Creating custom Sleeper API calls

The Sleeper API is pretty extensive. If there is something you’d like to access that’s beyond the current scope of ffscrapr, you can use the lower-level “sleeper_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 pull Sleeper’s trending players data!

We’ll start by opening up this page, https://docs.sleeper.app/#trending-players, which is the documentation page for this particular endpoint. From here, we can see that Sleeper’s documentation says the endpoint is:

https://api.sleeper.app/v1/players/<sport>/trending/<type>?lookback_hours=<hours>&limit=<int>

On first glance, you can see that it takes two parameters within the endpoint call itself (sport and type) and we can further adjust the query with HTTP parameters lookback_hours and limit. The sleeper_getendpoint function already has the https://api.sleeper.app/v1/ part encoded, so all we’ll need to do is pass in the remaining part of the URL as the endpoint, and pass the HTTP parameters in as arguments to the function (these are case sensitive!)

We can use the glue package to parameterise this, although you can also use base R’s paste function just as easily.


type <- "add"

query <- glue::glue('players/nfl/trending/{type}')

query
#> players/nfl/trending/add

response_trending <- sleeper_getendpoint(query,lookback_hours = 48, limit = 10)

str(response_trending, max.level = 1)
#> List of 3
#>  $ content :List of 10
#>  $ query   : chr "https://api.sleeper.app/v1/players/nfl/trending/add?lookback_hours=48&limit=10"
#>  $ response:List of 9
#>   ..- attr(*, "class")= chr "response"
#>  - attr(*, "class")= chr "sleeper_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_trending <- response_trending %>% 
  purrr::pluck("content") %>% 
  dplyr::bind_rows()

head(df_trending)
#> # A tibble: 6 x 2
#>   player_id  count
#>   <chr>      <int>
#> 1 4187      270285
#> 2 6156      142272
#> 3 1169      129162
#> 4 5100       84788
#> 5 4381       64578
#> 6 333        62680

This isn’t very helpful without knowing who these players are, so let’s pull the players endpoint in as well - this one has a convenient function!


players <- sleeper_players() %>% 
  select(player_id, player_name, pos, team, age)

trending <- df_trending %>% 
  left_join(players, by = "player_id")

trending
#> # A tibble: 10 x 6
#>    player_id  count player_name       pos   team    age
#>    <chr>      <int> <chr>             <chr> <chr> <dbl>
#>  1 4187      270285 Brian Hill        RB    ATL    25.1
#>  2 6156      142272 Benny Snell       RB    PIT    22.8
#>  3 1169      129162 Robert Griffin    QB    BAL    30.8
#>  4 5100       84788 Jordan Wilkins    RB    IND    26.4
#>  5 4381       64578 Taysom Hill       QB    NO     30.3
#>  6 333        62680 Ryan Fitzpatrick  QB    MIA    38  
#>  7 NYG        57945 <NA>              DEF   NYG    NA  
#>  8 4147       52537 Samaje Perine     RB    CIN    25.2
#>  9 3976       50102 Mitchell Trubisky QB    CHI    26.3
#> 10 943        45866 Kyle Rudolph      TE    MIN    31.1

There - this means something to us now! As of this writing (2020-11-10), Kalen Ballage was the most added player. Haven’t we been bitten by this before?