Getting Started: MFL Basics

Tan Ho

2020-11-15

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>

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    21.6
#> 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.

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))
#> `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>

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?