inst/doc/sleeper_basics.R

In ffscrapr: API Client for Fantasy Football League Platforms

## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

options(dplyr.summarise.inform = FALSE,
        rmarkdown.html_vignette.check_title = FALSE)

eval <- TRUE

tryCatch(expr = {
  
  download.file("https://github.com/ffverse/ffscrapr-tests/archive/1.4.7.zip","f.zip")
  unzip('f.zip', exdir = ".")
  
  httptest::.mockPaths(new = "ffscrapr-tests-1.4.7")},
  warning = function(e) eval <<- FALSE,
  error = function(e) eval <<- FALSE)

httptest::use_mock_api()

## ----setup, message=FALSE, eval = eval----------------------------------------
  library(ffscrapr)
  library(dplyr)
  library(tidyr)

## ----eval = eval--------------------------------------------------------------
solarpool_leagues <- sleeper_userleagues("solarpool",2020)

head(solarpool_leagues)

## ----eval = eval--------------------------------------------------------------
jml_id <- solarpool_leagues %>% 
  filter(league_name == "The JanMichaelLarkin Dynasty League") %>% 
  pull(league_id)

jml_id # For quick analyses, I'm not above copy-pasting the league ID instead!

jml <- sleeper_connect(season = 2020, league_id = jml_id)

jml

## ----eval = eval--------------------------------------------------------------
jml_summary <- ff_league(jml)

str(jml_summary)

## ----eval = eval--------------------------------------------------------------
jml_rosters <- ff_rosters(jml)

head(jml_rosters)

## ----eval = eval--------------------------------------------------------------
player_values <- dp_values("values-players.csv")

# The values are stored by fantasypros ID since that's where the data comes from. 
# To join it to our rosters, we'll need playerID mappings.

player_ids <- dp_playerids() %>% 
  select(sleeper_id,fantasypros_id)

player_values <- player_values %>% 
  left_join(player_ids, by = c("fp_id" = "fantasypros_id")) %>% 
  select(sleeper_id,ecr_1qb,ecr_pos,value_1qb)

# Drilling down to just 1QB values and IDs, we'll be joining it onto rosters and don't need the extra stuff

jml_values <- jml_rosters %>% 
  left_join(player_values, by = c("player_id"="sleeper_id")) %>% 
  arrange(franchise_id,desc(value_1qb))

head(jml_values)

## ----eval = eval--------------------------------------------------------------
value_summary <- jml_values %>% 
  group_by(franchise_id,franchise_name,pos) %>% 
  summarise(total_value = sum(value_1qb,na.rm = TRUE)) %>%
  ungroup() %>% 
  group_by(franchise_id,franchise_name) %>% 
  mutate(team_value = sum(total_value)) %>% 
  ungroup() %>% 
  pivot_wider(names_from = pos, values_from = total_value) %>% 
  arrange(desc(team_value))

value_summary

## ----eval = eval--------------------------------------------------------------
value_summary_pct <- value_summary %>% 
  mutate_at(c("team_value","QB","RB","WR","TE"),~.x/sum(.x)) %>% 
  mutate_at(c("team_value","QB","RB","WR","TE"),round, 3)

value_summary_pct

## ----eval = eval--------------------------------------------------------------
age_summary <- jml_values %>% 
  group_by(franchise_id,pos) %>% 
  mutate(position_value = sum(value_1qb,na.rm=TRUE)) %>% 
  ungroup() %>% 
  mutate(weighted_age = age*value_1qb/position_value,
         weighted_age = round(weighted_age, 1)) %>% 
  group_by(franchise_id,franchise_name,pos) %>% 
  summarise(count = n(),
            age = sum(weighted_age,na.rm = TRUE)) %>% 
  pivot_wider(names_from = pos,
              values_from = c(age,count))

age_summary

## ----include = FALSE----------------------------------------------------------
httptest::stop_mocking()

unlink(c("ffscrapr-tests-1.4.7","f.zip"), recursive = TRUE, force = TRUE)