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.

Fixture

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 rows

Lineup

We 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: ¹​player.playerName.givenName, ²​player.playerName.surname
#> # ℹ 1 more variable: teamStatus <chr>

Results

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: ¹​homeTeamScore.matchScore.totalScore,
#> #   ²​awayTeamScore.matchScore.totalScore

Ladder

We 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           265

Stats

Lastly - 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: ¹​player.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>, …

Player Details

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>

Coaches Votes

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)             1

Legacy/Advanced Stats

We 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, 0

Detailed stats

The 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