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"

str(aaa_summary)
#> tibble [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_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       13325 Austin Eke… RB    LAC   e5b8c439-a48…
#> 2      1578553 Running Bear       12926 Chris Godw… WR    TB    baa61bb5-f8d…
#> 3      1578553 Running Bear       15531 Brandon Ai… WR    SF    c90471cc-fa6…
#> 4      1578553 Running Bear        9338 Robert Woo… WR    LAR   618bedee-925…
#> 5      1578553 Running Bear       12159 Dak Presco… QB    DAL   86197778-8d4…
#> 6      1578553 Running Bear       13788 Michael Ga… WR    DAL   9e174ff2-ca0…
#> # … with 4 more variables: age <lgl>, 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      1581803 ZachFarni's Team         40718  2026 20237 18428    27
#>  2      1582416 Ray Jay Team             39961  1245 15838 12686 10192
#>  3      1581719 Jmuthers's Team          38982  3180 12345 13653  9804
#>  4      1581753 fede_mndz's Team         37853   627 19882 16248  1096
#>  5      1581722 syd12nyjets's Team       36579  3327  7987 22732  2533
#>  6      1581726 SCJaguars's Team         34270   443 21417 12325    85
#>  7      1581988 The DK Crew              33879  2442  8881 18056  4406
#>  8      1581718 AlexG5386's Team         33534  1582 20534  6896  4522
#>  9      1581720 brosene's Team           32887  3826 15919  9468  3674
#> 10      1581721 Mjenkyns2004's Team      32645  8830  7047 16325   443
#> 11      1582423 The Verblanders          32357  3912 12489 15409   547
#> 12      1578553 Running Bear             23608  2994  6348 13364   902

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         0.098 0.059 0.12  0.105 0.001
#>  2      1582416 Ray Jay Team             0.096 0.036 0.094 0.072 0.267
#>  3      1581719 Jmuthers's Team          0.093 0.092 0.073 0.078 0.256
#>  4      1581753 fede_mndz's Team         0.091 0.018 0.118 0.093 0.029
#>  5      1581722 syd12nyjets's Team       0.088 0.097 0.047 0.129 0.066
#>  6      1581726 SCJaguars's Team         0.082 0.013 0.127 0.07  0.002
#>  7      1581988 The DK Crew              0.081 0.071 0.053 0.103 0.115
#>  8      1581718 AlexG5386's Team         0.08  0.046 0.122 0.039 0.118
#>  9      1581720 brosene's Team           0.079 0.111 0.094 0.054 0.096
#> 10      1581721 Mjenkyns2004's Team      0.078 0.256 0.042 0.093 0.012
#> 11      1582423 The Verblanders          0.078 0.114 0.074 0.088 0.014
#> 12      1578553 Running Bear             0.057 0.087 0.038 0.076 0.024

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        0      0      0      0        6        6
#>  2      1581718 AlexG5386's T…      0      0      0      0        3       12
#>  3      1581719 Jmuthers's Te…      0      0      0      0        5        8
#>  4      1581720 brosene's Team      0      0      0      0        6       10
#>  5      1581721 Mjenkyns2004'…      0      0      0      0        5        9
#>  6      1581722 syd12nyjets's…      0      0      0      0        5        7
#>  7      1581726 SCJaguars's T…      0      0      0      0        5        7
#>  8      1581753 fede_mndz's T…      0      0      0      0        6       12
#>  9      1581803 ZachFarni's T…      0      0      0      0        5        9
#> 10      1581988 The DK Crew         0      0      0      0        4        6
#> 11      1582416 Ray Jay Team        0      0      0      0        4        8
#> 12      1582423 The Verblande…      0      0      0      0        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?