rugger

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rugger is a rrrrugby package 🏉. Get stats on teams, players, rankings and calculate changes in the rankings for a certain match.

Installation

You can install rugger with:

remotes::install_github("RobertMyles/rugger")

I won’t be burdening CRAN with it.

Usage

You can see the current world rankings with get_rankings():

library(rugger)
get_rankings()
#> # A tibble: 105 x 7
#>    team         team_abbr points  rank played previous_points previous_rank
#>    <chr>        <chr>      <dbl> <int>  <int>           <dbl>         <int>
#>  1 South Africa RSA         94.2     1    212            94.2             1
#>  2 New Zealand  NZL         92.1     2    220            92.1             2
#>  3 England      ENG         88.8     3    204            88.8             3
#>  4 Wales        WAL         85.0     4    212            85.0             4
#>  5 Ireland      IRE         84.4     5    196            84.4             5
#>  6 Australia    AUS         81.9     6    227            81.9             6
#>  7 France       FRA         80.9     7    199            80.9             7
#>  8 Japan        JPN         79.3     8    174            79.3             8
#>  9 Scotland     SCO         79.2     9    190            79.2             9
#> 10 Argentina    ARG         78.3    10    190            78.3            10
#> # … with 95 more rows

Hmmm, what would happen if New Zealand played Ireland tomorrow, and Ireland won by 5 points?

calculate_rank("New Zealand", "Ireland", score = c(15, 20))
#> # A tibble: 2 x 6
#>   team        points  rank points_exchanged new_points new_rank
#>   <chr>        <dbl> <int>            <dbl>      <dbl>    <int>
#> 1 New Zealand   92.1     2               -2       90.1        1
#> 2 Ireland       84.4     5                2       86.4        2

Let’s have a look at the history between England and Scotland, the first two teams to play the game:

get_team_records("England", "Scotland")
#> Data courtesy of ESPN, http://stats.espnscrum.com/statsguru/rugby/
#> # A tibble: 1 x 15
#>   team  start_year end_year matches   won  lost  draw percent_won `for` against
#>   <chr>      <dbl>    <dbl>   <int> <int> <int> <int>       <dbl> <int>   <int>
#> 1 Engl…       1871     2019     137    75    43    19        61.7  1674    1225
#> # … with 5 more variables: difference <int>, tries <int>, conversions <int>,
#> #   penalties <int>, dropgoals <int>

England winning almost 62% of the matches there.

I wonder which player has scored most tries in rugby?

library(dplyr)

get_team_records(type = "player") %>% 
  arrange(desc(points)) %>% 
  select(player, points)
#> # A tibble: 50 x 2
#>    player                    points
#>    <chr>                      <int>
#>  1 DW Carter (NZ)              1598
#>  2 RJR O'Gara (Ire/Lions)      1083
#>  3 SM Jones (Lions/Wales)       970
#>  4 FA Vlaicu (Rom)              951
#>  5 M Kvirikashvili (Georg)      840
#>  6 CD Paterson (Scot)           809
#>  7 Y Kushnarev (Russ)           777
#>  8 MJ Giteau (Aust)             698
#>  9 BG Habana (SA)               335
#> 10 BG O'Driscoll (Ire/Lions)    250
#> # … with 40 more rows

The sublime Dan Carter!

Data etc.

All of the data acessed by this package is obviously for informational/educational use, and a big thanks to all for making it available.

It belongs to World Rugby and ESPN. The algorithm that calculates the rankings also belongs to World Rugby, you can read about it here. Some of the data is also pulled from Pick and Go by Lassen Creative Technologies.

1. Black flags?! They’re Github’s fault. The flags represent the rankings as of the end of 2018 – New Zealand, Ireland, Wales, England, South Africa, Australia, Scotland and Fiji. You can see them in R with emo::ji("wales"), for example, using the emo package, available from https://github.com/hadley/emo.