fastbreak 0.2.1

Async NBA statistics API client

fastbreak

Async Python client for the NBA Stats API, fully typed with Pydantic. Pandas and polars exports are optional.

Installation

Requires Python 3.12+.

pip install fastbreak

With DataFrame support:

pip install fastbreak pandas   # or polars

Quick Start

import asyncio
from fastbreak.clients import NBAClient
from fastbreak.endpoints import LeagueStandings

async def main():
    async with NBAClient() as client:
        standings = await client.get(LeagueStandings(season="2025-26"))

        for team in standings.standings[:5]:
            print(f"{team.team_name}: {team.wins}-{team.losses}")

asyncio.run(main())

Convert to DataFrame

from fastbreak.models import TeamStanding

# To pandas
df = TeamStanding.to_pandas(standings.standings)

# To polars
df = TeamStanding.to_polars(standings.standings)

Batch Requests

Fetch multiple endpoints concurrently with get_many():

from fastbreak.clients import NBAClient
from fastbreak.endpoints import BoxScoreTraditional

game_ids = ["0022500001", "0022500002", "0022500003"]

async with NBAClient() as client:
    results = await client.get_many(
        [BoxScoreTraditional(game_id=gid) for gid in game_ids],
        max_concurrency=5,
    )

Results come back in the same order as the input. If any request fails, all in-flight requests are cancelled and an ExceptionGroup is raised.

Caching

Pass cache_ttl to avoid hitting the API twice for the same response:

from fastbreak.clients import NBAClient
from fastbreak.endpoints import LeagueStandings

async with NBAClient(cache_ttl=300, cache_maxsize=256) as client:
    result = await client.get(LeagueStandings(season="2025-26"))  # cached for 5 min
    print(client.cache_info)  # {'size': 1, 'maxsize': 256, 'ttl': 300}
    client.clear_cache()

Features

• Async I/O via aiohttp (asyncio only)
• Strict mypy typing
• Pydantic models for all responses, with pandas/polars export
• Retries and rate-limit handling built in
• get_many() for concurrent batch requests
• Optional TTL response caching
• Pure-computation analytics: BPM, VORP, PER, true shooting, win shares, Pythagorean wins

Utility Modules

Skip the endpoint boilerplate for common operations. Import directly and pass client as the first argument.

Data Access

Module	Key Functions
fastbreak.players	search_players, get_player_game_log, get_player_stats, get_league_leaders
fastbreak.teams	search_teams, get_team_stats, get_team_game_log, get_lineup_stats
fastbreak.games	get_todays_games, get_games_on_date, get_box_scores, get_play_by_play
fastbreak.standings	get_standings, get_conference_standings, magic_number
fastbreak.schedule	get_team_schedule, is_back_to_back, travel_distance, schedule_density
fastbreak.seasons	get_season_from_date, season_start_year — sync, no client needed

Shooting & Defense

Module	Key Functions
fastbreak.shots	get_shot_chart, zone_breakdown, shot_quality_vs_league, xfg_pct
fastbreak.defense	get_team_defense_zones, get_team_opponent_stats, defensive_shot_quality_vs_league
fastbreak.tracking	get_player_tracking_profile, get_team_tracking_profile, get_player_shot_defense

Matchups, Lineups & Rotations

Module	Key Functions
fastbreak.matchups	get_primary_defenders, get_defensive_assignments, get_team_matchup_summary
fastbreak.lineups	get_league_lineups, get_top_lineups, get_two_man_combos, get_lineup_efficiency
fastbreak.rotations	get_game_rotations, get_rotation_summary, lineup_stints, minutes_distribution

Analytics & Comparison

Module	Key Functions
fastbreak.metrics	bpm, vorp, per, true_shooting, usage_pct, pythagorean_win_pct
fastbreak.compare	get_player_comparison, comparison_deltas, comparison_edges, stat_leader
fastbreak.clutch	get_player_clutch_profile, get_league_clutch_leaders, clutch_score
fastbreak.estimated	get_player_estimated_metrics, get_team_estimated_metrics, get_estimated_leaders
fastbreak.splits	get_player_splits_profile, get_team_splits_profile
fastbreak.hot_hand	get_hot_hand_stats, hot_hand_result, miller_sanjurjo_bias, extract_shot_sequences
fastbreak.transition	get_transition_stats, classify_possessions, transition_frequency, transition_efficiency

from fastbreak.players import search_players, get_player_game_log
from fastbreak.games import get_todays_games

async with NBAClient() as client:
    players = await search_players(client, "Jokić")
    log     = await get_player_game_log(client, player_id=players[0].person_id)
    games   = await get_todays_games(client)

Available Endpoints

Over 115 endpoints covering:

Category	Examples
Box Scores	BoxScoreTraditional, BoxScoreAdvanced, BoxScoreFourFactors, BoxScorePlayerTrack, BoxScoreHustle, BoxScoreMatchups
Players	PlayerCareerStats, PlayerGameLogs, PlayerDashboardByClutch, PlayerEstimatedMetrics
Teams	TeamDetails, TeamGameLog, TeamPlayerDashboard, TeamEstimatedMetrics
League	LeagueStandings, LeagueLeaders, LeagueGameLog, LeagueDashLineups
Tracking	PlayerDashPtShots, PlayerDashPtPass, PlayerDashPtReb, PlayerDashPtShotDefend
Matchups	BoxScoreMatchupsV3, LeagueSeasonMatchups, MatchupsRollup, PlayerVsPlayer
Shooting	ShotChartDetail, ShotChartLeaguewide, LeagueDashTeamShotLocations
Play-by-Play	PlayByPlay, GameRotation
Hustle	HustleStatsBoxscore, LeagueHustleStatsPlayer, LeagueHustleStatsTeam
Splits	PlayerDashboardByGeneralSplits, PlayerDashboardByShootingSplits, TeamDashboardByGeneralSplits
Draft	DraftHistory, DraftCombineStats, DraftCombineDrillResults

See fastbreak.endpoints for the full list.

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

MIT License - see LICENSE for details.

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