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"
#> No encoding supplied: defaulting to UTF-8.
str(ssb_summary)
#> tibble[,13] [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, Evan TE NYG 26.6
#> 2 0001 Team Pikachu 11680 Landry, Jarvis WR CLE 28.4
#> 3 0001 Team Pikachu 13645 Smith, Tre'Quan WR NOS 25.3
#> 4 0001 Team Pikachu 12110 Brate, Cameron TE TBB 29.8
#> 5 0001 Team Pikachu 13168 Reynolds, Josh WR LAR 26.2
#> 6 0001 Team Pikachu 13793 Valdes-Scantling, Mar… WR GBP 26.5
#> # … 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")
#> 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-Helaire, Clyde RB KCC 22
#> 2 0001 Team Pikachu 14835 Higgins, Tee WR CIN 22.2
#> 3 0001 Team Pikachu 14779 Herbert, Justin QB LAC 23.1
#> 4 0001 Team Pikachu 14777 Burrow, Joe QB CIN 24.3
#> 5 0001 Team Pikachu 14838 Shenault, Laviska WR JAC 22.5
#> 6 0001 Team Pikachu 11680 Landry, Jarvis WR CLE 28.4
#> # … 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 8
#> franchise_id franchise_name team_value QB RB TE WR `NA`
#> <chr> <chr> <int> <int> <int> <int> <int> <int>
#> 1 0010 Team Yoshi 43137 4230 16375 8288 14244 NA
#> 2 0009 Team Link 39922 3344 11766 6445 18367 NA
#> 3 0004 Team Ice Climbers 38018 132 19446 3414 15026 NA
#> 4 0006 Team King Dedede 36417 6066 3894 1436 25021 NA
#> 5 0003 Team Captain Falcon 34697 1843 7569 7425 17860 NA
#> 6 0007 Team Kirby 30241 3843 16002 474 9922 NA
#> 7 0005 Team Dr. Mario 28969 36 7290 3304 18339 0
#> 8 0011 Team Diddy Kong 28452 818 13559 2514 11561 NA
#> 9 0002 Team Simon Belmont 27776 12 12296 14 15454 NA
#> 10 0012 Team Mewtwo 25081 460 18246 1338 5037 NA
#> 11 0013 Team Ness 21949 1108 16966 1958 1917 0
#> 12 0014 Team Luigi 21267 1967 4430 1013 13857 NA
#> 13 0001 Team Pikachu 17637 3450 6079 884 7224 NA
#> 14 0008 Team Fox 14357 4739 5226 39 4353 NASo 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 8
#> franchise_id franchise_name team_value QB RB TE WR `NA`
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 0010 Team Yoshi 0.106 0.132 0.103 0.215 0.08 NA
#> 2 0009 Team Link 0.098 0.104 0.074 0.167 0.103 NA
#> 3 0004 Team Ice Climbers 0.093 0.004 0.122 0.089 0.084 NA
#> 4 0006 Team King Dedede 0.089 0.189 0.024 0.037 0.14 NA
#> 5 0003 Team Captain Falcon 0.085 0.058 0.048 0.193 0.1 NA
#> 6 0007 Team Kirby 0.074 0.12 0.101 0.012 0.056 NA
#> 7 0005 Team Dr. Mario 0.071 0.001 0.046 0.086 0.103 0
#> 8 0011 Team Diddy Kong 0.07 0.026 0.085 0.065 0.065 NA
#> 9 0002 Team Simon Belmont 0.068 0 0.077 0 0.087 NA
#> 10 0012 Team Mewtwo 0.061 0.014 0.115 0.035 0.028 NA
#> 11 0013 Team Ness 0.054 0.035 0.107 0.051 0.011 0
#> 12 0014 Team Luigi 0.052 0.061 0.028 0.026 0.078 NA
#> 13 0001 Team Pikachu 0.043 0.108 0.038 0.023 0.041 NA
#> 14 0008 Team Fox 0.035 0.148 0.033 0.001 0.024 NAArmed 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 12
#> # Groups: franchise_id, franchise_name [14]
#> franchise_id franchise_name age_QB age_RB age_TE age_WR age_NA count_QB
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 0001 Team Pikachu 23.7 22.7 26.2 23.3 NA 3
#> 2 0002 Team Simon Belmont 22.9 24.9 24.8 24.2 NA 8
#> 3 0003 Team Captain Falcon 25.1 23.4 31.4 26.8 NA 5
#> 4 0004 Team Ice Climbers 29.3 25.3 26.8 27.2 NA 5
#> 5 0005 Team Dr. Mario 32.2 7.16 24.7 24.6 0 2
#> 6 0006 Team King Dedede 25.7 25.9 26.3 24.8 NA 3
#> 7 0007 Team Kirby 24.0 24.9 28.9 28.3 NA 4
#> 8 0008 Team Fox 26.0 26.7 33.5 28.1 NA 4
#> 9 0009 Team Link 26.2 26.2 28.2 28.1 NA 2
#> 10 0010 Team Yoshi 28.1 22.1 27.6 25.7 NA 2
#> 11 0011 Team Diddy Kong 31.8 26.6 24.1 23.6 NA 4
#> 12 0012 Team Mewtwo 31.7 24.1 24.6 24.0 NA 5
#> 13 0013 Team Ness 32.2 23.5 23.5 26.0 0 6
#> 14 0014 Team Luigi 32.5 24.7 23.4 26.8 NA 3
#> # … with 4 more variables: count_RB <int>, count_TE <int>, count_WR <int>,
#> # count_NA <int>