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 [1 × 14] (S3: tbl_df/tbl/data.frame)
#>  $ league_id      : chr "54040"
#>  $ league_name    : chr "The Super Smash Bros Dynasty League"
#>  $ season         : int 2020
#>  $ 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

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
#>   <chr>        <chr>          <chr>     <chr>                  <chr> <chr> <dbl>
#> 1 0001         Team Pikachu   13189     Engram, Evan           TE    NYG    26.9
#> 2 0001         Team Pikachu   11680     Landry, Jarvis         WR    CLE    28.7
#> 3 0001         Team Pikachu   13645     Smith, Tre'Quan        WR    NOS    25.5
#> 4 0001         Team Pikachu   12110     Brate, Cameron         TE    TBB    30.1
#> 5 0001         Team Pikachu   13168     Reynolds, Josh         WR    LAR    26.4
#> 6 0001         Team Pikachu   13793     Valdes-Scantling, Mar… WR    GBP    26.8
#> # … 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 × 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    22.3
#> 2 0001         Team Pikachu   14835     Higgins, Tee           WR    CIN    22.5
#> 3 0001         Team Pikachu   14779     Herbert, Justin        QB    LAC    23.4
#> 4 0001         Team Pikachu   14777     Burrow, Joe            QB    CIN    24.6
#> 5 0001         Team Pikachu   14838     Shenault, Laviska      WR    JAC    22.8
#> 6 0001         Team Pikachu   11680     Landry, Jarvis         WR    CLE    28.7
#> # … 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))

value_summary
#> # A tibble: 14 × 7
#>    franchise_id franchise_name     team_value    QB    RB    TE    WR
#>    <chr>        <chr>                   <int> <int> <int> <int> <int>
#>  1 0010         Team Yoshi              41170  4753 14710  7284 14423
#>  2 0006         Team King Dedede        35184  6458  2513   597 25616
#>  3 0004         Team Ice Climbers       35091   115 19362  2470 13144
#>  4 0009         Team Link               33078  1168 10578  5188 16144
#>  5 0003         Team Donkey Kong        30043  1299  6034  7220 15490
#>  6 0007         Team Kirby              27880  4890 14108   182  8700
#>  7 0005         Team Dr. Mario          27659    17  7137  2586 17919
#>  8 0011         Team Diddy Kong         26143   564 12406  2583 10590
#>  9 0002         Team Simon Belmont      25905    40 11318    12 14535
#> 10 0012         Team Mewtwo             24317   618 17670  1340  4689
#> 11 0013         Team Ness               20004   803 15980  1744  1477
#> 12 0014         Team Luigi              19761  1738  5828  1068 11127
#> 13 0001         Team Pikachu            17651  4323  6293   833  6202
#> 14 0008         Team Bowser             13150  5673  4069    25  3383

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 0010         Team Yoshi              0.109 0.146 0.099 0.22  0.088
#>  2 0006         Team King Dedede        0.093 0.199 0.017 0.018 0.157
#>  3 0004         Team Ice Climbers       0.093 0.004 0.131 0.075 0.08 
#>  4 0009         Team Link               0.088 0.036 0.071 0.157 0.099
#>  5 0003         Team Donkey Kong        0.08  0.04  0.041 0.218 0.095
#>  6 0007         Team Kirby              0.074 0.151 0.095 0.005 0.053
#>  7 0005         Team Dr. Mario          0.073 0.001 0.048 0.078 0.11 
#>  8 0011         Team Diddy Kong         0.069 0.017 0.084 0.078 0.065
#>  9 0002         Team Simon Belmont      0.069 0.001 0.076 0     0.089
#> 10 0012         Team Mewtwo             0.064 0.019 0.119 0.04  0.029
#> 11 0013         Team Ness               0.053 0.025 0.108 0.053 0.009
#> 12 0014         Team Luigi              0.052 0.054 0.039 0.032 0.068
#> 13 0001         Team Pikachu            0.047 0.133 0.043 0.025 0.038
#> 14 0008         Team Bowser             0.035 0.175 0.027 0.001 0.021

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         23.9   22.8   26.2   23.3        4        8
#>  2 0002         Team Simon Belmont   24.9   25.5   24.7   24.5        8       11
#>  3 0003         Team Donkey Kong     25.0   23.7   31.7   27.2        5        8
#>  4 0004         Team Ice Climbers    28.7   25.5   26.7   27.1        5        9
#>  5 0005         Team Dr. Mario       35.9   23.3   25.0   24.8        2        7
#>  6 0006         Team King Dedede     25.9   25.9   26.6   25.2        3       10
#>  7 0007         Team Kirby           24.6   25.3   28.9   28.8        4        9
#>  8 0008         Team Bowser          26.2   26.9   33.2   28.4        4       11
#>  9 0009         Team Link            27.4   26.3   28.6   28.4        3       11
#> 10 0010         Team Yoshi           27.9   22.4   27.9   26.1        2        6
#> 11 0011         Team Diddy Kong      31.6   27.0   24.2   23.5        4       12
#> 12 0012         Team Mewtwo          33.2   24.1   24.6   24.0        5        7
#> 13 0013         Team Ness            32.9   23.8   23.7   25.4        5        9
#> 14 0014         Team Luigi           32.7   25.4   23.8   27.2        3       10
#> # … 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?