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 26.2
#> 2 0001 Team Pikachu 11680 Landry, Ja… WR CLE 28
#> 3 0001 Team Pikachu 13645 Smith, Tre… WR NOS 24.9
#> 4 0001 Team Pikachu 12110 Brate, Cam… TE TBB 29.4
#> 5 0001 Team Pikachu 13168 Reynolds, … WR LAR 25.8
#> 6 0001 Team Pikachu 13793 Valdes-Sca… WR GBP 26.1
#> # … 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 21.6
#> 2 0001 Team Pikachu 14835 Higgins, T… WR CIN 21.8
#> 3 0001 Team Pikachu 14777 Burrow, Joe QB CIN 24
#> 4 0001 Team Pikachu 11680 Landry, Ja… WR CLE 28
#> 5 0001 Team Pikachu 13189 Engram, Ev… TE NYG 26.2
#> 6 0001 Team Pikachu 14838 Shenault, … WR JAC 22.1
#> # … 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))
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 41148 2595 13996 7671 16886
#> 2 0004 Team Ice Climbers 39753 599 20313 3199 15642
#> 3 0009 Team Link 39661 2761 12203 4037 20660
#> 4 0006 Team King Dedede 38290 5813 6801 2144 23532
#> 5 0007 Team Kirby 35213 3787 17214 1502 12710
#> 6 0003 Team Captain Falcon 34524 2290 10666 5967 15601
#> 7 0005 Team Dr. Mario 29461 53 6598 3703 19107
#> 8 0002 Team Simon Belmont 29330 476 11446 71 17337
#> 9 0011 Team Diddy Kong 26820 1280 12478 2654 10408
#> 10 0014 Team Luigi 26250 3069 5676 979 16526
#> 11 0012 Team Mewtwo 24862 1121 16546 1443 5752
#> 12 0013 Team Ness 23539 1040 17686 2790 2023
#> 13 0001 Team Pikachu 21103 2287 8930 1678 8208
#> 14 0008 Team Fox 18604 5352 8273 201 4778So 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 0.096 0.08 0.083 0.202 0.089
#> 2 0004 Team Ice Climbers 0.093 0.018 0.12 0.084 0.083
#> 3 0009 Team Link 0.093 0.085 0.072 0.106 0.109
#> 4 0006 Team King Dedede 0.089 0.179 0.04 0.056 0.124
#> 5 0007 Team Kirby 0.082 0.116 0.102 0.039 0.067
#> 6 0003 Team Captain Falcon 0.081 0.07 0.063 0.157 0.082
#> 7 0005 Team Dr. Mario 0.069 0.002 0.039 0.097 0.101
#> 8 0002 Team Simon Belmont 0.068 0.015 0.068 0.002 0.092
#> 9 0011 Team Diddy Kong 0.063 0.039 0.074 0.07 0.055
#> 10 0014 Team Luigi 0.061 0.094 0.034 0.026 0.087
#> 11 0012 Team Mewtwo 0.058 0.034 0.098 0.038 0.03
#> 12 0013 Team Ness 0.055 0.032 0.105 0.073 0.011
#> 13 0001 Team Pikachu 0.049 0.07 0.053 0.044 0.043
#> 14 0008 Team Fox 0.043 0.165 0.049 0.005 0.025Armed 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 23.6 22.0 25.8 23.4 3 6
#> 2 0002 Team Simon Be… 30.6 24.8 24.4 24.2 8 11
#> 3 0003 Team Captain … 24.6 23.3 30.7 26.6 5 8
#> 4 0004 Team Ice Clim… 28.6 25.0 25.8 27.5 5 9
#> 5 0005 Team Dr. Mario 26.3 22.8 24.3 24.3 2 7
#> 6 0006 Team King Ded… 25.3 25.5 25.9 24.7 3 10
#> 7 0007 Team Kirby 24.0 24.5 28.7 27.5 4 10
#> 8 0008 Team Fox 25.7 26.3 32.6 27.7 4 11
#> 9 0009 Team Link 26.0 26.0 27.0 27.7 2 11
#> 10 0010 Team Yoshi 28.8 21.9 27.2 24.7 2 6
#> 11 0011 Team Diddy Ko… 31.9 25.8 24.0 23.4 4 11
#> 12 0012 Team Mewtwo 30.3 24.2 24.4 23.9 5 7
#> 13 0013 Team Ness 31.4 23.2 23.5 26.0 6 11
#> 14 0014 Team Luigi 32.2 24.5 25.4 26.4 3 12
#> # … with 2 more variables: count_TE <int>, count_WR <int>