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)
#> Using request.R from "ffscrapr"
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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 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)
#> No encoding supplied: defaulting to UTF-8.

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)
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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            Paper Champs   2025      Albert Wilson   WR    MIA    28.6
#> 2 1            Paper Champs   4089      Gerald Everett  TE    LAR    26.7
#> 3 1            Paper Champs   6068      Devine Ozigbo   RB    JAX    24.4
#> 4 1            Paper Champs   1339      Zach Ertz       TE    PHI    30.3
#> 5 1            Paper Champs   5068      Kerryon Johnson RB    DET    23.7
#> 6 1            Paper Champs   5965      Miles Boykin    WR    BAL    24.4

Values

Cool! Let’s pull in some additional context by adding DynastyProcess player values.

player_values <- dp_values("values-players.csv")
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# 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)
#> No encoding supplied: defaulting to UTF-8.

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            Paper Champs   4866      Saquon Barkley RB    NYG    24.1     2.4
#> 2 1            Paper Champs   1426      DeAndre Hopki… WR    ARI    28.7    12  
#> 3 1            Paper Champs   4199      Aaron Jones    RB    GB     26.3    20.6
#> 4 1            Paper Champs   4037      Chris Godwin   WR    TB     25      29.3
#> 5 1            Paper Champs   4098      Kareem Hunt    RB    CLE    25.6    51  
#> 6 1            Paper Champs   5022      Dallas Goedert TE    PHI    26.2    77.3
#> # … 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))

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              49886  7575 26031  1135 15145    NA
#>  2 4            The FANTom Menace      45927  3093 11543  2139 29152    NA
#>  3 11           Permian Panthers       44801  4025 13572  7492 19712    NA
#>  4 1            Paper Champs           41544   734 21364  3048 16398    NA
#>  5 8            Hocka Flocka           39536  1897 22538  2717 12384    NA
#>  6 12           jaydk                  38510  2263 18955  4144 13148    NA
#>  7 5            Barbarians             37144  5894 21055  5083  5112    NA
#>  8 6            sox05syd               32176  3704  5138  8377 14957    NA
#>  9 9            ZPMiller97             27036  2722 12705  2518  9091    NA
#> 10 7            Flipadelphia05         23118  1953  9386   245 11534    NA
#> 11 2            KingGabe               21133   115  6515    21 14482    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              0.12  0.22  0.154 0.03  0.086    NA
#>  2 4            The FANTom Menace      0.11  0.09  0.068 0.056 0.166    NA
#>  3 11           Permian Panthers       0.107 0.117 0.08  0.196 0.112    NA
#>  4 1            Paper Champs           0.1   0.021 0.126 0.08  0.093    NA
#>  5 8            Hocka Flocka           0.095 0.055 0.133 0.071 0.071    NA
#>  6 12           jaydk                  0.092 0.066 0.112 0.108 0.075    NA
#>  7 5            Barbarians             0.089 0.172 0.125 0.133 0.029    NA
#>  8 6            sox05syd               0.077 0.108 0.03  0.219 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 0.006 0.066    NA
#> 11 2            KingGabe               0.051 0.003 0.039 0.001 0.082    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            Paper Champs        33.2   25.2   26.2   27.2     NA        3
#>  2 10           JMLarkin            28.7   27.1   25.8   25.2      0        3
#>  3 11           Permian Panthers    24.2   22.9   31.3   25.4     NA        4
#>  4 12           jaydk               31.3   25.2   25.6   27.6     NA        5
#>  5 2            KingGabe            26.1   22.2   27.1   22.8     NA        5
#>  6 3            solarpool           25.8   25.3   26.3   27.7     NA        4
#>  7 4            The FANTom Menace   27.6   24.4   24     26.5     NA        3
#>  8 5            Barbarians          25.1   24.5   28.4   26.5     NA        2
#>  9 6            sox05syd            23.6   23.6   26.8   25.2     NA        4
#> 10 7            Flipadelphia05      33.3   24.9   27.8   26.5     NA        2
#> 11 8            Hocka Flocka        31     24.1   24.3   23.3     NA        3
#> 12 9            ZPMiller97          24.5   23.7   26.3   25.3     NA        2
#> # … 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?