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 × 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    26.4
#> 2 0001         Team Pikachu   11680     Landry, Ja… WR    CLE    28.1
#> 3 0001         Team Pikachu   13645     Smith, Tre… WR    NOS    25  
#> 4 0001         Team Pikachu   12110     Brate, Cam… TE    TBB    29.5
#> 5 0001         Team Pikachu   13168     Reynolds, … WR    LAR    25.9
#> 6 0001         Team Pikachu   13793     Valdes-Sca… WR    GBP    26.3
#> # … 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    21.8
#> 2 0001         Team Pikachu   14835     Higgins, T… WR    CIN    22  
#> 3 0001         Team Pikachu   14779     Herbert, J… QB    LAC    22.9
#> 4 0001         Team Pikachu   14777     Burrow, Joe QB    CIN    24.1
#> 5 0001         Team Pikachu   11680     Landry, Ja… WR    CLE    28.1
#> 6 0001         Team Pikachu   13189     Engram, Ev… TE    NYG    26.4
#> # … 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 7
#>    franchise_id franchise_name      team_value    QB    RB    TE    WR
#>    <chr>        <chr>                    <int> <int> <int> <int> <int>
#>  1 0004         Team Ice Climbers        40939   511 20200  3707 16521
#>  2 0009         Team Link                40931  3633 11516  4571 21211
#>  3 0006         Team King Dedede         38114  5471  4651  2141 25851
#>  4 0010         Team Yoshi               36425  4813  7842  7910 15860
#>  5 0003         Team Captain Falcon      33830  3046  6803  7268 16713
#>  6 0007         Team Kirby               33327  6662 12653  1332 12680
#>  7 0011         Team Diddy Kong          29163  1161 14460  2393 11149
#>  8 0002         Team Simon Belmont       27768   121 10700    40 16907
#>  9 0012         Team Mewtwo              27401   968 17563  2242  6628
#> 10 0005         Team Dr. Mario           26622   137  4036  4349 18100
#> 11 0014         Team Luigi               22807  2091  3901  1319 15496
#> 12 0001         Team Pikachu             21370  4260  7307  1748  8055
#> 13 0008         Team Fox                 20853  6357  8178   312  6006
#> 14 0013         Team Ness                18766  1541 12818  1930  2477

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        0.098 0.013 0.142 0.09  0.085
#>  2 0009         Team Link                0.098 0.089 0.081 0.111 0.11 
#>  3 0006         Team King Dedede         0.091 0.134 0.033 0.052 0.133
#>  4 0010         Team Yoshi               0.087 0.118 0.055 0.192 0.082
#>  5 0003         Team Captain Falcon      0.081 0.075 0.048 0.176 0.086
#>  6 0007         Team Kirby               0.08  0.163 0.089 0.032 0.065
#>  7 0011         Team Diddy Kong          0.07  0.028 0.101 0.058 0.058
#>  8 0002         Team Simon Belmont       0.066 0.003 0.075 0.001 0.087
#>  9 0012         Team Mewtwo              0.066 0.024 0.123 0.054 0.034
#> 10 0005         Team Dr. Mario           0.064 0.003 0.028 0.105 0.093
#> 11 0014         Team Luigi               0.055 0.051 0.027 0.032 0.08 
#> 12 0001         Team Pikachu             0.051 0.104 0.051 0.042 0.042
#> 13 0008         Team Fox                 0.05  0.156 0.057 0.008 0.031
#> 14 0013         Team Ness                0.045 0.038 0.09  0.047 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))

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   22.4   26.3   23.8        3        6
#>  2 0002         Team Simon Be…   32.6   24.8   24.5   24.4        8       11
#>  3 0003         Team Captain …   24.9   23.4   31.0   26.6        5        8
#>  4 0004         Team Ice Clim…   29.4   25.1   26.1   27.3        5        9
#>  5 0005         Team Dr. Mario   27.3   23.0   24.5   24.3        2        7
#>  6 0006         Team King Ded…   25.3   25.7   26.2   24.6        3       10
#>  7 0007         Team Kirby       23.8   24.7   29.6   27.9        4       10
#>  8 0008         Team Fox         25.6   26.5   33.3   27.7        4       11
#>  9 0009         Team Link        26.3   26.0   27.9   27.9        2       11
#> 10 0010         Team Yoshi       29.1   22.0   27.5   25.3        2        6
#> 11 0011         Team Diddy Ko…   31.9   26.2   24.0   24.2        4       11
#> 12 0012         Team Mewtwo      32.1   23.9   24.4   24.0        5        7
#> 13 0013         Team Ness        31.8   23.4   23.3   26.7        6       11
#> 14 0014         Team Luigi       32.5   24.8   23.8   26.5        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?