Here’s what a basic dynasty league analysis might look like on MFL!
suppressPackageStartupMessages({ library(ffscrapr) library(dplyr) library(tidyr) })
Set up the connection to the league:
ssb <- mfl_connect(season = 2020, league_id = 54040, rate_limit_number = 3, rate_limit_seconds = 6) ssb #> <MFL connection 2020_54040> #> List of 5 #> $ platform : chr "MFL" #> $ season : num 2020 #> $ league_id : chr "54040" #> $ APIKEY : NULL #> $ auth_cookie: NULL #> - attr(*, "class")= chr "mfl_conn"
Cool! Let’s have a deeper look at what this league is like.
ssb_summary <- ff_league(ssb) str(ssb_summary) #> tibble [1 x 13] (S3: tbl_df/tbl/data.frame) #> $ league_id : chr "54040" #> $ league_name : chr "The Super Smash Bros Dynasty League" #> $ franchise_count: num 14 #> $ qb_type : chr "1QB" #> $ idp : logi FALSE #> $ scoring_flags : chr "0.5_ppr, TEPrem, PP1D" #> $ best_ball : logi TRUE #> $ salary_cap : logi FALSE #> $ player_copies : num 1 #> $ years_active : chr "2018-2020" #> $ qb_count : chr "1" #> $ roster_size : num 35 #> $ league_depth : num 490
Okay, so it’s the Smash Bros Dynasty League, it’s a 1QB league with 14 teams, best ball scoring, half ppr and point-per-first-down settings.
Let’s grab the rosters now.
ssb_rosters <- ff_rosters(ssb) head(ssb_rosters) #> # A tibble: 6 x 11 #> franchise_id franchise_name player_id player_name pos team age #> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> #> 1 0001 Team Pikachu 13129 Fournette,~ RB JAC 25.6 #> 2 0001 Team Pikachu 13189 Engram, Ev~ TE NYG 26 #> 3 0001 Team Pikachu 11680 Landry, Ja~ WR CLE 27.7 #> 4 0001 Team Pikachu 13290 Cohen, Tar~ RB CHI 25.1 #> 5 0001 Team Pikachu 13158 Westbrook,~ WR JAC 26.8 #> 6 0001 Team Pikachu 10273 Newton, Cam QB NEP 31.3 #> # ... with 4 more variables: roster_status <chr>, drafted <chr>, #> # draft_year <chr>, draft_round <chr>
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(mfl_id,fantasypros_id) player_values <- player_values %>% left_join(player_ids, by = c("fp_id" = "fantasypros_id")) %>% select(mfl_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 ssb_values <- ssb_rosters %>% left_join(player_values, by = c("player_id"="mfl_id")) %>% arrange(franchise_id,desc(value_1qb)) head(ssb_values) #> # A tibble: 6 x 14 #> franchise_id franchise_name player_id player_name pos team age #> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> #> 1 0001 Team Pikachu 14803 Edwards-He~ RB KCC 21.4 #> 2 0001 Team Pikachu 13129 Fournette,~ RB JAC 25.6 #> 3 0001 Team Pikachu 11680 Landry, Ja~ WR CLE 27.7 #> 4 0001 Team Pikachu 13189 Engram, Ev~ TE NYG 26 #> 5 0001 Team Pikachu 14777 Burrow, Joe QB CIN 23.7 #> 6 0001 Team Pikachu 14838 Shenault, ~ WR JAC 21.9 #> # ... with 7 more variables: roster_status <chr>, drafted <chr>, #> # draft_year <chr>, draft_round <chr>, ecr_1qb <dbl>, ecr_pos <dbl>, #> # value_1qb <int>
Let’s do some team summaries now!
value_summary <- ssb_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: 14 x 7 #> franchise_id franchise_name team_value QB RB TE WR #> <chr> <chr> <int> <int> <int> <int> <int> #> 1 0004 Team Ice Climbers 41539 514 19742 2048 19235 #> 2 0009 Team Link 39052 2834 11141 2217 22860 #> 3 0006 Team King Dedede 36504 5941 7635 1781 21147 #> 4 0007 Team Kirby 35036 3302 23817 2660 5257 #> 5 0003 Team Captain Falcon 34320 2327 10258 6394 15341 #> 6 0010 Team Yoshi 33997 1666 7952 6599 17780 #> 7 0014 Team Luigi 33383 2082 342 795 30164 #> 8 0012 Team Mewtwo 28963 1070 17832 1384 8677 #> 9 0011 Team Diddy Kong 27987 1906 13442 1702 10937 #> 10 0002 Team Simon Belmont 27004 415 9816 90 16683 #> 11 0008 Team Fox 24055 7283 10027 625 6120 #> 12 0005 Team Dr. Mario 23073 8 1763 3804 17498 #> 13 0013 Team Ness 20714 449 16255 1508 2502 #> 14 0001 Team Pikachu 19728 1391 11575 2275 4487
So with that, we’ve got a team summary of values! I like applying some context, so let’s turn these into percentages.
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: 14 x 7 #> franchise_id franchise_name team_value QB RB TE WR #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 0004 Team Ice Climbers 0.098 0.016 0.122 0.06 0.097 #> 2 0009 Team Link 0.092 0.091 0.069 0.065 0.115 #> 3 0006 Team King Dedede 0.086 0.19 0.047 0.053 0.106 #> 4 0007 Team Kirby 0.082 0.106 0.147 0.079 0.026 #> 5 0003 Team Captain Falcon 0.081 0.075 0.063 0.189 0.077 #> 6 0010 Team Yoshi 0.08 0.053 0.049 0.195 0.089 #> 7 0014 Team Luigi 0.078 0.067 0.002 0.023 0.152 #> 8 0012 Team Mewtwo 0.068 0.034 0.11 0.041 0.044 #> 9 0011 Team Diddy Kong 0.066 0.061 0.083 0.05 0.055 #> 10 0002 Team Simon Belmont 0.063 0.013 0.061 0.003 0.084 #> 11 0008 Team Fox 0.057 0.234 0.062 0.018 0.031 #> 12 0005 Team Dr. Mario 0.054 0 0.011 0.112 0.088 #> 13 0013 Team Ness 0.049 0.014 0.101 0.045 0.013 #> 14 0001 Team Pikachu 0.046 0.045 0.072 0.067 0.023
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.
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!
age_summary <- ssb_values %>% group_by(franchise_id,pos) %>% mutate(position_value = sum(value_1qb,na.rm=TRUE)) %>% ungroup() %>% mutate(weighted_age = age*value_1qb/position_value) %>% 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: 14 x 10 #> # Groups: franchise_id, franchise_name [14] #> franchise_id franchise_name age_QB age_RB age_TE age_WR count_QB count_RB #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int> <int> #> 1 0001 Team Pikachu 24.1 23.0 25.9 26.3 6 9 #> 2 0002 Team Simon Be~ 32.2 24.4 24.0 24.2 7 10 #> 3 0003 Team Captain ~ 24.7 23.7 30.2 26.4 5 9 #> 4 0004 Team Ice Clim~ 27.7 24.6 26.0 27.9 5 7 #> 5 0005 Team Dr. Mario 37.8 23.6 24 24.4 3 9 #> 6 0006 Team King Ded~ 24.8 25.5 25.7 24.8 3 11 #> 7 0007 Team Kirby 23.9 24.2 29.5 25.9 4 10 #> 8 0008 Team Fox 24.8 26.2 28.4 26.8 3 11 #> 9 0009 Team Link 25.8 25.8 27.2 27.5 2 9 #> 10 0010 Team Yoshi 28.7 21.5 26.9 24.2 4 4 #> 11 0011 Team Diddy Ko~ 30.8 25.5 23.6 25.5 3 14 #> 12 0012 Team Mewtwo 29.3 24.0 24.4 23.5 4 7 #> 13 0013 Team Ness 30.8 23.1 23.0 25.8 6 12 #> 14 0014 Team Luigi 31.9 28.0 24.7 26.5 3 9 #> # ... with 2 more variables: count_TE <int>, count_WR <int>