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:

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)
#> Using request.R from "ffscrapr"
#> No encoding supplied: defaulting to UTF-8.

str(aaa_summary)
#> tibble[,14] [1 × 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        7369 A.J. Green   WR    CIN   c9701373-23f6-…
#> 6      1578553 Running Bear       13782 Anthony Mil… WR    CHI   bfaedf99-7618-…

Values

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

player_values <- dp_values("values-players.csv")
#> No encoding supplied: defaulting to UTF-8.

# 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))
#> No encoding supplied: defaulting to UTF-8.

# 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       12926 Chris Godwin WR    TB    baa61bb5-f8d0-…
#> 3      1578553 Running Bear       12032 Carson Wentz QB    PHI   e9a5c16b-4472-…
#> 4      1578553 Running Bear       13325 Austin Ekel… RB    LAC   e5b8c439-a48a-…
#> 5      1578553 Running Bear       15531 Brandon Aiy… WR    SF    c90471cc-fa60-…
#> 6      1578553 Running Bear        9338 Robert Woods WR    LAR   618bedee-9259-…
#> # … 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      1581803 ZachFarni's Team         45658 10299 19968 15334    57
#>  2      1581722 syd12nyjets's Team       39773 14666  4596 19094  1417
#>  3      1581719 Jmuthers's Team          36800  8073 10710 10055  7962
#>  4      1581721 Mjenkyns2004's Team      35061 19338  3680 11598   445
#>  5      1582416 Ray Jay Team             34369  6201 11128  9012  8028
#>  6      1581753 fede_mndz's Team         34022  3071 16590 13803   558
#>  7      1581988 The DK Crew              34020  9167  6627 15292  2834
#>  8      1582423 The Verblanders          33492 10661 11273 11186   372
#>  9      1581720 brosene's Team           33432 12479 12380  6184  2389
#> 10      1581718 AlexG5386's Team         32997  8185 15644  4575  4593
#> 11      1581726 SCJaguars's Team         27219  3397 15703  8041    78
#> 12      1578553 Running Bear             23630 10533  3585  8257  1255

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      1581803 ZachFarni's Team         0.111 0.089 0.151 0.116 0.002
#>  2      1581722 syd12nyjets's Team       0.097 0.126 0.035 0.144 0.047
#>  3      1581719 Jmuthers's Team          0.09  0.07  0.081 0.076 0.266
#>  4      1581721 Mjenkyns2004's Team      0.085 0.167 0.028 0.088 0.015
#>  5      1582416 Ray Jay Team             0.084 0.053 0.084 0.068 0.268
#>  6      1581753 fede_mndz's Team         0.083 0.026 0.126 0.104 0.019
#>  7      1581988 The DK Crew              0.083 0.079 0.05  0.115 0.095
#>  8      1582423 The Verblanders          0.082 0.092 0.085 0.084 0.012
#>  9      1581720 brosene's Team           0.081 0.108 0.094 0.047 0.08 
#> 10      1581718 AlexG5386's Team         0.08  0.071 0.119 0.035 0.153
#> 11      1581726 SCJaguars's Team         0.066 0.029 0.119 0.061 0.003
#> 12      1578553 Running Bear             0.058 0.091 0.027 0.062 0.042

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         27.7   25.8   26.2   25.4        6        6
#>  2      1581718 AlexG5386's Team     30.7   24.9   28.4   26.4        3       12
#>  3      1581719 Jmuthers's Team      24.9   24.4   26.6   28.8        5        8
#>  4      1581720 brosene's Team       29.7   25.6   24.7   26.4        6       10
#>  5      1581721 Mjenkyns2004's Te…   25.8   24     26.8   26.6        5        9
#>  6      1581722 syd12nyjets's Team   24.8   22.4   24.4   22.2        5        7
#>  7      1581726 SCJaguars's Team     23.8   24.9   33     24.3        5        7
#>  8      1581753 fede_mndz's Team     35.7   24.7   24.5   27.9        6       12
#>  9      1581803 ZachFarni's Team     28.2   21.9   25.8   24.2        5        9
#> 10      1581988 The DK Crew          27     22.6   24.9   25          4        6
#> 11      1582416 Ray Jay Team         29.6   26.4   30.2   27          4        8
#> 12      1582423 The Verblanders      24.3   25.3   26.1   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?