Fleaflicker: Basics

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 14] (S3: tbl_df/tbl/data.frame)
#>  $ league_id      : chr "312861"
#>  $ league_name    : chr "Avid Auctioneers Alliance"
#>  $ 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 28

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    PHI   e9a5c16b-4472-~
#> 2      1578553 Running Bear        7378 Cam Newton   QB    NE    214e55e4-a089-~
#> 3      1578553 Running Bear       15622 Joshua Kell~ RB    LAC   62542e04-3c44-~
#> 4      1578553 Running Bear       13358 Matt Breida  RB    MIA   6249d2c0-75dc-~
#> 5      1578553 Running Bear        8429 Marvin Jones WR    DET   1a2fbc23-e6db-~
#> 6      1578553 Running Bear        7369 A.J. Green   WR    CIN   c9701373-23f6-~

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_1qb,ecr_pos,value_1qb)

# 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_1qb))

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       12926 Chris Godw~ WR    TB    baa61bb5-f8d~
#> 2      1578553 Running Bear       13325 Austin Eke~ RB    LAC   e5b8c439-a48~
#> 3      1578553 Running Bear        9338 Robert Woo~ WR    LAR   618bedee-925~
#> 4      1578553 Running Bear       12159 Dak Presco~ QB    DAL   86197778-8d4~
#> 5      1578553 Running Bear       13788 Michael Ga~ WR    DAL   9e174ff2-ca0~
#> 6      1578553 Running Bear       12974 Jonnu Smith TE    TEN   e4f25a37-74d~
#> # ... with 4 more variables: age <dbl>, ecr_1qb <dbl>, ecr_pos <dbl>,
#> #   value_1qb <int>

Let’s do some team summaries now!

value_summary <- aaa_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)) %>% 
  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      1581753 fede_mndz's Team         41975   975 22111 17916   973
#>  2      1581803 ZachFarni's Team         40387  2056 16789 21308   234
#>  3      1581722 syd12nyjets's Team       40013  1400  9767 26510  2336
#>  4      1581988 The DK Crew              38807  2131  8463 22123  6018
#>  5      1581718 AlexG5386's Team         36750  3275 23097  7776  2602
#>  6      1581719 Jmuthers's Team          36719  1866 12235 13624  8994
#>  7      1582416 Ray Jay Team             35218   771 15050 11178  8219
#>  8      1581721 Mjenkyns2004's Team      34321  7993  7234 18181   913
#>  9      1581726 SCJaguars's Team         34219   872 20415 12586   346
#> 10      1582423 The Verblanders          33252  4210 13008 14772  1262
#> 11      1581720 brosene's Team           32284  3750 16051  8804  3679
#> 12      1578553 Running Bear             25065  3237  6220 13636  1972

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      1581753 fede_mndz's Team         0.098 0.03  0.13  0.095 0.026
#>  2      1581803 ZachFarni's Team         0.094 0.063 0.099 0.113 0.006
#>  3      1581722 syd12nyjets's Team       0.093 0.043 0.057 0.141 0.062
#>  4      1581988 The DK Crew              0.09  0.065 0.05  0.117 0.16 
#>  5      1581718 AlexG5386's Team         0.086 0.101 0.136 0.041 0.069
#>  6      1581719 Jmuthers's Team          0.086 0.057 0.072 0.072 0.24 
#>  7      1582416 Ray Jay Team             0.082 0.024 0.088 0.059 0.219
#>  8      1581721 Mjenkyns2004's Team      0.08  0.246 0.042 0.096 0.024
#>  9      1581726 SCJaguars's Team         0.08  0.027 0.12  0.067 0.009
#> 10      1582423 The Verblanders          0.078 0.129 0.076 0.078 0.034
#> 11      1581720 brosene's Team           0.075 0.115 0.094 0.047 0.098
#> 12      1578553 Running Bear             0.058 0.099 0.036 0.072 0.053

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_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
#> # 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     27.6   25.8   25.4   25.5        6        6
#>  2      1581718 AlexG5386's T~   31.3   24.4   28.1   26.1        3       12
#>  3      1581719 Jmuthers's Te~   25.2   24.5   26.2   28.4        5        8
#>  4      1581720 brosene's Team   27     25.1   24.4   26.9        6       10
#>  5      1581721 Mjenkyns2004'~   25.3   24.9   26.4   26          5        9
#>  6      1581722 syd12nyjets's~   25.6   22.2   24.9   22.3        5        7
#>  7      1581726 SCJaguars's T~   23.7   24.8   33.4   24.4        5        7
#>  8      1581753 fede_mndz's T~   34.1   24.2   24.7   27.6        5       12
#>  9      1581803 ZachFarni's T~   27.2   21.8   25.5   24          5        9
#> 10      1581988 The DK Crew      26.7   22.7   24.9   25.2        4        6
#> 11      1582416 Ray Jay Team     29.3   25.9   29.9   26.7        4        8
#> 12      1582423 The Verblande~   24.4   24.9   25.1   27.3        4        8
#> # ... 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?