The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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 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…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
#> # ¹franchise_name, ²player_id, ³player_name, ⁴sportradar_id
#> # ℹ Use `colnames()` to see all variable namesLet’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.
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 ¹franchise_id, ²franchise_name, ³count_QB,
#> # ⁴count_RB, ⁵count_TE, ⁶count_WRIn 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?
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.