Fleaflicker: Basics
Tan Ho
2024-05-29
Source:vignettes/fleaflicker_basics.Rmd
fleaflicker_basics.RmdIn 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 × 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 31Okay, 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 12159 Dak Prescott QB DAL 86197778-8d4…
#> 2 1578553 Running Bear 16259 Trey Lance QB DAL 676a508c-c65…
#> 3 1578553 Running Bear 14736 Devin Singlet… RB HOU a961b0d4-5d7…
#> 4 1578553 Running Bear 13772 Rashaad Penny RB PHI 2b119688-83b…
#> 5 1578553 Running Bear 12017 Laquon Treadw… WR BAL 2e0badcd-b78…
#> 6 1578553 Running Bear 15531 Brandon Aiyuk WR SF c90471cc-fa6…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 franchise_name player_id player_name pos team sportradar_id
#> <int> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 1578553 Running Bear 16250 Ja'Marr Chase WR CIN fa99e984-d63…
#> 2 1578553 Running Bear 12159 Dak Prescott QB DAL 86197778-8d4…
#> 3 1578553 Running Bear 15531 Brandon Aiyuk WR SF c90471cc-fa6…
#> 4 1578553 Running Bear 12926 Chris Godwin WR TB baa61bb5-f8d…
#> 5 1578553 Running Bear 16552 Khalil Herbert RB CHI 02af99e0-3c8…
#> 6 1578553 Running Bear 14736 Devin Singlet… RB HOU a961b0d4-5d7…
#> # ℹ 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 × 7
#> franchise_id franchise_name team_value QB RB WR TE
#> <int> <chr> <int> <int> <int> <int> <int>
#> 1 1581722 syd12nyjets's Team 44863 19449 3083 22260 71
#> 2 1581719 Jmuthers's Team 37355 9881 10151 11267 6056
#> 3 1581803 ZachFarni's Team 30026 7048 5096 17718 164
#> 4 1581721 Mjenkyns2004's Team 28058 12496 5628 9508 426
#> 5 1581988 The DK Crew 26267 13954 7683 4496 134
#> 6 1581720 brosene's Team 23041 11281 5516 1599 4645
#> 7 1578553 Running Bear 21771 5998 698 15002 73
#> 8 1582416 Ray Jay Team 16853 903 1130 10890 3930
#> 9 1581718 Officially Rebuilding 13491 2362 858 8757 1514
#> 10 1581726 SCJaguars's Team 13039 11225 789 1017 8
#> 11 1582423 The Verblanders 12439 6890 2178 3350 21
#> 12 1581753 fede_mndz's Team 11846 164 4596 6356 730So 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 0.161 0.191 0.065 0.198 0.004
#> 2 1581719 Jmuthers's Team 0.134 0.097 0.214 0.1 0.341
#> 3 1581803 ZachFarni's Team 0.108 0.069 0.107 0.158 0.009
#> 4 1581721 Mjenkyns2004's Team 0.101 0.123 0.119 0.085 0.024
#> 5 1581988 The DK Crew 0.094 0.137 0.162 0.04 0.008
#> 6 1581720 brosene's Team 0.083 0.111 0.116 0.014 0.261
#> 7 1578553 Running Bear 0.078 0.059 0.015 0.134 0.004
#> 8 1582416 Ray Jay Team 0.06 0.009 0.024 0.097 0.221
#> 9 1581718 Officially Rebuilding 0.048 0.023 0.018 0.078 0.085
#> 10 1581726 SCJaguars's Team 0.047 0.11 0.017 0.009 0
#> 11 1582423 The Verblanders 0.045 0.068 0.046 0.03 0.001
#> 12 1581753 fede_mndz's Team 0.042 0.002 0.097 0.057 0.041Armed 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]
#> 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 30.2 25.9 27.9 24.7 5 6
#> 2 1581718 Officially Rebuil… 34.1 30.5 23.3 26.3 4 13
#> 3 1581719 Jmuthers's Team 27.5 27.3 27.1 25.7 5 7
#> 4 1581720 brosene's Team 27.3 26.7 27.7 27.7 6 17
#> 5 1581721 Mjenkyns2004's Te… 28.1 24.9 27.4 29.3 6 8
#> 6 1581722 syd12nyjets's Team 25.9 27.5 31 24.7 5 8
#> 7 1581726 SCJaguars's Team 24.5 25.2 29.5 25 5 6
#> 8 1581753 fede_mndz's Team 25.2 26.5 24.9 28.3 5 12
#> 9 1581803 ZachFarni's Team 25.9 24.8 28.4 25.8 4 8
#> 10 1581988 The DK Crew 26.6 25 27.3 27.1 4 8
#> 11 1582416 Ray Jay Team 33.3 29.9 32.8 28.5 5 5
#> 12 1582423 The Verblanders 26.8 28.6 25.6 29.9 3 8
#> # ℹ 2 more variables: count_TE <int>, count_WR <int>