The goal of fitzRoy is to provide a set of functions that allows for users to easily get access to AFL data from sources such as afltables.com and footywire.com. There are also tools for processing and cleaning that data. Future versions will include basic ELO processing functions.
You can install fitzRoy from github with:
# install.packages("devtools")
devtools::install_github("jimmyday12/fitzRoy")The fitzRoy package can be used to simply get data from various sources. Some minimal working examples are below.
Primarily, the tool can be used to access data from various sources. Data is included in the package and can be access directly however this will not be up to date. Each source of data has functions for updating data during the season.
You can access the basic afl tables match results data. This includes all matches from 1897-current. It is generally updated on the day after a round finishes.
You can access the data directly from the package using match_results. This will be updated periodically but you will need to update your R package to get access to the latest data. It is better to use get_match_results directly, as this will give you up to date results.
library(fitzRoy)
results <- get_match_results()
#> Warning: package 'bindrcpp' was built under R version 3.4.4
tail(results)
#> # A tibble: 6 x 16
#> Game Date Round Home.Team Home.Goals Home.Behinds Home.Points
#> <dbl> <date> <chr> <chr> <int> <int> <int>
#> 1 15375. 2018-08-11 R21 Port Adelai… 9 4 58
#> 2 15376. 2018-08-11 R21 GWS 15 16 106
#> 3 15377. 2018-08-11 R21 Collingwood 14 20 104
#> 4 15378. 2018-08-12 R21 North Melbo… 12 13 85
#> 5 15379. 2018-08-12 R21 Melbourne 10 18 78
#> # ... with 1 more row, and 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 also convert this format into a more analysis friendly "long" format using the helper function convert_results.
results_long <- convert_results(results)
head(results_long)
#> # A tibble: 6 x 13
#> Game Date Round Venue Margin Season Round.Type Round.Number
#> <dbl> <date> <chr> <chr> <dbl> <dbl> <chr> <int>
#> 1 1. 1897-05-08 R1 Brunswick … 33. 1897. Regular 1
#> 2 1. 1897-05-08 R1 Brunswick … -33. 1897. Regular 1
#> 3 2. 1897-05-08 R1 Victoria P… 25. 1897. Regular 1
#> 4 2. 1897-05-08 R1 Victoria P… -25. 1897. Regular 1
#> 5 3. 1897-05-08 R1 Corio Oval -23. 1897. Regular 1
#> # ... with 1 more row, and 5 more variables: Status <chr>, Behinds <chr>,
#> # Goals <chr>, Points <chr>, Team <chr>A new function will return all detailed player stats from afltables.com. Primarily, the easiest way to use this is simply to call get_afltables_stats with your required start_date and end_date.
#stats <- get_afltables_stats(start_date = "2000-01-01", end_date = "2018-06-01")
#tail(stats)You can access the fixture using get_fixture function. This will download the fixture for the current calendar year by default.
fixture <- get_fixture()
head(fixture)
#> # A tibble: 6 x 7
#> Date Season Season.Game Round Home.Team Away.Team Venue
#> <dttm> <int> <int> <int> <chr> <chr> <chr>
#> 1 2018-03-22 19:25:00 2018 1 1 Richmond Carlton MCG
#> 2 2018-03-23 19:50:00 2018 1 1 Essendon Adelaide Etiha…
#> 3 2018-03-24 15:35:00 2018 1 1 St Kilda Brisbane… Etiha…
#> 4 2018-03-24 16:05:00 2018 1 1 Port Adel… Fremantle Adela…
#> 5 2018-03-24 18:25:00 2018 1 1 Gold Coast North Me… Cazal…
#> # ... with 1 more rowFootywire data is available in the form of advanced player match statistics from 2010 games onwards. This is when advanced statistics became available.
Footywire data from 2010-2017 is included in the package. This will be updated periodically but you will need to update your R package to get access to the latest data.
## Show the top of player_stats
head(fitzRoy::player_stats)
#> Date Season Round Venue Player Team Opposition
#> 1 2010-03-25 2010 Round 1 MCG Daniel Connors Richmond Carlton
#> 2 2010-03-25 2010 Round 1 MCG Daniel Jackson Richmond Carlton
#> 3 2010-03-25 2010 Round 1 MCG Brett Deledio Richmond Carlton
#> 4 2010-03-25 2010 Round 1 MCG Ben Cousins Richmond Carlton
#> 5 2010-03-25 2010 Round 1 MCG Trent Cotchin Richmond Carlton
#> 6 2010-03-25 2010 Round 1 MCG Dustin Martin Richmond Carlton
#> Status Match_id CP UP ED DE CM GA MI5 One.Percenters BO TOG K HB D M
#> 1 Home 5089 8 15 16 66.7 0 0 0 1 0 69 14 10 24 3
#> 2 Home 5089 11 10 14 60.9 1 0 0 0 0 80 11 12 23 2
#> 3 Home 5089 7 14 16 76.2 0 0 0 0 0 89 12 9 21 5
#> 4 Home 5089 9 10 11 57.9 0 1 0 0 0 69 13 6 19 1
#> 5 Home 5089 8 10 13 68.4 1 0 0 0 1 77 11 8 19 6
#> 6 Home 5089 6 12 16 88.9 0 0 0 1 0 81 5 13 18 4
#> G B T HO GA1 I50 CL CG R50 FF FA AF SC CCL SCL SI MG TO ITC T5
#> 1 0 0 1 0 0 2 2 4 6 2 0 77 85 NA NA NA NA NA NA NA
#> 2 0 0 5 0 0 8 5 4 1 2 0 85 89 NA NA NA NA NA NA NA
#> 3 1 0 6 0 0 4 3 4 3 1 2 94 93 NA NA NA NA NA NA NA
#> 4 1 0 1 0 1 1 2 3 4 1 0 65 70 NA NA NA NA NA NA NA
#> 5 0 0 1 0 0 2 3 3 2 0 2 65 63 NA NA NA NA NA NA NA
#> 6 0 0 3 0 0 2 3 1 0 0 1 62 72 NA NA NA NA NA NA NAWe can also use the update_footywire_stats function to get the most up to date data. This will merge data from 2010-current with any new data points.
## Update footywire data
dat <- update_footywire_stats()
#> Getting match ID's...
#> Downloading new data for 63 matches...
#>
#> Checking Github
#> Warning in ids == git_ids: longer object length is not a multiple of
#> shorter object length
#> Finished getting data
tail(dat)
#> Date Season Round Venue Player Team
#> 78535 2018-07-29 2018 Round 19 Optus Stadium James Worpel Hawthorn
#> 78536 2018-07-29 2018 Round 19 Optus Stadium Jarman Impey Hawthorn
#> 78537 2018-07-29 2018 Round 19 Optus Stadium Ryan Schoenmakers Hawthorn
#> 78538 2018-07-29 2018 Round 19 Optus Stadium Jonathon Ceglar Hawthorn
#> 78539 2018-07-29 2018 Round 19 Optus Stadium Brendan Whitecross Hawthorn
#> 78540 2018-07-29 2018 Round 19 Optus Stadium Paul Puopolo Hawthorn
#> Opposition Status Match_id CP UP ED DE CM GA MI5 One.Percenters BO
#> 78535 Fremantle Away 9675 5 10 12 75.0 0 1 1 0 1
#> 78536 Fremantle Away 9675 3 13 10 66.7 0 0 0 3 0
#> 78537 Fremantle Away 9675 6 8 8 61.5 2 0 4 2 0
#> 78538 Fremantle Away 9675 4 8 9 75.0 1 0 1 3 0
#> 78539 Fremantle Away 9675 3 8 9 90.0 0 1 0 1 0
#> 78540 Fremantle Away 9675 6 5 8 80.0 0 1 0 1 0
#> TOG K HB D M G B T HO GA1 I50 CL CG R50 FF FA AF SC CCL SCL SI MG
#> 78535 83 8 8 16 2 0 1 3 0 1 5 2 0 0 0 0 59 72 0 2 8 296
#> 78536 77 11 4 15 4 0 0 3 0 0 1 1 3 2 0 1 62 63 0 1 4 227
#> 78537 82 7 6 13 6 2 2 3 0 0 2 0 0 0 0 0 77 81 0 0 8 172
#> 78538 76 7 5 12 4 0 0 3 27 0 1 1 3 1 1 1 80 63 1 0 4 188
#> 78539 74 6 4 10 6 0 0 4 0 1 2 0 1 1 0 0 60 51 0 0 4 179
#> 78540 80 4 6 10 1 1 0 5 0 1 2 0 1 0 1 0 54 76 0 0 4 100
#> TO ITC T5
#> 78535 3 2 2
#> 78536 5 1 0
#> 78537 2 0 2
#> 78538 2 0 0
#> 78539 1 1 2
#> 78540 1 3 2We have also included weather data for the 2017 season. This is a work in progress but includes rainfall data from the nearest observation station to each ground. This data is included in the package as results_weather.
library(ggplot2)
library(dplyr)
# Get 2017 weather data
weather <- fitzRoy::results_weather %>%
filter(Season == 2017)
# Plot total rainfal for each home team
ggplot(dat = weather, aes(x = Home.Team, y = Rainfall)) +
geom_col() +
coord_flip()
You can access data from the Squiggle API where the tips of well known AFL tipping models are collected. See full instructions on the above link.
# You can get the sources
sources <- get_squiggle_data("sources")
head(sources)
#> name url id
#> 1 Squiggle https://live.squiggle.com.au/ 1
#> 2 The Arc https://thearcfooty.com/ 2
#> 3 Figuring Footy http://figuringfooty.com/ 3
#> 4 Matter of Stats http://www.matterofstats.com/ 4
#> 5 Punters 5
#> 6 Footy Maths Institute https://footymaths.blogspot.com.au 6# Get all tips
tips <- get_squiggle_data("tips")
head(tips)
#> updated err hconfidence confidence date
#> 1 2017-07-11 13:59:46 42.00 50.0 50.0 2017-03-23 19:20:00
#> 2 2017-04-10 12:18:02 NA 42.0 58.0 2017-03-23 19:20:00
#> 3 2017-07-11 13:59:46 48.39 56.7 56.7 2017-03-23 19:20:00
#> 4 2017-07-11 13:59:46 3.69 37.3 62.7 2017-03-24 19:50:00
#> 5 2017-07-11 13:59:46 3.00 38.0 62.0 2017-03-24 19:50:00
#> 6 2017-07-11 13:59:46 53.00 50.0 50.0 2017-03-26 15:20:00
#> tip hteam tipteamid round correct bits hteamid
#> 1 Richmond Carlton 14 1 1 0.0000 3
#> 2 Richmond Carlton 14 1 1 0.2141 3
#> 3 Carlton Carlton 3 1 0 -0.2076 3
#> 4 Western Bulldogs Collingwood 18 1 1 0.3265 4
#> 5 Western Bulldogs Collingwood 18 1 1 0.3103 4
#> 6 Adelaide Adelaide 1 1 1 0.0000 1
#> venue year sourceid margin gameid ateam
#> 1 M.C.G. 2017 1 1.00 1 Richmond
#> 2 M.C.G. 2017 3 NA 1 Richmond
#> 3 M.C.G. 2017 4 5.39 1 Richmond
#> 4 M.C.G. 2017 4 10.31 2 Western Bulldogs
#> 5 M.C.G. 2017 1 17.00 2 Western Bulldogs
#> 6 Adelaide Oval 2017 1 3.00 8 Greater Western Sydney
#> source ateamid
#> 1 Squiggle 14
#> 2 Figuring Footy 14
#> 3 Matter of Stats 14
#> 4 Matter of Stats 18
#> 5 Squiggle 18
#> 6 Squiggle 9# Get` just tips from round 1, 2018
tips <- get_squiggle_data("tips", round = 1, year = 2018)
head(tips)
#> tip hconfidence sourceid tipteamid hteamid gameid
#> 1 Adelaide 44.00 1 1 5 373
#> 2 Adelaide 40.20 2 1 5 373
#> 3 Adelaide 40.50 4 1 5 373
#> 4 Essendon 52.08 5 5 5 373
#> 5 Adelaide 34.00 6 1 5 373
#> 6 Adelaide 44.84 7 1 5 373
#> updated round hteam correct date venue
#> 1 2018-03-23 22:54:38 1 Essendon 0 2018-03-23 19:50:00 Docklands
#> 2 2018-03-23 22:54:38 1 Essendon 0 2018-03-23 19:50:00 Docklands
#> 3 2018-03-23 22:54:38 1 Essendon 0 2018-03-23 19:50:00 Docklands
#> 4 2018-03-23 22:54:38 1 Essendon 1 2018-03-23 19:50:00 Docklands
#> 5 2018-03-23 22:54:38 1 Essendon 0 2018-03-23 19:50:00 Docklands
#> 6 2018-03-23 22:54:38 1 Essendon 0 2018-03-23 19:50:00 Docklands
#> ateamid year source err bits margin ateam
#> 1 1 2018 Squiggle 23.00 -0.1844 11.00 Adelaide
#> 2 1 2018 The Arc 21.00 -0.3147 9.00 Adelaide
#> 3 1 2018 Matter of Stats 21.78 -0.3040 9.78 Adelaide
#> 4 1 2018 Punters NA 0.0588 NA Adelaide
#> 5 1 2018 Footy Maths Institute 33.00 -0.5564 21.00 Adelaide
#> 6 1 2018 PlusSixOne 20.00 -0.1571 8.00 Adelaide
#> confidence
#> 1 56.00
#> 2 59.80
#> 3 59.50
#> 4 52.08
#> 5 66.00
#> 6 55.16Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.