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)
#> Using request.R from "ffscrapr"
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 28.2
#> 3 0001 Team Pikachu 13645 Smith, Tre… WR NOS 25.1
#> 4 0001 Team Pikachu 12110 Brate, Cam… TE TBB 29.6
#> 5 0001 Team Pikachu 13168 Reynolds, … WR LAR 26
#> 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")
#> No encoding supplied: defaulting to UTF-8.
# 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)
#> No encoding supplied: defaulting to UTF-8.
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 24.2
#> 5 0001 Team Pikachu 11680 Landry, Ja… WR CLE 28.2
#> 6 0001 Team Pikachu 14838 Shenault, … WR JAC 22.3
#> # … 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 0010 Team Yoshi 43044 4369 15185 8374 15116
#> 2 0009 Team Link 38971 3303 12234 4948 18486
#> 3 0004 Team Ice Climbers 38468 375 19909 2769 15415
#> 4 0006 Team King Dedede 37255 6388 5096 1590 24181
#> 5 0003 Team Captain Falcon 36856 2139 9605 7912 17200
#> 6 0007 Team Kirby 33337 4542 17316 563 10916
#> 7 0011 Team Diddy Kong 29416 900 16062 2510 9944
#> 8 0005 Team Dr. Mario 28275 61 7304 3915 16995
#> 9 0012 Team Mewtwo 27646 810 20016 1670 5150
#> 10 0002 Team Simon Belmont 27529 52 12410 18 15049
#> 11 0013 Team Ness 21179 1173 16394 1785 1827
#> 12 0014 Team Luigi 20398 1465 3999 920 14014
#> 13 0001 Team Pikachu 18611 3666 6994 1214 6737
#> 14 0008 Team Fox 16558 5197 6561 89 4711So 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 0010 Team Yoshi 0.103 0.127 0.09 0.219 0.086
#> 2 0009 Team Link 0.093 0.096 0.072 0.129 0.105
#> 3 0004 Team Ice Climbers 0.092 0.011 0.118 0.072 0.088
#> 4 0006 Team King Dedede 0.089 0.185 0.03 0.042 0.138
#> 5 0003 Team Captain Falcon 0.088 0.062 0.057 0.207 0.098
#> 6 0007 Team Kirby 0.08 0.132 0.102 0.015 0.062
#> 7 0011 Team Diddy Kong 0.07 0.026 0.095 0.066 0.057
#> 8 0005 Team Dr. Mario 0.068 0.002 0.043 0.102 0.097
#> 9 0012 Team Mewtwo 0.066 0.024 0.118 0.044 0.029
#> 10 0002 Team Simon Belmont 0.066 0.002 0.073 0 0.086
#> 11 0013 Team Ness 0.051 0.034 0.097 0.047 0.01
#> 12 0014 Team Luigi 0.049 0.043 0.024 0.024 0.08
#> 13 0001 Team Pikachu 0.045 0.106 0.041 0.032 0.038
#> 14 0008 Team Fox 0.04 0.151 0.039 0.002 0.027Armed 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 23.5 22.6 26.1 23.4 3 6
#> 2 0002 Team Simon Be… 35.8 24.9 24.5 24.3 8 11
#> 3 0003 Team Captain … 24.8 23.4 31.1 26.7 5 8
#> 4 0004 Team Ice Clim… 29.7 25.1 26.2 27.1 5 9
#> 5 0005 Team Dr. Mario 29.4 22.9 24.6 24.4 2 7
#> 6 0006 Team King Ded… 25.5 25.9 26.2 24.8 3 10
#> 7 0007 Team Kirby 24.4 24.7 28.4 28.4 4 10
#> 8 0008 Team Fox 25.8 26.5 33.2 27.8 4 11
#> 9 0009 Team Link 26.5 26.1 28.1 27.9 2 11
#> 10 0010 Team Yoshi 28.3 22.0 27.4 25.6 2 6
#> 11 0011 Team Diddy Ko… 32.6 26.3 24.0 23.6 4 11
#> 12 0012 Team Mewtwo 32.0 24.2 24.5 24.1 5 7
#> 13 0013 Team Ness 32.0 23.3 23.2 26.1 6 11
#> 14 0014 Team Luigi 32.4 24.6 23.6 26.7 3 12
#> # … with 2 more variables: count_TE <int>, count_WR <int>