The package provides easy access to AFLW data from the AFL.com.au
website. This can be accessed via the normal fetch_
functions in the same way you would access Men’s data.
This vignette talks through the main data sources. To fully
understand how the fetch_ functions work - please read the
Main
Fetch Functions vignette.
Firstly, we can return the fixture for a season or particular round.
not_cran
#> [1] TRUE
online
#> [1] TRUE
eval_param
#> [1] TRUE
fetch_fixture(season = 2021, comp = "AFLW") %>%
select(
compSeason.name, round.name,
home.team.name, away.team.name,
venue.name
)#> # A tibble: 68 × 5
#> compSeason.name round.name home.team.name away.team.name venue.name
#> <chr> <chr> <chr> <chr> <chr>
#> 1 2021 NAB AFLW Competition Round 1 Carlton Collingwood Ikon Park
#> 2 2021 NAB AFLW Competition Round 1 St Kilda Western Bulld… RSEA Park
#> 3 2021 NAB AFLW Competition Round 1 Gold Coast Su… Melbourne Metricon …
#> 4 2021 NAB AFLW Competition Round 1 West Coast Ea… Adelaide Crows Mineral R…
#> 5 2021 NAB AFLW Competition Round 1 Geelong Cats Kangaroos GMHBA Sta…
#> 6 2021 NAB AFLW Competition Round 1 Richmond Brisbane Lions Swinburne…
#> 7 2021 NAB AFLW Competition Round 1 Fremantle GWS Giants Fremantle…
#> 8 2021 NAB AFLW Competition Round 2 Western Bulld… Carlton Victoria …
#> 9 2021 NAB AFLW Competition Round 2 Collingwood Geelong Cats Victoria …
#> 10 2021 NAB AFLW Competition Round 2 Melbourne Richmond Casey Fie…
#> # ℹ 58 more rowsWe can get the lineup for a given set of matches in a particular round.
fetch_lineup(2021, round_number = 1, comp = "AFLW") %>%
select(
round.name, status, teamName,
player.playerName.givenName,
player.playerName.surname, teamStatus
)#> # A tibble: 294 × 6
#> round.name status teamName player.playerName.give…¹ player.playerName.su…²
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Round 1 CONCLUDED Carlton Brooke Vernon
#> 2 Round 1 CONCLUDED Carlton Natalie Plane
#> 3 Round 1 CONCLUDED Carlton Vaomua Laloifi
#> 4 Round 1 CONCLUDED Carlton Charlotte Wilson
#> 5 Round 1 CONCLUDED Carlton Kerryn Harrington
#> 6 Round 1 CONCLUDED Carlton Lauren Brazzale
#> 7 Round 1 CONCLUDED Carlton Elise O'Dea
#> 8 Round 1 CONCLUDED Carlton Katie Loynes
#> 9 Round 1 CONCLUDED Carlton Abbie McKay
#> 10 Round 1 CONCLUDED Carlton Tayla Harris
#> # ℹ 284 more rows
#> # ℹ abbreviated names: ¹player.playerName.givenName, ²player.playerName.surname
#> # ℹ 1 more variable: teamStatus <chr>The match results, including the teams playing, venue information and
final scores are returned via fetch_results.
fetch_results(2020, round_number = 1, comp = "AFLW") %>%
select(
match.date, match.name,
homeTeamScore.matchScore.totalScore, awayTeamScore.matchScore.totalScore
)#> # A tibble: 7 × 4
#> match.date match.name homeTeamScore.matchS…¹ awayTeamScore.matchS…²
#> <dttm> <chr> <int> <int>
#> 1 2020-02-07 08:45:00 Richmond Vs… 14 48
#> 2 2020-02-08 02:10:00 GWS Giants … 9 8
#> 3 2020-02-08 04:10:00 Melbourne V… 22 20
#> 4 2020-02-08 06:10:00 Brisbane Li… 34 21
#> 5 2020-02-09 02:10:00 Collingwood… 38 11
#> 6 2020-02-09 04:10:00 St Kilda Vs… 14 39
#> 7 2020-02-09 06:10:00 Fremantle V… 44 28
#> # ℹ abbreviated names: ¹homeTeamScore.matchScore.totalScore,
#> # ²awayTeamScore.matchScore.totalScoreWe can also get the ladder at any point with
fetch_ladder.
fetch_ladder(2020, round_number = 6, comp = "AFLW") %>%
select(
season, round_name,
position, team.name, played,
pointsFor, pointsAgainst
)#> # A tibble: 14 × 7
#> season round_name position team.name played pointsFor pointsAgainst
#> <dbl> <chr> <int> <chr> <int> <int> <int>
#> 1 2020 Semi Finals 1 Kangaroos 6 309 136
#> 2 2020 Semi Finals 2 GWS Giants 6 175 142
#> 3 2020 Semi Finals 3 Brisbane Lions 6 198 185
#> 4 2020 Semi Finals 4 Gold Coast Suns 6 154 152
#> 5 2020 Semi Finals 5 Geelong Cats 6 211 261
#> 6 2020 Semi Finals 6 Adelaide Crows 6 180 224
#> 7 2020 Semi Finals 7 Richmond 6 115 322
#> 8 2020 Semi Finals 1 Fremantle 6 277 179
#> 9 2020 Semi Finals 2 Carlton 6 249 164
#> 10 2020 Semi Finals 3 Melbourne 6 204 124
#> 11 2020 Semi Finals 4 Collingwood 6 229 149
#> 12 2020 Semi Finals 5 St Kilda 6 154 170
#> 13 2020 Semi Finals 6 Western Bulldogs 6 179 246
#> 14 2020 Semi Finals 7 West Coast Eagles 6 85 265Lastly - we have basic player stats. This will return player level statistics such as possessions, time on ground, fantasy points and a myriad of other statistics.
fetch_player_stats(2020, round_number = 1, comp = "AFLW") %>%
select(player.player.player.givenName:clearances.totalClearances)#> # A tibble: 294 × 46
#> player.player.player.givenName player.player.player.surn…¹ teamId gamesPlayed
#> <chr> <chr> <chr> <lgl>
#> 1 Gabrielle Seymour CD_T8… NA
#> 2 Kodi Jacques CD_T8… NA
#> 3 Sabrina Frederick CD_T8… NA
#> 4 Hannah Burchell CD_T8… NA
#> 5 Tayla Stahl CD_T8… NA
#> 6 Katie Brennan CD_T8… NA
#> 7 Madeline Brancatisano CD_T8… NA
#> 8 Laura Bailey CD_T8… NA
#> 9 Monique Conti CD_T8… NA
#> 10 Christina Bernardi CD_T8… NA
#> # ℹ 284 more rows
#> # ℹ abbreviated name: ¹player.player.player.surname
#> # ℹ 42 more variables: timeOnGroundPercentage <dbl>, goals <dbl>,
#> # behinds <dbl>, superGoals <lgl>, kicks <dbl>, handballs <dbl>,
#> # disposals <dbl>, marks <dbl>, bounces <dbl>, tackles <dbl>,
#> # contestedPossessions <dbl>, uncontestedPossessions <dbl>,
#> # totalPossessions <dbl>, inside50s <dbl>, marksInside50 <dbl>, …We can return player details such as data of birth and listed height and weight.
details_aflw <- fetch_player_details(team = "Western Bulldogs", current = TRUE, comp = "AFLW", source = "AFL")
head(details_aflw)#> # A tibble: 6 × 15
#> firstName surname id team season jumperNumber position providerId
#> <chr> <chr> <int> <chr> <dbl> <int> <chr> <chr>
#> 1 Hannah Scott 1502 Western Bull… 2022 22 MEDIUM_… CD_I10016…
#> 2 Ashleigh Guest 1630 Western Bull… 2022 19 MEDIUM_… CD_I10044…
#> 3 Brooke Lochland 1447 Western Bull… 2022 1 MEDIUM_… CD_I10044…
#> 4 Ellyse Gamble 1445 Western Bull… 2022 14 RUCK CD_I10053…
#> 5 Bailey Hunt 1649 Western Bull… 2022 21 MEDIUM_… CD_I10070…
#> 6 Kirsten McLeod 1668 Western Bull… 2022 6 MEDIUM_… CD_I10070…
#> # ℹ 7 more variables: dateOfBirth <chr>, heightInCm <int>, weightInKg <int>,
#> # recruitedFrom <chr>, debutYear <chr>, draftType <chr>, data_accessed <date>We can also return the coaches votes for a particular season, team or round.
fetch_coaches_votes(season = 2021, round_number = 9, comp = "AFLW", team = "Western Bulldogs")#> Season Round Home.Team Away.Team Player.Name Coaches.Votes
#> 9.1 2021 9 Western Bulldogs Richmond Kirsty Lamb (WB) 10
#> 9.2 2021 9 Western Bulldogs Richmond Brooke Lochland (WB) 8
#> 9.3 2021 9 Western Bulldogs Richmond Ellie Blackburn (WB) 5
#> 9.4 2021 9 Western Bulldogs Richmond Katie Brennan (RICH) 4
#> 9.5 2021 9 Western Bulldogs Richmond Rebecca Miller (RICH) 2
#> 9.6 2021 9 Western Bulldogs Richmond Eleanor Brown (WB) 1We have a legacy function to provide advanced AFLW stats. This is going to be deprecated in favour of a more robust solution but still works for now. The following code should show you how to use those functions. ### Match data
A good thing to check is that the cookie is working. Often this gets changed or moved and without it, the code won’t work.
cookie <- get_afl_cookie()
print(cookie)#> [1] "f992e7a132ed7631850b31ac6703fbae"Note - if this is NULL the rest of this Vignette won’t
show any outputs but the code will remain!
We can use the fetch_results() function to retrieve
match data matches.
match_data <- fetch_results(2020, round_number = 1, comp = "AFLW")Note that there will be warnings if a fixture is available but no match data has been added yet. If this is the case, make sure you don’t try to request detailed match stats for these match IDs.
glimpse(match_data)
#> Rows: 7
#> Columns: 75
#> $ match.name <chr> "Richmond Vs Carlton", "GWS Giants…
#> $ match.date <dttm> 2020-02-07 08:45:00, 2020-02-08 0…
#> $ match.status <chr> "CONCLUDED", "CONCLUDED", "CONCLUD…
#> $ match.matchId <chr> "CD_M20202640101", "CD_M2020264010…
#> $ match.venue <chr> "CD_V50", "CD_V402", "CD_V371", "C…
#> $ match.utcStartTime <chr> "2020-02-07T08:45:00", "2020-02-08…
#> $ match.homeTeamId <chr> "CD_T8788", "CD_T7889", "CD_T7386"…
#> $ match.awayTeamId <chr> "CD_T8096", "CD_T8786", "CD_T8466"…
#> $ match.round <chr> "CD_R202026401", "CD_R202026401", …
#> $ match.venueLocalStartTime <chr> "2020-02-07T19:45:00", "2020-02-08…
#> $ match.abbr <chr> "RICH V CARL", "GWS V GCFC", "MELB…
#> $ match.twitterHashTag <lgl> NA, NA, NA, NA, NA, NA, NA
#> $ match.homeTeam.name <chr> "Richmond", "GWS Giants", "Melbour…
#> $ match.homeTeam.timeZone <lgl> NA, NA, NA, NA, NA, NA, NA
#> $ match.homeTeam.teamId <chr> "CD_T8788", "CD_T7889", "CD_T7386"…
#> $ match.homeTeam.abbr <chr> "RICH", "GWS", "MELB", "BL", "COLL…
#> $ match.homeTeam.nickname <chr> "Richmond", "Giants", "Demons", "L…
#> $ match.awayTeam.name <chr> "Carlton", "Gold Coast Suns", "Kan…
#> $ match.awayTeam.timeZone <lgl> NA, NA, NA, NA, NA, NA, NA
#> $ match.awayTeam.teamId <chr> "CD_T8096", "CD_T8786", "CD_T8466"…
#> $ match.awayTeam.abbr <chr> "CARL", "GCFC", "NMFC", "ADEL", "W…
#> $ match.awayTeam.nickname <chr> "Blues", "Suns", "Kangaroos", "Cro…
#> $ venue.address <chr> "Melbourne", "Sydney", "Melbourne"…
#> $ venue.name <chr> "Ikon Park", "Blacktown Internatio…
#> $ venue.state <chr> "VIC", "NSW", "VIC", "QLD", "VIC",…
#> $ venue.timeZone <chr> "Australia/Melbourne", "Australia/…
#> $ venue.venueId <chr> "CD_V50", "CD_V402", "CD_V371", "C…
#> $ venue.abbreviation <chr> "IKP", "BISP", "CAS", "HP", "VIPC"…
#> $ venue.capacity <lgl> NA, NA, NA, NA, NA, NA, NA
#> $ venue.groundDimension <lgl> NA, NA, NA, NA, NA, NA, NA
#> $ venue.latitude <lgl> NA, NA, NA, NA, NA, NA, NA
#> $ venue.longitude <lgl> NA, NA, NA, NA, NA, NA, NA
#> $ round.name <chr> "Round 1", "Round 1", "Round 1", "…
#> $ round.year <chr> "2020", "2020", "2020", "2020", "2…
#> $ round.roundId <chr> "CD_R202026401", "CD_R202026401", …
#> $ round.abbreviation <chr> "Rd 1", "Rd 1", "Rd 1", "Rd 1", "R…
#> $ round.competitionId <chr> "CD_S2020264", "CD_S2020264", "CD_…
#> $ round.roundNumber <int> 1, 1, 1, 1, 1, 1, 1
#> $ status <chr> "CONCLUDED", "CONCLUDED", "CONCLUD…
#> $ matchId <chr> "CD_M20202640101", "CD_M2020264010…
#> $ scoreWorm <lgl> NA, NA, NA, NA, NA, NA, NA
#> $ scoreMap <lgl> NA, NA, NA, NA, NA, NA, NA
#> $ lastUpdated <chr> "2020-02-07T11:04:44.540+0000", "2…
#> $ homeTeamScore.periodScore <list> [<data.frame[4 x 5]>], [<data.fram…
#> $ homeTeamScore.rushedBehinds <int> 0, 0, 0, 1, 0, 0, 2
#> $ homeTeamScore.minutesInFront <int> 0, 9, 12, 56, 39, 0, 37
#> $ homeTeamScore.matchScore.totalScore <int> 14, 9, 22, 34, 38, 14, 44
#> $ homeTeamScore.matchScore.goals <int> 2, 1, 3, 5, 5, 2, 6
#> $ homeTeamScore.matchScore.behinds <int> 2, 3, 4, 4, 8, 2, 8
#> $ homeTeamScore.matchScore.superGoals <lgl> NA, NA, NA, NA, NA, NA, NA
#> $ awayTeamScore.periodScore <list> [<data.frame[4 x 5]>], [<data.fram…
#> $ awayTeamScore.rushedBehinds <int> 4, 0, 0, 3, 0, 0, 3
#> $ awayTeamScore.minutesInFront <int> 56, 23, 29, 0, 20, 60, 10
#> $ awayTeamScore.matchScore.totalScore <int> 48, 8, 20, 21, 11, 39, 28
#> $ awayTeamScore.matchScore.goals <int> 6, 1, 3, 3, 1, 6, 4
#> $ awayTeamScore.matchScore.behinds <int> 12, 2, 2, 3, 5, 3, 4
#> $ awayTeamScore.matchScore.superGoals <lgl> NA, NA, NA, NA, NA, NA, NA
#> $ matchClock.periods <list> [<data.frame[4 x 5]>], [<data.fram…
#> $ weather.description <chr> "Humid Partly cloudy", "Rain, heav…
#> $ weather.tempInCelsius <dbl> 26, 21, 28, 18, 26, 26, 18
#> $ weather.weatherType <chr> "OVERCAST", "RAIN", "OVERCAST", "M…
#> $ homeTeamScoreChart.goals <int> 2, 1, 3, 5, 5, 2, 6
#> $ homeTeamScoreChart.leftBehinds <int> 1, 2, 3, 1, 3, 0, 2
#> $ homeTeamScoreChart.rightBehinds <int> 1, 1, 1, 1, 3, 2, 4
#> $ homeTeamScoreChart.leftPosters <int> 0, 0, 0, 0, 1, 0, 0
#> $ homeTeamScoreChart.rightPosters <int> 0, 0, 0, 1, 1, 0, 0
#> $ homeTeamScoreChart.rushedBehinds <int> 0, 0, 0, 1, 0, 0, 2
#> $ homeTeamScoreChart.touchedBehinds <int> 0, 0, 0, 0, 0, 0, 0
#> $ awayTeamScoreChart.goals <int> 6, 1, 3, 3, 1, 6, 4
#> $ awayTeamScoreChart.leftBehinds <int> 3, 1, 1, 0, 1, 2, 0
#> $ awayTeamScoreChart.rightBehinds <int> 3, 1, 1, 0, 4, 0, 1
#> $ awayTeamScoreChart.leftPosters <int> 0, 0, 0, 0, 0, 1, 0
#> $ awayTeamScoreChart.rightPosters <int> 1, 0, 0, 0, 0, 0, 0
#> $ awayTeamScoreChart.rushedBehinds <int> 4, 0, 0, 3, 0, 0, 3
#> $ awayTeamScoreChart.touchedBehinds <int> 1, 0, 0, 0, 0, 0, 0The get_aflw_detailed_data() can be used to return more
detailed data than the match data shown above. It takes a vector of
match IDs as an argument. For example, let’s say we want detailed stats
for the first 10 games in match_data above. Then we would
do:
first10 <- head(match_data, 10)
first10_ids <- first10$Match.Id
first10_ids
#> NULL
detailed <- get_aflw_detailed_data(first10_ids)
glimpse(detailed)#> Rows: 0
#> Columns: 0