Here’s what a basic dynasty league analysis might look like on MFL!
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 × 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 28 #> $ league_depth : num 392
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 13189 Engram, Ev… TE NYG 26.1 #> 2 0001 Team Pikachu 11680 Landry, Ja… WR CLE 27.9 #> 3 0001 Team Pikachu 13290 Cohen, Tar… RB CHI 25.2 #> 4 0001 Team Pikachu 13158 Westbrook,… WR JAC 26.9 #> 5 0001 Team Pikachu 10273 Newton, Cam QB NEP 31.4 #> 6 0001 Team Pikachu 14085 Pollard, T… RB DAL 23.5 #> # … 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 21.5 #> 2 0001 Team Pikachu 11680 Landry, Ja… WR CLE 27.9 #> 3 0001 Team Pikachu 14838 Shenault, … WR JAC 22 #> 4 0001 Team Pikachu 14835 Higgins, T… WR CIN 21.7 #> 5 0001 Team Pikachu 14777 Burrow, Joe QB CIN 23.8 #> 6 0001 Team Pikachu 13189 Engram, Ev… TE NYG 26.1 #> # … 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 41412 536 19983 2033 18860 #> 2 0009 Team Link 39578 2520 11265 2940 22853 #> 3 0006 Team King Dedede 38702 6533 6177 1640 24352 #> 4 0007 Team Kirby 37656 3483 16761 1950 15462 #> 5 0010 Team Yoshi 35492 2857 7908 6684 18043 #> 6 0003 Team Captain Falcon 35212 1907 11164 6880 15261 #> 7 0014 Team Luigi 31730 2851 5797 668 22414 #> 8 0012 Team Mewtwo 30060 778 20861 1252 7169 #> 9 0002 Team Simon Belmont 29028 503 11031 135 17359 #> 10 0011 Team Diddy Kong 26588 1498 12154 1903 11033 #> 11 0005 Team Dr. Mario 23708 124 2433 3479 17672 #> 12 0008 Team Fox 23234 7758 8258 963 6255 #> 13 0013 Team Ness 20545 648 14961 2189 2747 #> 14 0001 Team Pikachu 20144 2334 9542 1533 6735
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 0.096 0.016 0.126 0.059 0.091 #> 2 0009 Team Link 0.091 0.073 0.071 0.086 0.111 #> 3 0006 Team King Dedede 0.089 0.19 0.039 0.048 0.118 #> 4 0007 Team Kirby 0.087 0.101 0.106 0.057 0.075 #> 5 0010 Team Yoshi 0.082 0.083 0.05 0.195 0.087 #> 6 0003 Team Captain Falcon 0.081 0.056 0.071 0.201 0.074 #> 7 0014 Team Luigi 0.073 0.083 0.037 0.02 0.109 #> 8 0012 Team Mewtwo 0.069 0.023 0.132 0.037 0.035 #> 9 0002 Team Simon Belmont 0.067 0.015 0.07 0.004 0.084 #> 10 0011 Team Diddy Kong 0.061 0.044 0.077 0.056 0.054 #> 11 0005 Team Dr. Mario 0.055 0.004 0.015 0.102 0.086 #> 12 0008 Team Fox 0.054 0.226 0.052 0.028 0.03 #> 13 0013 Team Ness 0.047 0.019 0.095 0.064 0.013 #> 14 0001 Team Pikachu 0.047 0.068 0.06 0.045 0.033
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 24.8 21.7 25.8 24.6 4 5 #> 2 0002 Team Simon Be… 32.5 24.6 24.3 24.2 6 12 #> 3 0003 Team Captain … 24.7 23.3 30.2 26.5 5 8 #> 4 0004 Team Ice Clim… 27.0 24.8 25.7 27.6 7 5 #> 5 0005 Team Dr. Mario 26.1 23.6 24.1 24.5 2 9 #> 6 0006 Team King Ded… 25.2 25.5 26.2 24.7 3 8 #> 7 0007 Team Kirby 24.2 24.4 29.4 27.3 4 10 #> 8 0008 Team Fox 25.0 26.4 27.3 27.1 2 10 #> 9 0009 Team Link 25.9 26.0 27.1 27.6 3 10 #> 10 0010 Team Yoshi 27.7 21.6 27.1 24.5 3 7 #> 11 0011 Team Diddy Ko… 31.4 25.8 23.6 25.0 3 11 #> 12 0012 Team Mewtwo 28.9 23.9 24.3 23.4 4 8 #> 13 0013 Team Ness 30.7 23.1 23.1 25.6 3 12 #> 14 0014 Team Luigi 32.3 24.5 24.8 26.0 2 8 #> # … with 2 more variables: count_TE <int>, count_WR <int>