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 22

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
#> # 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]>   
#> # … with 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 4242335   Jonathan Taylor IND   RB    <chr [7]>   
#> 2            1 The Early GGod 4241985   J.K. Dobbins    BAL   RB    <chr [7]>   
#> 3            1 The Early GGod 2976316   Michael Thomas  NOS   WR    <chr [7]>   
#> 4            1 The Early GGod 4040715   Jalen Hurts     PHI   QB    <chr [5]>   
#> 5            1 The Early GGod 4239993   Tee Higgins     CIN   WR    <chr [8]>   
#> 6            1 The Early GGod 4241479   Tua Tagovailoa  MIA   QB    <chr [5]>   
#> # … with 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"                  49693  8628 18592 16747  5726
#>  2            6 "OBJ's Personal Porta Potty"      46447 20424 22738   997  2288
#>  3            7 "Tony El Tigre"                   44547 15508 17020  5921  6098
#>  4            2 "Coom  Dumpster"                  41599 13314  2329 24668  1288
#>  5            4 "I'm Also Sad "                   37216  1467 15597 16565  3587
#>  6            3 "PAKI STANS"                      32697  6048 11980 12829  1840
#>  7            1 "The Early GGod"                  32247  6642 13916  9406  2283
#>  8            9 "RAFI CUNADO"                     31958  6878 10726 13365   989
#>  9            8 "Big Coomers"                     21493  7416  1453 12408   216
#> 10           10 "Austin 🐐Drew Lock🐐"            20832  7153   276 13324    79

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)

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"                  0.139 0.092 0.162 0.133 0.235
#>  2            6 "OBJ's Personal Porta Potty"      0.129 0.218 0.198 0.008 0.094
#>  3            7 "Tony El Tigre"                   0.124 0.166 0.148 0.047 0.25 
#>  4            2 "Coom  Dumpster"                  0.116 0.142 0.02  0.195 0.053
#>  5            4 "I'm Also Sad "                   0.104 0.016 0.136 0.131 0.147
#>  6            3 "PAKI STANS"                      0.091 0.065 0.105 0.102 0.075
#>  7            1 "The Early GGod"                  0.09  0.071 0.121 0.075 0.094
#>  8            9 "RAFI CUNADO"                     0.089 0.074 0.094 0.106 0.041
#>  9            8 "Big Coomers"                     0.06  0.079 0.013 0.098 0.009
#> 10           10 "Austin 🐐Drew Lock🐐"            0.058 0.077 0.002 0.106 0.003

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.

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"     23.5   22.4   24.7   26          4        6
#>  2            2 "Coom  Dumpster"     28.7   25.9   26.8   25.6        4        7
#>  3            3 "PAKI STANS"         29.1   25.3   23.9   25.9        3        6
#>  4            4 "I'm Also Sad "      35.4   24.8   28.7   27.4        2        5
#>  5            5 "The Juggernaut"     25     24.6   31.6   25.3        3        8
#>  6            6 "OBJ's Personal P…   24.8   24.7   25.3   23.4        3        6
#>  7            7 "Tony El Tigre"      24.8   25.3   27.8   26.6        3        5
#>  8            8 "Big Coomers"        23.5   26     27.2   27.1        3        7
#>  9            9 "RAFI CUNADO"        35.1   25.9   26.4   24.4        3        5
#> 10           10 "Austin 🐐Drew Lo…   32.2   24.4   32.2   25.5        3        5
#> # … with 2 more variables: count_TE <int>, count_WR <int>

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?