ESPN: Basics

  collapse = TRUE,
  comment = "#>"

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

eval <- TRUE

tryCatch(expr = {

  unzip('', exdir = ".")

  httptest::.mockPaths(new = "ffscrapr-tests-1.4.7")},
  warning = function(e) eval <<- FALSE,
  error = function(e) eval <<- FALSE)


In this vignette, I'll walk through how to get started with a basic dynasty value analysis on ESPN, pulling in roster data.

We'll start by loading the packages:


In ESPN, you can find the league ID by looking in the URL - it's the number immediately after ?leagueId in this example URL:

Let's set up a connection to this league:

sucioboys <- espn_connect(season = 2020, league_id = 899513)


I've done this with the espn_connect() function, although you can also do this from the ff_connect() call - they are equivalent. Most if not all of the remaining functions after this point are prefixed with "ff_".

Cool! Let's have a quick look at what this league is like.

sucioboys_summary <- ff_league(sucioboys)


Okay, so it's the Sucio Boys league, it's a 2QB league with 12 teams, half ppr scoring, and rosters about 240 players.

Let's grab the rosters now.

sucioboys_rosters <- ff_rosters(sucioboys)

head(sucioboys_rosters) # quick snapshot of rosters


Cool! Let's pull in some additional context by adding DynastyProcess player values.

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(espn_id,fantasypros_id) %>% 

# We'll be joining it onto rosters, so we can trim down the values dataframe
# to just IDs, age, and values

player_values <- player_values %>% 
  left_join(player_ids, by = c("fp_id" = "fantasypros_id")) %>% 

# we can join the roster's player_ids on the values' espn_id, with a bit of a type conversion first
sucioboys_values <- sucioboys_rosters %>% 
  mutate(player_id = as.character(player_id)) %>% 
  left_join(player_values, by = c("player_id"="espn_id")) %>% 


Let's do some team summaries now!

value_summary <- sucioboys_values %>% 
  group_by(franchise_id,franchise_name,pos) %>% 
  summarise(total_value = sum(value_2qb,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)) %>% 


So with that, we've got a team summary of values! I like applying some context, so let's turn these into percentages - this helps normalise it to your league environment.

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)


Armed with a value summary like this, we can see team strengths and weaknesses pretty quickly, and figure out who might be interested in your positional surpluses and who might have a surplus at a position you want to look at.


Another question you might ask: what is the average age of any given team?

I like looking at average age by position, but weighted by dynasty value. This helps give a better idea of age for each team - including who might be looking to offload an older veteran!

age_summary <- sucioboys_values %>% 
  filter(pos %in% c("QB","RB","WR","TE")) %>% 
  group_by(franchise_id,pos) %>% 
  mutate(position_value = sum(value_2qb,na.rm=TRUE)) %>% 
  ungroup() %>% 
  mutate(weighted_age = age*value_2qb/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))


Next steps

In this vignette, I've used only a few functions: ff_connect, ff_league, ff_rosters, and dp_values. Now that you've gotten this far, why not check out some of the other possibilities?


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

Try the ffscrapr package in your browser

Any scripts or data that you put into this service are public.

ffscrapr documentation built on Feb. 16, 2023, 10:55 p.m.