Sleeper: Get Endpoint

Creating custom Sleeper API calls

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

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.com/#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)
#> No encoding supplied: defaulting to UTF-8.

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 8
#>   ..- 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 × 2
#>   player_id count
#>   <chr>     <int>
#> 1 6002      14287
#> 2 7904      11553
#> 3 7703      11403
#> 4 4622       8548
#> 5 2711       7165
#> 6 6271       5452

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 × 6
#>    player_id count player_name       pos   team    age
#>    <chr>     <int> <chr>             <chr> <chr> <dbl>
#>  1 6002      14287 Qadree Ollison    RB    ATL    26.4
#>  2 7904      11553 Stevie Scott      RB    NO     NA  
#>  3 7703      11403 Jacob Harris      TE    LAR    NA  
#>  4 4622       8548 Keelan Cole       WR    NYJ    29.8
#>  5 2711       7165 Taylor Heinicke   QB    WAS    29.9
#>  6 6271       5452 Olamide Zaccheaus WR    ATL    25.6
#>  7 5199       4462 Byron Pringle     WR    KC     29.2
#>  8 7622       4082 Sammis Reyes      TE    WAS    27.3
#>  9 7530       3646 Frank Darby       WR    ATL    NA  
#> 10 2583       3201 Tyrell Williams   WR    DET    31

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?