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| import json | |
| import pandas as pd | |
| from stqdm import stqdm | |
| from typing import Optional | |
| from simulate import ( | |
| calculate_scenario_probabilities, | |
| create_simulate_summary, | |
| run_simulations, | |
| ) | |
| from yahoo_client import YahooFantasyClient | |
| def calculate_luck(df: pd.DataFrame, as_of_week: Optional[int] = None, include_current: bool = False) -> pd.DataFrame: | |
| if as_of_week: | |
| df_complete = df[df.week <= as_of_week] | |
| else: | |
| status_list = ["postevent"] | |
| if include_current: | |
| status_list.append("midevent") | |
| df_complete = df[df.matchup_status.isin(status_list)] | |
| df_complete["actual_wins"] = df_complete["win_probability"].apply(lambda x: x > 0.5) | |
| df_list = [] | |
| n_teams = df.team_name.nunique() | |
| for week, df_week in df_complete.groupby("week"): | |
| if len(df_week) != n_teams: | |
| next | |
| else: | |
| df_week["against_all_wins"] = ((df_week.team_points.rank().astype("float") - 1) / (n_teams - 1)).round(2) | |
| df_week["against_all_losses"] = 1 - df_week["against_all_wins"] | |
| df_week["half_wins"] = (df_week["against_all_wins"] >= 0.5) * 1.0 | |
| df_week["half_losses"] = 1 - df_week["half_wins"] | |
| df_week["against_all_luck"] = df_week["actual_wins"] - df_week["against_all_wins"] | |
| df_week["half_luck"] = df_week["actual_wins"] - df_week["half_wins"] | |
| df_week["earned_wins"] = ((df_week["against_all_wins"] + df_week["half_wins"]) / 2).round(2) | |
| df_week["luck_wins"] = df_week["actual_wins"] - df_week["earned_wins"] | |
| df_list.append(df_week) | |
| df_luck = pd.concat(df_list) | |
| return df_luck | |
| def get_grouped_luck(df_luck_all_weeks: pd.DataFrame) -> pd.DataFrame: | |
| summ_cols = [ | |
| "team_name", | |
| "team_points", | |
| "against_all_wins", | |
| "half_wins", | |
| "actual_wins", | |
| "earned_wins", | |
| "luck_wins", | |
| ] | |
| sort_by = "luck_wins" | |
| return df_luck_all_weeks[summ_cols].groupby("team_name").sum().sort_values(sort_by, ascending=False) | |
| def summarize_remaining_wins_from_matches_map(matches_map): | |
| """ | |
| Return map for all teams to map of number remaining wins | |
| to array of playoff and bye prob, respectively. | |
| """ | |
| remaining_map = {} | |
| for team_name, team_matches_map in matches_map.items(): | |
| team_remaining_map = {} | |
| for match_binary_str, prob_list in team_matches_map.items(): | |
| n_wins = sum([int(x) for x in match_binary_str]) | |
| if n_wins in team_remaining_map: | |
| incr_obs, incr_playoff_prob, incr_bye_prob = prob_list | |
| if incr_obs == 0: | |
| continue | |
| current_obs, current_playoff_prob, current_bye_prob = team_remaining_map[n_wins] | |
| new_obs = current_obs + incr_obs | |
| new_playoff_prob = round( | |
| (current_obs * current_playoff_prob + incr_obs * incr_playoff_prob) / new_obs, 3 | |
| ) | |
| new_bye_prob = round((current_obs * current_bye_prob + incr_obs * incr_bye_prob) / new_obs, 3) | |
| team_remaining_map[n_wins] = [new_obs, new_playoff_prob, new_bye_prob] | |
| else: | |
| team_remaining_map[n_wins] = prob_list | |
| remaining_map[team_name] = team_remaining_map | |
| return remaining_map | |
| def analyze_league(league_key: str, yahoo_client: YahooFantasyClient) -> None: | |
| df_scores = yahoo_client.full_schedule_dataframe(league_key) | |
| league_settings = yahoo_client.parse_league_settings(league_key) | |
| name_str = league_settings.name.strip().replace(" ", "_").lower() | |
| sim_completed_weeks = league_settings.current_week - 1 | |
| print(f"{sim_completed_weeks=}") | |
| stqdm.pandas() | |
| df_sims = run_simulations( | |
| df_scores, | |
| complete_weeks=sim_completed_weeks, | |
| n_sims=10000, | |
| n_playoff=league_settings.num_playoff_teams, | |
| ) | |
| df_sim_sum = create_simulate_summary(df_sims) | |
| df_sim_sum.to_csv(f"{name_str}_sim_sum.csv") | |
| scenario_probs = calculate_scenario_probabilities(df_sims) | |
| with open(f"{name_str}_scenario_probs.json", "w") as f: | |
| json.dump(scenario_probs, f) | |
| remaining_wins_to_probs_map = summarize_remaining_wins_from_matches_map(scenario_probs) | |
| with open(f"{name_str}_remaining_wins_probs.json", "w") as f: | |
| json.dump(remaining_wins_to_probs_map, f) | |