MFL: Basics

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)
  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    26.3
#> 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>

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 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.

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 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>

Next steps

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?