If you were to try and analyze statistics for your favorite hockey team, or try to predict an outcome of the next match you’d probably browse the internet in search of convenient tools to get the data you want from the NHL website. While there are a bunch of NHL API packages available in python, there is limited availability of many R packages. Therefore, the rpuck library is designed to allow users the ability to conveniently get both relevant and historical statistics from the publicly available but as of yet undocumented NHL.com API. As of now the package has limited functionality, and is considered a work-in-progress. We will add in additional functionality in the coming weeks which can easily be extended based on feedback we recieve.
Installation
You can install the development version of rpuck from GitHub with:
# install.packages("devtools")
devtools::install_github("UBC-MDS/rpuck")Features
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draft_pick(pick_number=NULL, round_number=NULL, year=NULL):- The
draft_pick( )function makes an API call to the drafts summary on the NHL.com API. The function returns information about draft picks for the specified arguments and stores them in a data frame.
- The
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attendance(regular=True, playoffs=True, start_season=NULL, end_season=NULL):- The attendance( ) function makes a query to the Attendance API to get the seasonal and playoff attendance numbers. The function displays attendance numbers in a ggplot chart.
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team_stats(start_season=NULL, end_season=NULL):- The
team_stats( )function makes an API call to the team summary endpoint on the NHL.com API. The function returns team seasonal stats for given seasons sorted by total team points.
- The
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player_stats(start_date=NULL, end_date=NULL):- The
player_stats( )function makes an API call to the player summary endpoint on the NHL.com API. The function returns the top 100 player stats for a given date range as sorted by total points.
- The
You can also find function descriptions and their use cases in package vignettes
Usage Example
The package can extract and visualize data from NHL statistics in convenient format. Below is an example of how one can use rpuck.
To load the package:
Get a chart showing the attendance over a specified time period:
attendance(regular=TRUE, playoffs=FALSE, start_season= 1990, end_season=2018)
Result of draft by pick number, draft number and year in summary report:
draft_pick(pick_number = 1, round_number = 2, year = 2019)
#> Player triCode Pick_number Round_number Year
#> 11402 Shane Pinto OTT 1 2 2019Query the top 100 player’s stats from the player’s summary report:
player_s <- player_stats(start_date = "2019-10-02", end_date = "2020-02-28")
head(player_s)
#> assists evGoals evPoints faceoffWinPct gameWinningGoals gamesPlayed goals
#> 1 62 24 59 0.51809 10 64 37
#> 2 44 27 54 0.37500 9 65 46
#> 3 56 25 67 0.50000 4 63 32
#> 4 56 20 48 0.47892 5 58 31
#> 5 52 23 56 0.42693 4 63 33
#> 6 55 20 52 0.40816 5 65 26
#> lastName otGoals penaltyMinutes playerId plusMinus points pointsPerGame
#> 1 Draisaitl 2 16 8477934 -11 99 1.54687
#> 2 Pastrnak 1 34 8477956 20 90 1.38461
#> 3 Panarin 0 18 8478550 35 88 1.39682
#> 4 McDavid 0 28 8478402 -7 87 1.50000
#> 5 MacKinnon 2 12 8477492 9 85 1.34920
#> 6 Marchand 0 72 8473419 24 81 1.24615
#> positionCode ppGoals ppPoints shGoals shPoints shootingPct shootsCatches
#> 1 C 13 40 0 0 0.18686 L
#> 2 R 19 36 0 0 0.18039 R
#> 3 L 7 21 0 0 0.16494 R
#> 4 C 11 39 0 0 0.16062 L
#> 5 C 10 29 0 0 0.11262 R
#> 6 L 5 26 1 3 0.14857 L
#> shots skaterFullName timeOnIcePerGame
#> 1 198 Leon Draisaitl 1361.516
#> 2 255 David Pastrnak 1146.369
#> 3 194 Artemi Panarin 1219.460
#> 4 193 Connor McDavid 1323.000
#> 5 293 Nathan MacKinnon 1278.889
#> 6 175 Brad Marchand 1178.246Stats for teams specified by start year or start year and end year:
attendance <- team_stats(start_season = "19801981", end_season = "19891990")
head(attendance)
#> faceoffWinPct gamesPlayed goalsAgainst goalsAgainstPerGame goalsFor
#> 1 NA 80 338 4.2250 230
#> 2 NA 80 336 4.2000 343
#> 3 NA 80 317 3.9625 321
#> 4 NA 80 287 3.5875 306
#> 5 NA 80 394 4.9250 257
#> 6 NA 80 333 4.1625 311
#> goalsForPerGame losses otLosses penaltyKillNetPct penaltyKillPct pointPct
#> 1 2.8750 49 NA 0.757679 0.733789 0.30000
#> 2 4.2875 34 NA 0.751445 0.734105 0.50000
#> 3 4.0125 34 NA 0.798449 0.771318 0.48750
#> 4 3.8250 35 NA 0.792880 0.754046 0.50000
#> 5 3.2125 53 NA 0.732323 0.724748 0.28125
#> 6 3.8875 39 NA 0.767068 0.730924 0.46250
#> points powerPlayNetPct powerPlayPct regulationAndOtWins seasonId
#> 1 48 0.182119 0.215231 17 19821983
#> 2 80 0.185185 0.205387 34 19821983
#> 3 78 0.216117 0.238095 32 19821983
#> 4 80 0.203821 0.229299 35 19821983
#> 5 45 0.183333 0.225000 18 19821983
#> 6 74 0.208459 0.229607 33 19821983
#> shotsAgainstPerGame shotsForPerGame teamFullName teamId ties wins
#> 1 32.0875 25.6875 New Jersey Devils 1 14 17
#> 2 32.5375 28.9125 Quebec Nordiques 32 12 34
#> 3 33.0875 28.5875 Calgary Flames 20 14 32
#> 4 29.8000 28.5250 New York Rangers 3 10 35
#> 5 32.6875 30.2250 Pittsburgh Penguins 5 9 18
#> 6 31.6375 31.7625 Winnipeg Jets (1979) 33 8 33
#> winsInRegulation winsInShootout
#> 1 17 0
#> 2 34 0
#> 3 32 0
#> 4 35 0
#> 5 18 0
#> 6 33 0Tests
We have included a variety of tests for each function in the tests\testthat directory. The tests check that the functions error gracefully and that proper function calls return the correct data/objects.
