Fleaflicker: Basics
Tan Ho
2022-06-22
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 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 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 16250 Ja'Marr Chase WR CIN fa99e984-d63b…
#> 3 1578553 Running Bear 12926 Chris Godwin WR TB baa61bb5-f8d0…
#> 4 1578553 Running Bear 16259 Trey Lance QB SF 676a508c-c65f…
#> 5 1578553 Running Bear 13325 Austin Ekeler RB LAC e5b8c439-a48a…
#> 6 1578553 Running Bear 15531 Brandon Aiyuk WR SF c90471cc-fa60…
#> # … 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 × 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 1965So 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 0.07Armed 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 26 26 8.4 23.5 6 5
#> 2 1581718 Officially Rebuil… 31.4 24.5 21.1 26.4 3 8
#> 3 1581719 Jmuthers's Team 24.7 24.8 26.7 28.4 4 5
#> 4 1581720 brosene's Team NA 25.7 24.6 26.3 NA 7
#> 5 1581721 Mjenkyns2004's Te… 25.7 23 23.4 26.6 4 6
#> 6 1581722 syd12nyjets's Team 24.4 23 25.5 22.5 5 5
#> 7 1581726 SCJaguars's Team 22.2 24.3 26.3 24 5 7
#> 8 1581753 fede_mndz's Team 30.3 24.9 23.5 27.7 5 9
#> 9 1581803 ZachFarni's Team 28.3 22.3 26.7 24.3 4 6
#> 10 1581988 The DK Crew 25.8 22.5 24.2 27.7 5 6
#> 11 1582416 Ray Jay Team 30.8 27.2 29.9 25.7 3 3
#> 12 1582423 The Verblanders 24.4 25.7 26.7 27.3 3 5
#> # … with 2 more variables: count_TE <int>, count_WR <int>