W | 54 | 47 | 7 | 11 | 2 | 12 |\n", "| 510 | Team10 | 26 | 36 | 1 | Team04 | H | 1 | 1 | D | 55 | 48 | 7 | 11 | 3 | 12 |\n", "| 523 | Team10 | 27 | 36 | 0 | Team03 | A | 1 | 4 | L | 56 | 52 | 4 | 11 | 3 | 13 |\n", "| 550 | Team10 | 28 | 39 | 3 | Team07 | H | 3 | 1 | W | 59 | 53 | 6 | 12 | 3 | 13 |\n", "| 570 | Team10 | 29 | 42 | 3 | Team16 | H | 2 | 1 | W | 61 | 54 | 7 | 13 | 3 | 13 |\n", "| 587 | Team10 | 30 | 45 | 3 | Team06 | A | 1 | 0 | W | 62 | 54 | 8 | 14 | 3 | 13 |\n", "| 610 | Team10 | 31 | 45 | 0 | Team02 | H | 0 | 3 | L | 62 | 57 | 5 | 14 | 3 | 14 |\n", "| 635 | Team10 | 32 | 45 | 0 | Team12 | A | 0 | 2 | L | 62 | 59 | 3 | 14 | 3 | 15 |\n", "| 650 | Team10 | 33 | 48 | 3 | Team15 | H | 5 | 2 | W | 67 | 61 | 6 | 15 | 3 | 15 |\n", "| 668 | Team10 | 34 | 48 | 0 | Team01 | H | 0 | 1 | L | 67 | 62 | 5 | 15 | 3 | 16 |\n", "| 689 | Team10 | 35 | 48 | 0 | Team09 | A | 0 | 1 | L | 67 | 63 | 4 | 15 | 3 | 17 |\n", "| 8 | Team11 | 1 | 3 | 3 | Team17 | H | 2 | 1 | W | 2 | 1 | 1 | 1 | 0 | 0 |\n", "| 27 | Team11 | 2 | 3 | 0 | Team07 | A | 0 | 3 | L | 2 | 4 | -2 | 1 | 0 | 1 |\n", "| 41 | Team11 | 3 | 3 | 0 | Team01 | A | 1 | 5 | L | 3 | 9 | -6 | 1 | 0 | 2 |\n", "| 71 | Team11 | 4 | 3 | 0 | Team15 | A | 1 | 3 | L | 4 | 12 | -8 | 1 | 0 | 3 |\n", "| 83 | Team11 | 5 | 3 | 0 | Team04 | A | 0 | 4 | L | 4 | 16 | -12 | 1 | 0 | 4 |\n", "| 103 | Team11 | 6 | 3 | 0 | Team08 | A | 0 | 6 | L | 4 | 22 | -18 | 1 | 0 | 5 |\n", "| 130 | Team11 | 7 | 6 | 3 | Team05 | H | 2 | 0 | W | 6 | 22 | -16 | 2 | 0 | 5 |\n", "| 155 | Team11 | 8 | 7 | 1 | Team16 | A | 3 | 3 | D | 9 | 25 | -16 | 2 | 1 | 5 |\n", "| 175 | Team11 | 9 | 10 | 3 | Team18 | A | 3 | 1 | W | 12 | 26 | -14 | 3 | 1 | 5 |\n", "| 191 | Team11 | 10 | 11 | 1 | Team14 | A | 1 | 1 | D | 13 | 27 | -14 | 3 | 2 | 5 |\n", "| 208 | Team11 | 11 | 11 | 0 | Team06 | H | 0 | 2 | L | 13 | 29 | -16 | 3 | 2 | 6 |\n", "| 228 | Team11 | 12 | 14 | 3 | Team09 | H | 3 | 0 | W | 16 | 29 | -13 | 4 | 2 | 6 |\n", "| 248 | Team11 | 13 | 17 | 3 | Team12 | H | 7 | 2 | W | 23 | 31 | -8 | 5 | 2 | 6 |\n", "| 279 | Team11 | 14 | 20 | 3 | Team20 | A | 6 | 0 | W | 29 | 31 | -2 | 6 | 2 | 6 |\n", "| 297 | Team11 | 15 | 20 | 0 | Team19 | A | 0 | 4 | L | 29 | 35 | -6 | 6 | 2 | 7 |\n", "| 309 | Team11 | 16 | 23 | 3 | Team13 | A | 2 | 1 | W | 31 | 36 | -5 | 7 | 2 | 7 |\n", "| 328 | Team11 | 17 | 23 | 0 | Team03 | H | 0 | 1 | L | 31 | 37 | -6 | 7 | 2 | 8 |\n", "| 349 | Team11 | 18 | 26 | 3 | Team10 | A | 3 | 1 | W | 34 | 38 | -4 | 8 | 2 | 8 |\n", "| 361 | Team11 | 19 | 26 | 0 | Team02 | A | 1 | 2 | L | 35 | 40 | -5 | 8 | 2 | 9 |\n", "| 390 | Team11 | 20 | 29 | 3 | Team10 | H | 2 | 1 | W | 37 | 41 | -4 | 9 | 2 | 9 |\n", "| 412 | Team11 | 21 | 29 | 0 | Team04 | H | 1 | 2 | L | 38 | 43 | -5 | 9 | 2 | 10 |\n", "| 437 | Team11 | 22 | 30 | 1 | Team17 | A | 1 | 1 | D | 39 | 44 | -5 | 9 | 3 | 10 |\n", "| 449 | Team11 | 23 | 31 | 1 | Team09 | A | 2 | 2 | D | 41 | 46 | -5 | 9 | 4 | 10 |\n", "| 470 | Team11 | 24 | 31 | 0 | Team07 | H | 0 | 2 | L | 41 | 48 | -7 | 9 | 4 | 11 |\n", "| 492 | Team11 | 25 | 34 | 3 | Team14 | H | 4 | 2 | W | 45 | 50 | -5 | 10 | 4 | 11 |\n", "| 507 | Team11 | 26 | 34 | 0 | Team06 | A | 1 | 2 | L | 46 | 52 | -6 | 10 | 4 | 12 |\n", "| 534 | Team11 | 27 | 34 | 0 | Team08 | H | 0 | 3 | L | 46 | 55 | -9 | 10 | 4 | 13 |\n", "| 543 | Team11 | 28 | 34 | 0 | Team03 | A | 0 | 5 | L | 46 | 60 | -14 | 10 | 4 | 14 |\n", "| 573 | Team11 | 29 | 35 | 1 | Team12 | A | 1 | 1 | D | 47 | 61 | -14 | 10 | 5 | 14 |\n", "| 592 | Team11 | 30 | 38 | 3 | Team15 | H | 3 | 0 | W | 50 | 61 | -11 | 11 | 5 | 14 |\n", "| 612 | Team11 | 31 | 38 | 0 | Team01 | H | 2 | 5 | L | 52 | 66 | -14 | 11 | 5 | 15 |\n", "| 632 | Team11 | 32 | 41 | 3 | Team13 | H | 2 | 1 | W | 54 | 67 | -13 | 12 | 5 | 15 |\n", "| 652 | Team11 | 33 | 44 | 3 | Team19 | H | 3 | 0 | W | 57 | 67 | -10 | 13 | 5 | 15 |\n", "| 665 | Team11 | 34 | 44 | 0 | Team05 | A | 1 | 2 | L | 58 | 69 | -11 | 13 | 5 | 16 |\n", "| 690 | Team11 | 35 | 44 | 0 | Team02 | H | 1 | 4 | L | 59 | 73 | -14 | 13 | 5 | 17 |\n", "| 10 | Team12 | 1 | 0 | 0 | Team03 | H | 1 | 2 | L | 1 | 2 | -1 | 0 | 0 | 1 |\n", "| 35 | Team12 | 2 | 3 | 3 | Team16 | A | 3 | 0 | W | 4 | 2 | 2 | 1 | 0 | 1 |\n", "| 55 | Team12 | 3 | 6 | 3 | Team18 | A | 4 | 1 | W | 8 | 3 | 5 | 2 | 0 | 1 |\n", "| 79 | Team12 | 4 | 6 | 0 | Team20 | A | 0 | 3 | L | 8 | 6 | 2 | 2 | 0 | 2 |\n", "| 81 | Team12 | 5 | 6 | 0 | Team01 | A | 3 | 5 | L | 11 | 11 | 0 | 2 | 0 | 3 |\n", "| 107 | Team12 | 6 | 6 | 0 | Team13 | A | 1 | 3 | L | 12 | 14 | -2 | 2 | 0 | 4 |\n", "| 129 | Team12 | 7 | 6 | 0 | Team09 | A | 0 | 5 | L | 12 | 19 | -7 | 2 | 0 | 5 |\n", "| 153 | Team12 | 8 | 6 | 0 | Team15 | A | 1 | 7 | L | 13 | 26 | -13 | 2 | 0 | 6 |\n", "| 165 | Team12 | 9 | 9 | 3 | Team08 | A | 4 | 2 | W | 17 | 28 | -11 | 3 | 0 | 6 |\n", "| 185 | Team12 | 10 | 9 | 0 | Team07 | A | 1 | 2 | L | 18 | 30 | -12 | 3 | 0 | 7 |\n", "| 210 | Team12 | 11 | 9 | 0 | Team05 | H | 0 | 2 | L | 18 | 32 | -14 | 3 | 0 | 8 |\n", "| 231 | Team12 | 12 | 9 | 0 | Team14 | A | 0 | 3 | L | 18 | 35 | -17 | 3 | 0 | 9 |\n", "| 249 | Team12 | 13 | 9 | 0 | Team11 | A | 2 | 7 | L | 20 | 42 | -22 | 3 | 0 | 10 |\n", "| 261 | Team12 | 14 | 9 | 0 | Team02 | A | 0 | 1 | L | 20 | 43 | -23 | 3 | 0 | 11 |\n", "| 283 | Team12 | 15 | 9 | 0 | Team04 | A | 0 | 4 | L | 20 | 47 | -27 | 3 | 0 | 12 |\n", "| 307 | Team12 | 16 | 12 | 3 | Team10 | A | 2 | 1 | W | 22 | 48 | -26 | 4 | 0 | 12 |\n", "| 339 | Team12 | 17 | 12 | 0 | Team19 | A | 0 | 1 | L | 22 | 49 | -27 | 4 | 0 | 13 |\n", "| 345 | Team12 | 18 | 13 | 1 | Team06 | A | 0 | 0 | D | 22 | 49 | -27 | 4 | 1 | 13 |\n", "| 375 | Team12 | 19 | 14 | 1 | Team17 | A | 1 | 1 | D | 23 | 50 | -27 | 4 | 2 | 13 |\n", "| 392 | Team12 | 20 | 15 | 1 | Team06 | H | 1 | 1 | D | 24 | 51 | -27 | 4 | 3 | 13 |\n", "| 414 | Team12 | 21 | 15 | 0 | Team01 | H | 0 | 1 | L | 24 | 52 | -28 | 4 | 3 | 14 |\n", "| 423 | Team12 | 22 | 15 | 0 | Team03 | A | 0 | 2 | L | 24 | 54 | -30 | 4 | 3 | 15 |\n", "| 452 | Team12 | 23 | 15 | 0 | Team14 | H | 1 | 3 | L | 25 | 57 | -32 | 4 | 3 | 16 |\n", "| 472 | Team12 | 24 | 18 | 3 | Team16 | H | 1 | 0 | W | 26 | 57 | -31 | 5 | 3 | 16 |\n", "| 494 | Team12 | 25 | 18 | 0 | Team07 | H | 1 | 2 | L | 27 | 59 | -32 | 5 | 3 | 17 |\n", "| 505 | Team12 | 26 | 18 | 0 | Team05 | A | 1 | 3 | L | 28 | 62 | -34 | 5 | 3 | 18 |\n", "| 536 | Team12 | 27 | 21 | 3 | Team13 | H | 2 | 0 | W | 30 | 62 | -32 | 6 | 3 | 18 |\n", "| 552 | Team12 | 28 | 24 | 3 | Team19 | H | 3 | 2 | W | 33 | 64 | -31 | 7 | 3 | 18 |\n", "| 572 | Team12 | 29 | 25 | 1 | Team11 | H | 1 | 1 | D | 34 | 65 | -31 | 7 | 4 | 18 |\n", "| 594 | Team12 | 30 | 26 | 1 | Team20 | H | 2 | 2 | D | 36 | 67 | -31 | 7 | 5 | 18 |\n", "| 614 | Team12 | 31 | 26 | 0 | Team18 | H | 0 | 1 | L | 36 | 68 | -32 | 7 | 5 | 19 |\n", "| 634 | Team12 | 32 | 29 | 3 | Team10 | H | 2 | 0 | W | 38 | 68 | -30 | 8 | 5 | 19 |\n", "| 654 | Team12 | 33 | 29 | 0 | Team04 | H | 0 | 1 | L | 38 | 69 | -31 | 8 | 5 | 20 |\n", "| 670 | Team12 | 34 | 30 | 1 | Team09 | H | 1 | 1 | D | 39 | 70 | -31 | 8 | 6 | 20 |\n", "| 692 | Team12 | 35 | 33 | 3 | Team17 | H | 6 | 1 | W | 45 | 71 | -26 | 9 | 6 | 20 |\n", "| 5 | Team13 | 1 | 0 | 0 | Team07 | A | 2 | 3 | L | 2 | 3 | -1 | 0 | 0 | 1 |\n", "| 30 | Team13 | 2 | 3 | 3 | Team10 | H | 3 | 1 | W | 5 | 4 | 1 | 1 | 0 | 1 |\n", "| 48 | Team13 | 3 | 6 | 3 | Team17 | H | 4 | 2 | W | 9 | 6 | 3 | 2 | 0 | 1 |\n", "| 68 | Team13 | 4 | 6 | 0 | Team05 | H | 2 | 3 | L | 11 | 9 | 2 | 2 | 0 | 2 |\n", "| 88 | Team13 | 5 | 7 | 1 | Team02 | H | 1 | 1 | D | 12 | 10 | 2 | 2 | 1 | 2 |\n", "| 106 | Team13 | 6 | 10 | 3 | Team12 | H | 3 | 1 | W | 15 | 11 | 4 | 3 | 1 | 2 |\n", "| 139 | Team13 | 7 | 13 | 3 | Team18 | A | 3 | 1 | W | 18 | 12 | 6 | 4 | 1 | 2 |\n", "| 150 | Team13 | 8 | 13 | 0 | Team06 | H | 1 | 3 | L | 19 | 15 | 4 | 4 | 1 | 3 |\n", "| 168 | Team13 | 9 | 13 | 0 | Team03 | H | 1 | 4 | L | 20 | 19 | 1 | 4 | 1 | 4 |\n", "| 188 | Team13 | 10 | 16 | 3 | Team19 | H | 4 | 1 | W | 24 | 20 | 4 | 5 | 1 | 4 |\n", "| 201 | Team13 | 11 | 16 | 0 | Team01 | A | 1 | 4 | L | 25 | 24 | 1 | 5 | 1 | 5 |\n", "| 227 | Team13 | 12 | 16 | 0 | Team08 | A | 0 | 2 | L | 25 | 26 | -1 | 5 | 1 | 6 |\n", "| 253 | Team13 | 13 | 16 | 0 | Team15 | A | 0 | 1 | L | 25 | 27 | -2 | 5 | 1 | 7 |\n", "| 268 | Team13 | 14 | 19 | 3 | Team09 | H | 2 | 1 | W | 27 | 28 | -1 | 6 | 1 | 7 |\n", "| 299 | Team13 | 15 | 19 | 0 | Team20 | A | 0 | 1 | L | 27 | 29 | -2 | 6 | 1 | 8 |\n", "| 308 | Team13 | 16 | 19 | 0 | Team11 | H | 1 | 2 | L | 28 | 31 | -3 | 6 | 1 | 9 |\n", "| 335 | Team13 | 17 | 19 | 0 | Team16 | A | 0 | 2 | L | 28 | 33 | -5 | 6 | 1 | 10 |\n", "| 350 | Team13 | 18 | 20 | 1 | Team04 | H | 2 | 2 | D | 30 | 35 | -5 | 6 | 2 | 10 |\n", "| 370 | Team13 | 19 | 20 | 0 | Team14 | H | 1 | 2 | L | 31 | 37 | -6 | 6 | 2 | 11 |\n", "| 383 | Team13 | 20 | 20 | 0 | Team04 | A | 1 | 2 | L | 32 | 39 | -7 | 6 | 2 | 12 |\n", "| 401 | Team13 | 21 | 20 | 0 | Team02 | A | 0 | 5 | L | 32 | 44 | -12 | 6 | 2 | 13 |\n", "| 432 | Team13 | 22 | 20 | 0 | Team07 | H | 0 | 2 | L | 32 | 46 | -14 | 6 | 2 | 14 |\n", "| 454 | Team13 | 23 | 20 | 0 | Team08 | H | 1 | 2 | L | 33 | 48 | -15 | 6 | 2 | 15 |\n", "| 469 | Team13 | 24 | 20 | 0 | Team10 | A | 0 | 1 | L | 33 | 49 | -16 | 6 | 2 | 16 |\n", "| 499 | Team13 | 25 | 23 | 3 | Team19 | A | 2 | 1 | W | 35 | 50 | -15 | 7 | 2 | 16 |\n", "| 512 | Team13 | 26 | 24 | 1 | Team01 | H | 0 | 0 | D | 35 | 50 | -15 | 7 | 3 | 16 |\n", "| 537 | Team13 | 27 | 24 | 0 | Team12 | A | 0 | 2 | L | 35 | 52 | -17 | 7 | 3 | 17 |\n", "| 554 | Team13 | 28 | 27 | 3 | Team16 | H | 2 | 0 | W | 37 | 52 | -15 | 8 | 3 | 17 |\n", "| 574 | Team13 | 29 | 27 | 0 | Team15 | H | 0 | 3 | L | 37 | 55 | -18 | 8 | 3 | 18 |\n", "| 585 | Team13 | 30 | 27 | 0 | Team05 | A | 0 | 6 | L | 37 | 61 | -24 | 8 | 3 | 19 |\n", "| 619 | Team13 | 31 | 30 | 3 | Team17 | A | 2 | 1 | W | 39 | 62 | -23 | 9 | 3 | 19 |\n", "| 633 | Team13 | 32 | 30 | 0 | Team11 | A | 1 | 2 | L | 40 | 64 | -24 | 9 | 3 | 20 |\n", "| 656 | Team13 | 33 | 33 | 3 | Team20 | H | 4 | 2 | W | 44 | 66 | -22 | 10 | 3 | 20 |\n", "| 672 | Team13 | 34 | 36 | 3 | Team18 | H | 6 | 2 | W | 50 | 68 | -18 | 11 | 3 | 20 |\n", "| 695 | Team13 | 35 | 37 | 1 | Team14 | A | 1 | 1 | D | 51 | 69 | -18 | 11 | 4 | 20 |\n", "| 12 | Team14 | 1 | 3 | 3 | Team10 | H | 2 | 1 | W | 2 | 1 | 1 | 1 | 0 | 0 |\n", "| 25 | Team14 | 2 | 3 | 0 | Team04 | A | 1 | 4 | L | 3 | 5 | -2 | 1 | 0 | 1 |\n", "| 45 | Team14 | 3 | 3 | 0 | Team07 | A | 0 | 1 | L | 3 | 6 | -3 | 1 | 0 | 2 |\n", "| 77 | Team14 | 4 | 4 | 1 | Team18 | A | 1 | 1 | D | 4 | 7 | -3 | 1 | 1 | 2 |\n", "| 97 | Team14 | 5 | 4 | 0 | Team19 | A | 1 | 4 | L | 5 | 11 | -6 | 1 | 1 | 3 |\n", "| 109 | Team14 | 6 | 7 | 3 | Team15 | A | 4 | 0 | W | 9 | 11 | -2 | 2 | 1 | 3 |\n", "| 132 | Team14 | 7 | 8 | 1 | Team03 | H | 2 | 2 | D | 11 | 13 | -2 | 2 | 2 | 3 |\n", "| 141 | Team14 | 8 | 8 | 0 | Team01 | A | 1 | 3 | L | 12 | 16 | -4 | 2 | 2 | 4 |\n", "| 173 | Team14 | 9 | 9 | 1 | Team16 | A | 1 | 1 | D | 13 | 17 | -4 | 2 | 3 | 4 |\n", "| 190 | Team14 | 10 | 10 | 1 | Team11 | H | 1 | 1 | D | 14 | 18 | -4 | 2 | 4 | 4 |\n", "| 212 | Team14 | 11 | 13 | 3 | Team17 | H | 4 | 1 | W | 18 | 19 | -1 | 3 | 4 | 4 |\n", "| 230 | Team14 | 12 | 16 | 3 | Team12 | H | 3 | 0 | W | 21 | 19 | 2 | 4 | 4 | 4 |\n", "| 250 | Team14 | 13 | 16 | 0 | Team05 | H | 1 | 6 | L | 22 | 25 | -3 | 4 | 4 | 5 |\n", "| 265 | Team14 | 14 | 16 | 0 | Team08 | A | 1 | 5 | L | 23 | 30 | -7 | 4 | 4 | 6 |\n", "| 288 | Team14 | 15 | 19 | 3 | Team09 | H | 3 | 2 | W | 26 | 32 | -6 | 5 | 4 | 6 |\n", "| 319 | Team14 | 16 | 20 | 1 | Team20 | A | 1 | 1 | D | 27 | 33 | -6 | 5 | 5 | 6 |\n", "| 330 | Team14 | 17 | 20 | 0 | Team06 | H | 0 | 5 | L | 27 | 38 | -11 | 5 | 5 | 7 |\n", "| 341 | Team14 | 18 | 20 | 0 | Team02 | A | 2 | 5 | L | 29 | 43 | -14 | 5 | 5 | 8 |\n", "| 371 | Team14 | 19 | 23 | 3 | Team13 | A | 2 | 1 | W | 31 | 44 | -13 | 6 | 5 | 8 |\n", "| 394 | Team14 | 20 | 23 | 0 | Team02 | H | 2 | 5 | L | 33 | 49 | -16 | 6 | 5 | 9 |\n", "| 416 | Team14 | 21 | 26 | 3 | Team19 | H | 3 | 1 | W | 36 | 50 | -14 | 7 | 5 | 9 |\n", "| 431 | Team14 | 22 | 29 | 3 | Team10 | A | 2 | 1 | W | 38 | 51 | -13 | 8 | 5 | 9 |\n", "| 453 | Team14 | 23 | 32 | 3 | Team12 | A | 3 | 1 | W | 41 | 52 | -11 | 9 | 5 | 9 |\n", "| 474 | Team14 | 24 | 32 | 0 | Team04 | H | 0 | 1 | L | 41 | 53 | -12 | 9 | 5 | 10 |\n", "| 493 | Team14 | 25 | 32 | 0 | Team11 | A | 2 | 4 | L | 43 | 57 | -14 | 9 | 5 | 11 |\n", "| 515 | Team14 | 26 | 35 | 3 | Team17 | A | 4 | 2 | W | 47 | 59 | -12 | 10 | 5 | 11 |\n", "| 538 | Team14 | 27 | 38 | 3 | Team15 | H | 2 | 0 | W | 49 | 59 | -10 | 11 | 5 | 11 |\n", "| 547 | Team14 | 28 | 38 | 0 | Team06 | A | 1 | 5 | L | 50 | 64 | -14 | 11 | 5 | 12 |\n", "| 565 | Team14 | 29 | 38 | 0 | Team05 | A | 1 | 3 | L | 51 | 67 | -16 | 11 | 5 | 13 |\n", "| 596 | Team14 | 30 | 41 | 3 | Team18 | H | 6 | 0 | W | 57 | 67 | -10 | 12 | 5 | 13 |\n", "| 616 | Team14 | 31 | 41 | 0 | Team07 | H | 0 | 1 | L | 57 | 68 | -11 | 12 | 5 | 14 |\n", "| 636 | Team14 | 32 | 44 | 3 | Team20 | H | 4 | 1 | W | 61 | 69 | -8 | 13 | 5 | 14 |\n", "| 649 | Team14 | 33 | 44 | 0 | Team09 | A | 0 | 3 | L | 61 | 72 | -11 | 13 | 5 | 15 |\n", "| 663 | Team14 | 34 | 44 | 0 | Team03 | A | 1 | 5 | L | 62 | 77 | -15 | 13 | 5 | 16 |\n", "| 694 | Team14 | 35 | 45 | 1 | Team13 | H | 1 | 1 | D | 63 | 78 | -15 | 13 | 6 | 16 |\n", "| 17 | Team15 | 1 | 0 | 0 | Team18 | A | 3 | 4 | L | 3 | 4 | -1 | 0 | 0 | 1 |\n", "| 32 | Team15 | 2 | 1 | 1 | Team03 | H | 1 | 1 | D | 4 | 5 | -1 | 0 | 1 | 1 |\n", "| 50 | Team15 | 3 | 1 | 0 | Team05 | H | 0 | 4 | L | 4 | 9 | -5 | 0 | 1 | 2 |\n", "| 70 | Team15 | 4 | 4 | 3 | Team11 | H | 3 | 1 | W | 7 | 10 | -3 | 1 | 1 | 2 |\n", "| 90 | Team15 | 5 | 5 | 1 | Team06 | H | 2 | 2 | D | 9 | 12 | -3 | 1 | 2 | 2 |\n", "| 108 | Team15 | 6 | 5 | 0 | Team14 | H | 0 | 4 | L | 9 | 16 | -7 | 1 | 2 | 3 |\n", "| 134 | Team15 | 7 | 8 | 3 | Team20 | H | 1 | 0 | W | 10 | 16 | -6 | 2 | 2 | 3 |\n", "| 152 | Team15 | 8 | 11 | 3 | Team12 | H | 7 | 1 | W | 17 | 17 | 0 | 3 | 2 | 3 |\n", "| 170 | Team15 | 9 | 14 | 3 | Team09 | H | 1 | 0 | W | 18 | 17 | 1 | 4 | 2 | 3 |\n", "| 192 | Team15 | 10 | 14 | 0 | Team17 | H | 0 | 3 | L | 18 | 20 | -2 | 4 | 2 | 4 |\n", "| 214 | Team15 | 11 | 15 | 1 | Team19 | H | 0 | 0 | D | 18 | 20 | -2 | 4 | 3 | 4 |\n", "| 232 | Team15 | 12 | 15 | 0 | Team02 | H | 0 | 3 | L | 18 | 23 | -5 | 4 | 3 | 5 |\n", "| 252 | Team15 | 13 | 18 | 3 | Team13 | H | 1 | 0 | W | 19 | 23 | -4 | 5 | 3 | 5 |\n", "| 270 | Team15 | 14 | 21 | 3 | Team04 | H | 2 | 1 | W | 21 | 24 | -3 | 6 | 3 | 5 |\n", "| 290 | Team15 | 15 | 24 | 3 | Team10 | H | 1 | 0 | W | 22 | 24 | -2 | 7 | 3 | 5 |\n", "| 310 | Team15 | 16 | 24 | 0 | Team07 | H | 0 | 2 | L | 22 | 26 | -4 | 7 | 3 | 6 |\n", "| 332 | Team15 | 17 | 24 | 0 | Team08 | H | 1 | 2 | L | 23 | 28 | -5 | 7 | 3 | 7 |\n", "| 352 | Team15 | 18 | 24 | 0 | Team16 | H | 1 | 2 | L | 24 | 30 | -6 | 7 | 3 | 8 |\n", "| 372 | Team15 | 19 | 24 | 0 | Team01 | H | 0 | 3 | L | 24 | 33 | -9 | 7 | 3 | 9 |\n", "| 397 | Team15 | 20 | 24 | 0 | Team16 | A | 2 | 3 | L | 26 | 36 | -10 | 7 | 3 | 10 |\n", "| 407 | Team15 | 21 | 24 | 0 | Team06 | A | 0 | 3 | L | 26 | 39 | -13 | 7 | 3 | 11 |\n", "| 434 | Team15 | 22 | 25 | 1 | Team18 | H | 2 | 2 | D | 28 | 41 | -13 | 7 | 4 | 11 |\n", "| 441 | Team15 | 23 | 25 | 0 | Team02 | A | 1 | 2 | L | 29 | 43 | -14 | 7 | 4 | 12 |\n", "| 461 | Team15 | 24 | 25 | 0 | Team03 | A | 1 | 4 | L | 30 | 47 | -17 | 7 | 4 | 13 |\n", "| 497 | Team15 | 25 | 28 | 3 | Team17 | A | 2 | 1 | W | 32 | 48 | -16 | 8 | 4 | 13 |\n", "| 517 | Team15 | 26 | 29 | 1 | Team19 | A | 1 | 1 | D | 33 | 49 | -16 | 8 | 5 | 13 |\n", "| 539 | Team15 | 27 | 29 | 0 | Team14 | A | 0 | 2 | L | 33 | 51 | -18 | 8 | 5 | 14 |\n", "| 549 | Team15 | 28 | 29 | 0 | Team08 | A | 0 | 3 | L | 33 | 54 | -21 | 8 | 5 | 15 |\n", "| 575 | Team15 | 29 | 32 | 3 | Team13 | A | 3 | 0 | W | 36 | 54 | -18 | 9 | 5 | 15 |\n", "| 593 | Team15 | 30 | 32 | 0 | Team11 | A | 0 | 3 | L | 36 | 57 | -21 | 9 | 5 | 16 |\n", "| 605 | Team15 | 31 | 32 | 0 | Team05 | A | 1 | 5 | L | 37 | 62 | -25 | 9 | 5 | 17 |\n", "| 629 | Team15 | 32 | 32 | 0 | Team07 | A | 0 | 4 | L | 37 | 66 | -29 | 9 | 5 | 18 |\n", "| 651 | Team15 | 33 | 32 | 0 | Team10 | A | 2 | 5 | L | 39 | 71 | -32 | 9 | 5 | 19 |\n", "| 679 | Team15 | 34 | 33 | 1 | Team20 | A | 0 | 0 | D | 39 | 71 | -32 | 9 | 6 | 19 |\n", "| 681 | Team15 | 35 | 33 | 0 | Team01 | A | 1 | 3 | L | 40 | 74 | -34 | 9 | 6 | 20 |\n", "| 14 | Team16 | 1 | 1 | 1 | Team08 | H | 1 | 1 | D | 1 | 1 | 0 | 0 | 1 | 0 |\n", "| 34 | Team16 | 2 | 1 | 0 | Team12 | H | 0 | 3 | L | 1 | 4 | -3 | 0 | 1 | 1 |\n", "| 52 | Team16 | 3 | 1 | 0 | Team09 | H | 0 | 2 | L | 1 | 6 | -5 | 0 | 1 | 2 |\n", "| 72 | Team16 | 4 | 2 | 1 | Team04 | H | 1 | 1 | D | 2 | 7 | -5 | 0 | 2 | 2 |\n", "| 92 | Team16 | 5 | 5 | 3 | Team05 | H | 1 | 0 | W | 3 | 7 | -4 | 1 | 2 | 2 |\n", "| 110 | Team16 | 6 | 5 | 0 | Team07 | H | 1 | 4 | L | 4 | 11 | -7 | 1 | 2 | 3 |\n", "| 136 | Team16 | 7 | 5 | 0 | Team02 | H | 1 | 3 | L | 5 | 14 | -9 | 1 | 2 | 4 |\n", "| 154 | Team16 | 8 | 6 | 1 | Team11 | H | 3 | 3 | D | 8 | 17 | -9 | 1 | 3 | 4 |\n", "| 172 | Team16 | 9 | 7 | 1 | Team14 | H | 1 | 1 | D | 9 | 18 | -9 | 1 | 4 | 4 |\n", "| 194 | Team16 | 10 | 7 | 0 | Team03 | H | 1 | 5 | L | 10 | 23 | -13 | 1 | 4 | 5 |\n", "| 216 | Team16 | 11 | 8 | 1 | Team20 | H | 0 | 0 | D | 10 | 23 | -13 | 1 | 5 | 5 |\n", "| 234 | Team16 | 12 | 11 | 3 | Team17 | H | 4 | 3 | W | 14 | 26 | -12 | 2 | 5 | 5 |\n", "| 254 | Team16 | 13 | 11 | 0 | Team10 | H | 1 | 2 | L | 15 | 28 | -13 | 2 | 5 | 6 |\n", "| 272 | Team16 | 14 | 11 | 0 | Team01 | H | 0 | 5 | L | 15 | 33 | -18 | 2 | 5 | 7 |\n", "| 292 | Team16 | 15 | 11 | 0 | Team06 | H | 1 | 3 | L | 16 | 36 | -20 | 2 | 5 | 8 |\n", "| 317 | Team16 | 16 | 11 | 0 | Team19 | A | 0 | 2 | L | 16 | 38 | -22 | 2 | 5 | 9 |\n", "| 334 | Team16 | 17 | 14 | 3 | Team13 | H | 2 | 0 | W | 18 | 38 | -20 | 3 | 5 | 9 |\n", "| 353 | Team16 | 18 | 17 | 3 | Team15 | A | 2 | 1 | W | 20 | 39 | -19 | 4 | 5 | 9 |\n", "| 377 | Team16 | 19 | 20 | 3 | Team18 | A | 2 | 1 | W | 22 | 40 | -18 | 5 | 5 | 9 |\n", "| 396 | Team16 | 20 | 23 | 3 | Team15 | H | 3 | 2 | W | 25 | 42 | -17 | 6 | 5 | 9 |\n", "| 405 | Team16 | 21 | 23 | 0 | Team05 | A | 0 | 1 | L | 25 | 43 | -18 | 6 | 5 | 10 |\n", "| 429 | Team16 | 22 | 23 | 0 | Team08 | A | 0 | 3 | L | 25 | 46 | -21 | 6 | 5 | 11 |\n", "| 457 | Team16 | 23 | 23 | 0 | Team17 | A | 1 | 2 | L | 26 | 48 | -22 | 6 | 5 | 12 |\n", "| 473 | Team16 | 24 | 23 | 0 | Team12 | A | 0 | 1 | L | 26 | 49 | -23 | 6 | 5 | 13 |\n", "| 483 | Team16 | 25 | 23 | 0 | Team03 | A | 0 | 2 | L | 26 | 51 | -25 | 6 | 5 | 14 |\n", "| 519 | Team16 | 26 | 24 | 1 | Team20 | A | 2 | 2 | D | 28 | 53 | -25 | 6 | 6 | 14 |\n", "| 531 | Team16 | 27 | 24 | 0 | Team07 | A | 1 | 3 | L | 29 | 56 | -27 | 6 | 6 | 15 |\n", "| 555 | Team16 | 28 | 24 | 0 | Team13 | A | 0 | 2 | L | 29 | 58 | -29 | 6 | 6 | 16 |\n", "| 571 | Team16 | 29 | 24 | 0 | Team10 | A | 1 | 2 | L | 30 | 60 | -30 | 6 | 6 | 17 |\n", "| 583 | Team16 | 30 | 24 | 0 | Team04 | A | 0 | 4 | L | 30 | 64 | -34 | 6 | 6 | 18 |\n", "| 609 | Team16 | 31 | 24 | 0 | Team09 | A | 2 | 3 | L | 32 | 67 | -35 | 6 | 6 | 19 |\n", "| 638 | Team16 | 32 | 27 | 3 | Team19 | H | 5 | 2 | W | 37 | 69 | -32 | 7 | 6 | 19 |\n", "| 647 | Team16 | 33 | 28 | 1 | Team06 | A | 3 | 3 | D | 40 | 72 | -32 | 7 | 7 | 19 |\n", "| 661 | Team16 | 34 | 28 | 0 | Team02 | A | 0 | 4 | L | 40 | 76 | -36 | 7 | 7 | 20 |\n", "| 696 | Team16 | 35 | 31 | 3 | Team18 | H | 4 | 0 | W | 44 | 76 | -32 | 8 | 7 | 20 |\n", "| 9 | Team17 | 1 | 0 | 0 | Team11 | A | 1 | 2 | L | 1 | 2 | -1 | 0 | 0 | 1 |\n", "| 39 | Team17 | 2 | 1 | 1 | Team20 | A | 2 | 2 | D | 3 | 4 | -1 | 0 | 1 | 1 |\n", "| 49 | Team17 | 3 | 1 | 0 | Team13 | A | 2 | 4 | L | 5 | 8 | -3 | 0 | 1 | 2 |\n", "| 74 | Team17 | 4 | 4 | 3 | Team19 | H | 2 | 0 | W | 7 | 8 | -1 | 1 | 1 | 2 |\n", "| 87 | Team17 | 5 | 4 | 0 | Team08 | A | 0 | 5 | L | 7 | 13 | -6 | 1 | 1 | 3 |\n", "| 112 | Team17 | 6 | 4 | 0 | Team06 | H | 1 | 3 | L | 8 | 16 | -8 | 1 | 1 | 4 |\n", "| 125 | Team17 | 7 | 4 | 0 | Team07 | A | 1 | 3 | L | 9 | 19 | -10 | 1 | 1 | 5 |\n", "| 143 | Team17 | 8 | 4 | 0 | Team02 | A | 0 | 6 | L | 9 | 25 | -16 | 1 | 1 | 6 |\n", "| 167 | Team17 | 9 | 4 | 0 | Team10 | A | 2 | 4 | L | 11 | 29 | -18 | 1 | 1 | 7 |\n", "| 193 | Team17 | 10 | 7 | 3 | Team15 | A | 3 | 0 | W | 14 | 29 | -15 | 2 | 1 | 7 |\n", "| 213 | Team17 | 11 | 7 | 0 | Team14 | A | 1 | 4 | L | 15 | 33 | -18 | 2 | 1 | 8 |\n", "| 235 | Team17 | 12 | 7 | 0 | Team16 | A | 3 | 4 | L | 18 | 37 | -19 | 2 | 1 | 9 |\n", "| 241 | Team17 | 13 | 7 | 0 | Team01 | A | 0 | 6 | L | 18 | 43 | -25 | 2 | 1 | 10 |\n", "| 274 | Team17 | 14 | 7 | 0 | Team03 | H | 0 | 1 | L | 18 | 44 | -26 | 2 | 1 | 11 |\n", "| 295 | Team17 | 15 | 10 | 3 | Team18 | A | 2 | 1 | W | 20 | 45 | -25 | 3 | 1 | 11 |\n", "| 312 | Team17 | 16 | 10 | 0 | Team05 | H | 0 | 2 | L | 20 | 47 | -27 | 3 | 1 | 12 |\n", "| 323 | Team17 | 17 | 10 | 0 | Team04 | A | 0 | 4 | L | 20 | 51 | -31 | 3 | 1 | 13 |\n", "| 354 | Team17 | 18 | 11 | 1 | Team09 | H | 0 | 0 | D | 20 | 51 | -31 | 3 | 2 | 13 |\n", "| 374 | Team17 | 19 | 12 | 1 | Team12 | H | 1 | 1 | D | 21 | 52 | -31 | 3 | 3 | 13 |\n", "| 389 | Team17 | 20 | 12 | 0 | Team09 | A | 1 | 3 | L | 22 | 55 | -33 | 3 | 3 | 14 |\n", "| 418 | Team17 | 21 | 12 | 0 | Team08 | H | 1 | 2 | L | 23 | 57 | -34 | 3 | 3 | 15 |\n", "| 436 | Team17 | 22 | 13 | 1 | Team11 | H | 1 | 1 | D | 24 | 58 | -34 | 3 | 4 | 15 |\n", "| 456 | Team17 | 23 | 16 | 3 | Team16 | H | 2 | 1 | W | 26 | 59 | -33 | 4 | 4 | 15 |\n", "| 476 | Team17 | 24 | 19 | 3 | Team20 | H | 3 | 0 | W | 29 | 59 | -30 | 5 | 4 | 15 |\n", "| 496 | Team17 | 25 | 19 | 0 | Team15 | H | 1 | 2 | L | 30 | 61 | -31 | 5 | 4 | 16 |\n", "| 514 | Team17 | 26 | 19 | 0 | Team14 | H | 2 | 4 | L | 32 | 65 | -33 | 5 | 4 | 17 |\n", "| 529 | Team17 | 27 | 19 | 0 | Team06 | A | 1 | 2 | L | 33 | 67 | -34 | 5 | 4 | 18 |\n", "| 556 | Team17 | 28 | 19 | 0 | Team04 | H | 1 | 3 | L | 34 | 70 | -36 | 5 | 4 | 19 |\n", "| 576 | Team17 | 29 | 19 | 0 | Team01 | H | 0 | 2 | L | 34 | 72 | -38 | 5 | 4 | 20 |\n", "| 599 | Team17 | 30 | 19 | 0 | Team19 | A | 2 | 5 | L | 36 | 77 | -41 | 5 | 4 | 21 |\n", "| 618 | Team17 | 31 | 19 | 0 | Team13 | H | 1 | 2 | L | 37 | 79 | -42 | 5 | 4 | 22 |\n", "| 627 | Team17 | 32 | 19 | 0 | Team05 | A | 0 | 5 | L | 37 | 84 | -47 | 5 | 4 | 23 |\n", "| 658 | Team17 | 33 | 22 | 3 | Team18 | H | 1 | 0 | W | 38 | 84 | -46 | 6 | 4 | 23 |\n", "| 674 | Team17 | 34 | 22 | 0 | Team07 | H | 1 | 3 | L | 39 | 87 | -48 | 6 | 4 | 24 |\n", "| 693 | Team17 | 35 | 22 | 0 | Team12 | A | 1 | 6 | L | 40 | 93 | -53 | 6 | 4 | 25 |\n", "| 16 | Team18 | 1 | 3 | 3 | Team15 | H | 4 | 3 | W | 4 | 3 | 1 | 1 | 0 | 0 |\n", "| 36 | Team18 | 2 | 3 | 0 | Team05 | H | 1 | 2 | L | 5 | 5 | 0 | 1 | 0 | 1 |\n", "| 54 | Team18 | 3 | 3 | 0 | Team12 | H | 1 | 4 | L | 6 | 9 | -3 | 1 | 0 | 2 |\n", "| 76 | Team18 | 4 | 4 | 1 | Team14 | H | 1 | 1 | D | 7 | 10 | -3 | 1 | 1 | 2 |\n", "| 94 | Team18 | 5 | 4 | 0 | Team03 | H | 1 | 2 | L | 8 | 12 | -4 | 1 | 1 | 3 |\n", "| 114 | Team18 | 6 | 4 | 0 | Team04 | H | 0 | 5 | L | 8 | 17 | -9 | 1 | 1 | 4 |\n", "| 138 | Team18 | 7 | 4 | 0 | Team13 | H | 1 | 3 | L | 9 | 20 | -11 | 1 | 1 | 5 |\n", "| 156 | Team18 | 8 | 4 | 0 | Team09 | H | 3 | 5 | L | 12 | 25 | -13 | 1 | 1 | 6 |\n", "| 174 | Team18 | 9 | 4 | 0 | Team11 | H | 1 | 3 | L | 13 | 28 | -15 | 1 | 1 | 7 |\n", "| 196 | Team18 | 10 | 5 | 1 | Team06 | H | 1 | 1 | D | 14 | 29 | -15 | 1 | 2 | 7 |\n", "| 218 | Team18 | 11 | 5 | 0 | Team08 | H | 1 | 2 | L | 15 | 31 | -16 | 1 | 2 | 8 |\n", "| 236 | Team18 | 12 | 5 | 0 | Team10 | H | 0 | 5 | L | 15 | 36 | -21 | 1 | 2 | 9 |\n", "| 256 | Team18 | 13 | 5 | 0 | Team02 | H | 0 | 8 | L | 15 | 44 | -29 | 1 | 2 | 10 |\n", "| 276 | Team18 | 14 | 5 | 0 | Team07 | H | 0 | 4 | L | 15 | 48 | -33 | 1 | 2 | 11 |\n", "| 294 | Team18 | 15 | 5 | 0 | Team17 | H | 1 | 2 | L | 16 | 50 | -34 | 1 | 2 | 12 |\n", "| 314 | Team18 | 16 | 5 | 0 | Team01 | H | 1 | 4 | L | 17 | 54 | -37 | 1 | 2 | 13 |\n", "| 336 | Team18 | 17 | 8 | 3 | Team20 | H | 2 | 0 | W | 19 | 54 | -35 | 2 | 2 | 13 |\n", "| 357 | Team18 | 18 | 11 | 3 | Team19 | A | 3 | 2 | W | 22 | 56 | -34 | 3 | 2 | 13 |\n", "| 376 | Team18 | 19 | 11 | 0 | Team16 | H | 1 | 2 | L | 23 | 58 | -35 | 3 | 2 | 14 |\n", "| 398 | Team18 | 20 | 14 | 3 | Team19 | H | 1 | 0 | W | 24 | 58 | -34 | 4 | 2 | 14 |\n", "| 403 | Team18 | 21 | 14 | 0 | Team03 | A | 1 | 7 | L | 25 | 65 | -40 | 4 | 2 | 15 |\n", "| 435 | Team18 | 22 | 15 | 1 | Team15 | A | 2 | 2 | D | 27 | 67 | -40 | 4 | 3 | 15 |\n", "| 451 | Team18 | 23 | 15 | 0 | Team10 | A | 0 | 6 | L | 27 | 73 | -46 | 4 | 3 | 16 |\n", "| 463 | Team18 | 24 | 18 | 3 | Team05 | A | 2 | 1 | W | 29 | 74 | -45 | 5 | 3 | 16 |\n", "| 487 | Team18 | 25 | 21 | 3 | Team06 | A | 4 | 2 | W | 33 | 76 | -43 | 6 | 3 | 16 |\n", "| 509 | Team18 | 26 | 21 | 0 | Team08 | A | 0 | 1 | L | 33 | 77 | -44 | 6 | 3 | 17 |\n", "| 525 | Team18 | 27 | 21 | 0 | Team04 | A | 1 | 5 | L | 34 | 82 | -48 | 6 | 3 | 18 |\n", "| 559 | Team18 | 28 | 21 | 0 | Team20 | A | 1 | 2 | L | 35 | 84 | -49 | 6 | 3 | 19 |\n", "| 561 | Team18 | 29 | 21 | 0 | Team02 | A | 0 | 6 | L | 35 | 90 | -55 | 6 | 3 | 20 |\n", "| 597 | Team18 | 30 | 21 | 0 | Team14 | A | 0 | 6 | L | 35 | 96 | -61 | 6 | 3 | 21 |\n", "| 615 | Team18 | 31 | 24 | 3 | Team12 | A | 1 | 0 | W | 36 | 96 | -60 | 7 | 3 | 21 |\n", "| 621 | Team18 | 32 | 24 | 0 | Team01 | A | 0 | 5 | L | 36 | 101 | -65 | 7 | 3 | 22 |\n", "| 659 | Team18 | 33 | 24 | 0 | Team17 | A | 0 | 1 | L | 36 | 102 | -66 | 7 | 3 | 23 |\n", "| 673 | Team18 | 34 | 24 | 0 | Team13 | A | 2 | 6 | L | 38 | 108 | -70 | 7 | 3 | 24 |\n", "| 697 | Team18 | 35 | 24 | 0 | Team16 | A | 0 | 4 | L | 38 | 112 | -74 | 7 | 3 | 25 |\n", "| 18 | Team19 | 1 | 0 | 0 | Team05 | H | 0 | 3 | L | 0 | 3 | -3 | 0 | 0 | 1 |\n", "| 23 | Team19 | 2 | 0 | 0 | Team02 | A | 1 | 8 | L | 1 | 11 | -10 | 0 | 0 | 2 |\n", "| 56 | Team19 | 3 | 0 | 0 | Team04 | H | 0 | 4 | L | 1 | 15 | -14 | 0 | 0 | 3 |\n", "| 75 | Team19 | 4 | 0 | 0 | Team17 | A | 0 | 2 | L | 1 | 17 | -16 | 0 | 0 | 4 |\n", "| 96 | Team19 | 5 | 3 | 3 | Team14 | H | 4 | 1 | W | 5 | 18 | -13 | 1 | 0 | 4 |\n", "| 116 | Team19 | 6 | 3 | 0 | Team01 | H | 1 | 7 | L | 6 | 25 | -19 | 1 | 0 | 5 |\n", "| 127 | Team19 | 7 | 3 | 0 | Team08 | A | 0 | 3 | L | 6 | 28 | -22 | 1 | 0 | 6 |\n", "| 149 | Team19 | 8 | 3 | 0 | Team10 | A | 2 | 4 | L | 8 | 32 | -24 | 1 | 0 | 7 |\n", "| 176 | Team19 | 9 | 3 | 0 | Team07 | H | 0 | 4 | L | 8 | 36 | -28 | 1 | 0 | 8 |\n", "| 189 | Team19 | 10 | 3 | 0 | Team13 | A | 1 | 4 | L | 9 | 40 | -31 | 1 | 0 | 9 |\n", "| 215 | Team19 | 11 | 4 | 1 | Team15 | A | 0 | 0 | D | 9 | 40 | -31 | 1 | 1 | 9 |\n", "| 239 | Team19 | 12 | 7 | 3 | Team20 | A | 3 | 2 | W | 12 | 42 | -30 | 2 | 1 | 9 |\n", "| 258 | Team19 | 13 | 8 | 1 | Team09 | H | 1 | 1 | D | 13 | 43 | -30 | 2 | 2 | 9 |\n", "| 263 | Team19 | 14 | 8 | 0 | Team06 | A | 0 | 3 | L | 13 | 46 | -33 | 2 | 2 | 10 |\n", "| 296 | Team19 | 15 | 11 | 3 | Team11 | H | 4 | 0 | W | 17 | 46 | -29 | 3 | 2 | 10 |\n", "| 316 | Team19 | 16 | 14 | 3 | Team16 | H | 2 | 0 | W | 19 | 46 | -27 | 4 | 2 | 10 |\n", "| 338 | Team19 | 17 | 17 | 3 | Team12 | H | 1 | 0 | W | 20 | 46 | -26 | 5 | 2 | 10 |\n", "| 356 | Team19 | 18 | 17 | 0 | Team18 | H | 2 | 3 | L | 22 | 49 | -27 | 5 | 2 | 11 |\n", "| 363 | Team19 | 19 | 17 | 0 | Team03 | A | 1 | 8 | L | 23 | 57 | -34 | 5 | 2 | 12 |\n", "| 399 | Team19 | 20 | 17 | 0 | Team18 | A | 0 | 1 | L | 23 | 58 | -35 | 5 | 2 | 13 |\n", "| 417 | Team19 | 21 | 17 | 0 | Team14 | A | 1 | 3 | L | 24 | 61 | -37 | 5 | 2 | 14 |\n", "| 425 | Team19 | 22 | 17 | 0 | Team05 | A | 1 | 5 | L | 25 | 66 | -41 | 5 | 2 | 15 |\n", "| 458 | Team19 | 23 | 17 | 0 | Team20 | H | 0 | 1 | L | 25 | 67 | -42 | 5 | 2 | 16 |\n", "| 478 | Team19 | 24 | 17 | 0 | Team02 | H | 0 | 4 | L | 25 | 71 | -46 | 5 | 2 | 17 |\n", "| 498 | Team19 | 25 | 17 | 0 | Team13 | H | 1 | 2 | L | 26 | 73 | -47 | 5 | 2 | 18 |\n", "| 516 | Team19 | 26 | 18 | 1 | Team15 | H | 1 | 1 | D | 27 | 74 | -47 | 5 | 3 | 18 |\n", "| 521 | Team19 | 27 | 18 | 0 | Team01 | A | 0 | 5 | L | 27 | 79 | -52 | 5 | 3 | 19 |\n", "| 553 | Team19 | 28 | 18 | 0 | Team12 | A | 2 | 3 | L | 29 | 82 | -53 | 5 | 3 | 20 |\n", "| 569 | Team19 | 29 | 18 | 0 | Team09 | A | 1 | 3 | L | 30 | 85 | -55 | 5 | 3 | 21 |\n", "| 598 | Team19 | 30 | 21 | 3 | Team17 | H | 5 | 2 | W | 35 | 87 | -52 | 6 | 3 | 21 |\n", "| 603 | Team19 | 31 | 21 | 0 | Team04 | A | 2 | 6 | L | 37 | 93 | -56 | 6 | 3 | 22 |\n", "| 639 | Team19 | 32 | 21 | 0 | Team16 | A | 2 | 5 | L | 39 | 98 | -59 | 6 | 3 | 23 |\n", "| 653 | Team19 | 33 | 21 | 0 | Team11 | A | 0 | 3 | L | 39 | 101 | -62 | 6 | 3 | 24 |\n", "| 676 | Team19 | 34 | 21 | 0 | Team08 | H | 1 | 6 | L | 40 | 107 | -67 | 6 | 3 | 25 |\n", "| 698 | Team19 | 35 | 21 | 0 | Team03 | H | 1 | 5 | L | 41 | 112 | -71 | 6 | 3 | 26 |\n", "| 1 | Team20 | 1 | 0 | 0 | Team01 | A | 0 | 6 | L | 0 | 6 | -6 | 0 | 0 | 1 |\n", "| 38 | Team20 | 2 | 1 | 1 | Team17 | H | 2 | 2 | D | 2 | 8 | -6 | 0 | 1 | 1 |\n", "| 58 | Team20 | 3 | 1 | 0 | Team06 | H | 1 | 5 | L | 3 | 13 | -10 | 0 | 1 | 2 |\n", "| 78 | Team20 | 4 | 4 | 3 | Team12 | H | 3 | 0 | W | 6 | 13 | -7 | 1 | 1 | 2 |\n", "| 98 | Team20 | 5 | 4 | 0 | Team10 | H | 0 | 3 | L | 6 | 16 | -10 | 1 | 1 | 3 |\n", "| 118 | Team20 | 6 | 4 | 0 | Team09 | H | 1 | 3 | L | 7 | 19 | -12 | 1 | 1 | 4 |\n", "| 135 | Team20 | 7 | 4 | 0 | Team15 | A | 0 | 1 | L | 7 | 20 | -13 | 1 | 1 | 5 |\n", "| 158 | Team20 | 8 | 4 | 0 | Team03 | H | 0 | 2 | L | 7 | 22 | -15 | 1 | 1 | 6 |\n", "| 178 | Team20 | 9 | 4 | 0 | Team05 | H | 0 | 5 | L | 7 | 27 | -20 | 1 | 1 | 7 |\n", "| 198 | Team20 | 10 | 4 | 0 | Team02 | H | 0 | 4 | L | 7 | 31 | -24 | 1 | 1 | 8 |\n", "| 217 | Team20 | 11 | 5 | 1 | Team16 | A | 0 | 0 | D | 7 | 31 | -24 | 1 | 2 | 8 |\n", "| 238 | Team20 | 12 | 5 | 0 | Team19 | H | 2 | 3 | L | 9 | 34 | -25 | 1 | 2 | 9 |\n", "| 247 | Team20 | 13 | 6 | 1 | Team08 | A | 2 | 2 | D | 11 | 36 | -25 | 1 | 3 | 9 |\n", "| 278 | Team20 | 14 | 6 | 0 | Team11 | H | 0 | 6 | L | 11 | 42 | -31 | 1 | 3 | 10 |\n", "| 298 | Team20 | 15 | 9 | 3 | Team13 | H | 1 | 0 | W | 12 | 42 | -30 | 2 | 3 | 10 |\n", "| 318 | Team20 | 16 | 10 | 1 | Team14 | H | 1 | 1 | D | 13 | 43 | -30 | 2 | 4 | 10 |\n", "| 337 | Team20 | 17 | 10 | 0 | Team18 | A | 0 | 2 | L | 13 | 45 | -32 | 2 | 4 | 11 |\n", "| 358 | Team20 | 18 | 10 | 0 | Team07 | H | 0 | 3 | L | 13 | 48 | -35 | 2 | 4 | 12 |\n", "| 378 | Team20 | 19 | 10 | 0 | Team04 | H | 2 | 3 | L | 15 | 51 | -36 | 2 | 4 | 13 |\n", "| 387 | Team20 | 20 | 10 | 0 | Team07 | A | 0 | 1 | L | 15 | 52 | -37 | 2 | 4 | 14 |\n", "| 411 | Team20 | 21 | 10 | 0 | Team10 | A | 0 | 5 | L | 15 | 57 | -42 | 2 | 4 | 15 |\n", "| 438 | Team20 | 22 | 10 | 0 | Team01 | H | 1 | 5 | L | 16 | 62 | -46 | 2 | 4 | 16 |\n", "| 459 | Team20 | 23 | 13 | 3 | Team19 | A | 1 | 0 | W | 17 | 62 | -45 | 3 | 4 | 16 |\n", "| 477 | Team20 | 24 | 13 | 0 | Team17 | A | 0 | 3 | L | 17 | 65 | -48 | 3 | 4 | 17 |\n", "| 481 | Team20 | 25 | 13 | 0 | Team02 | A | 0 | 4 | L | 17 | 69 | -52 | 3 | 4 | 18 |\n", "| 518 | Team20 | 26 | 14 | 1 | Team16 | H | 2 | 2 | D | 19 | 71 | -52 | 3 | 5 | 18 |\n", "| 533 | Team20 | 27 | 14 | 0 | Team09 | A | 1 | 5 | L | 20 | 76 | -56 | 3 | 5 | 19 |\n", "| 558 | Team20 | 28 | 17 | 3 | Team18 | H | 2 | 1 | W | 22 | 77 | -55 | 4 | 5 | 19 |\n", "| 578 | Team20 | 29 | 17 | 0 | Team08 | H | 1 | 6 | L | 23 | 83 | -60 | 4 | 5 | 20 |\n", "| 595 | Team20 | 30 | 18 | 1 | Team12 | A | 2 | 2 | D | 25 | 85 | -60 | 4 | 6 | 20 |\n", "| 607 | Team20 | 31 | 18 | 0 | Team06 | A | 0 | 2 | L | 25 | 87 | -62 | 4 | 6 | 21 |\n", "| 637 | Team20 | 32 | 18 | 0 | Team14 | A | 1 | 4 | L | 26 | 91 | -65 | 4 | 6 | 22 |\n", "| 657 | Team20 | 33 | 18 | 0 | Team13 | A | 2 | 4 | L | 28 | 95 | -67 | 4 | 6 | 23 |\n", "| 678 | Team20 | 34 | 19 | 1 | Team15 | H | 0 | 0 | D | 28 | 95 | -67 | 4 | 7 | 23 |\n", "| 683 | Team20 | 35 | 19 | 0 | Team04 | A | 1 | 5 | L | 29 | 100 | -71 | 4 | 7 | 24 |\n" ] } ], "source": [ "print(train_ts.to_markdown())" ] }, { "cell_type": "code", "execution_count": 8, "id": "7f60d991", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from typing import Optional, Sequence\n", "\n", "def _conform_forecast_df(\n", " fcst: pd.DataFrame,\n", " team: str,\n", " ds_col: str = \"ds\",\n", " mean_col: Optional[str] = None,\n", " lo_col: Optional[str] = None,\n", " hi_col: Optional[str] = None,\n", " model_name: Optional[str] = None,\n", " level: Optional[int] = None,\n", ") -> pd.DataFrame:\n", " \"\"\"\n", " Normalize a forecast frame to columns: ds, yhat, yhat_lo, yhat_hi for a single team.\n", "\n", " Works with:\n", " - Generic frames already named: 'yhat', 'yhat_lo', 'yhat_hi'\n", " - StatsForecast output (wide): columns like ['AutoARIMA', 'AutoARIMA-lo-80', 'AutoARIMA-hi-80']\n", " - Any custom naming if you pass mean_col/lo_col/hi_col explicitly.\n", " \"\"\"\n", " g = fcst[fcst[\"unique_id\"] == team].copy()\n", "\n", " # If user specified columns, use them.\n", " if mean_col:\n", " g = g.rename(columns={mean_col: \"yhat\"})\n", " if lo_col: g = g.rename(columns={lo_col: \"yhat_lo\"})\n", " if hi_col: g = g.rename(columns={hi_col: \"yhat_hi\"})\n", " else:\n", " # Try common names\n", " if \"yhat\" in g.columns:\n", " pass\n", " else:\n", " # StatsForecast wide format\n", " # Guess model name if not provided: take the first non-id/ds column\n", " if model_name is None:\n", " candidate_cols = [c for c in g.columns if c not in {\"unique_id\", ds_col}]\n", " model_name = candidate_cols[0] if candidate_cols else None\n", " # Guess level if not provided: prefer 95, fall back to 80\n", " if level is None:\n", " level = 95 if f\"{model_name}-lo-95\" in g.columns else (80 if f\"{model_name}-lo-80\" in g.columns else None)\n", "\n", " mapping = {}\n", " if model_name and model_name in g.columns:\n", " mapping[model_name] = \"yhat\"\n", " if model_name and level is not None:\n", " lo_name = f\"{model_name}-lo-{level}\"\n", " hi_name = f\"{model_name}-hi-{level}\"\n", " if lo_name in g.columns: mapping[lo_name] = \"yhat_lo\"\n", " if hi_name in g.columns: mapping[hi_name] = \"yhat_hi\"\n", " g = g.rename(columns=mapping)\n", "\n", " keep = [\"unique_id\", ds_col, \"yhat\"] + [c for c in [\"yhat_lo\", \"yhat_hi\"] if c in g.columns]\n", " g = g[keep].rename(columns={ds_col: \"ds\"})\n", " return g\n", "\n", "def plot_team_cumpoints_with_forecast(\n", " ts_df: pd.DataFrame,\n", " team: str,\n", " fcst_df: Optional[pd.DataFrame] = None,\n", " *,\n", " ds_col: str = \"ds\",\n", " y_col: str = \"y\",\n", " mean_col: Optional[str] = None,\n", " lo_col: Optional[str] = None,\n", " hi_col: Optional[str] = None,\n", " model_name: Optional[str] = None,\n", " level: Optional[int] = None,\n", " title: Optional[str] = None,\n", " show: bool = True,\n", "):\n", " # Actuals (all available ds for the team)\n", " act = ts_df.loc[ts_df[\"unique_id\"] == team, [ds_col, y_col]].sort_values(ds_col).rename(\n", " columns={ds_col: \"ds\", y_col: \"y\"}\n", " )\n", "\n", " fig, ax = plt.subplots(figsize=(8, 4.5))\n", " ax.plot(act[\"ds\"].values, act[\"y\"].values, marker=\"o\", linewidth=1.5, label=\"Actual cum. points\")\n", "\n", " # Optional forecast\n", " if fcst_df is not None and len(fcst_df):\n", " g = _conform_forecast_df(\n", " fcst_df, team,\n", " ds_col=ds_col, mean_col=mean_col, lo_col=lo_col, hi_col=hi_col,\n", " model_name=model_name, level=level\n", " )\n", " # Shade interval if present\n", " if {\"yhat_lo\", \"yhat_hi\"}.issubset(g.columns):\n", " ax.fill_between(g[\"ds\"].values, g[\"yhat_lo\"].values, g[\"yhat_hi\"].values, alpha=0.2, label=\"Prediction interval\", color = 'lime')\n", "\n", " ax.plot(g[\"ds\"].values, g[\"yhat\"].values, linestyle=\"--\", linewidth=1.8, label=\"Forecast mean\", color = 'lime')\n", "\n", " # Draw a vertical line at the last observed ds (split point)\n", " if len(act):\n", " split = 36\n", " ax.axvline(split, linestyle=\":\", alpha=0.6)\n", " ax.text(split, ax.get_ylim()[1], \" Train/Forecast Split\", va=\"top\", ha=\"left\", fontsize=9)\n", "\n", " ax.set_xlabel(\"Match Day Number\", fontsize = 15)\n", " ax.set_ylabel(\"Championship Points\", fontsize = 15)\n", " ax.set_title(title or f\"{team}: Championship Points & Forecast\", fontsize = 15)\n", " ax.grid(True, alpha=0.25)\n", " ax.legend(loc=\"best\", fontsize = 15)\n", " if show:\n", " plt.savefig('/Users/pieropaialunga/Desktop/blog/images/championship_forecasting/title-image.svg')\n", " plt.show()\n", " return fig, ax\n", "\n", "\n", "def round_forecast_to_valid_points(forecast_df: pd.DataFrame) -> pd.DataFrame:\n", " \"\"\"\n", " Round forecast values to integers since football points must be whole numbers.\n", " \n", " In football, you can only earn 0, 1 (draw), or 3 (win) points per match.\n", " Therefore, cumulative points must always be integers.\n", " \n", " This function rounds all forecast columns (mean, lower/upper bounds) to the nearest integer.\n", " \"\"\"\n", " df = forecast_df.copy()\n", " for col in df.columns:\n", " if col not in ['unique_id', 'ds']:\n", " df[col] = df[col].round().astype(int)\n", " return df" ] }, { "cell_type": "code", "execution_count": null, "id": "0b2317b7", "metadata": {}, "outputs": [], "source": [ "\n", "train" ] }, { "cell_type": "code", "execution_count": 10, "id": "4d4fdee0", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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,\n", " )" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from statsforecast import StatsForecast\n", "from statsforecast.models import AutoARIMA\n", "\n", "# Train up to matchday 35, forecast remaining rounds\n", "train_data = prepare_forecasting_data(teams, season, strengths, seed=777, cutoff_matchday=35)\n", "train_ts = train_data[\"ts_df\"][[\"unique_id\",\"ds\",\"y\"]]\n", "forecast_horizon = train_data[\"h\"]\n", "\n", "sf = StatsForecast(models=[AutoARIMA()], freq=1)\n", "sf.fit(train_ts)\n", "\n", "# StatsForecast returns a wide frame: ['unique_id','ds','AutoARIMA','AutoARIMA-lo-95','AutoARIMA-hi-95'] if level=[95]\n", "forecast_raw = sf.predict(h=forecast_horizon, level=[95])\n", "\n", "# Round to valid integer points (football only allows 0, 1, or 3 points per match)\n", "forecast = round_forecast_to_valid_points(forecast_raw)\n", "\n", "# Add actual values to compare with predictions\n", "full_season_results = prepare_forecasting_data(teams, season, strengths, seed=777)\n", "full_season_ts = full_season_results[\"ts_df\"][[\"unique_id\", \"ds\", \"y\"]]\n", "\n", "# Merge actual values into the forecast dataframe\n", "forecast = forecast.merge(\n", " full_season_ts.rename(columns={\"y\": \"actual\"}),\n", " on=[\"unique_id\", \"ds\"],\n", " how=\"left\"\n", ")\n", "set_dark_mode()\n", "plot_team_cumpoints_with_forecast(\n", " ts_df=full_season_results[\"ts_df\"], # full actuals for context\n", " team=\"Team01\",\n", " fcst_df=forecast,\n", " model_name=\"AutoARIMA\", # tell the helper how to read the wide columns\n", " level=95\n", ")\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "b72ff19c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Forecast vs Actual for Team20:\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " ds AutoARIMA actual error AutoARIMA-lo-95 AutoARIMA-hi-95\n", "57 36 20 19 1 18 21\n", "58 37 20 19 1 17 23\n", "59 38 21 19 2 17 24" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Display forecast vs actual for one team\n", "team_to_check = \"Team20\"\n", "team_forecast = forecast[forecast[\"unique_id\"] == team_to_check].copy()\n", "\n", "# Calculate error if we have actuals\n", "if \"actual\" in team_forecast.columns:\n", " team_forecast[\"error\"] = team_forecast[\"AutoARIMA\"] - team_forecast[\"actual\"]\n", " team_forecast[\"abs_error\"] = team_forecast[\"error\"].abs()\n", "\n", "print(f\"\\nForecast vs Actual for {team_to_check}:\")\n", "team_forecast[[\"ds\", \"AutoARIMA\", \"actual\", \"error\", \"AutoARIMA-lo-95\", \"AutoARIMA-hi-95\"]]\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "6d9b6b1d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "=== Forecast Accuracy Metrics ===\n", "Mean Absolute Error (MAE): 1.67 points\n", "Root Mean Squared Error (RMSE): 1.97 points\n", "Mean Error (bias): 0.13 points\n", "95% Prediction Interval Coverage: 98.3% (should be ~95%)\n", "\n", "=== Top 5 Worst Predictions (by absolute error) ===\n", "unique_id ds AutoARIMA actual error\n", " Team09 38 45 50 -5\n", " Team09 37 43 47 -4\n", " Team11 37 46 50 -4\n", " Team16 38 35 31 4\n", " Team06 38 71 74 -3\n" ] } ], "source": [ "# Calculate overall forecast accuracy metrics\n", "if \"actual\" in forecast.columns:\n", " forecast[\"error\"] = forecast[\"AutoARIMA\"] - forecast[\"actual\"]\n", " forecast[\"abs_error\"] = forecast[\"error\"].abs()\n", " forecast[\"squared_error\"] = forecast[\"error\"] ** 2\n", " \n", " print(\"\\n=== Forecast Accuracy Metrics ===\")\n", " print(f\"Mean Absolute Error (MAE): {forecast['abs_error'].mean():.2f} points\")\n", " print(f\"Root Mean Squared Error (RMSE): {np.sqrt(forecast['squared_error'].mean()):.2f} points\")\n", " print(f\"Mean Error (bias): {forecast['error'].mean():.2f} points\")\n", " \n", " # Check prediction interval coverage\n", " in_interval = (\n", " (forecast[\"actual\"] >= forecast[\"AutoARIMA-lo-95\"]) & \n", " (forecast[\"actual\"] <= forecast[\"AutoARIMA-hi-95\"])\n", " )\n", " coverage = in_interval.mean() * 100\n", " print(f\"95% Prediction Interval Coverage: {coverage:.1f}% (should be ~95%)\")\n", " \n", " # Show worst predictions\n", " print(\"\\n=== Top 5 Worst Predictions (by absolute error) ===\")\n", " worst = forecast.nlargest(5, 'abs_error')[[\"unique_id\", \"ds\", \"AutoARIMA\", \"actual\", \"error\"]]\n", " print(worst.to_string(index=False))\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "88d2045f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "| | unique_id | ds | AutoARIMA | AutoARIMA-lo-95 | AutoARIMA-hi-95 | actual | error | abs_error | squared_error |\n", "|---:|:------------|-----:|------------:|------------------:|------------------:|---------:|--------:|------------:|----------------:|\n", "| 0 | Team01 | 36 | 89 | 87 | 92 | 90 | -1 | 1 | 1 |\n", "| 1 | Team01 | 37 | 92 | 89 | 95 | 93 | -1 | 1 | 1 |\n", "| 2 | Team01 | 38 | 94 | 91 | 98 | 94 | 0 | 0 | 0 |\n", "| 3 | Team02 | 36 | 83 | 81 | 85 | 84 | -1 | 1 | 1 |\n", "| 4 | Team02 | 37 | 86 | 83 | 89 | 87 | -1 | 1 | 1 |\n", "| 5 | Team02 | 38 | 88 | 85 | 92 | 87 | 1 | 1 | 1 |\n", "| 6 | Team03 | 36 | 78 | 76 | 81 | 79 | -1 | 1 | 1 |\n", "| 7 | Team03 | 37 | 81 | 77 | 84 | 82 | -1 | 1 | 1 |\n", "| 8 | Team03 | 38 | 83 | 78 | 88 | 85 | -2 | 2 | 4 |\n", "| 9 | Team04 | 36 | 85 | 83 | 88 | 86 | -1 | 1 | 1 |\n", "| 10 | Team04 | 37 | 88 | 85 | 91 | 89 | -1 | 1 | 1 |\n", "| 11 | Team04 | 38 | 90 | 86 | 94 | 90 | 0 | 0 | 0 |\n", "| 12 | Team05 | 36 | 72 | 69 | 75 | 70 | 2 | 2 | 4 |\n", "| 13 | Team05 | 37 | 75 | 70 | 79 | 73 | 2 | 2 | 4 |\n", "| 14 | Team05 | 38 | 78 | 72 | 84 | 76 | 2 | 2 | 4 |\n", "| 15 | Team06 | 36 | 67 | 64 | 70 | 68 | -1 | 1 | 1 |\n", "| 16 | Team06 | 37 | 69 | 65 | 73 | 71 | -2 | 2 | 4 |\n", "| 17 | Team06 | 38 | 71 | 66 | 75 | 74 | -3 | 3 | 9 |\n", "| 18 | Team07 | 36 | 73 | 70 | 76 | 71 | 2 | 2 | 4 |\n", "| 19 | Team07 | 37 | 75 | 71 | 79 | 74 | 1 | 1 | 1 |\n", "| 20 | Team07 | 38 | 77 | 72 | 82 | 77 | 0 | 0 | 0 |\n", "| 21 | Team08 | 36 | 71 | 68 | 74 | 70 | 1 | 1 | 1 |\n", "| 22 | Team08 | 37 | 73 | 69 | 77 | 73 | 0 | 0 | 0 |\n", "| 23 | Team08 | 38 | 75 | 70 | 79 | 73 | 2 | 2 | 4 |\n", "| 24 | Team09 | 36 | 42 | 40 | 45 | 44 | -2 | 2 | 4 |\n", "| 25 | Team09 | 37 | 43 | 40 | 47 | 47 | -4 | 4 | 16 |\n", "| 26 | Team09 | 38 | 45 | 40 | 49 | 50 | -5 | 5 | 25 |\n", "| 27 | Team10 | 36 | 49 | 47 | 52 | 51 | -2 | 2 | 4 |\n", "| 28 | Team10 | 37 | 51 | 47 | 55 | 51 | 0 | 0 | 0 |\n", "| 29 | Team10 | 38 | 52 | 47 | 57 | 54 | -2 | 2 | 4 |\n", "| 30 | Team11 | 36 | 45 | 42 | 48 | 47 | -2 | 2 | 4 |\n", "| 31 | Team11 | 37 | 46 | 43 | 50 | 50 | -4 | 4 | 16 |\n", "| 32 | Team11 | 38 | 48 | 43 | 52 | 51 | -3 | 3 | 9 |\n", "| 33 | Team12 | 36 | 34 | 31 | 36 | 36 | -2 | 2 | 4 |\n", "| 34 | Team12 | 37 | 35 | 31 | 38 | 36 | -1 | 1 | 1 |\n", "| 35 | Team12 | 38 | 36 | 31 | 40 | 39 | -3 | 3 | 9 |\n", "| 36 | Team13 | 36 | 38 | 35 | 41 | 37 | 1 | 1 | 1 |\n", "| 37 | Team13 | 37 | 39 | 35 | 43 | 37 | 2 | 2 | 4 |\n", "| 38 | Team13 | 38 | 40 | 35 | 45 | 37 | 3 | 3 | 9 |\n", "| 39 | Team14 | 36 | 46 | 44 | 49 | 45 | 1 | 1 | 1 |\n", "| 40 | Team14 | 37 | 47 | 44 | 51 | 45 | 2 | 2 | 4 |\n", "| 41 | Team14 | 38 | 49 | 44 | 53 | 48 | 1 | 1 | 1 |\n", "| 42 | Team15 | 36 | 34 | 31 | 36 | 33 | 1 | 1 | 1 |\n", "| 43 | Team15 | 37 | 35 | 31 | 38 | 33 | 2 | 2 | 4 |\n", "| 44 | Team15 | 38 | 36 | 31 | 40 | 33 | 3 | 3 | 9 |\n", "| 45 | Team16 | 36 | 33 | 31 | 35 | 31 | 2 | 2 | 4 |\n", "| 46 | Team16 | 37 | 34 | 30 | 38 | 31 | 3 | 3 | 9 |\n", "| 47 | Team16 | 38 | 35 | 29 | 40 | 31 | 4 | 4 | 16 |\n", "| 48 | Team17 | 36 | 23 | 21 | 25 | 22 | 1 | 1 | 1 |\n", "| 49 | Team17 | 37 | 24 | 21 | 26 | 22 | 2 | 2 | 4 |\n", "| 50 | Team17 | 38 | 24 | 22 | 27 | 22 | 2 | 2 | 4 |\n", "| 51 | Team18 | 36 | 25 | 22 | 27 | 24 | 1 | 1 | 1 |\n", "| 52 | Team18 | 37 | 25 | 22 | 28 | 24 | 1 | 1 | 1 |\n", "| 53 | Team18 | 38 | 26 | 22 | 30 | 25 | 1 | 1 | 1 |\n", "| 54 | Team19 | 36 | 22 | 19 | 24 | 21 | 1 | 1 | 1 |\n", "| 55 | Team19 | 37 | 22 | 19 | 25 | 21 | 1 | 1 | 1 |\n", "| 56 | Team19 | 38 | 23 | 19 | 27 | 21 | 2 | 2 | 4 |\n", "| 57 | Team20 | 36 | 20 | 18 | 21 | 19 | 1 | 1 | 1 |\n", "| 58 | Team20 | 37 | 20 | 17 | 23 | 19 | 1 | 1 | 1 |\n", "| 59 | Team20 | 38 | 21 | 17 | 24 | 19 | 2 | 2 | 4 |\n" ] } ], "source": [ "print(forecast.to_markdown())" ] }, { "cell_type": "code", "execution_count": null, "id": "edb503b3", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "4c7f2342", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "base", "language": "python", 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