Library for easy querying of advanced NHL statistics.
Project description
pyhockey
Tool for querying advanced NHL data provided by MoneyPuck.com and NaturalStatTrick.com
Overview
A straightforward library that uses DuckDB and Polars to query a seperately-maintained database of NHL data scraped from MoneyPuck (https://www.moneypuck.com) and NaturalStatTrick (https://www.naturalstattrick.com).
Allows for easy access to season-summary data at the player- or team-level, as well as game-by-game data for each team. Game-by-game data is also available for players, but as of October 2025, this is only available for the 2025/2026 NHL season.
Examples
Season Summaries
To get the stats for each skater (i.e. non-goalies) on a team in a specific season with a minimum 1000 minutes of icetime:
>>> import pyhockey
>>> pyhockey.skater_summary(team='TOR', season=2024, min_icetime=1000)
shape: (14, 21)
┌──────────┬────────┬──────────────────────┬──────┬──────────┬───────────┬─────────────┬─────────┬────────┬───────┬───────────┬──────────┬───────────────┬──────────────┬─────────────────┬─────────────────────┬──────────────────┬──────────────────────┬───────────────┬──────────────┬────────────────┐
│ playerID ┆ season ┆ name ┆ team ┆ position ┆ situation ┆ gamesPlayed ┆ iceTime ┆ points ┆ goals ┆ xGoalsFor ┆ goalsFor ┆ xGoalsAgainst ┆ goalsAgainst ┆ goalsForPerHour ┆ goalsAgainstPerHour ┆ xGoalsForPerHour ┆ xGoalsAgainstPerHour ┆ pointsPerHour ┆ goalsPerHour ┆ averageIceTime │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str ┆ str ┆ str ┆ str ┆ i64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════╪════════╪══════════════════════╪══════╪══════════╪═══════════╪═════════════╪═════════╪════════╪═══════╪═══════════╪══════════╪═══════════════╪══════════════╪═════════════════╪═════════════════════╪══════════════════╪══════════════════════╪═══════════════╪══════════════╪════════════════╡
│ 8476931 ┆ 2024 ┆ Jake McCabe ┆ TOR ┆ D ┆ all ┆ 66 ┆ 1419.83 ┆ 23.0 ┆ 2.0 ┆ 64.72 ┆ 76.0 ┆ 74.66 ┆ 75.0 ┆ 3.21 ┆ 3.17 ┆ 2.73 ┆ 3.16 ┆ 0.97 ┆ 0.08 ┆ 21.51 │
│ 8476872 ┆ 2024 ┆ Scott Laughton ┆ TOR ┆ C ┆ all ┆ 80 ┆ 1168.03 ┆ 31.0 ┆ 13.0 ┆ 47.29 ┆ 42.0 ┆ 59.62 ┆ 76.0 ┆ 2.16 ┆ 3.9 ┆ 2.43 ┆ 3.06 ┆ 1.59 ┆ 0.67 ┆ 14.6 │
│ 8479318 ┆ 2024 ┆ Auston Matthews ┆ TOR ┆ C ┆ all ┆ 67 ┆ 1371.63 ┆ 78.0 ┆ 33.0 ┆ 101.7 ┆ 105.0 ┆ 82.36 ┆ 78.0 ┆ 4.59 ┆ 3.41 ┆ 4.45 ┆ 3.6 ┆ 3.41 ┆ 1.44 ┆ 20.47 │
│ 8475690 ┆ 2024 ┆ Chris Tanev ┆ TOR ┆ D ┆ all ┆ 75 ┆ 1481.2 ┆ 18.0 ┆ 3.0 ┆ 64.67 ┆ 72.0 ┆ 71.17 ┆ 64.0 ┆ 2.92 ┆ 2.59 ┆ 2.62 ┆ 2.88 ┆ 0.73 ┆ 0.12 ┆ 19.75 │
│ 8482720 ┆ 2024 ┆ Matthew Knies ┆ TOR ┆ L ┆ all ┆ 78 ┆ 1444.17 ┆ 58.0 ┆ 29.0 ┆ 91.62 ┆ 103.0 ┆ 77.97 ┆ 74.0 ┆ 4.28 ┆ 3.07 ┆ 3.81 ┆ 3.24 ┆ 2.41 ┆ 1.2 ┆ 18.51 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 8478443 ┆ 2024 ┆ Brandon Carlo ┆ TOR ┆ D ┆ all ┆ 83 ┆ 1568.7 ┆ 12.0 ┆ 1.0 ┆ 59.18 ┆ 59.0 ┆ 85.97 ┆ 90.0 ┆ 2.26 ┆ 3.44 ┆ 2.26 ┆ 3.29 ┆ 0.46 ┆ 0.04 ┆ 18.9 │
│ 8477939 ┆ 2024 ┆ William Nylander ┆ TOR ┆ R ┆ all ┆ 82 ┆ 1599.78 ┆ 84.0 ┆ 45.0 ┆ 109.83 ┆ 122.0 ┆ 83.84 ┆ 67.0 ┆ 4.58 ┆ 2.51 ┆ 4.12 ┆ 3.14 ┆ 3.15 ┆ 1.69 ┆ 19.51 │
│ 8475171 ┆ 2024 ┆ Oliver Ekman-Larsson ┆ TOR ┆ D ┆ all ┆ 77 ┆ 1622.28 ┆ 29.0 ┆ 4.0 ┆ 82.82 ┆ 86.0 ┆ 71.64 ┆ 70.0 ┆ 3.18 ┆ 2.59 ┆ 3.06 ┆ 2.65 ┆ 1.07 ┆ 0.15 ┆ 21.07 │
│ 8481122 ┆ 2024 ┆ Simon Benoit ┆ TOR ┆ D ┆ all ┆ 78 ┆ 1290.62 ┆ 10.0 ┆ 1.0 ┆ 42.76 ┆ 48.0 ┆ 72.78 ┆ 58.0 ┆ 2.23 ┆ 2.7 ┆ 1.99 ┆ 3.38 ┆ 0.46 ┆ 0.05 ┆ 16.55 │
│ 8477503 ┆ 2024 ┆ Max Domi ┆ TOR ┆ C ┆ all ┆ 74 ┆ 1048.67 ┆ 33.0 ┆ 8.0 ┆ 48.57 ┆ 53.0 ┆ 43.75 ┆ 39.0 ┆ 3.03 ┆ 2.23 ┆ 2.78 ┆ 2.5 ┆ 1.89 ┆ 0.46 ┆ 14.17 │
└──────────┴────────┴──────────────────────┴──────┴──────────┴───────────┴─────────────┴─────────┴────────┴───────┴───────────┴──────────┴───────────────┴──────────────┴─────────────────┴─────────────────────┴──────────────────┴──────────────────────┴───────────────┴──────────────┴────────────────┘
Can pass multiple values for team or season, as well as specify the situation ('5on5', '4on5', or '5on4'):
>>> import pyhockey
>>> pyhockey.skater_summary(team=['TOR', 'MTL'], season=[2023, 2024], situation='5on5', min_icetime=1000)
shape: (35, 21)
┌──────────┬────────┬──────────────────────┬──────┬──────────┬───────────┬─────────────┬─────────┬────────┬───────┬───────────┬──────────┬───────────────┬──────────────┬─────────────────┬─────────────────────┬──────────────────┬──────────────────────┬───────────────┬──────────────┬────────────────┐
│ playerID ┆ season ┆ name ┆ team ┆ position ┆ situation ┆ gamesPlayed ┆ iceTime ┆ points ┆ goals ┆ xGoalsFor ┆ goalsFor ┆ xGoalsAgainst ┆ goalsAgainst ┆ goalsForPerHour ┆ goalsAgainstPerHour ┆ xGoalsForPerHour ┆ xGoalsAgainstPerHour ┆ pointsPerHour ┆ goalsPerHour ┆ averageIceTime │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str ┆ str ┆ str ┆ str ┆ i64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════╪════════╪══════════════════════╪══════╪══════════╪═══════════╪═════════════╪═════════╪════════╪═══════╪═══════════╪══════════╪═══════════════╪══════════════╪═════════════════╪═════════════════════╪══════════════════╪══════════════════════╪═══════════════╪══════════════╪════════════════╡
│ 8481540 ┆ 2023 ┆ Cole Caufield ┆ MTL ┆ R ┆ 5on5 ┆ 82 ┆ 1239.5 ┆ 38.0 ┆ 16.0 ┆ 50.03 ┆ 58.0 ┆ 53.99 ┆ 44.0 ┆ 2.81 ┆ 2.13 ┆ 2.42 ┆ 2.61 ┆ 1.84 ┆ 0.77 ┆ 15.12 │
│ 8476875 ┆ 2023 ┆ Mike Matheson ┆ MTL ┆ D ┆ 5on5 ┆ 82 ┆ 1472.85 ┆ 21.0 ┆ 4.0 ┆ 53.18 ┆ 49.0 ┆ 64.35 ┆ 60.0 ┆ 2.0 ┆ 2.44 ┆ 2.17 ┆ 2.62 ┆ 0.86 ┆ 0.16 ┆ 17.96 │
│ 8483515 ┆ 2023 ┆ Juraj Slafkovsky ┆ MTL ┆ L ┆ 5on5 ┆ 82 ┆ 1192.77 ┆ 33.0 ┆ 13.0 ┆ 51.15 ┆ 53.0 ┆ 53.25 ┆ 53.0 ┆ 2.67 ┆ 2.67 ┆ 2.57 ┆ 2.68 ┆ 1.66 ┆ 0.65 ┆ 14.55 │
│ 8482087 ┆ 2023 ┆ Kaiden Guhle ┆ MTL ┆ D ┆ 5on5 ┆ 70 ┆ 1253.7 ┆ 19.0 ┆ 4.0 ┆ 43.84 ┆ 48.0 ┆ 51.59 ┆ 59.0 ┆ 2.3 ┆ 2.82 ┆ 2.1 ┆ 2.47 ┆ 0.91 ┆ 0.19 ┆ 17.91 │
│ 8478133 ┆ 2023 ┆ Jake Evans ┆ MTL ┆ C ┆ 5on5 ┆ 82 ┆ 1015.2 ┆ 22.0 ┆ 5.0 ┆ 37.41 ┆ 34.0 ┆ 44.4 ┆ 42.0 ┆ 2.01 ┆ 2.48 ┆ 2.21 ┆ 2.62 ┆ 1.3 ┆ 0.3 ┆ 12.38 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 8478443 ┆ 2024 ┆ Brandon Carlo ┆ TOR ┆ D ┆ 5on5 ┆ 83 ┆ 1323.27 ┆ 11.0 ┆ 1.0 ┆ 49.1 ┆ 46.0 ┆ 52.38 ┆ 50.0 ┆ 2.09 ┆ 2.27 ┆ 2.23 ┆ 2.38 ┆ 0.5 ┆ 0.05 ┆ 15.94 │
│ 8477939 ┆ 2024 ┆ William Nylander ┆ TOR ┆ R ┆ 5on5 ┆ 82 ┆ 1241.83 ┆ 42.0 ┆ 24.0 ┆ 54.43 ┆ 61.0 ┆ 52.17 ┆ 41.0 ┆ 2.95 ┆ 1.98 ┆ 2.63 ┆ 2.52 ┆ 2.03 ┆ 1.16 ┆ 15.14 │
│ 8475171 ┆ 2024 ┆ Oliver Ekman-Larsson ┆ TOR ┆ D ┆ 5on5 ┆ 77 ┆ 1370.22 ┆ 24.0 ┆ 2.0 ┆ 54.29 ┆ 60.0 ┆ 54.14 ┆ 51.0 ┆ 2.63 ┆ 2.23 ┆ 2.38 ┆ 2.37 ┆ 1.05 ┆ 0.09 ┆ 17.8 │
│ 8481122 ┆ 2024 ┆ Simon Benoit ┆ TOR ┆ D ┆ 5on5 ┆ 78 ┆ 1110.95 ┆ 9.0 ┆ 1.0 ┆ 36.31 ┆ 44.0 ┆ 48.4 ┆ 34.0 ┆ 2.38 ┆ 1.84 ┆ 1.96 ┆ 2.61 ┆ 0.49 ┆ 0.05 ┆ 14.24 │
│ 8476931 ┆ 2024 ┆ Jake McCabe ┆ TOR ┆ D ┆ 5on5 ┆ 66 ┆ 1173.87 ┆ 21.0 ┆ 2.0 ┆ 46.64 ┆ 59.0 ┆ 41.37 ┆ 41.0 ┆ 3.02 ┆ 2.1 ┆ 2.38 ┆ 2.11 ┆ 1.07 ┆ 0.1 ┆ 17.79 │
└──────────┴────────┴──────────────────────┴──────┴──────────┴───────────┴─────────────┴─────────┴────────┴───────┴───────────┴──────────┴───────────────┴──────────────┴─────────────────┴─────────────────────┴──────────────────┴──────────────────────┴───────────────┴──────────────┴────────────────┘
When querying multiple seasons, can set combine_seasons=True to combine stats for all seasons into a single row:
>>> import pyhockey
>>> pyhockey.skater_summary(team='TOR', season=[2022, 2023, 2024], situation='5on5', min_icetime=1000, combine_seasons=True)
shape: (21, 21)
┌──────────┬────────────────┬──────────────────────┬──────┬──────────┬───────────┬─────────────┬─────────┬────────┬───────┬───────────┬──────────┬───────────────┬──────────────┬─────────────────┬─────────────────────┬──────────────────┬──────────────────────┬───────────────┬──────────────┬────────────────┐
│ playerID ┆ season ┆ name ┆ team ┆ position ┆ situation ┆ gamesPlayed ┆ iceTime ┆ points ┆ goals ┆ xGoalsFor ┆ goalsFor ┆ xGoalsAgainst ┆ goalsAgainst ┆ goalsForPerHour ┆ goalsAgainstPerHour ┆ xGoalsForPerHour ┆ xGoalsAgainstPerHour ┆ pointsPerHour ┆ goalsPerHour ┆ averageIceTime │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ str ┆ str ┆ str ┆ str ┆ str ┆ i16 ┆ f64 ┆ i16 ┆ i16 ┆ f64 ┆ i16 ┆ f64 ┆ i16 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════╪════════════════╪══════════════════════╪══════╪══════════╪═══════════╪═════════════╪═════════╪════════╪═══════╪═══════════╪══════════╪═══════════════╪══════════════╪═════════════════╪═════════════════════╪══════════════════╪══════════════════════╪═══════════════╪══════════════╪════════════════╡
│ 8470966 ┆ 2022 ┆ Mark Giordano ┆ TOR ┆ D ┆ 5on5 ┆ 78 ┆ 1215.2 ┆ 21 ┆ 2 ┆ 62.49 ┆ 56 ┆ 47.55 ┆ 39 ┆ 2.76 ┆ 1.93 ┆ 3.09 ┆ 2.35 ┆ 1.04 ┆ 0.1 ┆ 15.58 │
│ 8474568 ┆ 2022 ┆ Luke Schenn ┆ TOR ┆ D ┆ 5on5 ┆ 70 ┆ 1015.93 ┆ 20 ┆ 4 ┆ 40.18 ┆ 49 ┆ 47.92 ┆ 47 ┆ 2.89 ┆ 2.78 ┆ 2.37 ┆ 2.83 ┆ 1.18 ┆ 0.24 ┆ 14.51 │
│ 8474673 ┆ 2022,2023 ┆ TJ Brodie ┆ TOR ┆ D ┆ 5on5 ┆ 136 ┆ 2460.82 ┆ 34 ┆ 2 ┆ 108.41 ┆ 114 ┆ 98.62 ┆ 102 ┆ 2.78 ┆ 2.49 ┆ 2.64 ┆ 2.4 ┆ 0.83 ┆ 0.05 ┆ 18.09 │
│ 8475166 ┆ 2022,2023,2024 ┆ John Tavares ┆ TOR ┆ C ┆ 5on5 ┆ 235 ┆ 3278.6 ┆ 113 ┆ 51 ┆ 151.32 ┆ 157 ┆ 133.9 ┆ 123 ┆ 2.87 ┆ 2.25 ┆ 2.77 ┆ 2.45 ┆ 2.07 ┆ 0.93 ┆ 13.95 │
│ 8475171 ┆ 2024 ┆ Oliver Ekman-Larsson ┆ TOR ┆ D ┆ 5on5 ┆ 77 ┆ 1370.22 ┆ 24 ┆ 2 ┆ 54.29 ┆ 60 ┆ 54.14 ┆ 51 ┆ 2.63 ┆ 2.23 ┆ 2.38 ┆ 2.37 ┆ 1.05 ┆ 0.09 ┆ 17.8 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 8480043 ┆ 2022 ┆ Timothy Liljegren ┆ TOR ┆ D ┆ 5on5 ┆ 67 ┆ 1035.42 ┆ 13 ┆ 5 ┆ 52.53 ┆ 50 ┆ 41.75 ┆ 31 ┆ 2.9 ┆ 1.8 ┆ 3.04 ┆ 2.42 ┆ 0.75 ┆ 0.29 ┆ 15.45 │
│ 8480144 ┆ 2022 ┆ David Kampf ┆ TOR ┆ C ┆ 5on5 ┆ 82 ┆ 1007.92 ┆ 23 ┆ 7 ┆ 40.85 ┆ 37 ┆ 40.28 ┆ 38 ┆ 2.2 ┆ 2.26 ┆ 2.43 ┆ 2.4 ┆ 1.37 ┆ 0.42 ┆ 12.29 │
│ 8480950 ┆ 2023 ┆ Ilya Lyubushkin ┆ TOR ┆ D ┆ 5on5 ┆ 74 ┆ 1029.3 ┆ 8 ┆ 0 ┆ 37.94 ┆ 37 ┆ 46.46 ┆ 50 ┆ 2.16 ┆ 2.91 ┆ 2.21 ┆ 2.71 ┆ 0.47 ┆ 0.0 ┆ 13.91 │
│ 8481122 ┆ 2024 ┆ Simon Benoit ┆ TOR ┆ D ┆ 5on5 ┆ 78 ┆ 1110.95 ┆ 9 ┆ 1 ┆ 36.31 ┆ 44 ┆ 48.4 ┆ 34 ┆ 2.38 ┆ 1.84 ┆ 1.96 ┆ 2.61 ┆ 0.49 ┆ 0.05 ┆ 14.24 │
│ 8482720 ┆ 2023,2024 ┆ Matthew Knies ┆ TOR ┆ L ┆ 5on5 ┆ 158 ┆ 2103.37 ┆ 68 ┆ 37 ┆ 94.97 ┆ 108 ┆ 83.82 ┆ 84 ┆ 3.08 ┆ 2.4 ┆ 2.71 ┆ 2.39 ┆ 1.94 ┆ 1.06 ┆ 13.31 │
└──────────┴────────────────┴──────────────────────┴──────┴──────────┴───────────┴─────────────┴─────────┴────────┴───────┴───────────┴──────────┴───────────────┴──────────────┴─────────────────┴─────────────────────┴──────────────────┴──────────────────────┴───────────────┴──────────────┴────────────────┘
The goalie_summary() and team_summary() functions work in the same way:
# goalie_summary() uses min_games_played instead of min_icetime
>>> import pyhockey
>>> pyhockey.goalie_summary(team=['TOR', 'MTL'], season=[2023, 2024], min_games_played=10)
shape: (10, 18)
┌──────────┬────────┬──────────────────┬──────┬───────────┬─────────────┬─────────┬────────┬───────┬────────────────┬───────────────────┬─────────────────┬─────────────────┬────────────────────┬──────────────────┬────────────────┬───────────────────┬─────────────────┐
│ playerID ┆ season ┆ name ┆ team ┆ situation ┆ gamesPlayed ┆ iceTime ┆ xGoals ┆ goals ┆ lowDangerShots ┆ mediumDangerShots ┆ highDangerShots ┆ lowDangerxGoals ┆ mediumDangerxGoals ┆ highDangerxGoals ┆ lowDangerGoals ┆ mediumDangerGoals ┆ highDangerGoals │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str ┆ str ┆ str ┆ i64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════╪════════╪══════════════════╪══════╪═══════════╪═════════════╪═════════╪════════╪═══════╪════════════════╪═══════════════════╪═════════════════╪═════════════════╪════════════════════╪══════════════════╪════════════════╪═══════════════════╪═════════════════╡
│ 8478470 ┆ 2023 ┆ Sam Montembeault ┆ MTL ┆ all ┆ 41 ┆ 2428.77 ┆ 130.34 ┆ 127.0 ┆ 1432.0 ┆ 313.0 ┆ 151.0 ┆ 40.5 ┆ 39.47 ┆ 50.37 ┆ 46.0 ┆ 49.0 ┆ 32.0 │
│ 8480051 ┆ 2023 ┆ Cayden Primeau ┆ MTL ┆ all ┆ 23 ┆ 1324.83 ┆ 69.58 ┆ 66.0 ┆ 799.0 ┆ 182.0 ┆ 70.0 ┆ 22.73 ┆ 23.23 ┆ 23.62 ┆ 26.0 ┆ 25.0 ┆ 15.0 │
│ 8480051 ┆ 2024 ┆ Cayden Primeau ┆ MTL ┆ all ┆ 11 ┆ 523.7 ┆ 31.53 ┆ 41.0 ┆ 267.0 ┆ 88.0 ┆ 37.0 ┆ 9.05 ┆ 10.95 ┆ 11.52 ┆ 22.0 ┆ 14.0 ┆ 5.0 │
│ 8482487 ┆ 2024 ┆ Jakub Dobes ┆ MTL ┆ all ┆ 16 ┆ 874.78 ┆ 49.03 ┆ 40.0 ┆ 481.0 ┆ 141.0 ┆ 51.0 ┆ 14.42 ┆ 18.03 ┆ 16.58 ┆ 14.0 ┆ 20.0 ┆ 6.0 │
│ 8478470 ┆ 2024 ┆ Sam Montembeault ┆ MTL ┆ all ┆ 62 ┆ 3533.98 ┆ 190.59 ┆ 166.0 ┆ 1864.0 ┆ 497.0 ┆ 224.0 ┆ 54.67 ┆ 62.12 ┆ 73.8 ┆ 61.0 ┆ 66.0 ┆ 39.0 │
│ 8478492 ┆ 2023 ┆ Ilya Samsonov ┆ TOR ┆ all ┆ 40 ┆ 2300.97 ┆ 111.86 ┆ 120.0 ┆ 1251.0 ┆ 272.0 ┆ 136.0 ┆ 34.27 ┆ 32.97 ┆ 44.63 ┆ 44.0 ┆ 41.0 ┆ 35.0 │
│ 8474889 ┆ 2023 ┆ Martin Jones ┆ TOR ┆ all ┆ 22 ┆ 1169.67 ┆ 62.3 ┆ 56.0 ┆ 632.0 ┆ 153.0 ┆ 72.0 ┆ 19.05 ┆ 19.42 ┆ 23.84 ┆ 21.0 ┆ 21.0 ┆ 14.0 │
│ 8479361 ┆ 2023 ┆ Joseph Woll ┆ TOR ┆ all ┆ 25 ┆ 1471.65 ┆ 79.17 ┆ 72.0 ┆ 786.0 ┆ 212.0 ┆ 96.0 ┆ 22.72 ┆ 25.96 ┆ 30.49 ┆ 30.0 ┆ 23.0 ┆ 19.0 │
│ 8479361 ┆ 2024 ┆ Joseph Woll ┆ TOR ┆ all ┆ 42 ┆ 2442.33 ┆ 127.85 ┆ 111.0 ┆ 1354.0 ┆ 333.0 ┆ 145.0 ┆ 38.31 ┆ 40.97 ┆ 48.57 ┆ 45.0 ┆ 42.0 ┆ 24.0 │
│ 8476932 ┆ 2024 ┆ Anthony Stolarz ┆ TOR ┆ all ┆ 34 ┆ 1986.9 ┆ 96.78 ┆ 71.0 ┆ 1142.0 ┆ 248.0 ┆ 111.0 ┆ 30.41 ┆ 31.81 ┆ 34.58 ┆ 24.0 ┆ 27.0 ┆ 20.0 │
└──────────┴────────┴──────────────────┴──────┴───────────┴─────────────┴─────────┴────────┴───────┴────────────────┴───────────────────┴─────────────────┴─────────────────┴────────────────────┴──────────────────┴────────────────┴───────────────────┴─────────────────┘
>>> import pyhockey
>>> pyhockey.team_summary(season=list(range(2019, 2025)), combine_seasons=True)
shape: (33, 14)
┌───────────────────────────────┬──────┬───────────┬─────────────┬──────────┬─────────────┬──────────┬───────────────┬──────────────┬─────────────────┬─────────────────────┬──────────────────┬──────────────────────┬────────────────┐
│ season ┆ team ┆ situation ┆ gamesPlayed ┆ iceTime ┆ xGoalsFor ┆ goalsFor ┆ xGoalsAgainst ┆ goalsAgainst ┆ goalsForPerHour ┆ goalsAgainstPerHour ┆ xGoalsForPerHour ┆ xGoalsAgainstPerHour ┆ averageIceTime │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str ┆ i16 ┆ f64 ┆ f32 ┆ i16 ┆ f32 ┆ i16 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═══════════════════════════════╪══════╪═══════════╪═════════════╪══════════╪═════════════╪══════════╪═══════════════╪══════════════╪═════════════════╪═════════════════════╪══════════════════╪══════════════════════╪════════════════╡
│ 2019,2020,2021,2022,2023,2024 ┆ BOS ┆ all ┆ 454 ┆ 27590.15 ┆ 1351.319946 ┆ 1430 ┆ 1245.98999 ┆ 1185 ┆ 3.11 ┆ 2.58 ┆ 2.94 ┆ 2.71 ┆ 60.77 │
│ 2019,2020,2021,2022,2023,2024 ┆ PHI ┆ all ┆ 453 ┆ 27540.25 ┆ 1259.869995 ┆ 1278 ┆ 1328.959961 ┆ 1497 ┆ 2.78 ┆ 3.26 ┆ 2.74 ┆ 2.9 ┆ 60.8 │
│ 2019,2020,2021,2022,2023,2024 ┆ CAR ┆ all ┆ 452 ┆ 27429.78 ┆ 1507.25 ┆ 1474 ┆ 1173.410034 ┆ 1178 ┆ 3.22 ┆ 2.58 ┆ 3.3 ┆ 2.57 ┆ 60.69 │
│ 2019,2020,2021,2022,2023,2024 ┆ DAL ┆ all ┆ 453 ┆ 27557.75 ┆ 1378.920044 ┆ 1417 ┆ 1205.469971 ┆ 1235 ┆ 3.09 ┆ 2.69 ┆ 3.0 ┆ 2.62 ┆ 60.83 │
│ 2019,2020,2021,2022,2023,2024 ┆ NYI ┆ all ┆ 452 ┆ 27468.3 ┆ 1235.060059 ┆ 1279 ┆ 1275.469971 ┆ 1278 ┆ 2.79 ┆ 2.79 ┆ 2.7 ┆ 2.79 ┆ 60.77 │
│ 2019,2020,2021,2022,2023,2024 ┆ NSH ┆ all ┆ 453 ┆ 27477.9 ┆ 1337.890015 ┆ 1326 ┆ 1357.589966 ┆ 1376 ┆ 2.9 ┆ 3.0 ┆ 2.92 ┆ 2.96 ┆ 60.66 │
│ 2019,2020,2021,2022,2023,2024 ┆ CGY ┆ all ┆ 454 ┆ 27574.43 ┆ 1347.800049 ┆ 1381 ┆ 1256.810059 ┆ 1330 ┆ 3.0 ┆ 2.89 ┆ 2.93 ┆ 2.73 ┆ 60.74 │
│ 2019,2020,2021,2022,2023,2024 ┆ LAK ┆ all ┆ 454 ┆ 27551.97 ┆ 1333.219971 ┆ 1331 ┆ 1235.550049 ┆ 1277 ┆ 2.9 ┆ 2.78 ┆ 2.9 ┆ 2.69 ┆ 60.69 │
│ 2019,2020,2021,2022,2023,2024 ┆ VAN ┆ all ┆ 453 ┆ 27519.18 ┆ 1276.26001 ┆ 1399 ┆ 1326.109985 ┆ 1397 ┆ 3.05 ┆ 3.05 ┆ 2.78 ┆ 2.89 ┆ 60.75 │
│ 2019,2020,2021,2022,2023,2024 ┆ ANA ┆ all ┆ 455 ┆ 27609.75 ┆ 1138.800049 ┆ 1159 ┆ 1449.160034 ┆ 1554 ┆ 2.52 ┆ 3.38 ┆ 2.47 ┆ 3.15 ┆ 60.68 │
│ 2019,2020,2021,2022,2023,2024 ┆ DET ┆ all ┆ 455 ┆ 27613.32 ┆ 1203.930054 ┆ 1241 ┆ 1402.47998 ┆ 1550 ┆ 2.7 ┆ 3.37 ┆ 2.62 ┆ 3.05 ┆ 60.69 │
│ 2019,2020,2021,2022,2023,2024 ┆ OTT ┆ all ┆ 455 ┆ 27577.82 ┆ 1350.400024 ┆ 1320 ┆ 1371.02002 ┆ 1474 ┆ 2.87 ┆ 3.21 ┆ 2.94 ┆ 2.98 ┆ 60.61 │
│ 2019,2020,2021,2022,2023,2024 ┆ MIN ┆ all ┆ 453 ┆ 27557.93 ┆ 1292.089966 ┆ 1415 ┆ 1238.73999 ┆ 1340 ┆ 3.08 ┆ 2.92 ┆ 2.81 ┆ 2.7 ┆ 60.83 │
│ 2019,2020,2021,2022,2023,2024 ┆ PIT ┆ all ┆ 453 ┆ 27506.68 ┆ 1406.329956 ┆ 1439 ┆ 1307.709961 ┆ 1371 ┆ 3.14 ┆ 2.99 ┆ 3.07 ┆ 2.85 ┆ 60.72 │
│ 2019,2020,2021,2022,2023,2024 ┆ BUF ┆ all ┆ 453 ┆ 27470.68 ┆ 1252.420044 ┆ 1358 ┆ 1394.089966 ┆ 1525 ┆ 2.97 ┆ 3.33 ┆ 2.74 ┆ 3.04 ┆ 60.64 │
│ 2019,2020,2021,2022,2023,2024 ┆ MTL ┆ all ┆ 455 ┆ 27660.0 ┆ 1233.48999 ┆ 1286 ┆ 1458.719971 ┆ 1549 ┆ 2.79 ┆ 3.36 ┆ 2.68 ┆ 3.16 ┆ 60.79 │
│ 2019,2020,2021,2022,2023,2024 ┆ EDM ┆ all ┆ 455 ┆ 27543.68 ┆ 1499.869995 ┆ 1567 ┆ 1258.290039 ┆ 1346 ┆ 3.41 ┆ 2.93 ┆ 3.27 ┆ 2.74 ┆ 60.54 │
│ 2019,2020,2021,2022,2023,2024 ┆ WSH ┆ all ┆ 453 ┆ 27534.72 ┆ 1316.75 ┆ 1449 ┆ 1288.650024 ┆ 1357 ┆ 3.16 ┆ 2.96 ┆ 2.87 ┆ 2.81 ┆ 60.78 │
│ 2019,2020,2021,2022,2023,2024 ┆ CBJ ┆ all ┆ 454 ┆ 27579.42 ┆ 1206.550049 ┆ 1286 ┆ 1423.719971 ┆ 1558 ┆ 2.8 ┆ 3.39 ┆ 2.62 ┆ 3.1 ┆ 60.75 │
│ 2019,2020,2021,2022,2023,2024 ┆ CHI ┆ all ┆ 454 ┆ 27549.48 ┆ 1120.449951 ┆ 1184 ┆ 1402.569946 ┆ 1567 ┆ 2.58 ┆ 3.41 ┆ 2.44 ┆ 3.05 ┆ 60.68 │
│ 2019,2020,2021,2022,2023,2024 ┆ FLA ┆ all ┆ 453 ┆ 27482.93 ┆ 1518.189941 ┆ 1552 ┆ 1292.790039 ┆ 1310 ┆ 3.39 ┆ 2.86 ┆ 3.31 ┆ 2.82 ┆ 60.67 │
│ 2019,2020,2021,2022,2023,2024 ┆ NYR ┆ all ┆ 454 ┆ 27500.93 ┆ 1309.280029 ┆ 1465 ┆ 1309.890015 ┆ 1276 ┆ 3.2 ┆ 2.78 ┆ 2.86 ┆ 2.86 ┆ 60.57 │
│ 2021,2022,2023,2024 ┆ SEA ┆ all ┆ 328 ┆ 19868.17 ┆ 901.97998 ┆ 961 ┆ 940.97998 ┆ 1030 ┆ 2.9 ┆ 3.11 ┆ 2.72 ┆ 2.84 ┆ 60.57 │
│ 2019,2020,2021,2022,2023,2024 ┆ WPG ┆ all ┆ 455 ┆ 27521.73 ┆ 1347.530029 ┆ 1413 ┆ 1339.189941 ┆ 1218 ┆ 3.08 ┆ 2.66 ┆ 2.94 ┆ 2.92 ┆ 60.49 │
│ 2019,2020,2021,2022,2023,2024 ┆ COL ┆ all ┆ 454 ┆ 27520.2 ┆ 1411.180054 ┆ 1590 ┆ 1227.48999 ┆ 1259 ┆ 3.47 ┆ 2.74 ┆ 3.08 ┆ 2.68 ┆ 60.62 │
│ 2019,2020,2021,2022,2023,2024 ┆ STL ┆ all ┆ 455 ┆ 27588.88 ┆ 1224.920044 ┆ 1440 ┆ 1327.410034 ┆ 1372 ┆ 3.13 ┆ 2.98 ┆ 2.66 ┆ 2.89 ┆ 60.63 │
│ 2019,2020,2021,2022,2023,2024 ┆ NJD ┆ all ┆ 453 ┆ 27486.77 ┆ 1372.27002 ┆ 1368 ┆ 1315.619995 ┆ 1438 ┆ 2.99 ┆ 3.14 ┆ 3.0 ┆ 2.87 ┆ 60.68 │
│ 2019,2020,2021,2022,2023,2024 ┆ TOR ┆ all ┆ 454 ┆ 27537.9 ┆ 1479.26001 ┆ 1577 ┆ 1294.400024 ┆ 1332 ┆ 3.44 ┆ 2.9 ┆ 3.22 ┆ 2.82 ┆ 60.66 │
│ 2019,2020,2021,2022,2023,2024 ┆ TBL ┆ all ┆ 454 ┆ 27515.25 ┆ 1430.930054 ┆ 1563 ┆ 1306.390015 ┆ 1300 ┆ 3.41 ┆ 2.83 ┆ 3.12 ┆ 2.85 ┆ 60.61 │
│ 2019,2020,2021,2022,2023 ┆ ARI ┆ all ┆ 372 ┆ 22584.72 ┆ 961.919983 ┆ 1025 ┆ 1173.189941 ┆ 1235 ┆ 2.72 ┆ 3.28 ┆ 2.56 ┆ 3.12 ┆ 60.71 │
│ 2019,2020,2021,2022,2023,2024 ┆ SJS ┆ all ┆ 454 ┆ 27546.48 ┆ 1203.199951 ┆ 1158 ┆ 1454.609985 ┆ 1633 ┆ 2.52 ┆ 3.56 ┆ 2.62 ┆ 3.17 ┆ 60.68 │
│ 2019,2020,2021,2022,2023,2024 ┆ VGK ┆ all ┆ 455 ┆ 27630.25 ┆ 1404.969971 ┆ 1480 ┆ 1253.349976 ┆ 1257 ┆ 3.21 ┆ 2.73 ┆ 3.05 ┆ 2.72 ┆ 60.73 │
│ 2024 ┆ UTA ┆ all ┆ 82 ┆ 4994.22 ┆ 261.690002 ┆ 240 ┆ 240.179993 ┆ 247 ┆ 2.88 ┆ 2.97 ┆ 3.14 ┆ 2.89 ┆ 60.91 │
└───────────────────────────────┴──────┴───────────┴─────────────┴──────────┴─────────────┴──────────┴───────────────┴──────────────┴─────────────────┴─────────────────────┴──────────────────┴──────────────────────┴────────────────┘
Working with the data
If you're prefer working with pandas dataframes as opposed to polars ones, you can use the built-in to_pandas() method to make an easy conversion.
>>> import pyhockey
>>> df = pyhockey.skater_summary(team='TOR', season=2024)
>>> type(df)
<class 'polars.dataframe.frame.DataFrame'>
# Use `to_pandas()` to convert the results to a pandas dataframe
>>> df = df.to_pandas()
>>> type(df)
<class 'pandas.core.frame.DataFrame'>
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