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 x 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 26.2
#> 2 0001 Team Pikachu 11680 Landry, Ja~ WR CLE 28
#> 3 0001 Team Pikachu 14085 Pollard, T~ RB DAL 23.5
#> 4 0001 Team Pikachu 13645 Smith, Tre~ WR NOS 24.9
#> 5 0001 Team Pikachu 12110 Brate, Cam~ TE TBB 29.4
#> 6 0001 Team Pikachu 13168 Reynolds, ~ WR LAR 25.7
#> # ... 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 23.9
#> 4 0001 Team Pikachu 11680 Landry, Ja~ WR CLE 28
#> 5 0001 Team Pikachu 14838 Shenault, ~ WR JAC 22.1
#> 6 0001 Team Pikachu 14779 Herbert, J~ QB LAC 22.7
#> # ... 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 41671 583 20414 2586 18088
#> 2 0006 Team King Dedede 38280 5972 5244 1556 25508
#> 3 0009 Team Link 38244 2696 9503 4338 21707
#> 4 0010 Team Yoshi 35176 2802 8720 6715 16939
#> 5 0007 Team Kirby 34387 4111 15331 794 14151
#> 6 0003 Team Captain Falcon 33427 1872 9091 5836 16628
#> 7 0011 Team Diddy Kong 29432 1468 13640 2362 11962
#> 8 0002 Team Simon Belmont 29264 389 10102 51 18722
#> 9 0014 Team Luigi 28548 3588 5388 1167 18405
#> 10 0005 Team Dr. Mario 26642 60 4055 3480 19047
#> 11 0012 Team Mewtwo 25765 805 16638 1644 6678
#> 12 0001 Team Pikachu 20597 2918 8584 1498 7597
#> 13 0008 Team Fox 20450 6458 7634 259 6099
#> 14 0013 Team Ness 19976 881 14487 2033 2575
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 0.099 0.017 0.137 0.075 0.089
#> 2 0006 Team King Dedede 0.091 0.173 0.035 0.045 0.125
#> 3 0009 Team Link 0.091 0.078 0.064 0.126 0.106
#> 4 0010 Team Yoshi 0.083 0.081 0.059 0.196 0.083
#> 5 0007 Team Kirby 0.082 0.119 0.103 0.023 0.069
#> 6 0003 Team Captain Falcon 0.079 0.054 0.061 0.17 0.081
#> 7 0011 Team Diddy Kong 0.07 0.042 0.092 0.069 0.059
#> 8 0002 Team Simon Belmont 0.069 0.011 0.068 0.001 0.092
#> 9 0014 Team Luigi 0.068 0.104 0.036 0.034 0.09
#> 10 0005 Team Dr. Mario 0.063 0.002 0.027 0.101 0.093
#> 11 0012 Team Mewtwo 0.061 0.023 0.112 0.048 0.033
#> 12 0001 Team Pikachu 0.049 0.084 0.058 0.044 0.037
#> 13 0008 Team Fox 0.048 0.187 0.051 0.008 0.03
#> 14 0013 Team Ness 0.047 0.025 0.097 0.059 0.013
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 23.4 22.0 26.0 23.9 3 6
#> 2 0002 Team Simon Be~ 32.6 24.7 24.4 24.1 8 11
#> 3 0003 Team Captain ~ 24.6 23.4 30.7 26.5 5 9
#> 4 0004 Team Ice Clim~ 28.1 24.9 25.9 27.5 5 9
#> 5 0005 Team Dr. Mario 29.2 23.1 24.3 24.3 2 7
#> 6 0006 Team King Ded~ 25.4 25.5 25.8 24.8 3 9
#> 7 0007 Team Kirby 24.2 24.4 29.2 27.5 4 9
#> 8 0008 Team Fox 25.5 26.4 33.5 27.8 4 10
#> 9 0009 Team Link 25.8 26.0 26.8 27.7 4 10
#> 10 0010 Team Yoshi 28.4 21.9 27.3 24.7 3 7
#> 11 0011 Team Diddy Ko~ 31.3 26.1 24.2 24.0 4 11
#> 12 0012 Team Mewtwo 28.8 24.0 24.4 24.2 5 7
#> 13 0013 Team Ness 31.4 23.3 23.1 26.0 4 10
#> 14 0014 Team Luigi 32.3 24.6 25.8 26.4 2 9
#> # ... with 2 more variables: count_TE <int>, count_WR <int>
In this vignette, I’ve used ~three functions: ff_connect, ff_league, and ff_rosters. Now that you’ve gotten this far, why not check out some of the other possibilities?