Author: Tan Ho
Date: 2023-02-11

# Fleaflicker: Basics

#### Tan Ho

#### 2023-02-11

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 × 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 × 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 Ekeler RB    LAC   e5b8c439-a48a…
    #> 4      1578553 Running Bear       12926 Chris Godwin  WR    TB    baa61bb5-f8d0…
    #> 5      1578553 Running Bear       16250 Ja'Marr Chase WR    CIN   fa99e984-d63b…
    #> 6      1578553 Running Bear        6660 Antonio Brown 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 × 11
    #>   franchise_id franc…¹ playe…² playe…³ pos   team  sport…⁴   age ecr_2qb ecr_pos
    #>          <int> <chr>     <int> <chr>   <chr> <chr> <chr>   <dbl>   <dbl>   <dbl>
    #> 1      1578553 Runnin…   12159 Dak Pr… QB    DAL   861977…  27.9    10.3     4.4
    #> 2      1578553 Runnin…   16250 Ja'Mar… WR    CIN   fa99e9…  21.3    41.9    13.8
    #> 3      1578553 Runnin…   12926 Chris … WR    TB    baa61b…  25.3    42.6    13.8
    #> 4      1578553 Runnin…   16259 Trey L… QB    SF    676a50…  21.1    43.3    13
    #> 5      1578553 Runnin…   13325 Austin… RB    LAC   e5b8c4…  26.1    48.4    16.2
    #> 6      1578553 Runnin…   15531 Brando… WR    SF    c90471…  23.2    57.1    20.4
    #> # … with 1 more variable: value_2qb <int>, and abbreviated variable names
    #> #   ¹​franchise_name, ²​player_id, ³​player_name, ⁴​sportradar_id
    #> # ℹ Use `colnames()` to see all variable names

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 × 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 × 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 × 10
    #> # Groups:   franchise_id, franchise_name [12]
    #>    franchi…¹ franc…² age_QB age_RB age_TE age_WR count…³ count…⁴ count…⁵ count…⁶
    #>        <int> <chr>    <dbl>  <dbl>  <dbl>  <dbl>   <int>   <int>   <int>   <int>
    #>  1   1578553 Runnin…   26     26      8.4   23.5       6       5       3      14
    #>  2   1581718 Offici…   31.4   24.5   21.1   26.4       3       8       2       8
    #>  3   1581719 Jmuthe…   24.7   24.8   26.7   28.4       4       5       4       9
    #>  4   1581720 brosen…   NA     25.7   24.6   26.3      NA       7       2       5
    #>  5   1581721 Mjenky…   25.7   23     23.4   26.6       4       6       5       8
    #>  6   1581722 syd12n…   24.4   23     25.5   22.5       5       5       4      14
    #>  7   1581726 SCJagu…   22.2   24.3   26.3   24         5       7       4      10
    #>  8   1581753 fede_m…   30.3   24.9   23.5   27.7       5       9       3      10
    #>  9   1581803 ZachFa…   28.3   22.3   26.7   24.3       4       6       5      12
    #> 10   1581988 The DK…   25.8   22.5   24.2   27.7       5       6       5       8
    #> 11   1582416 Ray Ja…   30.8   27.2   29.9   25.7       3       3       7       7
    #> 12   1582423 The Ve…   24.4   25.7   26.7   27.3       3       5       2       9
    #> # … with abbreviated variable names ¹​franchise_id, ²​franchise_name, ³​count_QB,
    #> #   ⁴​count_RB, ⁵​count_TE, ⁶​count_WR

## 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?