Fleaflicker: Basics

Tan Ho

2021-06-12

In this vignette, I’ll walk through how to get started with a basic dynasty value analysis on Fleaflicker.

We’ll start by loading the packages:

  library(ffscrapr)
  library(dplyr)
  library(tidyr)

In Fleaflicker, you can find the league ID by looking in the URL - it’s the number immediately after /league/ in this example URL: https://www.fleaflicker.com/nfl/leagues/312861.

Let’s set up a connection to this league:

aaa <- fleaflicker_connect(season = 2020, league_id = 312861)

aaa
#> <Fleaflicker connection 2020_312861>
#> List of 4
#>  $ platform  : chr "Fleaflicker"
#>  $ season    : chr "2020"
#>  $ user_email: NULL
#>  $ league_id : chr "312861"
#>  - attr(*, "class")= chr "flea_conn"

I’ve done this with the fleaflicker_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.


aaa_summary <- ff_league(aaa)

str(aaa_summary)
#> tibble [1 x 15] (S3: tbl_df/tbl/data.frame)
#>  $ league_id      : chr "312861"
#>  $ league_name    : chr "Avid Auctioneers Alliance"
#>  $ season         : int 2020
#>  $ league_type    : chr "dynasty"
#>  $ franchise_count: num 12
#>  $ qb_type        : chr "2QB/SF"
#>  $ idp            : logi FALSE
#>  $ scoring_flags  : chr "0.5_ppr, PP1D"
#>  $ best_ball      : logi FALSE
#>  $ salary_cap     : logi FALSE
#>  $ player_copies  : num 1
#>  $ qb_count       : chr "1-2"
#>  $ roster_size    : int 28
#>  $ league_depth   : num 336
#>  $ keeper_count   : int 31

Okay, so it’s the Avid Auctioneers Alliance, it’s a 2QB league with 12 teams, half ppr scoring, and rosters about 340 players.

Let’s grab the rosters now.

aaa_rosters <- ff_rosters(aaa)

head(aaa_rosters)
#> # A tibble: 6 x 7
#>   franchise_id franchise_name player_id player_name  pos   team  sportradar_id  
#>          <int> <chr>              <int> <chr>        <chr> <chr> <chr>          
#> 1      1578553 Running Bear       12032 Carson Wentz QB    IND   e9a5c16b-4472-~
#> 2      1578553 Running Bear       12159 Dak Prescott QB    DAL   86197778-8d4b-~
#> 3      1578553 Running Bear       13325 Austin Ekel~ RB    LAC   e5b8c439-a48a-~
#> 4      1578553 Running Bear       12926 Chris Godwin WR    TB    baa61bb5-f8d0-~
#> 5      1578553 Running Bear       16250 Ja'Marr Cha~ WR    CIN   fa99e984-d63b-~
#> 6      1578553 Running Bear        6660 Antonio Bro~ WR    TB    16e33176-b73e-~

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(sportradar_id,fantasypros_id) %>% 
  filter(!is.na(sportradar_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(sportradar_id,age,ecr_2qb,ecr_pos,value_2qb)

# ff_rosters() will return the sportradar_id, which we can then match to our player values!

aaa_values <- aaa_rosters %>% 
  left_join(player_values, by = c("sportradar_id"="sportradar_id")) %>% 
  arrange(franchise_id,desc(value_2qb))

head(aaa_values)
#> # A tibble: 6 x 11
#>   franchise_id franchise_name player_id player_name  pos   team  sportradar_id  
#>          <int> <chr>              <int> <chr>        <chr> <chr> <chr>          
#> 1      1578553 Running Bear       12159 Dak Prescott QB    DAL   86197778-8d4b-~
#> 2      1578553 Running Bear       16250 Ja'Marr Cha~ WR    CIN   fa99e984-d63b-~
#> 3      1578553 Running Bear       12926 Chris Godwin WR    TB    baa61bb5-f8d0-~
#> 4      1578553 Running Bear       16259 Trey Lance   QB    SF    676a508c-c65f-~
#> 5      1578553 Running Bear       13325 Austin Ekel~ RB    LAC   e5b8c439-a48a-~
#> 6      1578553 Running Bear       15531 Brandon Aiy~ WR    SF    c90471cc-fa60-~
#> # ... with 4 more variables: age <dbl>, ecr_2qb <dbl>, ecr_pos <dbl>,
#> #   value_2qb <int>

Let’s do some team summaries now!

value_summary <- aaa_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: 12 x 7
#>    franchise_id franchise_name        team_value    QB    RB    WR    TE
#>           <int> <chr>                      <int> <int> <int> <int> <int>
#>  1      1581722 syd12nyjets's Team         42601 12120 10478 19263   740
#>  2      1581719 Jmuthers's Team            42502 12069 19213  3641  7579
#>  3      1581803 ZachFarni's Team           39715  5908 20641 12931   235
#>  4      1582416 Ray Jay Team               37522  3843  7855 15337 10487
#>  5      1582423 The Verblanders            31289 10117  9972 10878   322
#>  6      1581721 Mjenkyns2004's Team        30245 13421  4682 11325   817
#>  7      1581718 Officially Rebuilding      29336  6340  7124 12921  2951
#>  8      1578553 Running Bear               29082 14002  3464 11551    65
#>  9      1581988 The DK Crew                28349 11822  8480  6218  1773
#> 10      1581726 SCJaguars's Team           26596  6999 10538  8510   549
#> 11      1581753 fede_mndz's Team           26485  2489 10871 12673   452
#> 12      1581720 brosene's Team             16432    NA  8277  6190  1965

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: 12 x 7
#>    franchise_id franchise_name        team_value    QB    RB    WR    TE
#>           <int> <chr>                      <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1      1581722 syd12nyjets's Team         0.112    NA 0.086 0.147 0.026
#>  2      1581719 Jmuthers's Team            0.112    NA 0.158 0.028 0.271
#>  3      1581803 ZachFarni's Team           0.104    NA 0.17  0.098 0.008
#>  4      1582416 Ray Jay Team               0.099    NA 0.065 0.117 0.375
#>  5      1582423 The Verblanders            0.082    NA 0.082 0.083 0.012
#>  6      1581721 Mjenkyns2004's Team        0.08     NA 0.039 0.086 0.029
#>  7      1581718 Officially Rebuilding      0.077    NA 0.059 0.098 0.106
#>  8      1578553 Running Bear               0.077    NA 0.028 0.088 0.002
#>  9      1581988 The DK Crew                0.075    NA 0.07  0.047 0.063
#> 10      1581726 SCJaguars's Team           0.07     NA 0.087 0.065 0.02 
#> 11      1581753 fede_mndz's Team           0.07     NA 0.089 0.096 0.016
#> 12      1581720 brosene's Team             0.043    NA 0.068 0.047 0.07

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 <- aaa_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: 12 x 10
#> # Groups:   franchise_id, franchise_name [12]
#>    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      1578553 Running Bear         26     26      8.4   23.5        6        5
#>  2      1581718 Officially Rebuil~   31.4   24.5   21.1   26.4        3        8
#>  3      1581719 Jmuthers's Team      24.7   24.8   26.7   28.4        4        5
#>  4      1581720 brosene's Team       NA     25.7   24.6   26.3       NA        7
#>  5      1581721 Mjenkyns2004's Te~   25.7   23     23.4   26.6        4        6
#>  6      1581722 syd12nyjets's Team   24.4   23     25.5   22.5        5        5
#>  7      1581726 SCJaguars's Team     22.2   24.3   26.3   24          5        7
#>  8      1581753 fede_mndz's Team     30.3   24.9   23.5   27.7        5        9
#>  9      1581803 ZachFarni's Team     28.3   22.3   26.7   24.3        4        6
#> 10      1581988 The DK Crew          25.8   22.5   24.2   27.7        5        6
#> 11      1582416 Ray Jay Team         30.8   27.2   29.9   25.7        3        3
#> 12      1582423 The Verblanders      24.4   25.7   26.7   27.3        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?