Sleeper: Basics

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!

solarpool_leagues <- sleeper_userleagues("solarpool",2020)

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.

jml_id <- solarpool_leagues %>% 
  filter(league_name == "The JanMichaelLarkin Dynasty League") %>% 
  pull(league_id)

jml_id # For quick analyses, I'm not above copy-pasting the league ID instead!
#> [1] "522458773317046272"

jml <- sleeper_connect(season = 2020, league_id = jml_id)

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.


jml_summary <- ff_league(jml)

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.

jml_rosters <- ff_rosters(jml)

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

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(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_values <- jml_rosters %>% 
  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!

value_summary <- jml_values %>% 
  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_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 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.

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 <- jml_values %>% 
  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>

Next steps

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?