Sleeper: Basics

Tan Ho

2021-09-02

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)
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head(solarpool_leagues)
#> # A tibble: 3 x 4
#>   league_name                   league_id        franchise_name franchise_id    
#>   <chr>                         <chr>            <chr>          <chr>           
#> 1 The JanMichaelLarkin Dynasty~ 522458773317046~ solarpool      202892038360801~
#> 2 DLP Dynasty League            521379020332068~ DLP::thoriyan  202892038360801~
#> 3 z_dynastyprocess-test         633501761776197~ solarpool      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)
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str(jml_summary)
#> tibble [1 x 16] (S3: tbl_df/tbl/data.frame)
#>  $ league_id      : chr "522458773317046272"
#>  $ league_name    : chr "The JanMichaelLarkin Dynasty League"
#>  $ season         : int 2020
#>  $ 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)
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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    31.8
#> 2 1            Fake News      1339      Zach Ertz       TE    PHI    30.8
#> 3 1            Fake News      1426      DeAndre Hopkins WR    ARI    29.2
#> 4 1            Fake News      1825      Jarvis Landry   WR    CLE    28.8
#> 5 1            Fake News      2025      Albert Wilson   WR    MIA    29.1
#> 6 1            Fake News      2197      Brandin Cooks   WR    HOU    27.9

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 Barkley RB    NYG    24.6     3.4
#> 2 1            Fake News      4199      Aaron Jones    RB    GB     26.8    21  
#> 3 1            Fake News      1426      DeAndre Hopki~ WR    ARI    29.2    21.1
#> 4 1            Fake News      4037      Chris Godwin   WR    TB     25.5    33.7
#> 5 1            Fake News      4098      Kareem Hunt    RB    CLE    26.1    63.7
#> 6 1            Fake News      5022      Dallas Goedert TE    PHI    26.2    83.2
#> # ... 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              45406  7664 23920   529 13293    NA
#>  2 4            The FANTom Menace      41754  3051  7594  1820 29289    NA
#>  3 11           Permian Panthers       40081  3889  9902  6997 19293    NA
#>  4 1            Fake News              37716  1505 19221  2730 14260    NA
#>  5 8            Hocka Flocka           37314  1234 20459  2511 13110    NA
#>  6 12           jaydk                  33981  1696 17692  2936 11657    NA
#>  7 5            Barbarians             32614  3770 19492  4629  4723    NA
#>  8 6            sox05syd               30780  4329  5614  8136 12701    NA
#>  9 9            ZPMiller97             24697  2941 12782   998  7976    NA
#> 10 2            KingGabe               19931    41  6327    15 13548    NA
#> 11 7            Flipadelphia05         18140  1951  4799   789 10601    NA
#> 12 10           JMLarkin               14197   336    67   884 12908     2

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              0.121 0.236 0.162 0.016 0.081    NA
#>  2 4            The FANTom Menace      0.111 0.094 0.051 0.055 0.179    NA
#>  3 11           Permian Panthers       0.106 0.12  0.067 0.212 0.118    NA
#>  4 1            Fake News              0.1   0.046 0.13  0.083 0.087    NA
#>  5 8            Hocka Flocka           0.099 0.038 0.138 0.076 0.08     NA
#>  6 12           jaydk                  0.09  0.052 0.12  0.089 0.071    NA
#>  7 5            Barbarians             0.087 0.116 0.132 0.14  0.029    NA
#>  8 6            sox05syd               0.082 0.134 0.038 0.247 0.078    NA
#>  9 9            ZPMiller97             0.066 0.091 0.086 0.03  0.049    NA
#> 10 2            KingGabe               0.053 0.001 0.043 0     0.083    NA
#> 11 7            Flipadelphia05         0.048 0.06  0.032 0.024 0.065    NA
#> 12 10           JMLarkin               0.038 0.01  0     0.027 0.079     2

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           29     25.5   25.8   27.7   NA          3
#>  2 10           JMLarkin            28.7   27.4   26.1   25.3   30.4        3
#>  3 11           Permian Panthers    24.3   23.3   31.8   25.7   NA          4
#>  4 12           jaydk               32.4   25.8   26.3   28     NA          4
#>  5 2            KingGabe            29.2   22.6   27.4   22.5   NA          5
#>  6 3            solarpool           25.6   25.6   26.8   27.9   NA          5
#>  7 4            The FANTom Menace   29     23.7   24     27.1   NA          5
#>  8 5            Barbarians          25.3   24.8   29     26.5   NA          3
#>  9 6            sox05syd            24     24.2   27.5   25.5   NA          3
#> 10 7            Flipadelphia05      33.2   25.9   27.6   27     NA          2
#> 11 8            Hocka Flocka        31.6   24.4   24.1   23.8   NA          3
#> 12 9            ZPMiller97          24.7   24.1   26.8   25.4   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?