library(fitzRoy)
library(dplyr)
You can access data from the Squiggle API directly with the
fetch_squiggle_data
. This allows direct access to the
Squiggle API.
Note that we also provide some helper functions that map more closely
to our fetch_
functions such as
fetch_ladder_squiggle
.
Full instructions for constructing queries can be found at Squiggle API. One of the
following must be provided to the query
argument.
teams
- Info about teams (e.g. Richmond, Geelong, West
Coast)games
- Info about games (e.g. Round 1, 2019 Richmond v
Carlton)sources
- Info about models (e.g. Matter of Stats,
GRAFT, Swinburne)tips
- Info about tips and predictions made by
modelsstandings
- Info about team standings (i.e. the
ladder)ladder
- Info about predicted ladders generated by
modelsvirtual
- Info about Virtually Season 2020pav
- Info about Player Approximate Value from HPN
FootyOptional arguments can then be supplied based on the query.
For example, games
takes the following optional
arguments. * year
- Year * round
- Round *
game
- Game ID * complete
- Percent of game
complete
These can be supplied as named arguments after the query. For example, to return games from just 2020, we would use the following.
fetch_squiggle_data(query = "games", year = 2020)
Fetch info about one or more AFL teams.
fetch_squiggle_data("teams")
Fetch info about one or more games.
fetch_squiggle_data(query = "games", year = 2020)
Fetch info about one or more computer models.
# You can get the sources
fetch_squiggle_data("sources")
Fetch info about one or more tips made by computer models.
# Get all tips
fetch_squiggle_data("tips")
We can just look at one particular round.
# Get` just tips from round 1, 2018
fetch_squiggle_data("tips", round = 1, year = 2018)
Fetch info about team standings at a point in time, i.e. the ladder.
fetch_squiggle_data("standings", year = 2020, round = 1)
Fetch info about one or more projected ladders generated by computer models. For the actual ladder, see standings instead.
fetch_squiggle_data("ladder", year = 2019, round = 15, source = 1)
Fetch info about players using HPN Footy’s Player Approximate Value.
fetch_squiggle_data("pav",
firstname = "Dustin",
surname = "Martin",
year = 2017)