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:
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 26.3
#> 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 29.5
#> 5 0001 Team Pikachu 13168 Reynolds, ~ WR LAR 25.8
#> 6 0001 Team Pikachu 13793 Valdes-Sca~ WR GBP 26.2
#> # ... 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 21.7
#> 2 0001 Team Pikachu 14835 Higgins, T~ WR CIN 21.9
#> 3 0001 Team Pikachu 14838 Shenault, ~ WR JAC 22.2
#> 4 0001 Team Pikachu 11680 Landry, Ja~ WR CLE 28
#> 5 0001 Team Pikachu 14779 Herbert, J~ QB LAC 22.8
#> 6 0001 Team Pikachu 14777 Burrow, Joe QB CIN 24
#> # ... 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 0004 Team Ice Climbers 43313 1174 20498 3358 18283
#> 2 0006 Team King Dedede 42743 6344 5427 2370 28602
#> 3 0009 Team Link 41098 3005 10530 4937 22626
#> 4 0010 Team Yoshi 39274 3573 9614 7262 18825
#> 5 0007 Team Kirby 36661 4444 14023 1819 16375
#> 6 0003 Team Captain Falcon 35255 2496 8358 6323 18078
#> 7 0011 Team Diddy Kong 31658 1712 13790 3069 13087
#> 8 0005 Team Dr. Mario 29585 225 4070 4122 21168
#> 9 0002 Team Simon Belmont 28968 503 9537 145 18783
#> 10 0014 Team Luigi 27989 3617 4297 1740 18335
#> 11 0012 Team Mewtwo 27782 1235 16740 1879 7928
#> 12 0001 Team Pikachu 24350 3415 9832 1791 9312
#> 13 0008 Team Fox 19736 6196 7376 471 5693
#> 14 0013 Team Ness 19158 1446 12183 2488 3041
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 0.097 0.03 0.14 0.08 0.083
#> 2 0006 Team King Dedede 0.096 0.161 0.037 0.057 0.13
#> 3 0009 Team Link 0.092 0.076 0.072 0.118 0.103
#> 4 0010 Team Yoshi 0.088 0.091 0.066 0.174 0.086
#> 5 0007 Team Kirby 0.082 0.113 0.096 0.044 0.074
#> 6 0003 Team Captain Falcon 0.079 0.063 0.057 0.151 0.082
#> 7 0011 Team Diddy Kong 0.071 0.043 0.094 0.073 0.059
#> 8 0005 Team Dr. Mario 0.066 0.006 0.028 0.099 0.096
#> 9 0002 Team Simon Belmont 0.065 0.013 0.065 0.003 0.085
#> 10 0014 Team Luigi 0.063 0.092 0.029 0.042 0.083
#> 11 0012 Team Mewtwo 0.062 0.031 0.114 0.045 0.036
#> 12 0001 Team Pikachu 0.054 0.087 0.067 0.043 0.042
#> 13 0008 Team Fox 0.044 0.157 0.05 0.011 0.026
#> 14 0013 Team Ness 0.043 0.037 0.083 0.06 0.014
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))
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 22.1 26.0 23.8 3 6
#> 2 0002 Team Simon Be~ 30.7 24.6 24.4 24.1 8 11
#> 3 0003 Team Captain ~ 24.7 23.6 30.4 26.6 5 8
#> 4 0004 Team Ice Clim~ 29.3 25.0 25.9 27.1 5 9
#> 5 0005 Team Dr. Mario 30.0 23.0 24.5 24.4 2 7
#> 6 0006 Team King Ded~ 25.2 25.5 26.0 24.5 3 10
#> 7 0007 Team Kirby 24.2 24.6 28.2 27.2 4 10
#> 8 0008 Team Fox 25.6 26.4 32.1 27.8 4 11
#> 9 0009 Team Link 26.2 25.9 27.0 27.8 2 11
#> 10 0010 Team Yoshi 28.5 21.9 27.4 24.8 2 6
#> 11 0011 Team Diddy Ko~ 30.9 26.1 24.7 23.7 4 11
#> 12 0012 Team Mewtwo 30.0 23.7 24.5 23.8 5 7
#> 13 0013 Team Ness 31.5 23.4 24.0 26.0 6 11
#> 14 0014 Team Luigi 32.2 24.7 28.2 26.6 3 12
#> # ... 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?