NHL intro — sportsdataverse-py
Two hockey surfaces live under sportsdataverse.nhl:
- NHL api-web (native,
nhl_*) — the league's own modern feed: schedule, play-by-play (nhl_web_pbp), boxscores, standings, rosters, player game logs, plus player tracking (nhl_edge_*) and the stats-rest / records APIs (nhl_stats_rest_*,nhl_records_*). These return tidy frames directly (return_as_pandas=True). - ESPN NHL (
espn_nhl_*) — the same ESPN conventions used across every other league:espn_nhl_teams,espn_nhl_schedule,espn_nhl_pbp(a dict),espn_nhl_standings, etc.
Plus the load_nhl_* parquet loaders that read pre-built data releases.
R companion: fastRhockey (NHL + PWHL). Python neighbor: nhl-api-py. Part of the SportsDataverse.
Setup
pip install sportsdataverse
import polars as pl
import sportsdataverse as sdv
NHL api-web (native)
The native wrappers hit the league's api-web.nhle.com feed. Every one accepts return_as_pandas=True to get a tidy frame back (the default return_parsed=True already shapes the JSON into rows). Native game IDs look like 2023030417 (season + game-type + sequence), which is different from ESPN's 401675111 for the same game.
We'll use the 2024 Stanley Cup Final Game 7 throughout: Florida Panthers 2, Edmonton Oilers 1 (June 24, 2024).
Native: schedule
nhl_web_schedule(date='YYYY-MM-DD') returns the day's games with home_team_* / away_team_* columns and the native id.
nat_sched = sdv.nhl.nhl_web_schedule(date='2024-06-24', return_as_pandas=True)
pl.from_pandas(nat_sched).select([
'id', 'game_state',
'home_team_abbrev', 'home_team_score',
'away_team_abbrev', 'away_team_score',
]).head()
shape: (1, 6)
┌────────────┬────────────┬──────────────────┬─────────────────┬─────────────────┬─────────────────┐
│ id ┆ game_state ┆ home_team_abbrev ┆ home_team_score ┆ away_team_abbre ┆ away_team_score │
│ --- ┆ --- ┆ --- ┆ --- ┆ v ┆ --- │
│ i64 ┆ str ┆ str ┆ i64 ┆ --- ┆ i64 │
│ ┆ ┆ ┆ ┆ str ┆ │
╞════════════╪════════════╪══════════════════╪═════════════════╪═════════════════╪═════════════════╡
│ 2023030417 ┆ OFF ┆ FLA ┆ 2 ┆ EDM ┆ 1 │
└────────────┴────────────┴──────────────────┴─────────────────┴─────────────────┴─────────────────┘
Native: play-by-play
nhl_web_pbp(game_id=...) returns one row per event. Columns use snake_case (type_desc_key, time_in_period, period_descriptor_number) — not ESPN dot-notation.
nat_pbp = pl.from_pandas(sdv.nhl.nhl_web_pbp(game_id=2023030417, return_as_pandas=True))
print(nat_pbp.shape)
nat_pbp.select([
'period_descriptor_number', 'time_in_period', 'type_desc_key',
'details_event_owner_team_id', 'details_x_coord', 'details_y_coord',
]).head()
(331, 48)
shape: (5, 6)
┌────────────────┬────────────────┬───────────────┬────────────────┬───────────────┬───────────────┐
│ period_descrip ┆ time_in_period ┆ type_desc_key ┆ details_event_ ┆ details_x_coo ┆ details_y_coo │
│ tor_number ┆ --- ┆ --- ┆ owner_team_id ┆ rd ┆ rd │
│ --- ┆ str ┆ str ┆ --- ┆ --- ┆ --- │
│ i64 ┆ ┆ ┆ f64 ┆ f64 ┆ f64 │
╞════════════════╪════════════════╪═══════════════╪════════════════╪═══════════════╪═══════════════╡
│ 1 ┆ 00:00 ┆ period-start ┆ null ┆ null ┆ null │
│ 1 ┆ 00:00 ┆ faceoff ┆ 22.0 ┆ 0.0 ┆ 0.0 │
│ 1 ┆ 00:21 ┆ shot-on-goal ┆ 22.0 ┆ 82.0 ┆ -3.0 │
│ 1 ┆ 00:31 ┆ missed-shot ┆ 13.0 ┆ -81.0 ┆ 30.0 │
│ 1 ┆ 00:40 ┆ blocked-shot ┆ 13.0 ┆ -64.0 ┆ -36.0 │
└────────────────┴────────────────┴───────────────┴────────────────┴───────────────┴───────────────┘
# Event-type mix for the game (native uses `type_desc_key`, e.g. shot-on-goal, goal, hit)
(nat_pbp
.group_by('type_desc_key')
.agg(pl.len().alias('events'))
.sort('events', descending=True)
.head(10))
shape: (10, 2)
┌─────────────────┬────────┐
│ type_desc_key ┆ events │
│ --- ┆ --- │
│ str ┆ u32 │
╞═════════════════╪════════╡
│ faceoff ┆ 59 │
│ hit ┆ 53 │
│ stoppage ┆ 52 │
│ shot-on-goal ┆ 42 │
│ missed-shot ┆ 36 │
│ blocked-shot ┆ 32 │
│ giveaway ┆ 22 │
│ takeaway ┆ 19 │
│ delayed-penalty ┆ 3 │
│ period-start ┆ 3 │
└─────────────────┴────────┘
Native: boxscore
nhl_boxscore(game_id=...) returns one row per player (skaters + goalies) with home_away, position, and per-player stats.
box = pl.from_pandas(sdv.nhl.nhl_boxscore(game_id=2023030417, return_as_pandas=True))
(box
.filter(pl.col('position') != 'G')
.select(['name_default', 'home_away', 'position', 'goals', 'assists', 'points', 'sog', 'toi'])
.sort('points', descending=True)
.head())
shape: (5, 8)
┌──────────────┬───────────┬──────────┬───────┬─────────┬────────┬─────┬───────┐
│ name_default ┆ home_away ┆ position ┆ goals ┆ assists ┆ points ┆ sog ┆ toi │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ str │
╞══════════════╪═══════════╪══════════╪═══════╪═════════╪════════╪═════╪═══════╡
│ C. Verhaeghe ┆ home ┆ C ┆ 1.0 ┆ 1.0 ┆ 2.0 ┆ 3.0 ┆ 19:23 │
│ M. Janmark ┆ away ┆ C ┆ 1.0 ┆ 0.0 ┆ 1.0 ┆ 1.0 ┆ 12:40 │
│ C. Ceci ┆ away ┆ D ┆ 0.0 ┆ 1.0 ┆ 1.0 ┆ 1.0 ┆ 18:35 │
│ S. Reinhart ┆ home ┆ C ┆ 1.0 ┆ 0.0 ┆ 1.0 ┆ 2.0 ┆ 21:42 │
│ A. Lundell ┆ home ┆ C ┆ 0.0 ┆ 1.0 ┆ 1.0 ┆ 0.0 ┆ 14:39 │
└──────────────┴───────────┴──────────┴───────┴─────────┴────────┴─────┴───────┘
Native: standings
nhl_standings(date='YYYY-MM-DD') returns one row per team with conference/division context and points.
standings = pl.from_pandas(sdv.nhl.nhl_standings(date='2024-04-15', return_as_pandas=True))
(standings
.select(['team_name_default', 'conference_name', 'division_name', 'games_played', 'wins', 'losses', 'points'])
.sort('points', descending=True)
.head())
shape: (5, 7)
┌─────────────────────┬─────────────────┬───────────────┬──────────────┬──────┬────────┬────────┐
│ team_name_default ┆ conference_name ┆ division_name ┆ games_played ┆ wins ┆ losses ┆ points │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str ┆ i64 ┆ i64 ┆ i64 ┆ i64 │
╞═════════════════════╪═════════════════╪═══════════════╪══════════════╪══════╪════════╪════════╡
│ New York Rangers ┆ Eastern ┆ Metropolitan ┆ 82 ┆ 55 ┆ 23 ┆ 114 │
│ Carolina Hurricanes ┆ Eastern ┆ Metropolitan ┆ 81 ┆ 52 ┆ 22 ┆ 111 │
│ Dallas Stars ┆ Western ┆ Central ┆ 81 ┆ 51 ┆ 21 ┆ 111 │
│ Boston Bruins ┆ Eastern ┆ Atlantic ┆ 81 ┆ 47 ┆ 19 ┆ 109 │
│ Florida Panthers ┆ Eastern ┆ Atlantic ┆ 81 ┆ 51 ┆ 24 ┆ 108 │
└─────────────────────┴─────────────────┴───────────────┴──────────────┴──────┴────────┴────────┘
Native: roster & player game log
nhl_roster(team=ABBREV, season=...) lists a club's roster; nhl_player_game_log(player_id=..., season=...) returns a player's game-by-game line. Connor McDavid is 8478402; the season string is 20232024.
roster = pl.from_pandas(sdv.nhl.nhl_roster(team='FLA', season=20232024, return_as_pandas=True))
roster.select(['id', 'first_name_default', 'last_name_default', 'sweater_number', 'position_code', 'shoots_catches']).head()
shape: (5, 6)
┌─────────┬───────────────────┬──────────────────┬────────────────┬───────────────┬────────────────┐
│ id ┆ first_name_defaul ┆ last_name_defaul ┆ sweater_number ┆ position_code ┆ shoots_catches │
│ --- ┆ t ┆ t ┆ --- ┆ --- ┆ --- │
│ i64 ┆ --- ┆ --- ┆ i64 ┆ str ┆ str │
│ ┆ str ┆ str ┆ ┆ ┆ │
╞═════════╪═══════════════════╪══════════════════╪════════════════╪═══════════════╪════════════════╡
│ 8477493 ┆ Aleksander ┆ Barkov ┆ 16 ┆ C ┆ L │
│ 8477935 ┆ Sam ┆ Bennett ┆ 9 ┆ C ┆ L │
│ 8479981 ┆ Jonah ┆ Gadjovich ┆ 12 ┆ L ┆ L │
│ 8480825 ┆ Patrick ┆ Giles ┆ 36 ┆ R ┆ R │
│ 8479367 ┆ William ┆ Lockwood ┆ 67 ┆ R ┆ R │
└─────────┴───────────────────┴──────────────────┴────────────────┴───────────────┴────────────────┘
gamelog = pl.from_pandas(sdv.nhl.nhl_player_game_log(player_id=8478402, season=20232024, return_as_pandas=True))
print(gamelog.shape)
gamelog.select(['game_date', 'opponent_abbrev', 'goals', 'assists', 'points', 'shots', 'toi']).head()
(76, 22)
shape: (5, 7)
┌────────────┬─────────────────┬───────┬─────────┬────────┬───────┬───────┐
│ game_date ┆ opponent_abbrev ┆ goals ┆ assists ┆ points ┆ shots ┆ toi │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ i64 ┆ i64 ┆ i64 ┆ i64 ┆ str │
╞════════════╪═════════════════╪═══════╪═════════╪════════╪═══════╪═══════╡
│ 2024-04-17 ┆ ARI ┆ 0 ┆ 0 ┆ 0 ┆ 2 ┆ 18:10 │
│ 2024-04-15 ┆ SJS ┆ 1 ┆ 1 ┆ 2 ┆ 3 ┆ 15:45 │
│ 2024-04-06 ┆ CGY ┆ 0 ┆ 2 ┆ 2 ┆ 3 ┆ 20:39 │
│ 2024-04-05 ┆ COL ┆ 2 ┆ 0 ┆ 2 ┆ 9 ┆ 20:11 │
│ 2024-04-03 ┆ DAL ┆ 0 ┆ 0 ┆ 0 ┆ 8 ┆ 20:05 │
└────────────┴─────────────────┴───────┴─────────┴────────┴───────┴───────┘
Native: NHL EDGE (player tracking)
nhl_edge_* wraps the NHL EDGE puck-and-player tracking surface. The *_landing calls return a wide single-row leaderboard frame; per-player *_detail calls return a player's tracked values alongside the league average and percentile. Here's McDavid's skating-speed detail for 2023-24.
edge = pl.from_pandas(
sdv.nhl.nhl_edge_skater_skating_speed_detail(player_id=8478402, season=20232024, return_as_pandas=True)
)
edge.select([
'skating_speed_details_max_skating_speed_imperial',
'skating_speed_details_max_skating_speed_league_avg_imperial',
'skating_speed_details_bursts_over22_value',
'skating_speed_details_bursts_over22_percentile',
])
shape: (1, 4)
┌────────────────────────┬────────────────────────┬────────────────────────┬───────────────────────┐
│ skating_speed_details_ ┆ skating_speed_details_ ┆ skating_speed_details_ ┆ skating_speed_details │
│ max_skat… ┆ max_skat… ┆ bursts_o… ┆ _bursts_o… │
│ --- ┆ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ i64 ┆ f64 │
╞════════════════════════╪ ════════════════════════╪════════════════════════╪═══════════════════════╡
│ 24.191 ┆ 22.0904 ┆ 66 ┆ 0.9984 │
└────────────────────────┴────────────────────────┴────────────────────────┴───────────────────────┘
Native: stats-rest leaders
nhl_stats_rest_leaders_skaters(attribute=...) taps the api.nhle.com/stats/rest leaderboards — a clean top-10 frame per attribute (e.g. goals, points, assists).
leaders = pl.from_pandas(
sdv.nhl.nhl_stats_rest_leaders_skaters(attribute='goals', return_as_pandas=True)
)
leaders.select(['player_full_name', 'player_position_code', 'team_tri_code', 'goals']).head(10)
shape: (10, 4)
┌───────────────────┬──────────────────────┬───────────────┬───────┐
│ player_full_name ┆ player_position_code ┆ team_tri_code ┆ goals │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str ┆ i64 │
╞═══════════════════╪══════════════════════╪═══════════════╪═══════╡
│ Wayne Gretzky ┆ C ┆ EDM ┆ 92 │
│ Wayne Gretzky ┆ C ┆ EDM ┆ 87 │
│ Brett Hull ┆ R ┆ STL ┆ 86 │
│ Mario Lemieux ┆ C ┆ PIT ┆ 85 │
│ Phil Esposito ┆ C ┆ BOS ┆ 76 │
│ Teemu Selanne ┆ R ┆ WIN ┆ 76 │
│ Alexander Mogilny ┆ R ┆ BUF ┆ 76 │
│ Wayne Gretzky ┆ C ┆ EDM ┆ 73 │
│ Brett Hull ┆ R ┆ STL ┆ 72 │
│ Wayne Gretzky ┆ C ┆ EDM ┆ 71 │
└───────────────────┴──────────────────────┴───────────────┴───────┘
ESPN NHL (espn_nhl_*)
ESPN's surface follows the same conventions as every other league in the package: team-name columns are home_display_name / away_display_name, scores come back as strings, and espn_nhl_pbp returns a dict whose plays use raw ESPN dot-notation. ESPN game IDs look like 401675111.
ESPN: teams
teams = sdv.nhl.espn_nhl_teams()
print(teams.shape)
teams.select(['team_id', 'team_location', 'team_name', 'team_abbreviation', 'team_display_name']).head()
(32, 14)
shape: (5, 5)
┌─────────┬───────────────┬────────────┬───────────────────┬─────────────────────┐
│ team_id ┆ team_location ┆ team_name ┆ team_abbreviation ┆ team_display_name │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str ┆ str ┆ str │
╞═════════╪═══════════════╪════════════╪═══════════════════╪═════════════════════╡
│ 25 ┆ Anaheim ┆ Ducks ┆ ANA ┆ Anaheim Ducks │
│ 1 ┆ Boston ┆ Bruins ┆ BOS ┆ Boston Bruins │
│ 2 ┆ Buffalo ┆ Sabres ┆ BUF ┆ Buffalo Sabres │
│ 3 ┆ Calgary ┆ Flames ┆ CGY ┆ Calgary Flames │
│ 7 ┆ Carolina ┆ Hurricanes ┆ CAR ┆ Carolina Hurricanes │
└─────────┴───────────────┴────────────┴───────────────────┴─────────────────────┘
ESPN: schedule
espn_nhl_schedule(dates=YYYYMMDD). Team names are home_display_name / away_display_name; scores are strings, so cast before doing arithmetic.
schedule = sdv.nhl.espn_nhl_schedule(dates=20240624)
schedule.select([
'id', 'home_display_name', 'away_display_name',
pl.col('home_score').cast(pl.Int64, strict=False).alias('home_score'),
pl.col('away_score').cast(pl.Int64, strict=False).alias('away_score'),
]).head()
shape: (1, 5)
┌───────────┬───────────────────┬───────────────────┬────────────┬────────────┐
│ id ┆ home_display_name ┆ away_display_name ┆ home_score ┆ away_score │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str ┆ i64 ┆ i64 │
╞═══════════╪═══════════════════╪═══════════════════╪════════════╪════════════╡
│ 401675111 ┆ Florida Panthers ┆ Edmonton Oilers ┆ 2 ┆ 1 │
└───────────┴───────────────────┴───────────────────┴────────────┴────────────┘
ESPN: play-by-play
espn_nhl_pbp(game_id=...) returns a dict (keys like plays, boxscore, header, ...). pbp['plays'] is a list of raw dicts — build a frame with pl.DataFrame(..., infer_schema_length=None). Columns use ESPN dot-notation: period.number, clock.displayValue, scoringPlay, shootingPlay, type.text, coordinate.x / coordinate.y.
pbp = sdv.nhl.espn_nhl_pbp(game_id=401675111)
list(pbp.keys())[:8]
['gameId',
'plays',
'boxscore',
'header',
'format',
'broadcasts',
'videos',
'playByPlaySource']
plays = pl.DataFrame(pbp['plays'], infer_schema_length=None)
print(plays.shape)
plays.select(['period.number', 'clock.displayValue', 'text', 'type.text', 'scoringPlay']).head()
(328, 74)
shape: (5, 5)
┌───────────────┬────────────────────┬────────────────────────────────┬──────────────┬─────────────┐
│ period.number ┆ clock.displayValue ┆ text ┆ type.text ┆ scoringPlay │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ str ┆ str ┆ str ┆ bool │
╞═══════════════╪════════════════════╪════════════════════════════════╪══════════════╪═══════ ══════╡
│ 1 ┆ 0:00 ┆ Start of 1st Period ┆ Period Start ┆ false │
│ 1 ┆ 0:00 ┆ Adam Henrique faceoff won ┆ Face Off ┆ false │
│ ┆ ┆ agai… ┆ ┆ │
│ 1 ┆ 0:21 ┆ Adam Henrique Tip-In saved by ┆ Shot ┆ false │
│ ┆ ┆ … ┆ ┆ │
│ 1 ┆ 0:31 ┆ Anton Lundell Backhand Wide ┆ Missed ┆ false │
│ ┆ ┆ Le… ┆ ┆ │
│ 1 ┆ 0:40 ┆ Matthew Tkachuk shot blocked ┆ Blocked ┆ false │
│ ┆ ┆ b… ┆ ┆ │
└───────────────┴────────────────────┴────────────────────────────────┴──────────────┴─────────────┘
ESPN: shot-event filter
Filter the ESPN play-by-play to shooting plays — the simplest analysis primitive in hockey. shootingPlay is a Boolean column.
shots = plays.filter(pl.col('shootingPlay') == True)
shots.shape
(328, 74)
(shots
.group_by('type.text')
.agg(pl.len().alias('events'))
.sort('events', descending=True)
.head(10))
shape: (10, 2)
┌────────────┬── ──────┐
│ type.text ┆ events │
│ --- ┆ --- │
│ str ┆ u32 │
╞════════════╪════════╡
│ Face Off ┆ 59 │
│ Hit ┆ 53 │
│ Stoppage ┆ 52 │
│ Shot ┆ 42 │
│ Missed ┆ 36 │
│ Blocked ┆ 32 │
│ Giveaway ┆ 22 │
│ Takeaway ┆ 19 │
│ Penalty ┆ 3 │
│ Period End ┆ 3 │
└────────────┴────────┘
Parquet loaders (load_nhl_*)
The load_nhl_* loaders read pre-built parquet data releases (fastRhockey-era schema) and return polars frames — fast for multi-season work and offline-friendly. Pass seasons=[...]; add return_as_pandas=True for a pandas frame.
schedule_2024 = sdv.nhl.load_nhl_schedule(seasons=[2024])
print(schedule_2024.shape)
schedule_2024.select(['game_id', 'game_date', 'home_team_name', 'away_team_name', 'home_score', 'away_score']).head()
(179, 28)
shape: (5, 6)
┌────────────┬────────────┬─────────────────────┬───────────────────────┬────────────┬────────────┐
│ game_id ┆ game_date ┆ home_team_name ┆ away_team_name ┆ home_score ┆ away_score │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ i32 ┆ date ┆ str ┆ str ┆ i32 ┆ i32 │
╞════════════╪════════════╪═════════════════════╪══════════════ ═════════╪════════════╪════════════╡
│ 2023020176 ┆ 2023-11-07 ┆ Florida Panthers ┆ Columbus Blue Jackets ┆ 5 ┆ 4 │
│ 2023020177 ┆ 2023-11-07 ┆ Toronto Maple Leafs ┆ Tampa Bay Lightning ┆ 6 ┆ 5 │
│ 2023020178 ┆ 2023-11-07 ┆ Dallas Stars ┆ Boston Bruins ┆ 2 ┆ 3 │
│ 2023020179 ┆ 2023-11-07 ┆ Vancouver Canucks ┆ Edmonton Oilers ┆ 6 ┆ 2 │
│ 2023020174 ┆ 2023-11-06 ┆ Chicago Blackhawks ┆ New Jersey Devils ┆ 2 ┆ 4 │
└────────────┴────────────┴─────────────────────┴───────────────────────┴────────────┴────────────┘
team_box = sdv.nhl.load_nhl_team_box(seasons=[2024])
print(team_box.shape)
team_box.select(['game_id', 'team_name', 'goals', 'shots', 'hits', 'power_play_goals']).head()
(358, 18)
shape: (5, 6)
┌────────────┬───────────────────────┬─────── ┬───────┬──────┬──────────────────┐
│ game_id ┆ team_name ┆ goals ┆ shots ┆ hits ┆ power_play_goals │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ i32 ┆ str ┆ i32 ┆ i32 ┆ i32 ┆ i32 │
╞════════════╪═══════════════════════╪═══════╪═══════╪══════╪══════════════════╡
│ 2023020176 ┆ Columbus Blue Jackets ┆ 4 ┆ 22 ┆ 28 ┆ 1 │
│ 2023020176 ┆ Florida Panthers ┆ 5 ┆ 47 ┆ 8 ┆ 0 │
│ 2023020177 ┆ Tampa Bay Lightning ┆ 5 ┆ 31 ┆ 36 ┆ 2 │
│ 2023020177 ┆ Toronto Maple Leafs ┆ 6 ┆ 33 ┆ 32 ┆ 0 │
│ 2023020178 ┆ Boston Bruins ┆ 3 ┆ 29 ┆ 10 ┆ 1 │
└────────────┴───────────────────────┴───────┴───────┴──────┴──────────────────┘
Pipeline example: goals per period
Combine the ESPN play-by-play with polars: filter to scoring plays and group by period. (Game 7 ended 2-1, so this is a small, easy-to-read frame.)
(plays
.filter(pl.col('scoringPlay') == True)
.group_by('period.number')
.agg(pl.len().alias('goals'))
.sort('period.number'))
shape: (2, 2)
┌───────────────┬───────┐
│ period.number ┆ goals │
│ --- ┆ --- │
│ i64 ┆ u32 │
╞═══════════════╪═══════╡
│ 1 ┆ 2 │
│ 2 ┆ 1 │
└───────────────┴───────┘
Cross-references
- R companion: fastRhockey (NHL + PWHL)
- Data sources: NHL api-web (
api-web.nhle.com), NHL stats-rest (api.nhle.com), NHL EDGE, and the ESPN NHL API - Python alternative: nhl-api-py
- Plotting: matplotlib, plotnine
Where to go next
- API docs:
docs/docs/nhl/index.md - Women's hockey:
10_pwhl_intro.ipynb(PWHL, also via fastRhockey) - Cross-sport overview:
01_quickstart.ipynb