Scrape Team Summary Statistics

Web scrape (using rvest) team summary statistics for the current ECAC season

Usage

scrapeTeamStats(games = "all", gender = "women")

Arguments

games

collecting data for 'all' (default), 'conference', or 'nonconference' games. Currently no support for 'nonconference' games

gender

'women' (default) or 'men'

Value

data frame of summary team statistics

Examples

scrapeTeamStats()
#> # A tibble: 12 × 21
#>       Rk Name        GamesPlayed Goals Assists GoalsPerGame Shots PenaltyMinutes
#>    <int> <chr>             <int> <int>   <int>        <dbl> <int>          <int>
#>  1     1 Brown                29    52      80         1.79   616            178
#>  2     2 Clarkson             36   105     167         2.92  1300            221
#>  3     3 Colgate              39   146     251         3.74  1475            310
#>  4     4 Cornell              30    73     117         2.43   795            245
#>  5     5 Dartmouth            29    64      90         2.21   812            193
#>  6     6 Harvard              32   113     169         3.53  1161            249
#>  7     7 Princeton            33    58      99         1.76   978            230
#>  8     8 Quinnipiac           38   117     191         3.08  1262            188
#>  9     9 Rensselaer           32    57      85         1.78   756            242
#> 10    10 St. Lawren…          37    89     135         2.41   930            250
#> 11    11 Union                34    42      73         1.24   801            308
#> 12    12 Yale                 35   122     190         3.49  1197            196
#> # … with 13 more variables: PowerPlayGoals <int>, PowerPlayOpportunities <int>,
#> #   PowerPlayPercent <dbl>, PowerPlayGoalsAgainst <int>,
#> #   TimesShortHanded <int>, PenaltyKillPercent <dbl>, ShortHandedGoals <int>,
#> #   ShortHandedGoalsAgainst <int>, GoalsAgainst <int>,
#> #   GoalsAgainstAverage <dbl>, Saves <int>, SavePercent <dbl>,
#> #   EmptyNetGoalsAgainst <int>
scrapeTeamStats(games="conference", gender="men")
#> # A tibble: 12 × 21
#>       Rk Name        GamesPlayed Goals Assists GoalsPerGame Shots PenaltyMinutes
#>    <int> <chr>             <int> <int>   <int>        <dbl> <int>          <int>
#>  1     1 Brown                22    36      60         1.64   506            236
#>  2     2 Clarkson             23    89     160         3.87   657            196
#>  3     3 Colgate              23    56      99         2.43   692            260
#>  4     4 Cornell              22    73     123         3.32   630            273
#>  5     5 Dartmouth            22    45      77         2.05   459            240
#>  6     6 Harvard              24    77     135         3.21   779            209
#>  7     7 Princeton            22    54      87         2.45   605            191
#>  8     8 Quinnipiac           24    76     129         3.17   801            211
#>  9     9 Rensselaer           22    58      95         2.64   560            208
#> 10    10 St. Lawren…          22    44      74         2      628            218
#> 11    10 Union                22    52      91         2.36   586            162
#> 12    12 Yale                 22    38      61         1.73   527            276
#> # … with 13 more variables: PowerPlayGoals <int>, PowerPlayOpportunities <int>,
#> #   PowerPlayPercent <dbl>, PowerPlayGoalsAgainst <int>,
#> #   TimesShortHanded <int>, PenaltyKillPercent <dbl>, ShortHandedGoals <int>,
#> #   ShortHandedGoalsAgainst <int>, GoalsAgainst <int>,
#> #   GoalsAgainstAverage <dbl>, Saves <int>, SavePercent <dbl>,
#> #   EmptyNetGoalsAgainst <int>