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Fleaflicker: Get Endpoint

Tan Ho

2023-02-11

Creating custom Fleaflicker API calls

library(ffscrapr)
library(dplyr)
library(purrr)
library(glue)

The Fleaflicker API is fairly extensive. If there is something you’d like to access that’s beyond the current scope of ffscrapr, you can use the lower-level “fleaflicker_getendpoint” function to create a GET request and access the data, while still using the authentication and rate-limiting features I’ve already created.

Here is an example of how you can call one of the endpoints - in this case, let’s zoom in on the Fetch League Scoreboard endpoint, which is used inside a few different ffscrapr functions and summarised in a few ways.

We’ll start by opening up this page, https://www.fleaflicker.com/api-docs/index.html#operation--FetchLeagueScoreboard-get, which is the documentation page for this particular endpoint.

From here, we can see that Fleaflicker’s documentation says the endpoint and parameters are:

GET /FetchLeagueScoreboard

Parameters:

- sport: string NFL, MLB, NBA, NHL NFL
(in query)

- league_id: integer (int32)
(in query)

- season: integer (int32)
(in query)

- scoring_period: integer (int32)
(in query)

The fleaflicker_getendpoint function already has the base url encoded, so all we’ll need to do is pass in the endpoint without the /, and pass the HTTP parameters in as arguments to the function (these are case sensitive!)

sport <- "NFL"
league_id <- 206154
season <- 2020
week <- 5

response_scoreboard <- fleaflicker_getendpoint("FetchLeagueScoreboard",
                                               sport = sport, 
                                               league_id = league_id, 
                                               season = season, 
                                               scoring_period = week)
#> Using request.R from "ffscrapr"

str(response_scoreboard, max.level = 1)
#> List of 3
#>  $ content :List of 3
#>  $ query   : chr "https://www.fleaflicker.com/api/FetchLeagueScoreboard?sport=NFL&league_id=206154&season=2020&scoring_period=5"
#>  $ response:List of 9
#>   ..- attr(*, "class")= chr "response"
#>  - attr(*, "class")= chr "fleaflicker_api"

Along with the parsed content, the function also returns the query and the response that was sent by the server. These are helpful for debugging, but we can turn the content into a dataframe with some careful application of the tidyverse.

df_scoreboard <- response_scoreboard %>% 
  purrr::pluck("content","games") %>% 
  tibble::tibble() %>% 
  tidyr::unnest_wider(1) %>% 
  dplyr::mutate_at(c("away","home"),purrr::map_chr,purrr::pluck,"name") %>% 
  dplyr::mutate_at(c("homeScore","awayScore"),purrr::map_dbl,purrr::pluck,"score","value")

head(df_scoreboard)
#> # A tibble: 6 × 8
#>   id       away                    home  awayS…¹ homeS…² homeR…³ awayR…⁴ isFin…⁵
#>   <chr>    <chr>                   <chr>   <dbl>   <dbl> <chr>   <chr>   <lgl>  
#> 1 46301923 Winterfell Dire Wolves  Top …    207.    162. LOSE    WIN     TRUE   
#> 2 46301919 Goldenrod City Nightma… Wint…    145.    157. WIN     LOSE    TRUE   
#> 3 46301921 Winterthur Angry Ducks  Clut…    153.    192. WIN     LOSE    TRUE   
#> 4 46301922 Manitoba Marmots        Bame…    182.    183. WIN     LOSE    TRUE   
#> 5 46301920 Springfield Isotopes    Shan…    176.    129. LOSE    WIN     TRUE   
#> 6 46301926 Boomtown Sly Foxes      Phil…    199.    200. WIN     LOSE    TRUE   
#> # … with abbreviated variable names ¹​awayScore, ²​homeScore, ³​homeResult,
#> #   ⁴​awayResult, ⁵​isFinalScore

From here, we’ll be able to feed these IDs into the FetchLeagueBoxscore endpoint https://www.fleaflicker.com/api-docs/index.html#operation--FetchLeagueScoreboard-get as the fantasy_game_id - and from here you’ll be able to get to player-level points and actual stat data for each fantasy game!

# same variables as previous endpoint call!
onegame_lineups <- fleaflicker_getendpoint(
  "FetchLeagueBoxscore",
  sport = sport, 
  league_id = league_id, 
  # example for one call, but you can call this in a map or loop! 
  fantasy_game_id = df_scoreboard$id[[1]], 
  scoring_period = week) %>% 
  purrr::pluck('content','lineups') %>% 
  tibble::tibble() %>% 
  tidyr::unnest_wider(1) %>% 
  tidyr::unnest_longer('slots') %>% 
  tidyr::unnest_wider('slots') %>% 
  tidyr::pivot_longer(c("home","away"),names_to = "franchise",values_to = "player") %>% 
  tidyr::unnest_wider('player')

str(onegame_lineups,max.level = 2)
#> tibble [98 × 16] (S3: tbl_df/tbl/data.frame)
#>  $ group               : chr [1:98] "START" "START" "START" "START" ...
#>  $ position            :List of 98
#>  $ positionColor       :List of 98
#>  $ franchise           : chr [1:98] "home" "away" "home" "away" ...
#>  $ proPlayer           :List of 98
#>  $ requestedGames      :List of 98
#>  $ viewingActualPoints :List of 98
#>  $ viewingActualStats  :List of 98
#>  $ requestedGamesPeriod:List of 98
#>  $ viewingFormat       : chr [1:98] "TOTAL" "TOTAL" "TOTAL" "TOTAL" ...
#>  $ viewingRange        :List of 98
#>  $ owner               :List of 98
#>  $ displayGroup        : chr [1:98] "PASSER" "PASSER" "RUSHER" "RUSHER" ...
#>  $ rankFantasy         :List of 98
#>  $ rankDraft           :List of 98
#>  $ lastX               :List of 98

From here, you can keep unravelling - including the “viewingActualPoints” and “viewingActualStats” columns!

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.