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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

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"

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 drafted
#>   <chr>        <chr>          <chr>     <chr>          <chr> <chr> <dbl> <chr>  
#> 1 0001         Team Pikachu   13189     Engram, Evan   TE    NYG    29.7 3.04   
#> 2 0001         Team Pikachu   11680     Landry, Jarvis WR    CLE    31.5 4.02   
#> 3 0001         Team Pikachu   13645     Smith, Tre'Qu… WR    NOS    28.4 18.02  
#> 4 0001         Team Pikachu   12110     Brate, Cameron TE    TBB    32.9 19.04  
#> 5 0001         Team Pikachu   13168     Reynolds, Josh WR    LAR    29.3 20.02  
#> 6 0001         Team Pikachu   13793     Valdes-Scantl… WR    GBP    29.6 21.04  
#> # ℹ 3 more variables: roster_status <chr>, draft_year <int>, 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 drafted
#>   <chr>        <chr>          <chr>     <chr>          <chr> <chr> <dbl> <chr>  
#> 1 0001         Team Pikachu   14835     Higgins, Tee   WR    CIN    25.3 Trade  
#> 2 0001         Team Pikachu   14777     Burrow, Joe    QB    CIN    27.5 1.14   
#> 3 0001         Team Pikachu   14779     Herbert, Just… QB    LAC    26.2 2.11   
#> 4 0001         Team Pikachu   14085     Pollard, Tony  RB    DAL    27.1 13.04  
#> 5 0001         Team Pikachu   13189     Engram, Evan   TE    NYG    29.7 3.04   
#> 6 0001         Team Pikachu   13139     Williams, Jam… RB    GBP    29.2 15.04  
#> # ℹ 6 more variables: roster_status <chr>, draft_year <int>, 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 0006         Team King Dedede        29320  6382   679    82 22177
#>  2 0010         Team Yoshi              23903  5386  3962  1622 12933
#>  3 0011         Team Diddy Kong         20264     7  5300  5329  9628
#>  4 0004         Team Ice Climbers       20084   107 11355  1067  7555
#>  5 0003         Team Donkey Kong        17092  2410  4691  4034  5957
#>  6 0005         Team Dr. Mario          16299     2   265  4702 11330
#>  7 0001         Team Pikachu            16290  7460  3334   690  4806
#>  8 0002         Team Simon Belmont      11238   394  3923     5  6916
#>  9 0012         Team Mewtwo             10605    98  6884  1302  2321
#> 10 0009         Team Link                9984   694   875   806  7609
#> 11 0007         Team Kirby               9772  6973  1660    70  1069
#> 12 0013         Team Ness                8900    12  4546    45  4297
#> 13 0008         Team Bowser              3981  3842    12     2   125
#> 14 0014         Team Luigi               3183   296    56    90  2741

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 × 7
#>    franchise_id franchise_name     team_value    QB    RB    TE    WR
#>    <chr>        <chr>                   <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 0006         Team King Dedede        0.146 0.187 0.014 0.004 0.223
#>  2 0010         Team Yoshi              0.119 0.158 0.083 0.082 0.13 
#>  3 0011         Team Diddy Kong         0.101 0     0.111 0.269 0.097
#>  4 0004         Team Ice Climbers       0.1   0.003 0.239 0.054 0.076
#>  5 0003         Team Donkey Kong        0.085 0.071 0.099 0.203 0.06 
#>  6 0005         Team Dr. Mario          0.081 0     0.006 0.237 0.114
#>  7 0001         Team Pikachu            0.081 0.219 0.07  0.035 0.048
#>  8 0002         Team Simon Belmont      0.056 0.012 0.083 0     0.07 
#>  9 0012         Team Mewtwo             0.053 0.003 0.145 0.066 0.023
#> 10 0009         Team Link               0.05  0.02  0.018 0.041 0.077
#> 11 0007         Team Kirby              0.049 0.205 0.035 0.004 0.011
#> 12 0013         Team Ness               0.044 0     0.096 0.002 0.043
#> 13 0008         Team Bowser             0.02  0.113 0     0     0.001
#> 14 0014         Team Luigi              0.016 0.009 0.001 0.005 0.028

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 × 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         26.9   27.1   29.7   25.4        4        8
#>  2 0002         Team Simon Belmont   25.7   27.7   27.6   27.0        8       11
#>  3 0003         Team Donkey Kong     27.1   26.3   34.5   29.5        4        8
#>  4 0004         Team Ice Climbers    33.0   28.2   29.4   26.8        5        9
#>  5 0005         Team Dr. Mario       29.8   25.9   28.7   27.2        2        7
#>  6 0006         Team King Dedede     28.7   29.1   29.5   27.5        3       10
#>  7 0007         Team Kirby           26.0   28.2   29.0   32.6        4        9
#>  8 0008         Team Bowser          27.4   28.8   37.1   29.0        3        9
#>  9 0009         Team Link            31.1   31.9   29.2   30.8        3       11
#> 10 0010         Team Yoshi           28.2   25.4   30.7   28.9        2        6
#> 11 0011         Team Diddy Kong      34.7   29.3   26.9   25.1        4       12
#> 12 0012         Team Mewtwo          36.0   25.5   26.2   26.5        4        7
#> 13 0013         Team Ness            32.8   27.2   27.1   26.6        5        9
#> 14 0014         Team Luigi           35.5   28.6   28.7   30.8        3        7
#> # ℹ 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?