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 [1 × 17] (S3: tbl_df/tbl/data.frame)
#> $ league_id : chr "54040"
#> $ league_name : chr "The Super Smash Bros Dynasty League"
#> $ season : int 2020
#> $ league_type : chr NA
#> $ franchise_count : num 14
#> $ qb_type : chr "1QB"
#> $ idp : logi FALSE
#> $ scoring_flags : chr "0.5_ppr, TEPrem, PP1D"
#> $ best_ball : logi FALSE
#> $ salary_cap : logi FALSE
#> $ player_copies : num 1
#> $ years_active : chr "2018-2021"
#> $ qb_count : chr "1"
#> $ roster_size : num 33
#> $ league_depth : num 462
#> $ draft_type : chr " email draft"
#> $ draft_player_pool: chr "Both"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 × 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 27.8
#> 2 0001 Team Pikachu 11680 Landry, Jarvis WR CLE 29.6
#> 3 0001 Team Pikachu 13645 Smith, Tre'Quan WR NOS 26.5
#> 4 0001 Team Pikachu 12110 Brate, Cameron TE TBB 31
#> 5 0001 Team Pikachu 13168 Reynolds, Josh WR LAR 27.3
#> 6 0001 Team Pikachu 13793 Valdes-Scantling, Mar… WR GBP 27.7
#> # … with 4 more variables: roster_status <chr>, drafted <chr>,
#> # draft_year <chr>, draft_round <chr>Values
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 × 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 23.2
#> 2 0001 Team Pikachu 14835 Higgins, Tee WR CIN 23.4
#> 3 0001 Team Pikachu 14779 Herbert, Justin QB LAC 24.3
#> 4 0001 Team Pikachu 14777 Burrow, Joe QB CIN 25.5
#> 5 0001 Team Pikachu 14838 Shenault, Laviska WR JAC 23.7
#> 6 0001 Team Pikachu 11680 Landry, Jarvis WR CLE 29.6
#> # … 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 × 7
#> franchise_id franchise_name team_value QB RB TE WR
#> <chr> <chr> <int> <int> <int> <int> <int>
#> 1 0010 Team Yoshi 41170 4753 14710 7284 14423
#> 2 0006 Team King Dedede 35184 6458 2513 597 25616
#> 3 0004 Team Ice Climbers 35091 115 19362 2470 13144
#> 4 0009 Team Link 33078 1168 10578 5188 16144
#> 5 0003 Team Donkey Kong 30043 1299 6034 7220 15490
#> 6 0007 Team Kirby 27880 4890 14108 182 8700
#> 7 0005 Team Dr. Mario 27659 17 7137 2586 17919
#> 8 0011 Team Diddy Kong 26143 564 12406 2583 10590
#> 9 0002 Team Simon Belmont 25905 40 11318 12 14535
#> 10 0012 Team Mewtwo 24317 618 17670 1340 4689
#> 11 0013 Team Ness 20004 803 15980 1744 1477
#> 12 0014 Team Luigi 19761 1738 5828 1068 11127
#> 13 0001 Team Pikachu 17651 4323 6293 833 6202
#> 14 0008 Team Bowser 13150 5673 4069 25 3383So 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 × 7
#> franchise_id franchise_name team_value QB RB TE WR
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0010 Team Yoshi 0.109 0.146 0.099 0.22 0.088
#> 2 0006 Team King Dedede 0.093 0.199 0.017 0.018 0.157
#> 3 0004 Team Ice Climbers 0.093 0.004 0.131 0.075 0.08
#> 4 0009 Team Link 0.088 0.036 0.071 0.157 0.099
#> 5 0003 Team Donkey Kong 0.08 0.04 0.041 0.218 0.095
#> 6 0007 Team Kirby 0.074 0.151 0.095 0.005 0.053
#> 7 0005 Team Dr. Mario 0.073 0.001 0.048 0.078 0.11
#> 8 0011 Team Diddy Kong 0.069 0.017 0.084 0.078 0.065
#> 9 0002 Team Simon Belmont 0.069 0.001 0.076 0 0.089
#> 10 0012 Team Mewtwo 0.064 0.019 0.119 0.04 0.029
#> 11 0013 Team Ness 0.053 0.025 0.108 0.053 0.009
#> 12 0014 Team Luigi 0.052 0.054 0.039 0.032 0.068
#> 13 0001 Team Pikachu 0.047 0.133 0.043 0.025 0.038
#> 14 0008 Team Bowser 0.035 0.175 0.027 0.001 0.021Armed 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.
Age
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 × 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 24.8 23.7 27.1 24.3 4 8
#> 2 0002 Team Simon Belmont 25.8 26.4 25.7 25.4 8 11
#> 3 0003 Team Donkey Kong 25.9 24.7 32.6 28.1 5 8
#> 4 0004 Team Ice Climbers 29.6 26.4 27.6 28.0 5 9
#> 5 0005 Team Dr. Mario 36.8 24.2 25.9 25.7 2 7
#> 6 0006 Team King Dedede 26.8 26.8 27.5 26.1 3 10
#> 7 0007 Team Kirby 25.5 26.2 29.8 29.7 4 9
#> 8 0008 Team Bowser 27.2 27.8 34.1 29.3 4 11
#> 9 0009 Team Link 28.3 27.2 29.5 29.3 3 11
#> 10 0010 Team Yoshi 28.8 23.3 28.8 27.0 2 6
#> 11 0011 Team Diddy Kong 32.5 27.9 25.1 24.5 4 12
#> 12 0012 Team Mewtwo 34.1 25.0 25.5 24.9 5 7
#> 13 0013 Team Ness 33.8 24.7 24.6 26.3 5 9
#> 14 0014 Team Luigi 33.6 26.3 24.7 28.1 3 10
#> # … with 2 more variables: count_TE <int>, count_WR <int>