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)
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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 × 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 31.6
#> 2 1 Fake News 1339 Zach Ertz TE PHI 30.6
#> 3 1 Fake News 1426 DeAndre Hopkins WR ARI 29
#> 4 1 Fake News 1825 Jarvis Landry WR CLE 28.5
#> 5 1 Fake News 2025 Albert Wilson WR MIA 28.9
#> 6 1 Fake News 2197 Brandin Cooks WR HOU 27.7Cool! 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 24.3 3.4
#> 2 1 Fake News 4199 Aaron Jones RB GB 26.5 21
#> 3 1 Fake News 1426 DeAndre Hopki… WR ARI 29 21.1
#> 4 1 Fake News 4037 Chris Godwin WR TB 25.3 33.7
#> 5 1 Fake News 4098 Kareem Hunt RB CLE 25.9 63.7
#> 6 1 Fake News 5022 Dallas Goedert TE PHI 25.9 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 2So 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 2Armed 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.
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 28.7 25.3 25.5 27.4 NA 3
#> 2 10 JMLarkin 28.4 27.3 25.9 25.1 30.1 3
#> 3 11 Permian Panthers 24 23 31.6 25.5 NA 4
#> 4 12 jaydk 32.1 25.6 26.1 27.8 NA 4
#> 5 2 KingGabe 28.8 22.5 27.2 22.3 NA 5
#> 6 3 solarpool 25.3 25.5 26.6 27.6 NA 5
#> 7 4 The FANTom Menace 28.8 23.5 23.8 27 NA 5
#> 8 5 Barbarians 25.1 24.5 28.7 26.2 NA 3
#> 9 6 sox05syd 23.8 24 27.3 25.3 NA 3
#> 10 7 Flipadelphia05 32.9 25.7 27.4 26.7 NA 2
#> 11 8 Hocka Flocka 31.4 24.1 23.8 23.5 NA 3
#> 12 9 ZPMiller97 24.4 24 26.6 25 NA 3
#> # … with 4 more variables: count_RB <int>, count_TE <int>, count_WR <int>,
#> # count_FB <int>