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[,13] [1 × 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, Evan           TE    NYG    26.6
#> 2 0001         Team Pikachu   11680     Landry, Jarvis         WR    CLE    28.4
#> 3 0001         Team Pikachu   13645     Smith, Tre'Quan        WR    NOS    25.3
#> 4 0001         Team Pikachu   12110     Brate, Cameron         TE    TBB    29.8
#> 5 0001         Team Pikachu   13168     Reynolds, Josh         WR    LAR    26.2
#> 6 0001         Team Pikachu   13793     Valdes-Scantling, Mar… WR    GBP    26.5
#> # … 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")
#> No encoding supplied: defaulting to UTF-8.

# 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)
#> No encoding supplied: defaulting to UTF-8.

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-Helaire, Clyde RB    KCC    22  
#> 2 0001         Team Pikachu   14835     Higgins, Tee           WR    CIN    22.2
#> 3 0001         Team Pikachu   14779     Herbert, Justin        QB    LAC    23.1
#> 4 0001         Team Pikachu   14777     Burrow, Joe            QB    CIN    24.3
#> 5 0001         Team Pikachu   14838     Shenault, Laviska      WR    JAC    22.5
#> 6 0001         Team Pikachu   11680     Landry, Jarvis         WR    CLE    28.4
#> # … 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 8
#>    franchise_id franchise_name      team_value    QB    RB    TE    WR  `NA`
#>    <chr>        <chr>                    <int> <int> <int> <int> <int> <int>
#>  1 0010         Team Yoshi               43137  4230 16375  8288 14244    NA
#>  2 0009         Team Link                39922  3344 11766  6445 18367    NA
#>  3 0004         Team Ice Climbers        38018   132 19446  3414 15026    NA
#>  4 0006         Team King Dedede         36417  6066  3894  1436 25021    NA
#>  5 0003         Team Captain Falcon      34697  1843  7569  7425 17860    NA
#>  6 0007         Team Kirby               30241  3843 16002   474  9922    NA
#>  7 0005         Team Dr. Mario           28969    36  7290  3304 18339     0
#>  8 0011         Team Diddy Kong          28452   818 13559  2514 11561    NA
#>  9 0002         Team Simon Belmont       27776    12 12296    14 15454    NA
#> 10 0012         Team Mewtwo              25081   460 18246  1338  5037    NA
#> 11 0013         Team Ness                21949  1108 16966  1958  1917     0
#> 12 0014         Team Luigi               21267  1967  4430  1013 13857    NA
#> 13 0001         Team Pikachu             17637  3450  6079   884  7224    NA
#> 14 0008         Team Fox                 14357  4739  5226    39  4353    NA

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 8
#>    franchise_id franchise_name      team_value    QB    RB    TE    WR  `NA`
#>    <chr>        <chr>                    <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#>  1 0010         Team Yoshi               0.106 0.132 0.103 0.215 0.08     NA
#>  2 0009         Team Link                0.098 0.104 0.074 0.167 0.103    NA
#>  3 0004         Team Ice Climbers        0.093 0.004 0.122 0.089 0.084    NA
#>  4 0006         Team King Dedede         0.089 0.189 0.024 0.037 0.14     NA
#>  5 0003         Team Captain Falcon      0.085 0.058 0.048 0.193 0.1      NA
#>  6 0007         Team Kirby               0.074 0.12  0.101 0.012 0.056    NA
#>  7 0005         Team Dr. Mario           0.071 0.001 0.046 0.086 0.103     0
#>  8 0011         Team Diddy Kong          0.07  0.026 0.085 0.065 0.065    NA
#>  9 0002         Team Simon Belmont       0.068 0     0.077 0     0.087    NA
#> 10 0012         Team Mewtwo              0.061 0.014 0.115 0.035 0.028    NA
#> 11 0013         Team Ness                0.054 0.035 0.107 0.051 0.011     0
#> 12 0014         Team Luigi               0.052 0.061 0.028 0.026 0.078    NA
#> 13 0001         Team Pikachu             0.043 0.108 0.038 0.023 0.041    NA
#> 14 0008         Team Fox                 0.035 0.148 0.033 0.001 0.024    NA

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 12
#> # Groups:   franchise_id, franchise_name [14]
#>    franchise_id franchise_name      age_QB age_RB age_TE age_WR age_NA count_QB
#>    <chr>        <chr>                <dbl>  <dbl>  <dbl>  <dbl>  <dbl>    <int>
#>  1 0001         Team Pikachu          23.7  22.7    26.2   23.3     NA        3
#>  2 0002         Team Simon Belmont    22.9  24.9    24.8   24.2     NA        8
#>  3 0003         Team Captain Falcon   25.1  23.4    31.4   26.8     NA        5
#>  4 0004         Team Ice Climbers     29.3  25.3    26.8   27.2     NA        5
#>  5 0005         Team Dr. Mario        32.2   7.16   24.7   24.6      0        2
#>  6 0006         Team King Dedede      25.7  25.9    26.3   24.8     NA        3
#>  7 0007         Team Kirby            24.0  24.9    28.9   28.3     NA        4
#>  8 0008         Team Fox              26.0  26.7    33.5   28.1     NA        4
#>  9 0009         Team Link             26.2  26.2    28.2   28.1     NA        2
#> 10 0010         Team Yoshi            28.1  22.1    27.6   25.7     NA        2
#> 11 0011         Team Diddy Kong       31.8  26.6    24.1   23.6     NA        4
#> 12 0012         Team Mewtwo           31.7  24.1    24.6   24.0     NA        5
#> 13 0013         Team Ness             32.2  23.5    23.5   26.0      0        6
#> 14 0014         Team Luigi            32.5  24.7    23.4   26.8     NA        3
#> # … with 4 more variables: count_RB <int>, count_TE <int>, count_WR <int>,
#> #   count_NA <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?