In this vignette, I’ll walk through how to get started with a basic dynasty value analysis on Sleeper.
We’ll start by loading the packages:
library(ffscrapr)
library(dplyr)
library(tidyr)
In Sleeper, unlike in other platforms, it’s very unlikely that you’ll remember the league ID - both because most people use the mobile app, and because it happens to be an 18 digit number! It’s a little more natural to start analyses from the username, so let’s start there!
<- sleeper_userleagues("solarpool",2020)
solarpool_leagues
head(solarpool_leagues)
#> # A tibble: 3 x 4
#> league_name league_id franchise_name franchise_id
#> <chr> <chr> <chr> <chr>
#> 1 z_dynastyprocess-test 633501761776197~ solarpool 202892038360801~
#> 2 The JanMichaelLarkin Dynasty~ 522458773317046~ solarpool 202892038360801~
#> 3 DLP Dynasty League 521379020332068~ DLP::thoriyan 202892038360801~
Let’s pull the JML league ID from here for analysis, and set up a Sleeper connection object.
<- solarpool_leagues %>%
jml_id filter(league_name == "The JanMichaelLarkin Dynasty League") %>%
pull(league_id)
# For quick analyses, I'm not above copy-pasting the league ID instead!
jml_id #> [1] "522458773317046272"
<- sleeper_connect(season = 2020, league_id = jml_id)
jml
jml#> <Sleeper connection 2020_522458773317046272>
#> List of 5
#> $ platform : chr "Sleeper"
#> $ season : num 2020
#> $ user_name: NULL
#> $ league_id: chr "522458773317046272"
#> $ user_id : NULL
#> - attr(*, "class")= chr "sleeper_conn"
I’ve done this with the sleeper_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.
<- ff_league(jml)
jml_summary
str(jml_summary)
#> tibble [1 x 15] (S3: tbl_df/tbl/data.frame)
#> $ league_id : chr "522458773317046272"
#> $ league_name : chr "The JanMichaelLarkin Dynasty League"
#> $ league_type : chr "dynasty"
#> $ franchise_count: num 12
#> $ qb_type : chr "1QB"
#> $ idp : logi FALSE
#> $ scoring_flags : chr "0.5_ppr"
#> $ best_ball : logi FALSE
#> $ salary_cap : logi FALSE
#> $ player_copies : num 1
#> $ years_active : chr "2019-2020"
#> $ qb_count : chr "1"
#> $ roster_size : int 25
#> $ league_depth : num 300
#> $ prev_league_ids: chr "386236959468675072"
Okay, so it’s the JanMichaelLarkin Dynasty League, it’s a 1QB league with 12 teams, half ppr scoring, and rosters about 300 players.
Let’s grab the rosters now.
<- ff_rosters(jml)
jml_rosters
head(jml_rosters)
#> # A tibble: 6 x 7
#> franchise_id franchise_name player_id player_name pos team age
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 1 Fake News 1110 T.Y. Hilton WR IND 31.2
#> 2 1 Fake News 1339 Zach Ertz TE PHI 30.2
#> 3 1 Fake News 1426 DeAndre Hopkins WR ARI 28.7
#> 4 1 Fake News 1825 Jarvis Landry WR CLE 28.2
#> 5 1 Fake News 2025 Albert Wilson WR MIA 28.6
#> 6 1 Fake News 2197 Brandin Cooks WR HOU 27.4
Cool! Let’s pull in some additional context by adding DynastyProcess player values.
<- dp_values("values-players.csv")
player_values
# 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.
<- dp_playerids() %>%
player_ids select(sleeper_id,fantasypros_id)
<- player_values %>%
player_values left_join(player_ids, by = c("fp_id" = "fantasypros_id")) %>%
select(sleeper_id,ecr_1qb,ecr_pos,value_1qb)
# Drilling down to just 1QB values and IDs, we'll be joining it onto rosters and don't need the extra stuff
<- jml_rosters %>%
jml_values left_join(player_values, by = c("player_id"="sleeper_id")) %>%
arrange(franchise_id,desc(value_1qb))
head(jml_values)
#> # A tibble: 6 x 10
#> franchise_id franchise_name player_id player_name pos team age ecr_1qb
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 1 Fake News 4866 Saquon Bar~ RB NYG 24 2.4
#> 2 1 Fake News 1426 DeAndre Ho~ WR ARI 28.7 12
#> 3 1 Fake News 4199 Aaron Jones RB GB 26.2 20.6
#> 4 1 Fake News 4037 Chris Godw~ WR TB 24.9 29.3
#> 5 1 Fake News 4098 Kareem Hunt RB CLE 25.5 51
#> 6 1 Fake News 4017 Deshaun Wa~ QB HOU 25.4 56.6
#> # ... with 2 more variables: ecr_pos <dbl>, value_1qb <int>
Let’s do some team summaries now!
<- jml_values %>%
value_summary 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))
value_summary#> # A tibble: 12 x 8
#> franchise_id franchise_name team_value QB RB TE WR FB
#> <chr> <chr> <int> <int> <int> <int> <int> <int>
#> 1 3 solarpool 49737 7356 26111 1130 15140 NA
#> 2 1 Fake News 46198 3506 21307 3035 18350 NA
#> 3 4 The FANTom Menace 46152 3323 11561 2120 29148 NA
#> 4 11 Permian Panthers 44695 3838 13491 7632 19734 NA
#> 5 12 jaydk 38673 2260 19105 4164 13144 NA
#> 6 8 Hocka Flocka 38455 1897 21385 2717 12456 NA
#> 7 5 Barbarians 33916 3309 20991 4483 5133 NA
#> 8 6 sox05syd 33734 3701 6273 8844 14916 NA
#> 9 9 ZPMiller97 27014 2723 12684 2518 9089 NA
#> 10 7 Flipadelphia05 23128 1953 9380 257 11538 NA
#> 11 2 KingGabe 19205 115 6524 21 12545 NA
#> 12 10 JMLarkin 16321 386 196 1302 14437 0
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 %>%
value_summary_pct 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 8
#> franchise_id franchise_name team_value QB RB TE WR FB
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 3 solarpool 0.119 0.214 0.154 0.03 0.086 NA
#> 2 1 Fake News 0.111 0.102 0.126 0.079 0.104 NA
#> 3 4 The FANTom Menace 0.111 0.097 0.068 0.055 0.166 NA
#> 4 11 Permian Panthers 0.107 0.112 0.08 0.2 0.112 NA
#> 5 12 jaydk 0.093 0.066 0.113 0.109 0.075 NA
#> 6 8 Hocka Flocka 0.092 0.055 0.127 0.071 0.071 NA
#> 7 5 Barbarians 0.081 0.096 0.124 0.117 0.029 NA
#> 8 6 sox05syd 0.081 0.108 0.037 0.231 0.085 NA
#> 9 9 ZPMiller97 0.065 0.079 0.075 0.066 0.052 NA
#> 10 7 Flipadelphia05 0.055 0.057 0.056 0.007 0.066 NA
#> 11 2 KingGabe 0.046 0.003 0.039 0.001 0.071 NA
#> 12 10 JMLarkin 0.039 0.011 0.001 0.034 0.082 0
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.
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!
<- jml_values %>%
age_summary 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 12
#> # Groups: franchise_id, franchise_name [12]
#> franchise_id franchise_name age_QB age_RB age_TE age_WR age_FB count_QB
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 1 Fake News 27 25.1 26 27.3 NA 3
#> 2 10 JMLarkin 28.6 26.8 25.7 25.2 0 3
#> 3 11 Permian Panth~ 23.9 22.9 31.1 25.3 NA 3
#> 4 12 jaydk 31.2 25.2 25.6 27.6 NA 4
#> 5 2 KingGabe 26 22.2 27 22 NA 5
#> 6 3 solarpool 25.1 25.4 26.2 27.7 NA 5
#> 7 4 The FANTom Me~ 28.5 24.3 23.9 26.5 NA 4
#> 8 5 Barbarians 25 24.5 28.3 26.3 NA 3
#> 9 6 sox05syd 23.5 23.6 26.9 25 NA 3
#> 10 7 Flipadelphia05 33.1 24.9 27.6 26.4 NA 2
#> 11 8 Hocka Flocka 30.9 24.1 24.3 23.1 NA 3
#> 12 9 ZPMiller97 24.4 23.6 26.3 25.2 NA 3
#> # ... with 4 more variables: count_RB <int>, count_TE <int>, count_WR <int>,
#> # count_FB <int>
In this vignette, I’ve used ~three functions: ff_connect, ff_league, and ff_rosters. Now that you’ve gotten this far, why not check out some of the other possibilities?