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:

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 × 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 1            Fake News      1339      Zach Ertz       TE    PHI    30.1
#> 3 1            Fake News      1426      DeAndre Hopkins WR    ARI    28.5
#> 4 1            Fake News      1825      Jarvis Landry   WR    CLE    28  
#> 5 1            Fake News      2025      Albert Wilson   WR    MIA    28.4
#> 6 1            Fake News      2197      Brandin Cooks   WR    HOU    27.2

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    23.8     4.2
#> 2 1            Fake News      1426      DeAndre Ho… WR    ARI    28.5    11.5
#> 3 1            Fake News      4037      Chris Godw… WR    TB     24.8    17.5
#> 4 1            Fake News      4199      Aaron Jones RB    GB     26      22.8
#> 5 1            Fake News      4098      Kareem Hunt RB    CLE    25.3    45.2
#> 6 1            Fake News      4017      Deshaun Wa… QB    HOU    25.2    62.2
#> # … 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 1            Fake News              50555  3252 22254  4614 20435    NA
#>  2 4            The FANTom Menace      50251  3183 16623  3079 27366    NA
#>  3 3            solarpool              46544  6093 24096  1492 14863    NA
#>  4 11           Permian Panthers       44058  3770 14619  5890 19779    NA
#>  5 12           jaydk                  39880  2134 18254  4333 15159    NA
#>  6 8            Hocka Flocka           38902  1417 22410  2936 12139    NA
#>  7 9            ZPMiller97             34209  3102 14652  3360 13095    NA
#>  8 6            sox05syd               33604  2488  4557  7593 18966    NA
#>  9 7            Flipadelphia05         24230  3663  9746   383 10438    NA
#> 10 5            Barbarians             23951  1880 13871  2647  5553    NA
#> 11 2            KingGabe               22400   436  7091   207 14666    NA
#> 12 10           JMLarkin               18273   687   379  1175 16032     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 1            Fake News              0.118 0.101 0.132 0.122 0.108    NA
#>  2 4            The FANTom Menace      0.118 0.099 0.099 0.082 0.145    NA
#>  3 3            solarpool              0.109 0.19  0.143 0.04  0.079    NA
#>  4 11           Permian Panthers       0.103 0.117 0.087 0.156 0.105    NA
#>  5 12           jaydk                  0.093 0.066 0.108 0.115 0.08     NA
#>  6 8            Hocka Flocka           0.091 0.044 0.133 0.078 0.064    NA
#>  7 9            ZPMiller97             0.08  0.097 0.087 0.089 0.069    NA
#>  8 6            sox05syd               0.079 0.077 0.027 0.201 0.101    NA
#>  9 7            Flipadelphia05         0.057 0.114 0.058 0.01  0.055    NA
#> 10 5            Barbarians             0.056 0.059 0.082 0.07  0.029    NA
#> 11 2            KingGabe               0.052 0.014 0.042 0.005 0.078    NA
#> 12 10           JMLarkin               0.043 0.021 0.002 0.031 0.085     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        27.5   24.9   26.5   26.9     NA        3
#>  2 10           JMLarkin         28.9   26.3   26.1   25        0        3
#>  3 11           Permian Panth…   23.6   22.6   30.5   25.8     NA        3
#>  4 12           jaydk            29.2   25.2   25.5   27.2     NA        4
#>  5 2            KingGabe         24     22.2   31.1   21.7     NA        5
#>  6 3            solarpool        25.5   25.2   26     28       NA        5
#>  7 4            The FANTom Me…   28     23.9   23.8   26.3     NA        4
#>  8 5            Barbarians       24.6   24.1   27.9   26.3     NA        3
#>  9 6            sox05syd         24.4   23.4   26.8   24.3     NA        3
#> 10 7            Flipadelphia05   32.7   24.9   26.6   26       NA        2
#> 11 8            Hocka Flocka     30.6   24.1   24.4   23.7     NA        3
#> 12 9            ZPMiller97       24.3   23.9   26     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?