🏀 NBA hoops with sportsdataverse-py

This page is the executed notebook 04_nba_intro.ipynb: download it to run it yourself.

Welcome to the hardwood! 🎉 In just a few lines of Python you're about to pull a whole season of NBA data — teams, standings, rosters, play-by-play, box scores, schedules and statistical leaders — straight from ESPN and the SportsDataverse data releases. Everything comes back as a tidy polars DataFrame that's ready to slice, model, and chart. 🚀

We lead with the richest surface in the package: the espn_nba_* family, backed by ESPN's site / web / core APIs. If you know the R package hoopR, these names will feel like home. Python neighbor for the raw NBA Stats endpoints: nba_api. Let's lace 'em up! 👟

🧰 The toolbox

Here's the kit we'll reach for. The espn_nba_* wrappers (⭐ our premium source) hit ESPN live and parse the JSON into polars for you; the load_nba_* loaders pull pre-built season parquets from the sportsdataverse-data releases — fast and reliable. Click any name for the full reference.

Function	What it gives you	Source
espn_nba_teams	All 30 NBA teams (grab team_ids here)	⭐ ESPN
espn_nba_scoreboard	A day's slate — scores, status, matchups	⭐ ESPN
espn_nba_schedule	Schedule for a date / date-range	⭐ ESPN
espn_nba_standings	Conference standings (W-L, win%, streak)	⭐ ESPN
espn_nba_team_roster	A team's active roster	⭐ ESPN
espn_nba_team_schedule	One team's full-season schedule	⭐ ESPN
espn_nba_player_gamelog	A player's game-by-game log	⭐ ESPN
espn_nba_leaders	League statistical leaders	⭐ ESPN
espn_nba_pbp	Full game payload (play-by-play, win prob, box)	⭐ ESPN
espn_nba_game_rosters	Both teams' rosters for one game	⭐ ESPN
load_nba_schedule	Multi-season schedule parquet	📦 release
load_nba_player_boxscore	Player box scores, every game	📦 release
load_nba_standings	Historical standings	📦 release
espn_nba_injuries	League-wide injury report, one row per team	⭐ ESPN
load_nba_team_boxscore	Team box scores, every game (off/def, shooting)	📦 release
load_nba_shots	Every made shot with court coordinates	📦 release
most_recent_nba_season	The current season year helper	🧮 util

🔌 Setup

pip install sportsdataverse

No API key needed — ESPN's public endpoints and the data releases are open. 😊

import polars as pl
import sportsdataverse as sdv
from sportsdataverse.nba import most_recent_nba_season

pl.Config.set_tbl_rows(8)
SEASON = most_recent_nba_season()
print('current NBA season:', SEASON)

current NBA season: 2027

ESPN endpoints are live and seasonal, so we'll route every network call through a tiny safe() helper. When the feed is up you get the frame; when it's mid-offseason or briefly rate-limited you get a friendly one-liner instead of a scary traceback. 🛟

from sportsdataverse.errors import AssetFetchError, NoDataError

def safe(label, thunk):
    try:
        out = thunk()
        n = out.height if isinstance(out, pl.DataFrame) else (len(out) if hasattr(out, '__len__') else '?')
        print(f'✅ {label} — {n} rows')
        return out
    except (NoDataError, AssetFetchError) as e:
        print(f"\u23ed\ufe0f  {label}: {type(e).__name__}: {e}")
        return None

🏟️ Teams

Start with espn_nba_teams — one wide row per franchise. The team_id column is the key you'll pass into roster, schedule and standings calls everywhere else.

teams = safe('teams', sdv.nba.espn_nba_teams)
(teams.select(['team_id', 'team_location', 'team_name', 'team_abbreviation', 'team_color']).head(10)
 if teams is not None else 'teams feed unavailable')

✅ teams — 30 rows





shape: (10, 5)
┌─────────┬───────────────┬───────────┬───────────────────┬────────────┐
│ team_id ┆ team_location ┆ team_name ┆ team_abbreviation ┆ team_color │
│ ---     ┆ ---           ┆ ---       ┆ ---               ┆ ---        │
│ i64     ┆ str           ┆ str       ┆ str               ┆ str        │
╞═════════╪═══════════════╪═══════════╪═══════════════════╪════════════╡
│ 1       ┆ Atlanta       ┆ Hawks     ┆ ATL               ┆ c8102e     │
│ 2       ┆ Boston        ┆ Celtics   ┆ BOS               ┆ 008348     │
│ 17      ┆ Brooklyn      ┆ Nets      ┆ BKN               ┆ 000000     │
│ 30      ┆ Charlotte     ┆ Hornets   ┆ CHA               ┆ 008ca8     │
│ …       ┆ …             ┆ …         ┆ …                 ┆ …          │
│ 6       ┆ Dallas        ┆ Mavericks ┆ DAL               ┆ 0064b1     │
│ 7       ┆ Denver        ┆ Nuggets   ┆ DEN               ┆ 0e2240     │
│ 8       ┆ Detroit       ┆ Pistons   ┆ DET               ┆ 1d428a     │
│ 9       ┆ Golden State  ┆ Warriors  ┆ GS                ┆ fdb927     │
└─────────┴───────────────┴───────────┴───────────────────┴────────────┘

📅 Today on the slate (scoreboard)

espn_nba_scoreboard returns a tidy frame of every game for a date — final scores, live status, and matchups. Pass dates='YYYYMMDD' for one day. Here's a slice of the 2024 NBA Finals opener.

sb = safe('scoreboard', lambda: sdv.nba.espn_nba_scoreboard(dates='20240606'))
keep = ['game_id', 'short_name', 'home_abbreviation', 'away_abbreviation',
        'home_score', 'away_score', 'status_type_detail']
(sb.select([c for c in keep if c in sb.columns]).head()
 if sb is not None and sb.height else 'no games on that date')

✅ scoreboard — 1 rows





shape: (1, 7)
┌───────────┬────────────┬───────────────┬───────────────┬────────────┬────────────┬───────────────┐
│ game_id   ┆ short_name ┆ home_abbrevia ┆ away_abbrevia ┆ home_score ┆ away_score ┆ status_type_d │
│ ---       ┆ ---        ┆ tion          ┆ tion          ┆ ---        ┆ ---        ┆ etail         │
│ i64       ┆ str        ┆ ---           ┆ ---           ┆ str        ┆ str        ┆ ---           │
│           ┆            ┆ str           ┆ str           ┆            ┆            ┆ str           │
╞═══════════╪════════════╪═══════════════╪═══════════════╪════════════╪════════════╪═══════════════╡
│ 401656359 ┆ DAL @ BOS  ┆ BOS           ┆ DAL           ┆ 107        ┆ 89         ┆ Final         │
└───────────┴────────────┴───────────────┴───────────────┴────────────┴────────────┴───────────────┘

🏆 Standings

espn_nba_standings gives one row per team with wins, losses, win%, point differential and current streak. Pass season= (the end year of the season).

standings = safe('standings', lambda: sdv.nba.espn_nba_standings(season=SEASON))
cols = ['team_display_name', 'wins', 'losses', 'win_percent', 'games_behind',
        'point_differential', 'streak']
(standings.select([c for c in cols if c in standings.columns])
          .sort('win_percent', descending=True).head(10)
 if standings is not None and standings.height else 'standings unavailable')

✅ standings — 30 rows





shape: (10, 7)
┌───────────────────────┬──────┬────────┬─────────────┬──────────────┬────────────────────┬────────┐
│ team_display_name     ┆ wins ┆ losses ┆ win_percent ┆ games_behind ┆ point_differential ┆ streak │
│ ---                   ┆ ---  ┆ ---    ┆ ---         ┆ ---          ┆ ---                ┆ ---    │
│ str                   ┆ f64  ┆ f64    ┆ f64         ┆ f64          ┆ f64                ┆ f64    │
╞═══════════════════════╪══════╪════════╪═════════════╪══════════════╪════════════════════╪════════╡
│ Detroit Pistons       ┆ 3.0  ┆ 0.0    ┆ 1.0         ┆ 0.0          ┆ 38.0               ┆ 3.0    │
│ Portland Trail        ┆ 2.0  ┆ 0.0    ┆ 1.0         ┆ 0.5          ┆ 49.0               ┆ 2.0    │
│ Blazers               ┆      ┆        ┆             ┆              ┆                    ┆        │
│ Minnesota             ┆ 3.0  ┆ 0.0    ┆ 1.0         ┆ 0.0          ┆ 70.0               ┆ 3.0    │
│ Timberwolves          ┆      ┆        ┆             ┆              ┆                    ┆        │
│ Oklahoma City Thunder ┆ 3.0  ┆ 0.0    ┆ 1.0         ┆ 0.0          ┆ 31.0               ┆ 3.0    │
│ …                     ┆ …    ┆ …      ┆ …           ┆ …            ┆ …                  ┆ …      │
│ Houston Rockets       ┆ 3.0  ┆ 0.0    ┆ 1.0         ┆ 0.0          ┆ 29.0               ┆ 3.0    │
│ Philadelphia 76ers    ┆ 3.0  ┆ 1.0    ┆ 0.75        ┆ 0.5          ┆ 12.0               ┆ -1.0   │
│ Atlanta Hawks         ┆ 2.0  ┆ 1.0    ┆ 0.6666667   ┆ 1.0          ┆ 47.0               ┆ -1.0   │
│ Toronto Raptors       ┆ 2.0  ┆ 1.0    ┆ 0.6666667   ┆ 1.0          ┆ 30.0               ┆ -1.0   │
└───────────────────────┴──────┴────────┴─────────────┴──────────────┴────────────────────┴────────┘

🍳 Cookbook: common NBA tasks

Now the fun part — a handful of recipes you'll reach for again and again. Each one leans on the premium espn_nba_* wrappers.

Recipe 1 — A team and its roster 👥

Grab a team_id from espn_nba_teams, then pull the active roster with espn_nba_team_roster.

tid = None
if teams is not None and teams.height:
    # Boston Celtics if present, else the first team
    row = teams.filter(pl.col('team_abbreviation') == 'BOS')
    tid = int((row if row.height else teams)['team_id'][0])

roster = safe(f'roster {tid}', lambda: sdv.nba.espn_nba_team_roster(team_id=tid)) if tid else None
cols = ['full_name', 'jersey', 'position_abbreviation', 'height', 'weight', 'age']
(roster.select([c for c in cols if c in roster.columns]).head(10)
 if roster is not None and roster.height else 'roster unavailable')

✅ roster 2 — 17 rows





shape: (10, 6)
┌──────────────────┬────────┬───────────────────────┬────────┬────────┬─────┐
│ full_name        ┆ jersey ┆ position_abbreviation ┆ height ┆ weight ┆ age │
│ ---              ┆ ---    ┆ ---                   ┆ ---    ┆ ---    ┆ --- │
│ str              ┆ str    ┆ str                   ┆ f64    ┆ f64    ┆ i64 │
╞══════════════════╪════════╪═══════════════════════╪════════╪════════╪═════╡
│ Devin Carter     ┆ 99     ┆ G                     ┆ 74.0   ┆ 188.0  ┆ 24  │
│ Chris Cenac Jr.  ┆ 12     ┆ F                     ┆ 83.0   ┆ 240.0  ┆ 19  │
│ Mike Conley      ┆ 45     ┆ G                     ┆ 73.0   ┆ 175.0  ┆ 39  │
│ Luka Garza       ┆ 52     ┆ C                     ┆ 82.0   ┆ 243.0  ┆ 27  │
│ …                ┆ …      ┆ …                     ┆ …      ┆ …      ┆ …   │
│ Ron Harper Jr.   ┆ 8      ┆ G                     ┆ 77.0   ┆ 233.0  ┆ 26  │
│ Sam Hauser       ┆ 30     ┆ F                     ┆ 80.0   ┆ 221.0  ┆ 28  │
│ Dillon Mitchell  ┆ 20     ┆ F                     ┆ 80.0   ┆ 210.0  ┆ 23  │
│ Payton Pritchard ┆ 11     ┆ G                     ┆ 74.0   ┆ 211.0  ┆ 28  │
└──────────────────┴────────┴───────────────────────┴────────┴────────┴─────┘

Recipe 2 — One team's season schedule 📆

espn_nba_team_schedule returns every game on a team's calendar for a season — perfect for building a results table or a strength-of-schedule view.

tsched = safe(f'team schedule {tid}',
              lambda: sdv.nba.espn_nba_team_schedule(team_id=tid, season=SEASON)) if tid else None
cols = ['id', 'date', 'name', 'short_name', 'season_year']
(tsched.select([c for c in cols if c in tsched.columns]).head()
 if tsched is not None and tsched.height else 'team schedule unavailable')

✅ team schedule 2 — 80 rows





shape: (5, 5)
┌───────────┬───────────────────┬─────────────────────────────────┬────────────┬─────────────┐
│ id        ┆ date              ┆ name                            ┆ short_name ┆ season_year │
│ ---       ┆ ---               ┆ ---                             ┆ ---        ┆ ---         │
│ i64       ┆ str               ┆ str                             ┆ str        ┆ i64         │
╞═══════════╪═══════════════════╪═════════════════════════════════╪════════════╪═════════════╡
│ 401909088 ┆ 2026-10-20T19:00Z ┆ Boston Celtics at Detroit Pist… ┆ BOS @ DET  ┆ 2027        │
│ 401909095 ┆ 2026-10-23T23:00Z ┆ New York Knicks at Boston Celt… ┆ NY @ BOS   ┆ 2027        │
│ 401909870 ┆ 2026-10-26T23:30Z ┆ Chicago Bulls at Boston Celtic… ┆ CHI @ BOS  ┆ 2027        │
│ 401909877 ┆ 2026-10-27T23:30Z ┆ Brooklyn Nets at Boston Celtic… ┆ BKN @ BOS  ┆ 2027        │
│ 401909278 ┆ 2026-10-30T23:30Z ┆ Chicago Bulls at Boston Celtic… ┆ CHI @ BOS  ┆ 2027        │
└───────────┴───────────────────┴─────────────────────────────────┴────────────┴─────────────┘

Recipe 3 — A player's game log ⛹️

espn_nba_player_gamelog returns a game-by-game stat line for one athlete. The stat_* columns are positional (the ordered ESPN box categories); pair them with the opponent and result columns to see how the night went. (1966 = LeBron James.)

gamelog = safe('LeBron gamelog',
               lambda: sdv.nba.espn_nba_player_gamelog(athlete_id=1966, season=SEASON))
cols = ['event_date', 'opponent_abbreviation', 'home_away', 'game_result', 'score',
        'stat_0', 'stat_1', 'stat_2']
(gamelog.select([c for c in cols if c in gamelog.columns]).head()
 if gamelog is not None and gamelog.height else 'gamelog unavailable')

✅ LeBron gamelog — 5 rows





shape: (5, 8)
┌────────────┬───────────────────────┬───────────┬─────────────┬───────┬────────┬────────┬────────┐
│ event_date ┆ opponent_abbreviation ┆ home_away ┆ game_result ┆ score ┆ stat_0 ┆ stat_1 ┆ stat_2 │
│ ---        ┆ ---                   ┆ ---       ┆ ---         ┆ ---   ┆ ---    ┆ ---    ┆ ---    │
│ str        ┆ str                   ┆ str       ┆ str         ┆ str   ┆ str    ┆ str    ┆ str    │
╞════════════╪═══════════════════════╪═══════════╪═════════════╪═══════╪════════╪════════╪════════╡
│ null       ┆ null                  ┆ null      ┆ null        ┆ null  ┆ 33     ┆ 8-15   ┆ 53.3   │
│ null       ┆ null                  ┆ null      ┆ null        ┆ null  ┆ 33     ┆ 5-14   ┆ 35.7   │
│ null       ┆ null                  ┆ null      ┆ null        ┆ null  ┆ 34     ┆ 5-13   ┆ 38.5   │
│ null       ┆ null                  ┆ null      ┆ null        ┆ null  ┆ 25     ┆ 4-9    ┆ 44.4   │
│ null       ┆ null                  ┆ null      ┆ null        ┆ null  ┆ 18     ┆ 1-1    ┆ 100.0  │
└────────────┴───────────────────────┴───────────┴─────────────┴───────┴────────┴────────┴────────┘

Recipe 4 — Top scorers from the box-score release 🥇

For a whole-season leaderboard the load_nba_player_boxscore release is your friend — it's a fast parquet download, no live API needed. Here we average points per game and rank the top 10 scorers.

box = safe('player boxscore release', lambda: sdv.nba.load_nba_player_boxscore(seasons=[SEASON]))
if box is not None and box.height:
    leaders = (
        box.filter(pl.col('minutes') > 0)
        .group_by(['athlete_display_name', 'team_abbreviation'])
        .agg(pl.len().alias('gp'),
             pl.col('points').mean().round(1).alias('ppg'),
             pl.col('rebounds').mean().round(1).alias('rpg'),
             pl.col('assists').mean().round(1).alias('apg'))
        .filter(pl.col('gp') >= 20)
        .sort('ppg', descending=True)
        .head(10)
    )
    out = leaders
else:
    out = 'box-score release unavailable'
out

✅ player boxscore release — 995 rows





shape: (0, 6)
┌──────────────────────┬───────────────────┬─────┬─────┬─────┬─────┐
│ athlete_display_name ┆ team_abbreviation ┆ gp  ┆ ppg ┆ rpg ┆ apg │
│ ---                  ┆ ---               ┆ --- ┆ --- ┆ --- ┆ --- │
│ str                  ┆ str               ┆ u32 ┆ f64 ┆ f64 ┆ f64 │
╞══════════════════════╪═══════════════════╪═════╪═════╪═════╪═════╡
└──────────────────────┴───────────────────┴─────┴─────┴─────┴─────┘

Recipe 5 — Offense vs defense, every team 🛡️

The load_nba_team_boxscore release has one row per team-game with both team_score and opponent_team_score — so points-for, points-against and net rating are a single group_by away.

tbox = safe('team boxscore release', lambda: sdv.nba.load_nba_team_boxscore(seasons=[SEASON]))
if tbox is not None and tbox.height:
    netrtg = (
        tbox.group_by('team_abbreviation')
        .agg(pl.len().alias('gp'),
             pl.col('team_score').mean().round(1).alias('off_ppg'),
             pl.col('opponent_team_score').mean().round(1).alias('def_ppg'))
        .with_columns((pl.col('off_ppg') - pl.col('def_ppg')).round(1).alias('net'))
        .sort('net', descending=True)
        .head(10)
    )
    out = netrtg
else:
    out = 'team box-score release unavailable'
out

✅ team boxscore release — 72 rows





shape: (10, 5)
┌───────────────────┬─────┬─────────┬─────────┬──────┐
│ team_abbreviation ┆ gp  ┆ off_ppg ┆ def_ppg ┆ net  │
│ ---               ┆ --- ┆ ---     ┆ ---     ┆ ---  │
│ str               ┆ u32 ┆ f64     ┆ f64     ┆ f64  │
╞═══════════════════╪═════╪═════════╪═════════╪══════╡
│ TOR               ┆ 2   ┆ 123.5   ┆ 97.0    ┆ 26.5 │
│ POR               ┆ 2   ┆ 126.0   ┆ 101.5   ┆ 24.5 │
│ MIN               ┆ 2   ┆ 123.0   ┆ 99.5    ┆ 23.5 │
│ ATL               ┆ 3   ┆ 124.7   ┆ 109.0   ┆ 15.7 │
│ …                 ┆ …   ┆ …       ┆ …       ┆ …    │
│ OKC               ┆ 2   ┆ 106.0   ┆ 94.0    ┆ 12.0 │
│ MEM               ┆ 2   ┆ 117.0   ┆ 105.5   ┆ 11.5 │
│ HOU               ┆ 3   ┆ 121.0   ┆ 111.3   ┆ 9.7  │
│ LAC               ┆ 2   ┆ 119.0   ┆ 110.5   ┆ 8.5  │
└───────────────────┴─────┴─────────┴─────────┴──────┘

Recipe 6 — Who lived behind the arc? 🎯

Sum makes and attempts across the season to get each team's true three-point percentage (game-level percentages can't just be averaged). Reuses the tbox frame from Recipe 5 — no second download.

if tbox is not None and tbox.height:
    three_pt = (
        tbox.group_by('team_abbreviation')
        .agg(pl.col('three_point_field_goals_made').sum().alias('made'),
             pl.col('three_point_field_goals_attempted').sum().alias('att'))
        .with_columns((100 * pl.col('made') / pl.col('att')).round(1).alias('three_pt_pct'))
        .filter(pl.col('att') > 0)
        .sort('three_pt_pct', descending=True)
        .head(10)
    )
    out = three_pt
else:
    out = 'team box-score release unavailable'
out

shape: (10, 4)
┌───────────────────┬──────┬─────┬──────────────┐
│ team_abbreviation ┆ made ┆ att ┆ three_pt_pct │
│ ---               ┆ ---  ┆ --- ┆ ---          │
│ str               ┆ i32  ┆ i32 ┆ f64          │
╞═══════════════════╪══════╪═════╪══════════════╡
│ DET               ┆ 33   ┆ 78  ┆ 42.3         │
│ HOU               ┆ 40   ┆ 96  ┆ 41.7         │
│ LAL               ┆ 25   ┆ 60  ┆ 41.7         │
│ LAC               ┆ 30   ┆ 72  ┆ 41.7         │
│ …                 ┆ …    ┆ …   ┆ …            │
│ BOS               ┆ 32   ┆ 81  ┆ 39.5         │
│ MEM               ┆ 28   ┆ 73  ┆ 38.4         │
│ MIL               ┆ 45   ┆ 121 ┆ 37.2         │
│ NY                ┆ 27   ┆ 73  ┆ 37.0         │
└───────────────────┴──────┴─────┴──────────────┘

Recipe 7 — Double-double machines 💪

A double-double is double digits in two of points / rebounds / assists. Count the categories per player-game, keep the ones that cleared two, then tally them up — straight from load_nba_player_boxscore.

pbox = safe('player boxscore release', lambda: sdv.nba.load_nba_player_boxscore(seasons=[SEASON]))
if pbox is not None and pbox.height:
    dd = (
        pbox.filter(pl.col('minutes') > 0)
        .with_columns(
            ((pl.col('points') >= 10).cast(pl.Int8)
             + (pl.col('rebounds') >= 10).cast(pl.Int8)
             + (pl.col('assists') >= 10).cast(pl.Int8)).alias('cats10'))
        .filter(pl.col('cats10') >= 2)
        .group_by(['athlete_display_name', 'team_abbreviation'])
        .agg(pl.len().alias('double_doubles'))
        .sort('double_doubles', descending=True)
        .head(10)
    )
    out = dd
else:
    out = 'player box-score release unavailable'
out

✅ player boxscore release — 995 rows





shape: (10, 3)
┌──────────────────────┬───────────────────┬────────────────┐
│ athlete_display_name ┆ team_abbreviation ┆ double_doubles │
│ ---                  ┆ ---               ┆ ---            │
│ str                  ┆ str               ┆ u32            │
╞══════════════════════╪═══════════════════╪════════════════╡
│ Amen Thompson        ┆ HOU               ┆ 3              │
│ Trae Young           ┆ WSH               ┆ 2              │
│ Karl-Anthony Towns   ┆ NY                ┆ 2              │
│ Domantas Sabonis     ┆ SAC               ┆ 2              │
│ …                    ┆ …                 ┆ …              │
│ Victor Wembanyama    ┆ SA                ┆ 2              │
│ Davion Mitchell      ┆ MIA               ┆ 2              │
│ Jalen Johnson        ┆ ATL               ┆ 2              │
│ Paul Reed            ┆ DET               ┆ 1              │
└──────────────────────┴───────────────────┴────────────────┘

Recipe 8 — A tidy standings table 🏆

The load_nba_standings release ships in long format (one row per team × stat). Pivot the stats you care about into columns to get a classic standings grid, sorted by win percentage.

stload = safe('standings release', lambda: sdv.nba.load_nba_standings(seasons=[SEASON]))
wanted = ['wins', 'losses', 'winPercent', 'playoffSeed', 'pointDifferential']
if stload is not None and stload.height and {'stat_name', 'value'}.issubset(stload.columns):
    table = (
        stload.filter(pl.col('stat_name').is_in(wanted))
        .select(['team_abbreviation', 'group_name', 'stat_name', 'value'])
        .pivot(values='value', index=['team_abbreviation', 'group_name'], on='stat_name')
        .sort('winPercent', descending=True)
        .head(12)
    )
    out = table
else:
    out = 'standings release unavailable'
out

✅ standings release — 0 rows





'standings release unavailable'

Recipe 9 — Built on threes (shot release + a join) 🧱

load_nba_shots is one row per made shot with a score_value. Tally points from twos vs threes per team, then join team abbreviations from the box-score release to find who leaned hardest on the long ball.

shots = safe('shots release', lambda: sdv.nba.load_nba_shots(seasons=[SEASON]))
if shots is not None and shots.height and tbox is not None and tbox.height:
    fg = shots.filter(pl.col('score_value').is_in([2, 3]))
    reliance = (
        fg.group_by('team_id')
        .agg(pl.col('score_value').filter(pl.col('score_value') == 3).len().alias('threes_made'),
             pl.col('score_value').sum().alias('points_from_fg'))
        .with_columns((3 * pl.col('threes_made')).alias('points_from_threes'))
        .with_columns((100 * pl.col('points_from_threes') / pl.col('points_from_fg'))
                      .round(1).alias('pct_pts_from_3'))
        .filter(pl.col('threes_made') >= 500)  # drop All-Star / special rosters
    )
    abbr = tbox.select(['team_id', 'team_abbreviation']).unique()
    out = (reliance.join(abbr, on='team_id', how='left')
           .select(['team_abbreviation', 'threes_made', 'pct_pts_from_3'])
           .sort('pct_pts_from_3', descending=True).head(10))
else:
    out = 'shots / team box-score release unavailable'
out

✅ shots release — 8390 rows





shape: (0, 3)
┌───────────────────┬─────────────┬────────────────┐
│ team_abbreviation ┆ threes_made ┆ pct_pts_from_3 │
│ ---               ┆ ---         ┆ ---            │
│ str               ┆ u32         ┆ f64            │
╞═══════════════════╪═════════════╪════════════════╡
└───────────────────┴─────────────┴────────────────┘

Recipe 10 — Head-to-head, game by game 🤝

Filter the team box-score release to one matchup and you get the full season series — every meeting, the score, and who won. Swap the two abbreviations for any rivalry you like.

TEAM_A, TEAM_B = 'BOS', 'NY'
if tbox is not None and tbox.height and 'opponent_team_abbreviation' in tbox.columns:
    series = (
        tbox.filter((pl.col('team_abbreviation') == TEAM_A)
                    & (pl.col('opponent_team_abbreviation') == TEAM_B))
        .select([c for c in ['game_date', 'team_home_away', 'team_score',
                             'opponent_team_score', 'team_winner']
                 if c in tbox.columns])
        .sort('game_date')
    )
    out = series if series.height else f'no {TEAM_A} vs {TEAM_B} games in {SEASON}'
else:
    out = 'team box-score release unavailable'
out

shape: (1, 5)
┌────────────┬────────────────┬────────────┬─────────────────────┬─────────────┐
│ game_date  ┆ team_home_away ┆ team_score ┆ opponent_team_score ┆ team_winner │
│ ---        ┆ ---            ┆ ---        ┆ ---                 ┆ ---         │
│ date       ┆ str            ┆ i32        ┆ i32                 ┆ bool        │
╞════════════╪════════════════╪════════════╪═════════════════════╪═════════════╡
│ 2026-10-23 ┆ home           ┆ 107        ┆ 103                 ┆ true        │
└────────────┴────────────────┴────────────┴─────────────────────┴─────────────┘

Recipe 11 — Who's banged up? 🩹 (pandas interop)

espn_nba_injuries hits ESPN live for the league-wide injury report. Ask for a pandas frame with return_as_pandas=True (a handy interop point), count the listed players per team, then hand the result back to polars for the final sort.

import ast

inj = safe('injuries', lambda: sdv.nba.espn_nba_injuries(return_as_pandas=True))
if inj is not None and getattr(inj, 'shape', (0,))[0] and 'injuries' in inj.columns:
    def _n_listed(s):
        try:
            v = ast.literal_eval(s) if isinstance(s, str) else s
            return len(v) if isinstance(v, list) else 0
        except Exception:
            return 0
    inj = inj.copy()
    inj['players_listed'] = inj['injuries'].apply(_n_listed)
    out = (pl.from_pandas(inj[['display_name', 'players_listed']])
           .filter(pl.col('players_listed') > 0)
           .sort('players_listed', descending=True)
           .head(12))
else:
    out = 'injury report unavailable (off-season or feed down)'
out

✅ injuries — 27 rows





shape: (12, 2)
┌───────────────────────┬────────────────┐
│ display_name          ┆ players_listed │
│ ---                   ┆ ---            │
│ str                   ┆ i64            │
╞═══════════════════════╪════════════════╡
│ Dallas Mavericks      ┆ 6              │
│ Golden State Warriors ┆ 6              │
│ Oklahoma City Thunder ┆ 6              │
│ Philadelphia 76ers    ┆ 6              │
│ …                     ┆ …              │
│ Indiana Pacers        ┆ 4              │
│ Sacramento Kings      ┆ 4              │
│ Chicago Bulls         ┆ 3              │
│ Detroit Pistons       ┆ 3              │
└───────────────────────┴────────────────┘

🎬 Play-by-play & game rosters

Now for the granular stuff. espn_nba_pbp returns the whole game payload as a dict — play-by-play, win probability, box score, and header — keyed by game_id (an ESPN event id). Pair it with espn_nba_game_rosters for who actually suited up.

We'll use Game 1 of the 2024 Finals (game_id=401585660).

GAME_ID = 401585660
pbp = safe('pbp payload', lambda: sdv.nba.espn_nba_pbp(game_id=GAME_ID))
(list(pbp.keys())[:8] if isinstance(pbp, dict) else 'pbp unavailable')

✅ pbp payload — 22 rows





['gameId',
 'plays',
 'winprobability',
 'boxscore',
 'header',
 'format',
 'broadcasts',
 'videos']

plays = (pl.DataFrame(pbp['plays'], infer_schema_length=None)
         if isinstance(pbp, dict) and pbp.get('plays') else None)
cols = ['period.number', 'clock.displayValue', 'text', 'homeScore', 'awayScore', 'scoringPlay']
(plays.select([c for c in cols if c in plays.columns]).head()
 if plays is not None and plays.height else 'no plays parsed')

shape: (5, 6)
┌───────────────┬────────────────────┬───────────────────────┬───────────┬───────────┬─────────────┐
│ period.number ┆ clock.displayValue ┆ text                  ┆ homeScore ┆ awayScore ┆ scoringPlay │
│ ---           ┆ ---                ┆ ---                   ┆ ---       ┆ ---       ┆ ---         │
│ i64           ┆ str                ┆ str                   ┆ i64       ┆ i64       ┆ bool        │
╞═══════════════╪════════════════════╪═══════════════════════╪═══════════╪═══════════╪═════════════╡
│ 1             ┆ 12:00              ┆ Myles Turner vs.      ┆ 0         ┆ 0         ┆ false       │
│               ┆                    ┆ Anthony Davis…        ┆           ┆           ┆             │
│ 1             ┆ 11:42              ┆ Aaron Nesmith makes   ┆ 0         ┆ 3         ┆ true        │
│               ┆                    ┆ 26-foot th…           ┆           ┆           ┆             │
│ 1             ┆ 11:17              ┆ Austin Reaves misses  ┆ 0         ┆ 3         ┆ false       │
│               ┆                    ┆ driving l…            ┆           ┆           ┆             │
│ 1             ┆ 11:14              ┆ Austin Reaves         ┆ 0         ┆ 3         ┆ false       │
│               ┆                    ┆ offensive reboun…     ┆           ┆           ┆             │
│ 1             ┆ 11:12              ┆ Austin Reaves misses  ┆ 0         ┆ 3         ┆ false       │
│               ┆                    ┆ 14-foot t…            ┆           ┆           ┆             │
└───────────────┴────────────────────┴───────────────────────┴───────────┴───────────┴─────────────┘

Slice it: every 3-pointer in the game 🎯

The plays frame is just polars — so a scoring slice is one filter away. Here we pull made three-pointers in chronological order.

if plays is not None and plays.height:
    threes = (
        plays.filter(pl.col('scoringPlay') == True)
        .filter(pl.col('text').str.contains('(?i)three point|3pt|three-point'))
        .select([c for c in ['period.number', 'clock.displayValue', 'text',
                              'homeScore', 'awayScore'] if c in plays.columns])
    )
    out = threes.head(10) if threes.height else 'no three-pointers matched the text filter'
else:
    out = 'no plays to slice'
out

shape: (10, 5)
┌───────────────┬────────────────────┬─────────────────────────────────┬───────────┬───────────┐
│ period.number ┆ clock.displayValue ┆ text                            ┆ homeScore ┆ awayScore │
│ ---           ┆ ---                ┆ ---                             ┆ ---       ┆ ---       │
│ i64           ┆ str                ┆ str                             ┆ i64       ┆ i64       │
╞═══════════════╪════════════════════╪═════════════════════════════════╪═══════════╪═══════════╡
│ 1             ┆ 11:42              ┆ Aaron Nesmith makes 26-foot th… ┆ 0         ┆ 3         │
│ 1             ┆ 10:14              ┆ Andrew Nembhard makes 23-foot … ┆ 2         ┆ 6         │
│ 1             ┆ 9:58               ┆ LeBron James makes 26-foot thr… ┆ 5         ┆ 6         │
│ 1             ┆ 5:44               ┆ Myles Turner makes 25-foot thr… ┆ 15        ┆ 19        │
│ …             ┆ …                  ┆ …                               ┆ …         ┆ …         │
│ 2             ┆ 10:05              ┆ Max Christie makes 25-foot thr… ┆ 41        ┆ 40        │
│ 2             ┆ 9:39               ┆ Obi Toppin makes 27-foot three… ┆ 41        ┆ 43        │
│ 2             ┆ 8:44               ┆ Aaron Nesmith makes 26-foot th… ┆ 41        ┆ 49        │
│ 2             ┆ 7:02               ┆ Rui Hachimura makes 22-foot th… ┆ 51        ┆ 51        │
└───────────────┴────────────────────┴─────────────────────────────────┴───────────┴───────────┘

Who played? Game rosters 📋

espn_nba_game_rosters returns both teams' rosters for a single game, one row per athlete — including the starter flag and jersey number.

grosters = safe('game rosters', lambda: sdv.nba.espn_nba_game_rosters(game_id=GAME_ID))
cols = ['athlete_display_name', 'team_abbreviation', 'starter', 'jersey', 'position_name']
(grosters.select([c for c in cols if c in grosters.columns]).head(10)
 if grosters is not None and grosters.height else 'game rosters unavailable')

✅ game rosters — 26 rows





shape: (10, 5)
┌──────────────────────┬───────────────────┬─────────┬────────┬───────────────┐
│ athlete_display_name ┆ team_abbreviation ┆ starter ┆ jersey ┆ position_name │
│ ---                  ┆ ---               ┆ ---     ┆ ---    ┆ ---           │
│ str                  ┆ str               ┆ bool    ┆ str    ┆ str           │
╞══════════════════════╪═══════════════════╪═════════╪════════╪═══════════════╡
│ LeBron James         ┆ LAL               ┆ true    ┆ 23     ┆ Forward       │
│ Anthony Davis        ┆ LAL               ┆ true    ┆ 23     ┆ Forward       │
│ Rui Hachimura        ┆ LAL               ┆ true    ┆ 28     ┆ Forward       │
│ Spencer Dinwiddie    ┆ LAL               ┆ true    ┆ 26     ┆ Point Guard   │
│ …                    ┆ …                 ┆ …       ┆ …      ┆ …             │
│ Cam Reddish          ┆ LAL               ┆ false   ┆ 5      ┆ Small Forward │
│ Jaxson Hayes         ┆ LAL               ┆ false   ┆ 11     ┆ Center        │
│ Max Christie         ┆ LAL               ┆ false   ┆ 0      ┆ Guard         │
│ Harry Giles III      ┆ LAL               ┆ false   ┆ 20     ┆ Power Forward │
└──────────────────────┴───────────────────┴─────────┴────────┴───────────────┘

📦 Bulk season data with the loaders

When you want everything for a season at once — not one game at a time — the load_nba_* loaders pull pre-built parquet releases. They're fast, reliable, and don't depend on a live API being up.

Loader	Grain
load_nba_schedule	one row per game
load_nba_player_boxscore	one row per player-game
load_nba_standings	one row per team-season

sched = safe('schedule release', lambda: sdv.nba.load_nba_schedule(seasons=[SEASON]))
cols = ['id', 'date', 'home_display_name', 'away_display_name', 'home_score', 'away_score']
(sched.select([c for c in cols if c in sched.columns]).head()
 if sched is not None and sched.height else 'schedule release unavailable')

✅ schedule release — 1206 rows





shape: (5, 6)
┌───────────┬───────────────────┬────────────────────┬───────────────────┬────────────┬────────────┐
│ id        ┆ date              ┆ home_display_name  ┆ away_display_name ┆ home_score ┆ away_score │
│ ---       ┆ ---               ┆ ---                ┆ ---               ┆ ---        ┆ ---        │
│ i32       ┆ str               ┆ str                ┆ str               ┆ i32        ┆ i32        │
╞═══════════╪═══════════════════╪════════════════════╪═══════════════════╪════════════╪════════════╡
│ 401910945 ┆ 2027-04-12T00:30Z ┆ Dallas Mavericks   ┆ Memphis Grizzlies ┆ 0          ┆ 0          │
│ 401910946 ┆ 2027-04-12T00:30Z ┆ Minnesota          ┆ Houston Rockets   ┆ 0          ┆ 0          │
│           ┆                   ┆ Timberwolves       ┆                   ┆            ┆            │
│ 401910947 ┆ 2027-04-12T00:30Z ┆ San Antonio Spurs  ┆ Portland Trail    ┆ 0          ┆ 0          │
│           ┆                   ┆                    ┆ Blazers           ┆            ┆            │
│ 401910948 ┆ 2027-04-12T00:30Z ┆ Denver Nuggets     ┆ Oklahoma City     ┆ 0          ┆ 0          │
│           ┆                   ┆                    ┆ Thunder           ┆            ┆            │
│ 401910949 ┆ 2027-04-12T00:30Z ┆ Golden State       ┆ Utah Jazz         ┆ 0          ┆ 0          │
│           ┆                   ┆ Warriors           ┆                   ┆            ┆            │
└───────────┴───────────────────┴────────────────────┴───────────────────┴────────────┴────────────┘

Pipeline: the highest-scoring games of the season 🔥

With the schedule release in hand, a combined-points leaderboard is a quick polars pipeline — cast the scores, sum them, sort descending.

stats.nba.com surface (nba_stats_*)

Beyond the ESPN wrappers, sportsdataverse.nba also ships 128 wrappers for the official stats.nba.com API — the same source powering nba_api. The module is nba_stats and every function is named nba_stats_<slug>. These are the capture-confirmed live, non-deprecated endpoints; deprecated and never-populated endpoints are excluded (see ENDPOINT_HEALTH.md in sdv-internal-refs for the full active/dying/barren/dead matrix).

League routing is a single league_id parameter: "00" → NBA, "20" → G-League, "15" → Summer League. One stem, one host, league chosen per call.

curl_cffi required for live calls: stats.nba.com uses TLS/JA3 fingerprinting that silently blocks plain requests. Install the optional transport: pip install "sportsdataverse[all]" (or pip install curl_cffi). The wrappers run fully offline when fed a fixture — live calls simply need the extra installed.

Wrappers return a tidy polars DataFrame by default (parsed via the generic parse_nba_stats_result_sets parser — which also flattens the shot-location endpoints' grouped 2-level headers into composite columns and unrolls the scoreboardv3 game feed). Pass return_parsed=False for the raw Dict or return_as_pandas=True for pandas. Multi-result-set endpoints (e.g. playercareerstats) return dict[str, DataFrame].

from sportsdataverse.nba import nba_stats

# NBA player dashboard (polars DataFrame by default)
# Live calls require: pip install "sportsdataverse[all]"  (curl_cffi transport)
df = safe('nba_stats_leaguedashplayerstats (NBA)',
          lambda: nba_stats.nba_stats_leaguedashplayerstats(league_id="00"))

# Same endpoint, other leagues via league_id on the SAME stem:
gleague = safe('nba_stats_leaguedashplayerstats (G-League)',
               lambda: nba_stats.nba_stats_leaguedashplayerstats(league_id="20"))
summer  = safe('nba_stats_leaguedashplayerstats (Summer League)',
               lambda: nba_stats.nba_stats_leaguedashplayerstats(league_id="15"))

# Multi result-set endpoint -> dict[str, polars.DataFrame]
career = safe('nba_stats_playercareerstats (LeBron)',
              lambda: nba_stats.nba_stats_playercareerstats(player_id="2544"))

print('career result sets:', list(career.keys()) if isinstance(career, dict) else career)
df

✅ nba_stats_leaguedashplayerstats (NBA) — 582 rows


✅ nba_stats_leaguedashplayerstats (G-League) — 526 rows


✅ nba_stats_leaguedashplayerstats (Summer League) — 450 rows


✅ nba_stats_playercareerstats (LeBron) — 14 rows
career result sets: ['SeasonTotalsRegularSeason', 'CareerTotalsRegularSeason', 'SeasonTotalsPostSeason', 'CareerTotalsPostSeason', 'SeasonTotalsAllStarSeason', 'CareerTotalsAllStarSeason', 'SeasonTotalsCollegeSeason', 'CareerTotalsCollegeSeason', 'SeasonTotalsShowcaseSeason', 'CareerTotalsShowcaseSeason', 'SeasonRankingsRegularSeason', 'SeasonRankingsPostSeason', 'SeasonHighs', 'CareerHighs']





shape: (582, 69)
┌───────────┬────────────┬──────────┬───────────┬───┬──────────┬───────────┬───────────┬───────────┐
│ player_id ┆ player_nam ┆ nickname ┆ team_id   ┆ … ┆ td3_rank ┆ wnba_fant ┆ fp_high_s ┆ team_coun │
│ ---       ┆ e          ┆ ---      ┆ ---       ┆   ┆ ---      ┆ asy_pts_r ┆ core_rank ┆ t         │
│ i64       ┆ ---        ┆ str      ┆ i64       ┆   ┆ i64      ┆ ank       ┆ ---       ┆ ---       │
│           ┆ str        ┆          ┆           ┆   ┆          ┆ ---       ┆ i64       ┆ i64       │
│           ┆            ┆          ┆           ┆   ┆          ┆ i64       ┆           ┆           │
╞═══════════╪════════════╪══════════╪═══════════╪═══╪══════════╪═══════════╪═══════════╪═══════════╡
│ 1630639   ┆ A.J.       ┆ A.J.     ┆ 161061276 ┆ … ┆ 38       ┆ 449       ┆ 454       ┆ 1         │
│           ┆ Lawson     ┆          ┆ 1         ┆   ┆          ┆           ┆           ┆           │
│ 1631260   ┆ AJ Green   ┆ AJ       ┆ 161061274 ┆ … ┆ 38       ┆ 139       ┆ 167       ┆ 1         │
│           ┆            ┆          ┆ 9         ┆   ┆          ┆           ┆           ┆           │
│ 1642358   ┆ AJ Johnson ┆ AJ       ┆ 161061274 ┆ … ┆ 38       ┆ 413       ┆ 412       ┆ 2         │
│           ┆            ┆          ┆ 2         ┆   ┆          ┆           ┆           ┆           │
│ 203932    ┆ Aaron      ┆ Aaron    ┆ 161061274 ┆ … ┆ 38       ┆ 241       ┆ 250       ┆ 1         │
│           ┆ Gordon     ┆          ┆ 3         ┆   ┆          ┆           ┆           ┆           │
│ …         ┆ …          ┆ …        ┆ …         ┆ … ┆ …        ┆ …         ┆ …         ┆ …         │
│ 1630192   ┆ Zeke Nnaji ┆ Zeke     ┆ 161061274 ┆ … ┆ 38       ┆ 371       ┆ 373       ┆ 1         │
│           ┆            ┆          ┆ 3         ┆   ┆          ┆           ┆           ┆           │
│ 1630533   ┆ Ziaire     ┆ Ziaire   ┆ 161061275 ┆ … ┆ 38       ┆ 233       ┆ 244       ┆ 1         │
│           ┆ Williams   ┆          ┆ 1         ┆   ┆          ┆           ┆           ┆           │
│ 1629627   ┆ Zion       ┆ Zion     ┆ 161061274 ┆ … ┆ 38       ┆ 70        ┆ 65        ┆ 1         │
│           ┆ Williamson ┆          ┆ 0         ┆   ┆          ┆           ┆           ┆           │
│ 1642389   ┆ Zyon       ┆ Zyon     ┆ 161061275 ┆ … ┆ 38       ┆ 537       ┆ 533       ┆ 1         │
│           ┆ Pullin     ┆          ┆ 0         ┆   ┆          ┆           ┆           ┆           │
└───────────┴────────────┴──────────┴───────────┴───┴──────────┴───────────┴───────────┴───────────┘

if sched is not None and sched.height and {'home_score', 'away_score'}.issubset(sched.columns):
    hot = (
        sched.with_columns(
            (pl.col('home_score').cast(pl.Int64, strict=False)
             + pl.col('away_score').cast(pl.Int64, strict=False)).alias('total_points')
        )
        .filter(pl.col('total_points').is_not_null())
        .sort('total_points', descending=True)
        .select([c for c in ['date', 'home_display_name', 'away_display_name',
                              'home_score', 'away_score', 'total_points'] if c in sched.columns])
        .head(10)
    )
    out = hot
else:
    out = 'schedule release unavailable'
out

shape: (10, 5)
┌───────────────────┬──────────────────────┬───────────────────────┬────────────┬────────────┐
│ date              ┆ home_display_name    ┆ away_display_name     ┆ home_score ┆ away_score │
│ ---               ┆ ---                  ┆ ---                   ┆ ---        ┆ ---        │
│ str               ┆ str                  ┆ str                   ┆ i32        ┆ i32        │
╞═══════════════════╪══════════════════════╪═══════════════════════╪════════════╪════════════╡
│ 2026-10-25T01:00Z ┆ Denver Nuggets       ┆ New Orleans Pelicans  ┆ 141        ┆ 135        │
│ 2026-10-23T20:00Z ┆ Milwaukee Bucks      ┆ Chicago Bulls         ┆ 125        ┆ 132        │
│ 2026-10-21T23:00Z ┆ Orlando Magic        ┆ Atlanta Hawks         ┆ 125        ┆ 132        │
│ 2026-10-25T02:00Z ┆ Phoenix Suns         ┆ Golden State Warriors ┆ 131        ┆ 122        │
│ …                 ┆ …                    ┆ …                     ┆ …          ┆ …          │
│ 2026-10-25T00:00Z ┆ Chicago Bulls        ┆ Washington Wizards    ┆ 120        ┆ 124        │
│ 2026-10-21T23:00Z ┆ Washington Wizards   ┆ Milwaukee Bucks       ┆ 128        ┆ 116        │
│ 2026-10-24T23:30Z ┆ Philadelphia 76ers   ┆ Milwaukee Bucks       ┆ 127        ┆ 116        │
│ 2026-10-22T00:00Z ┆ New Orleans Pelicans ┆ Indiana Pacers        ┆ 120        ┆ 123        │
└───────────────────┴──────────────────────┴───────────────────────┴────────────┴────────────┘

🎉 Where to next

You just toured the premium espn_nba_* surface plus the season loaders — teams, scoreboard, standings, rosters, schedules, player game logs, play-by-play, and bulk box scores. A few parting tips:

• Pass return_as_pandas=True to any wrapper for a pandas frame instead of polars.
• ESPN espn_nba_* wrappers also accept return_parsed=False for the raw JSON dict.
• Full reference lives in the NBA section of the sidebar: ESPN site API · ESPN web API · ESPN core API · additional functions · loaders
• R user? The same surface lives in hoopR.
• Need raw NBA Stats endpoints? See nba_api.

Now go break down some film — and may your jumper always find the bottom of the net! 🏀🔥

🦓 Officiating

Last Two Minute reports and referee assignments, from official.nba.com and the NBA's own feeds.

from sportsdataverse.nba import nba_officiating

print("nba_officiating:", [n for n in dir(nba_officiating) if not n.startswith("_")][:8])
safe("referee assignments", lambda: nba_officiating.nba_referee_assignments("2024-04-01"))
safe("L2M games", lambda: nba_officiating.nba_l2m_games(2024))

nba_officiating: ['Any', 'AssetFetchError', 'L2M_CALLS_SCHEMA', 'L2M_GAME_SCHEMA', 'L2M_STATS_SCHEMA', 'Literal', 'NBA_REFEREE_ASSIGN_SCHEMA', 'NBA_REFEREE_REPLAY_SCHEMA']


✅ referee assignments — 2 rows


✅ L2M games — 413 rows





shape: (413, 4)
┌────────────┬────────┬─────────────┬─────────────────────────────────┐
│ game_id    ┆ season ┆ season_type ┆ label                           │
│ ---        ┆ ---    ┆ ---         ┆ ---                             │
│ str        ┆ i32    ┆ str         ┆ str                             │
╞════════════╪════════╪═════════════╪═════════════════════════════════╡
│ 0042300403 ┆ 2024   ┆ playoffs    ┆ Celtics 106, Mavericks 99       │
│ 0042300314 ┆ 2024   ┆ playoffs    ┆ Timberwolves 105, Mavericks 10… │
│ 0042300304 ┆ 2024   ┆ playoffs    ┆ Celtics 105, Pacers 102         │
│ 0042300303 ┆ 2024   ┆ playoffs    ┆ Celtics 114, Pacers 111         │
│ …          ┆ …      ┆ …           ┆ …                               │
│ 0022300068 ┆ 2024   ┆ regular     ┆ Heat 103, Pistons 102           │
│ 0022300069 ┆ 2024   ┆ regular     ┆ Raptors 97, Timberwolves 94     │
│ 0022300073 ┆ 2024   ┆ regular     ┆ Mavericks 126, Spurs 119        │
│ 0022300062 ┆ 2024   ┆ regular     ┆ Suns 108, Warriors 104          │
└────────────┴────────┴─────────────┴─────────────────────────────────┘