In this vignette, I’ll walk through how to get started with a basic dynasty value analysis on MFL.
We’ll start by loading the packages:
library(ffscrapr)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidyr)Set up the connection to the league:
ssb <- mfl_connect(season = 2020,
league_id = 54040, # from the URL of your league
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"I’ve done this with the mfl_connect() function, although you can also do this from the ff_connect() call - they are equivalent. Most if not all of the remaining functions are prefixed with “ff_”.
Cool! Let’s have a quick 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 392Okay, 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 26.4
#> 2 0001 Team Pikachu 11680 Landry, Ja… WR CLE 28.1
#> 3 0001 Team Pikachu 13645 Smith, Tre… WR NOS 25
#> 4 0001 Team Pikachu 12110 Brate, Cam… TE TBB 29.5
#> 5 0001 Team Pikachu 13168 Reynolds, … WR LAR 25.9
#> 6 0001 Team Pikachu 13793 Valdes-Sca… WR GBP 26.3
#> # … 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 21.8
#> 2 0001 Team Pikachu 14835 Higgins, T… WR CIN 22
#> 3 0001 Team Pikachu 14779 Herbert, J… QB LAC 22.9
#> 4 0001 Team Pikachu 14777 Burrow, Joe QB CIN 24.1
#> 5 0001 Team Pikachu 11680 Landry, Ja… WR CLE 28.1
#> 6 0001 Team Pikachu 13189 Engram, Ev… TE NYG 26.4
#> # … 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))
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 40939 511 20200 3707 16521
#> 2 0009 Team Link 40931 3633 11516 4571 21211
#> 3 0006 Team King Dedede 38114 5471 4651 2141 25851
#> 4 0010 Team Yoshi 36425 4813 7842 7910 15860
#> 5 0003 Team Captain Falcon 33830 3046 6803 7268 16713
#> 6 0007 Team Kirby 33327 6662 12653 1332 12680
#> 7 0011 Team Diddy Kong 29163 1161 14460 2393 11149
#> 8 0002 Team Simon Belmont 27768 121 10700 40 16907
#> 9 0012 Team Mewtwo 27401 968 17563 2242 6628
#> 10 0005 Team Dr. Mario 26622 137 4036 4349 18100
#> 11 0014 Team Luigi 22807 2091 3901 1319 15496
#> 12 0001 Team Pikachu 21370 4260 7307 1748 8055
#> 13 0008 Team Fox 20853 6357 8178 312 6006
#> 14 0013 Team Ness 18766 1541 12818 1930 2477So 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 0.013 0.142 0.09 0.085
#> 2 0009 Team Link 0.098 0.089 0.081 0.111 0.11
#> 3 0006 Team King Dedede 0.091 0.134 0.033 0.052 0.133
#> 4 0010 Team Yoshi 0.087 0.118 0.055 0.192 0.082
#> 5 0003 Team Captain Falcon 0.081 0.075 0.048 0.176 0.086
#> 6 0007 Team Kirby 0.08 0.163 0.089 0.032 0.065
#> 7 0011 Team Diddy Kong 0.07 0.028 0.101 0.058 0.058
#> 8 0002 Team Simon Belmont 0.066 0.003 0.075 0.001 0.087
#> 9 0012 Team Mewtwo 0.066 0.024 0.123 0.054 0.034
#> 10 0005 Team Dr. Mario 0.064 0.003 0.028 0.105 0.093
#> 11 0014 Team Luigi 0.055 0.051 0.027 0.032 0.08
#> 12 0001 Team Pikachu 0.051 0.104 0.051 0.042 0.042
#> 13 0008 Team Fox 0.05 0.156 0.057 0.008 0.031
#> 14 0013 Team Ness 0.045 0.038 0.09 0.047 0.013Armed 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))
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 23.4 22.4 26.3 23.8 3 6
#> 2 0002 Team Simon Be… 32.6 24.8 24.5 24.4 8 11
#> 3 0003 Team Captain … 24.9 23.4 31.0 26.6 5 8
#> 4 0004 Team Ice Clim… 29.4 25.1 26.1 27.3 5 9
#> 5 0005 Team Dr. Mario 27.3 23.0 24.5 24.3 2 7
#> 6 0006 Team King Ded… 25.3 25.7 26.2 24.6 3 10
#> 7 0007 Team Kirby 23.8 24.7 29.6 27.9 4 10
#> 8 0008 Team Fox 25.6 26.5 33.3 27.7 4 11
#> 9 0009 Team Link 26.3 26.0 27.9 27.9 2 11
#> 10 0010 Team Yoshi 29.1 22.0 27.5 25.3 2 6
#> 11 0011 Team Diddy Ko… 31.9 26.2 24.0 24.2 4 11
#> 12 0012 Team Mewtwo 32.1 23.9 24.4 24.0 5 7
#> 13 0013 Team Ness 31.8 23.4 23.3 26.7 6 11
#> 14 0014 Team Luigi 32.5 24.8 23.8 26.5 3 12
#> # … with 2 more variables: count_TE <int>, count_WR <int>