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"

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    28.2
#> 3 0001         Team Pikachu   13645     Smith, Tre… WR    NOS    25.1
#> 4 0001         Team Pikachu   12110     Brate, Cam… TE    TBB    29.6
#> 5 0001         Team Pikachu   13168     Reynolds, … WR    LAR    26  
#> 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")
#> 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-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    24.2
#> 5 0001         Team Pikachu   11680     Landry, Ja… WR    CLE    28.2
#> 6 0001         Team Pikachu   14838     Shenault, … WR    JAC    22.3
#> # … 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 0010         Team Yoshi               43044  4369 15185  8374 15116
#>  2 0009         Team Link                38971  3303 12234  4948 18486
#>  3 0004         Team Ice Climbers        38468   375 19909  2769 15415
#>  4 0006         Team King Dedede         37255  6388  5096  1590 24181
#>  5 0003         Team Captain Falcon      36856  2139  9605  7912 17200
#>  6 0007         Team Kirby               33337  4542 17316   563 10916
#>  7 0011         Team Diddy Kong          29416   900 16062  2510  9944
#>  8 0005         Team Dr. Mario           28275    61  7304  3915 16995
#>  9 0012         Team Mewtwo              27646   810 20016  1670  5150
#> 10 0002         Team Simon Belmont       27529    52 12410    18 15049
#> 11 0013         Team Ness                21179  1173 16394  1785  1827
#> 12 0014         Team Luigi               20398  1465  3999   920 14014
#> 13 0001         Team Pikachu             18611  3666  6994  1214  6737
#> 14 0008         Team Fox                 16558  5197  6561    89  4711

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 0010         Team Yoshi               0.103 0.127 0.09  0.219 0.086
#>  2 0009         Team Link                0.093 0.096 0.072 0.129 0.105
#>  3 0004         Team Ice Climbers        0.092 0.011 0.118 0.072 0.088
#>  4 0006         Team King Dedede         0.089 0.185 0.03  0.042 0.138
#>  5 0003         Team Captain Falcon      0.088 0.062 0.057 0.207 0.098
#>  6 0007         Team Kirby               0.08  0.132 0.102 0.015 0.062
#>  7 0011         Team Diddy Kong          0.07  0.026 0.095 0.066 0.057
#>  8 0005         Team Dr. Mario           0.068 0.002 0.043 0.102 0.097
#>  9 0012         Team Mewtwo              0.066 0.024 0.118 0.044 0.029
#> 10 0002         Team Simon Belmont       0.066 0.002 0.073 0     0.086
#> 11 0013         Team Ness                0.051 0.034 0.097 0.047 0.01 
#> 12 0014         Team Luigi               0.049 0.043 0.024 0.024 0.08 
#> 13 0001         Team Pikachu             0.045 0.106 0.041 0.032 0.038
#> 14 0008         Team Fox                 0.04  0.151 0.039 0.002 0.027

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     23.5   22.6   26.1   23.4        3        6
#>  2 0002         Team Simon Be…   35.8   24.9   24.5   24.3        8       11
#>  3 0003         Team Captain …   24.8   23.4   31.1   26.7        5        8
#>  4 0004         Team Ice Clim…   29.7   25.1   26.2   27.1        5        9
#>  5 0005         Team Dr. Mario   29.4   22.9   24.6   24.4        2        7
#>  6 0006         Team King Ded…   25.5   25.9   26.2   24.8        3       10
#>  7 0007         Team Kirby       24.4   24.7   28.4   28.4        4       10
#>  8 0008         Team Fox         25.8   26.5   33.2   27.8        4       11
#>  9 0009         Team Link        26.5   26.1   28.1   27.9        2       11
#> 10 0010         Team Yoshi       28.3   22.0   27.4   25.6        2        6
#> 11 0011         Team Diddy Ko…   32.6   26.3   24.0   23.6        4       11
#> 12 0012         Team Mewtwo      32.0   24.2   24.5   24.1        5        7
#> 13 0013         Team Ness        32.0   23.3   23.2   26.1        6       11
#> 14 0014         Team Luigi       32.4   24.6   23.6   26.7        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?