/
optimize_lineup.py
129 lines (122 loc) · 5.2 KB
/
optimize_lineup.py
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import pyfiglet
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='white')
from collections import defaultdict
import numpy as np
import click
from tabulate import tabulate
import logging
logging.basicConfig()
logging.getLogger().setLevel(logging.INFO)
from nba_matchup import get_league, simulate_h2h, CURRENT_WEEK, hill_climb, visualize_matchup, get_free_agents, simulated_annealing
league = get_league()
def winning_prob(cats, points, scores, num_samples):
unique, nums = np.unique(points, return_counts=True)
counts = defaultdict(int)
counts.update(dict(zip(unique, nums)))
return sum([counts[p] for p in range(5, 10)]) / num_samples
def ev(cats, points, scores, num_samples):
return points.mean()
@click.command()
@click.option('--team1', type=str, default=None)
@click.option('--team2', type=str, default=None)
@click.option('--num_days', type=int, default=30)
@click.option('--num_samples', type=int, default=50000)
@click.option('--week', type=int, default=CURRENT_WEEK)
@click.option('--num_fa', type=int, default=0)
@click.option('--num_iters', type=int, default=100)
@click.option('--ignore_player', type=str, multiple=True)
@click.option('--half_life', type=float, default=14)
@click.option('--metric', type=str, default='winning_probability')
@click.option('--ignore_injured', is_flag=True)
def main(team1, team2, num_days, num_samples, week, num_fa, num_iters,
ignore_player, half_life, metric, ignore_injured):
league = get_league()
decay_rate = np.log(2) / half_life
# if week == 19: week = 18
# if week >= 19:
# week -= 1
if team1 is None:
team1 = league.current_team
else:
team1 = league.team_by_owner(team1)
if team2 is None:
team2 = league.get_matchup(team1, week=week)
else:
team2 = league.team_by_owner(team2)
pyfiglet.print_figlet("%s vs. %s" % (team1.manager_name,
team2.manager_name), font='banner',
width=160)
if week:
pyfiglet.print_figlet("Week %u" % week, font='big')
if metric == 'ev':
metric_fn = ev
else:
metric_fn = winning_prob
def roster_score(roster):
cats, points, scores, _ = simulate_h2h(roster,
team2.roster(week=week),
num_days=num_days, num_samples=num_samples,
week=week, decay_rate=decay_rate)
return metric_fn(cats, points, scores, num_samples)
def reverse_roster_score(roster):
cats, points, scores, _ = simulate_h2h(roster,
team1.roster(week=week),
num_days=num_days, num_samples=num_samples,
week=week, decay_rate=decay_rate)
return metric_fn(cats, points, scores, num_samples)
print("%s's roster:" % team1.manager_name, roster_score(team1.roster(week=week)))
print(tabulate([
[position, player.name] for player, position in
team1.roster(week=week).positions.items() if position not in {"BN", "IL"}
]))
print("%s's roster:" % team2.manager_name, reverse_roster_score(team2.roster(week=week)))
print(tabulate([
[position, player.name] for player, position in
team2.roster(week=week).positions.items() if position not in {"BN", "IL"}
]))
print("Optimizing %s's lineup" % team1.manager_name)
print("===========================================")
roster = team1.roster(week=week)
old_roster = roster
print("Adding free agents:")
for agent in get_free_agents(num_fa):
print(agent.name)
roster = roster.add(agent, "BN")
team1.set_roster(roster)
print("Ignoring players:", ", ".join(ignore_player))
scores = []
for roster, score in simulated_annealing(roster, roster_score, ignore_players={team1.roster(week=week).player_by_name(n) for n in ignore_player},
num_steps=num_iters,
ignore_injured=ignore_injured):
scores.append(score)
# print(tabulate([
# [position, player.name] for player, position in
# roster.positions.items() if position not in {"BN", "IL"}
# ]))
print("%s's optimized roster:" % team1.manager_name, score)
print(tabulate([
[position, player.name] for player, position in
roster.positions.items() if position not in {"BN", "IL"}
]))
def team_generator():
for r in [old_roster, roster]:
team1.set_roster(r)
yield team1
projections = visualize_matchup(team_generator(), team2,
num_days=num_days, num_samples=100000,
week=week, decay_rate=decay_rate,
show_plots=False)
with pd.option_context('display.max_rows', None, 'display.max_columns',
None, 'display.expand_frame_repr', False):
for i, team in enumerate([team1, team2]):
print("===========================================")
print("%s's projections:" % team.manager_name)
print(projections[1][i].round(2))
plt.figure()
plt.plot(scores)
plt.show()
if __name__ == "__main__":
main()