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: https://fantasy.espn.com/football/team?leagueId=899513&seasonId=2020
Let’s set up a connection to this league:
sucioboys <- espn_connect(season = 2020, league_id = 899513)
sucioboys
#> <ESPN connection 2020_899513>
#> List of 4
#> $ platform : chr "ESPN"
#> $ season : chr "2020"
#> $ league_id: chr "899513"
#> $ cookies : NULL
#> - attr(*, "class")= chr "espn_conn"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)
#> Using request.R from "ffscrapr"
str(sucioboys_summary)
#> tibble [1 × 16] (S3: tbl_df/tbl/data.frame)
#> $ league_id : chr "899513"
#> $ league_name : chr "Sucio Boys"
#> $ season : int 2020
#> $ league_type : chr "keeper"
#> $ franchise_count: int 10
#> $ qb_type : chr "2QB/SF"
#> $ idp : logi FALSE
#> $ scoring_flags : chr "0.5_ppr"
#> $ best_ball : logi FALSE
#> $ salary_cap : logi FALSE
#> $ player_copies : num 1
#> $ years_active : chr "2018-2020"
#> $ qb_count : chr "1-2"
#> $ roster_size : int 24
#> $ league_depth : num 240
#> $ keeper_count : int 22Okay, 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
#> # A tibble: 6 × 10
#> franchise_id franchise_name player_id player_name team pos eligible_pos
#> <int> <chr> <int> <chr> <chr> <chr> <list>
#> 1 1 The Early GGod 4036348 Michael Gallup DAL WR <chr [7]>
#> 2 1 The Early GGod 4036131 Noah Fant DEN TE <chr [6]>
#> 3 1 The Early GGod -16003 Bears D/ST CHI DST <chr [3]>
#> 4 1 The Early GGod 15920 Latavius Murray NOS RB <chr [6]>
#> 5 1 The Early GGod 3055899 Harrison Butker KCC K <chr [3]>
#> 6 1 The Early GGod 4241372 Marquise Brown BAL WR <chr [7]>
#> # ℹ 3 more variables: status <chr>, acquisition_type <chr>,
#> # acquisition_date <dttm>Values
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) %>%
filter(!is.na(espn_id),!is.na(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")) %>%
select(espn_id,age,ecr_2qb,ecr_pos,value_2qb)
# 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")) %>%
arrange(franchise_id,desc(value_2qb))
head(sucioboys_values)
#> # A tibble: 6 × 14
#> franchise_id franchise_name player_id player_name team pos eligible_pos
#> <int> <chr> <chr> <chr> <chr> <chr> <list>
#> 1 1 The Early GGod 4040715 Jalen Hurts PHI QB <chr [5]>
#> 2 1 The Early GGod 4241479 Tua Tagovailoa MIA QB <chr [5]>
#> 3 1 The Early GGod 4242335 Jonathan Taylor IND RB <chr [7]>
#> 4 1 The Early GGod 4239993 Tee Higgins CIN WR <chr [8]>
#> 5 1 The Early GGod 3046779 Jared Goff DET QB <chr [4]>
#> 6 1 The Early GGod 4241372 Marquise Brown BAL WR <chr [7]>
#> # ℹ 7 more variables: status <chr>, acquisition_type <chr>,
#> # acquisition_date <dttm>, age <dbl>, ecr_2qb <dbl>, ecr_pos <dbl>,
#> # value_2qb <int>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)) %>%
select(franchise_id,franchise_name,team_value,QB,RB,WR,TE)
value_summary
#> # A tibble: 10 × 7
#> franchise_id franchise_name team_value QB RB WR TE
#> <int> <chr> <int> <int> <int> <int> <int>
#> 1 5 "The Juggernaut" 31373 9041 7889 11645 2798
#> 2 1 "The Early GGod" 30207 19367 5110 5611 119
#> 3 6 "OBJ's Personal Porta Potty" 26092 17342 3441 1518 3791
#> 4 7 "Tony El Tigre" 25681 18717 4617 1062 1285
#> 5 2 "Coom Dumpster" 23380 6392 11 16113 864
#> 6 8 "Big Coomers" 21063 8703 617 11671 72
#> 7 9 "RAFI CUNADO" 19258 1417 2477 14721 643
#> 8 3 "PAKI STANS" 16631 2253 6900 2658 4820
#> 9 4 "I'm Also Sad " 12077 3 4583 7309 182
#> 10 10 "Austin 🐐Drew Lock🐐" 8840 2110 0 6727 3So 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)
value_summary_pct
#> # A tibble: 10 × 7
#> franchise_id franchise_name team_value QB RB WR TE
#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 5 "The Juggernaut" 0.146 0.106 0.221 0.147 0.192
#> 2 1 "The Early GGod" 0.141 0.227 0.143 0.071 0.008
#> 3 6 "OBJ's Personal Porta Potty" 0.122 0.203 0.097 0.019 0.26
#> 4 7 "Tony El Tigre" 0.12 0.219 0.13 0.013 0.088
#> 5 2 "Coom Dumpster" 0.109 0.075 0 0.204 0.059
#> 6 8 "Big Coomers" 0.098 0.102 0.017 0.148 0.005
#> 7 9 "RAFI CUNADO" 0.09 0.017 0.069 0.186 0.044
#> 8 3 "PAKI STANS" 0.077 0.026 0.194 0.034 0.331
#> 9 4 "I'm Also Sad " 0.056 0 0.129 0.092 0.012
#> 10 10 "Austin 🐐Drew Lock🐐" 0.041 0.025 0 0.085 0Armed 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.
Age
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))
age_summary
#> # A tibble: 10 × 10
#> # Groups: franchise_id, franchise_name [10]
#> franchise_id franchise_name age_QB age_RB age_TE age_WR count_QB count_RB
#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 1 "The Early GGod" 26 25 27.8 25.9 4 6
#> 2 2 "Coom Dumpster" 30.2 28.5 28.9 26.7 4 7
#> 3 3 "PAKI STANS" 28.2 27.5 26.5 27.7 3 6
#> 4 4 "I'm Also Sad " 36.1 26.5 31.2 30.1 2 5
#> 5 5 "The Juggernaut" 26.9 27 34.2 27.1 3 8
#> 6 6 "OBJ's Personal P… 27.5 26.7 28.3 27.2 3 6
#> 7 7 "Tony El Tigre" 27.2 27.1 30.3 27.1 3 5
#> 8 8 "Big Coomers" 25.7 31.6 30.9 28.1 3 7
#> 9 9 "RAFI CUNADO" 35.7 28 29.2 25.8 3 5
#> 10 10 "Austin 🐐Drew Lo… 35.1 0 23.3 27.5 3 5
#> # ℹ 2 more variables: count_TE <int>, count_WR <int>