Getting Started: 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      2025      Albert Wilson   WR    MIA    28.3
#> 2 1            Fake News      4089      Gerald Everett  TE    LAR    26.4
#> 3 1            Fake News      6068      Devine Ozigbo   RB    JAX    24.1
#> 4 1            Fake News      4036      Corey Davis     WR    TEN    25.8
#> 5 1            Fake News      1339      Zach Ertz       TE    PHI    30  
#> 6 1            Fake News      5068      Kerryon Johnson RB    DET    23.4

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      1426      DeAndre Ho~ WR    ARI    28.4     6.7
#> 2 1            Fake News      4866      Saquon Bar~ RB    NYG    23.8     7.7
#> 3 1            Fake News      4037      Chris Godw~ WR    TB     24.7    15.7
#> 4 1            Fake News      4199      Aaron Jones RB    GB     26      28.3
#> 5 1            Fake News      4098      Kareem Hunt RB    CLE    25.3    47.7
#> 6 1            Fake News      4137      James Conn~ RB    PIT    25.5    71.3
#> # ... 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))
#> `summarise()` regrouping output by 'franchise_id', 'franchise_name' (override with `.groups` argument)

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 4            The FANTom Menace      51911  3133 15663  2637 30478    NA
#>  2 1            Fake News              47811   654 20073  4097 22987    NA
#>  3 3            solarpool              46000  5907 21904  1321 16868    NA
#>  4 11           Permian Panthers       43204  4187 11979  5266 21772    NA
#>  5 12           jaydk                  39289  1972 15583  4343 17391    NA
#>  6 8            Hocka Flocka           35007  1235 19661  2304 11807    NA
#>  7 6            sox05syd               33925  3288  4000  7099 19538    NA
#>  8 9            ZPMiller97             30612  4122 11320  2585 12585    NA
#>  9 5            Barbarians             29758  4391 16191  2713  6463    NA
#> 10 7            Flipadelphia05         23353  4078  7371   414 11490    NA
#> 11 10           JMLarkin               21571   926   396  1389 18860     0
#> 12 2            KingGabe               18830   543  4577   148 13562    NA

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 4            The FANTom Menace      0.123 0.091 0.105 0.077 0.15     NA
#>  2 1            Fake News              0.113 0.019 0.135 0.119 0.113    NA
#>  3 3            solarpool              0.109 0.172 0.147 0.038 0.083    NA
#>  4 11           Permian Panthers       0.103 0.122 0.081 0.153 0.107    NA
#>  5 12           jaydk                  0.093 0.057 0.105 0.127 0.085    NA
#>  6 8            Hocka Flocka           0.083 0.036 0.132 0.067 0.058    NA
#>  7 6            sox05syd               0.081 0.095 0.027 0.207 0.096    NA
#>  8 9            ZPMiller97             0.073 0.12  0.076 0.075 0.062    NA
#>  9 5            Barbarians             0.071 0.128 0.109 0.079 0.032    NA
#> 10 7            Flipadelphia05         0.055 0.118 0.05  0.012 0.056    NA
#> 11 10           JMLarkin               0.051 0.027 0.003 0.04  0.093     0
#> 12 2            KingGabe               0.045 0.016 0.031 0.004 0.067    NA

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))
#> `summarise()` regrouping output by 'franchise_id', 'franchise_name' (override with `.groups` argument)

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        33.1   24.8   26     27       NA        3
#>  2 10           JMLarkin         29.1   25.8   26.6   25.2      0        3
#>  3 11           Permian Panth~   23.8   22.9   31     25.7     NA        3
#>  4 12           jaydk            29.7   25.1   25.7   27.5     NA        4
#>  5 2            KingGabe         24.4   22     31.3   21.7     NA        5
#>  6 3            solarpool        25.4   25.2   26.2   27.8     NA        5
#>  7 4            The FANTom Me~   28.6   24     24     26.3     NA        4
#>  8 5            Barbarians       24.8   24.3   27.8   26.2     NA        2
#>  9 6            sox05syd         24.4   23.4   26.8   24.5     NA        3
#> 10 7            Flipadelphia05   32.6   25.4   26.2   26.1     NA        2
#> 11 8            Hocka Flocka     29.9   24.3   24.1   23.6     NA        3
#> 12 9            ZPMiller97       24.2   24.1   25.9   24.9     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?