This is data from the 2021 Women's Australian Football League. These are average player statistics across the season, with game statistics provided by the fitzRoy package. If you are new to the game of AFL, there is a nice explanation on Wikipedia. The primary analysis is to summarise the variation using principal component analysis, which gives information about relationships between the statistics or skills sets common in players. One also might be tempted to cluster the players, but there are no obvious clusters so it could be frustrating. At best one could partition the players into groups, while recognising there are no absolutely distinct and separated groups.
Details
- id, given_name, surname, number, position, team
player identification details
- time_pct, ..., clearances
player statistics for the match
Examples
require(dplyr)
#> Loading required package: dplyr
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
data(aflw)
glimpse(aflw)
#> Rows: 381
#> Columns: 35
#> $ id <chr> "CD_I1001678", "CD_I1001679", "CD_I1001681", "CD_I1001…
#> $ given_name <chr> "Jordan", "Brianna", "Jodie", "Ebony", "Emma", "Pepa",…
#> $ surname <chr> "Zanchetta", "Green", "Hicks", "Antonio", "King", "Ran…
#> $ number <int> 2, 3, 5, 12, 60, 21, 22, 23, 35, 14, 3, 8, 16, 12, 19,…
#> $ team <chr> "Brisbane Lions", "West Coast Eagles", "GWS Giants", "…
#> $ position <chr> "INT", "INT", "HFFR", "WL", "RK", "BPL", "INT", "INT",…
#> $ time_pct <dbl> 63.00000, 61.25000, 76.50000, 74.90000, 85.10000, 77.4…
#> $ goals <dbl> 0.0000000, 0.0000000, 0.0000000, 0.1000000, 0.6000000,…
#> $ behinds <dbl> 0.0000000, 0.0000000, 0.5000000, 0.4000000, 0.4000000,…
#> $ kicks <dbl> 5.000000, 2.500000, 3.750000, 8.800000, 4.100000, 3.22…
#> $ handballs <dbl> 2.500000, 3.750000, 3.000000, 3.600000, 2.700000, 2.22…
#> $ disposals <dbl> 7.500000, 6.250000, 6.750000, 12.400000, 6.800000, 5.4…
#> $ marks <dbl> 1.5000000, 0.2500000, 1.0000000, 3.7000000, 2.2000000,…
#> $ bounces <dbl> 0.0000000, 0.0000000, 0.0000000, 0.6000000, 0.1000000,…
#> $ tackles <dbl> 3.000000, 2.250000, 2.250000, 3.900000, 2.000000, 1.77…
#> $ contested <dbl> 3.500000, 2.250000, 3.500000, 5.700000, 4.400000, 2.66…
#> $ uncontested <dbl> 3.500000, 4.500000, 3.000000, 7.000000, 2.800000, 1.77…
#> $ possessions <dbl> 7.000000, 6.750000, 6.500000, 12.700000, 7.200000, 4.4…
#> $ marks_in50 <dbl> 1.0000000, 0.0000000, 0.2500000, 0.5000000, 0.9000000,…
#> $ contested_marks <dbl> 1.0000000, 0.0000000, 0.0000000, 0.4000000, 1.2000000,…
#> $ hitouts <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 19.4000000…
#> $ one_pct <dbl> 0.0000000, 1.5000000, 0.5000000, 1.2000000, 2.6000000,…
#> $ disposal <dbl> 60.25000, 67.15000, 37.20000, 65.96000, 61.72000, 66.8…
#> $ clangers <dbl> 2.000000, 0.500000, 2.500000, 3.100000, 2.400000, 1.33…
#> $ frees_for <dbl> 1.0000000, 0.5000000, 0.2500000, 2.5000000, 0.5000000,…
#> $ frees_against <dbl> 1.0000000, 0.5000000, 1.2500000, 1.3000000, 1.1000000,…
#> $ rebounds_in50 <dbl> 0.0000000, 0.5000000, 0.2500000, 1.1000000, 0.0000000,…
#> $ assists <dbl> 0.00000000, 0.00000000, 0.00000000, 0.20000000, 0.2000…
#> $ accuracy <dbl> 0.00000, 0.00000, 0.00000, 5.00000, 30.00000, 0.00000,…
#> $ turnovers <dbl> 1.500000, 1.000000, 2.500000, 4.000000, 1.700000, 1.22…
#> $ intercepts <dbl> 2.0000000, 2.0000000, 0.5000000, 5.3000000, 1.3000000,…
#> $ tackles_in50 <dbl> 0.5000000, 0.0000000, 0.7500000, 0.5000000, 0.5000000,…
#> $ shots <dbl> 0.5000000, 0.0000000, 0.7500000, 1.0000000, 1.2000000,…
#> $ metres <dbl> 72.50000, 58.50000, 76.00000, 225.90000, 89.80000, 76.…
#> $ clearances <dbl> 0.5000000, 0.2500000, 1.2500000, 0.4000000, 0.9000000,…