The goal of fitzRoy is to make it easy to access data from the AFLM and AFLW competitions. It provides a simple and consistent API to access data such as match results, fixtures and player statistics from multiple data sources.
Primarily, fitzRoy can be used to access data from
various sources using the fetch_ functions. For a detailed
view on how the API works - view the Main
Fetch Functions vignette.
There are 5 main data sources for data in fitzRoy. Where possible, we do not edit the data from how we receive it, although in some cases, we do need to aggregate and calculate certain fields based on the structure of the site.
You can choose your data source as an argument to any
fetch_ function using the source =
argument.
We provide data from the (AFL website)[https://www.afl.com.au/]
as the default to any fetch_ function. This data is from
the official AFL data provider. With this data, we can return data for
both the Mens and Womens competitions. The oldest data is from 2012. It
provides access to all data types including results, fixtures, ladders,
lineups and stats.
AFL Tables has historically been the main source of data in fitzRoy. It is the most complete source of data about AFL that exists (to our knowledge at least!). It contains data from 1897 and is the only data source included in fitzRoy with such historical data. The types of data it contains are results, ladders and stats.
Footywire has traditionally been the main source of player statistics in fitzRoy. It contains data dating back to 2012 and was generally used as a supplement to AFL Tables data. The types of data it returns are results, fixtures and statistics.
Squiggle is a famous AFL
Prediction and Analysis website run by Max Barry. In recent years,
Squiggle has become the main place to aggregate various predictive
models. Max has provided a nice and well documented API that fitzRoy uses to return
data. Helper functions included in the fetch_ family will
return results, fixtures and ladders but the
fetch_squiggle_data function provides direct access to the
API. Read the Squiggle
API vignette for more details.
Twitter user Fryzigg has provided
access to some advanced player statistics. These are included in the
fetch_player_stats function. Read the Fryzigg
API vignette for more information.
In most cases, trying to use the same source for all of your analysis will be most beneficial. This is not always possible as some sources only go back so far (the AFL website only has data back to 2011), while some data is not available (AFL Tables doesn’t have decent fixture data). If you are mixing sources, be careful to understand differences in naming structures, team names and player names.
It is also a good idea to avoid regularly fetching whole datasets. Where possible, try to keep an off-line version of your data and only request the smallest amount possible to get the new data you require. This is both faster (less data transferred over your Internet connection and less data living in your computer memory) but also helps to reduce traffic on the data providers servers.
Fixture data is available from multiple places. The most reliable and
complete data usually comes from the AFL website. From that website you
can specify either the Mens or Womens competitions using the
comp argument.
fixture <- fetch_fixture(2021, comp = "AFLW")
fixture %>%
select(
utcStartTime, round.name,
home.team.name, away.team.name, venue.name
)#> # A tibble: 68 × 5
#> utcStartTime round.name home.team.name away.team.name venue.name
#> <chr> <chr> <chr> <chr> <chr>
#> 1 2021-01-28T08:15:00.000+… Round 1 Carlton Collingwood Ikon Park
#> 2 2021-01-29T08:10:00.000+… Round 1 St Kilda Western Bulld… RSEA Park
#> 3 2021-01-30T04:10:00.000+… Round 1 Gold Coast Su… Melbourne Metricon …
#> 4 2021-01-30T06:10:00.000+… Round 1 West Coast Ea… Adelaide Crows Mineral R…
#> 5 2021-01-31T01:10:00.000+… Round 1 Geelong Cats Kangaroos GMHBA Sta…
#> 6 2021-01-31T03:10:00.000+… Round 1 Richmond Brisbane Lions Swinburne…
#> 7 2021-01-31T05:10:00.000+… Round 1 Fremantle GWS Giants Fremantle…
#> 8 2021-02-05T08:45:00.000+… Round 2 Western Bulld… Carlton Victoria …
#> 9 2021-02-06T04:10:00.000+… Round 2 Collingwood Geelong Cats Victoria …
#> 10 2021-02-06T06:10:00.000+… Round 2 Melbourne Richmond Casey Fie…
#> # ℹ 58 more rowsIf wanted, you could return just a single round.
fetch_fixture(2021, round_number = 5, comp = "AFLM") %>%
select(
utcStartTime, round.name,
home.team.name, away.team.name, venue.name
)#> # A tibble: 9 × 5
#> utcStartTime round.name home.team.name away.team.name venue.name
#> <chr> <chr> <chr> <chr> <chr>
#> 1 2021-04-15T09:20:00.000+0… Round 5 St Kilda Richmond Marvel St…
#> 2 2021-04-16T10:10:00.000+0… Round 5 West Coast Ea… Collingwood Optus Sta…
#> 3 2021-04-17T06:35:00.000+0… Round 5 Western Bulld… Gold Coast Su… Marvel St…
#> 4 2021-04-17T06:35:00.000+0… Round 5 Sydney Swans GWS Giants SCG
#> 5 2021-04-17T09:25:00.000+0… Round 5 Carlton Port Adelaide MCG
#> 6 2021-04-17T09:25:00.000+0… Round 5 Brisbane Lions Essendon Gabba
#> 7 2021-04-18T03:10:00.000+0… Round 5 Adelaide Crows Fremantle Adelaide …
#> 8 2021-04-18T05:20:00.000+0… Round 5 Hawthorn Melbourne MCG
#> 9 2021-04-18T06:40:00.000+0… Round 5 Geelong Cats North Melbour… GMHBA Sta…You can get results data from other sources including
Squiggle and Footywire. The default source for
fetch_results() is the AFL.com.au website.
fixture_afl <- fetch_fixture(2020)
fixture_aflw <- fetch_fixture(2020, round_number = 1, comp = "AFLW")
fixture_squiggle <- fetch_fixture_squiggle(2020, round_number = 10)
fixture_footywire <- fetch_fixture_squiggle(2018)You can get the lineup for a particular round. This is usually useful when running after the teams have been announced but before the match has been played.
The only data source with lineup data is the AFL.com.au website.
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>You can access AFL match results data from various sources. The most complete is the AFL Tables data, which includes all matches from 1897-current.
results <- fetch_match_results_afltables(1897:2019)
results#> # A tibble: 15,614 × 16
#> Game Date Round Home.Team Home.Goals Home.Behinds Home.Points
#> <dbl> <date> <chr> <chr> <int> <int> <int>
#> 1 1 1897-05-08 R1 Fitzroy 6 13 49
#> 2 2 1897-05-08 R1 Collingwood 5 11 41
#> 3 3 1897-05-08 R1 Geelong 3 6 24
#> 4 4 1897-05-08 R1 Sydney 3 9 27
#> 5 5 1897-05-15 R2 Sydney 6 4 40
#> 6 6 1897-05-15 R2 Essendon 4 6 30
#> 7 7 1897-05-15 R2 St Kilda 3 8 26
#> 8 8 1897-05-15 R2 Melbourne 9 10 64
#> 9 9 1897-05-22 R3 Collingwood 6 5 41
#> 10 10 1897-05-22 R3 Fitzroy 5 9 39
#> # ℹ 15,604 more rows
#> # ℹ 9 more variables: Away.Team <chr>, Away.Goals <int>, Away.Behinds <int>,
#> # Away.Points <int>, Venue <chr>, Margin <int>, Season <dbl>,
#> # Round.Type <chr>, Round.Number <int>While it is possible to return all historical data, it is usually good practice to only return a small amount of data - such as a single season or round - and keep your own offline database of historical data.
results_new <- fetch_results_afltables(2021)
bind_rows(results, results_new)#> # A tibble: 15,983 × 16
#> Game Date Round Home.Team Home.Goals Home.Behinds Home.Points
#> <dbl> <date> <chr> <chr> <int> <int> <int>
#> 1 1 1897-05-08 R1 Fitzroy 6 13 49
#> 2 2 1897-05-08 R1 Collingwood 5 11 41
#> 3 3 1897-05-08 R1 Geelong 3 6 24
#> 4 4 1897-05-08 R1 Sydney 3 9 27
#> 5 5 1897-05-15 R2 Sydney 6 4 40
#> 6 6 1897-05-15 R2 Essendon 4 6 30
#> 7 7 1897-05-15 R2 St Kilda 3 8 26
#> 8 8 1897-05-15 R2 Melbourne 9 10 64
#> 9 9 1897-05-22 R3 Collingwood 6 5 41
#> 10 10 1897-05-22 R3 Fitzroy 5 9 39
#> # ℹ 15,973 more rows
#> # ℹ 9 more variables: Away.Team <chr>, Away.Goals <int>, Away.Behinds <int>,
#> # Away.Points <int>, Venue <chr>, Margin <int>, Season <dbl>,
#> # Round.Type <chr>, Round.Number <int>You can get results data from other sources including
AFL, Squiggle and Footywire. The
default source for fetch_results() is the AFL.com.au
website.
results_afl <- fetch_results(2020, round_number = 11)
results_aflw <- fetch_results(2020, comp = "AFLW")
results_squiggle <- fetch_results_squiggle(2019, round_number = 1)
results_footywire <- fetch_results_footywire(1990)You can get AFLW results by using the comp argument.
fetch_results(2020, comp = "AFLW") %>%
select(
match.name, venue.name, round.name,
homeTeamScore.matchScore.totalScore,
awayTeamScore.matchScore.totalScore
)#> # A tibble: 46 × 5
#> match.name venue.name round.name homeTeamScore.matchS…¹
#> <chr> <chr> <chr> <int>
#> 1 Richmond Vs Carlton Ikon Park Round 1 14
#> 2 GWS Giants Vs Gold Coast Suns Blacktown… Round 1 9
#> 3 Melbourne Vs Kangaroos Casey Fie… Round 1 22
#> 4 Brisbane Lions Vs Adelaide Crows Hickey Pa… Round 1 34
#> 5 Collingwood Vs West Coast Eagles Victoria … Round 1 38
#> 6 St Kilda Vs Western Bulldogs RSEA Park Round 1 14
#> 7 Fremantle Vs Geelong Cats Fremantle… Round 1 44
#> 8 Western Bulldogs Vs Melbourne Victoria … Round 2 12
#> 9 Kangaroos Vs GWS Giants Universit… Round 2 37
#> 10 Gold Coast Suns Vs Richmond Metricon … Round 2 33
#> # ℹ 36 more rows
#> # ℹ abbreviated name: ¹homeTeamScore.matchScore.totalScore
#> # ℹ 1 more variable: awayTeamScore.matchScore.totalScore <int>The ladder for a particular round can be returned using
fetch_ladder. Usually this only makes sense to return for
one round at a time, although it is possible to return multiple
rounds.
ladder <- fetch_ladder(2020, round_number = 7, comp = "AFLW") %>%
select(
season, round_name, position,
team.name, pointsFor, pointsAgainst, form
)
ladder#> # A tibble: 14 × 7
#> season round_name position team.name pointsFor pointsAgainst form
#> <dbl> <chr> <int> <chr> <int> <int> <chr>
#> 1 2020 Semi Finals 1 Kangaroos 309 136 LWWWWW
#> 2 2020 Semi Finals 2 GWS Giants 175 142 WLWLWW
#> 3 2020 Semi Finals 3 Brisbane Lions 198 185 WWDWLL
#> 4 2020 Semi Finals 4 Gold Coast Suns 154 152 LWDLLW
#> 5 2020 Semi Finals 5 Geelong Cats 211 261 LLLWWL
#> 6 2020 Semi Finals 6 Adelaide Crows 180 224 LWWLLL
#> 7 2020 Semi Finals 7 Richmond 115 322 LLLLLL
#> 8 2020 Semi Finals 1 Fremantle 277 179 WWWWWW
#> 9 2020 Semi Finals 2 Carlton 249 164 WLWWWW
#> 10 2020 Semi Finals 3 Melbourne 204 124 WWLWWL
#> 11 2020 Semi Finals 4 Collingwood 229 149 WWLLWW
#> 12 2020 Semi Finals 5 St Kilda 154 170 LLWLLW
#> 13 2020 Semi Finals 6 Western Bulldogs 179 246 WLLLLL
#> 14 2020 Semi Finals 7 West Coast Eagles 85 265 LLLWLLThere are many variables included in the AFL.com.au ladder.
ladder <- fetch_ladder(2020, round_number = 7, comp = "AFLW")
ncol(ladder)#> [1] 86You can get ladder data from other sources including
Squiggle and Afltables. The default source for
fetch_ladder() is the AFL.com.au website.
ladder_afl <- fetch_ladder(2020, round_number = 11)
ladder_aflw <- fetch_ladder(2020, comp = "AFLW")
ladder_squiggle <- fetch_ladder_squiggle(2019, round_number = 1)
ladder_afltables <- fetch_ladder_afltables(1990)We can return player statistics for a set of matches. The exact stats that are included varies quite a bit between data sources.
The default is again the AFL.com.au which is fairly comprehensive.
fetch_player_stats(2020, comp = "AFLW")#> # A tibble: 1,932 × 68
#> providerId utcStartTime status compSeason.shortName round.name
#> <chr> <chr> <chr> <chr> <chr>
#> 1 CD_M20202640101 2020-02-07T08:45:00.0… CONCL… 2020 AFL Womens Round 1
#> 2 CD_M20202640101 2020-02-07T08:45:00.0… CONCL… 2020 AFL Womens Round 1
#> 3 CD_M20202640101 2020-02-07T08:45:00.0… CONCL… 2020 AFL Womens Round 1
#> 4 CD_M20202640101 2020-02-07T08:45:00.0… CONCL… 2020 AFL Womens Round 1
#> 5 CD_M20202640101 2020-02-07T08:45:00.0… CONCL… 2020 AFL Womens Round 1
#> 6 CD_M20202640101 2020-02-07T08:45:00.0… CONCL… 2020 AFL Womens Round 1
#> 7 CD_M20202640101 2020-02-07T08:45:00.0… CONCL… 2020 AFL Womens Round 1
#> 8 CD_M20202640101 2020-02-07T08:45:00.0… CONCL… 2020 AFL Womens Round 1
#> 9 CD_M20202640101 2020-02-07T08:45:00.0… CONCL… 2020 AFL Womens Round 1
#> 10 CD_M20202640101 2020-02-07T08:45:00.0… CONCL… 2020 AFL Womens Round 1
#> # ℹ 1,922 more rows
#> # ℹ 63 more variables: round.roundNumber <int>, venue.name <chr>,
#> # home.team.club.name <chr>, away.team.club.name <chr>,
#> # player.jumperNumber <int>, player.photoURL <chr>,
#> # player.player.position <chr>, player.player.player.playerId <chr>,
#> # player.player.player.captain <lgl>,
#> # player.player.player.playerJumperNumber <int>, …We also have detailed player stats courtesy of Fryzigg.
fetch_player_stats(2019, source = "fryzigg")#> # A tibble: 9,108 × 81
#> venue_name match_id match_home_team match_away_team match_date
#> <chr> <int> <chr> <chr> <chr>
#> 1 MCG 15408 Carlton Richmond 2019-03-21
#> 2 MCG 15408 Carlton Richmond 2019-03-21
#> 3 MCG 15408 Carlton Richmond 2019-03-21
#> 4 MCG 15408 Carlton Richmond 2019-03-21
#> 5 MCG 15408 Carlton Richmond 2019-03-21
#> 6 MCG 15408 Carlton Richmond 2019-03-21
#> 7 MCG 15408 Carlton Richmond 2019-03-21
#> 8 MCG 15408 Carlton Richmond 2019-03-21
#> 9 MCG 15408 Carlton Richmond 2019-03-21
#> 10 MCG 15408 Carlton Richmond 2019-03-21
#> # ℹ 9,098 more rows
#> # ℹ 76 more variables: match_local_time <chr>, match_attendance <int>,
#> # match_round <chr>, match_home_team_goals <int>,
#> # match_home_team_behinds <int>, match_home_team_score <int>,
#> # match_away_team_goals <int>, match_away_team_behinds <int>,
#> # match_away_team_score <int>, match_margin <int>, match_winner <chr>,
#> # match_weather_temp_c <int>, match_weather_type <chr>, player_id <int>, …Other providers include Afltables and Footywire.
stats_afl <- fetch_player_stats(2020, round_number = 11)
stats_aflw <- fetch_player_stats(2020, source = "AFL", comp = "AFLW")
stats_footywire <- fetch_player_stats(2019, round_number = 1, source = "footywire")
stats_afltables <- fetch_player_stats_afltables(1990)