Here’s what a basic dynasty league analysis might look like on MFL!

Set up the connection to the league:

ssb <- mfl_connect(season = 2020, league_id = 54040, 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"

Cool! Let’s have a deeper look at what this league is like.

ssb_summary <- ff_league(ssb)

str(ssb_summary)
#> tibble [1 x 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 35
#>  $ league_depth   : num 490

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   13129     Fournette,~ RB    JAC    25.6
#> 2 0001         Team Pikachu   13189     Engram, Ev~ TE    NYG    26  
#> 3 0001         Team Pikachu   11680     Landry, Ja~ WR    CLE    27.7
#> 4 0001         Team Pikachu   13290     Cohen, Tar~ RB    CHI    25.1
#> 5 0001         Team Pikachu   13158     Westbrook,~ WR    JAC    26.8
#> 6 0001         Team Pikachu   10273     Newton, Cam QB    NEP    31.3
#> # ... 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    21.4
#> 2 0001         Team Pikachu   13129     Fournette,~ RB    JAC    25.6
#> 3 0001         Team Pikachu   11680     Landry, Ja~ WR    CLE    27.7
#> 4 0001         Team Pikachu   13189     Engram, Ev~ TE    NYG    26  
#> 5 0001         Team Pikachu   14777     Burrow, Joe QB    CIN    23.7
#> 6 0001         Team Pikachu   14838     Shenault, ~ WR    JAC    21.9
#> # ... 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))
#> `summarise()` regrouping output by 'franchise_id', 'franchise_name' (override with `.groups` argument)

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        41539   514 19742  2048 19235
#>  2 0009         Team Link                39052  2834 11141  2217 22860
#>  3 0006         Team King Dedede         36504  5941  7635  1781 21147
#>  4 0007         Team Kirby               35036  3302 23817  2660  5257
#>  5 0003         Team Captain Falcon      34320  2327 10258  6394 15341
#>  6 0010         Team Yoshi               33997  1666  7952  6599 17780
#>  7 0014         Team Luigi               33383  2082   342   795 30164
#>  8 0012         Team Mewtwo              28963  1070 17832  1384  8677
#>  9 0011         Team Diddy Kong          27987  1906 13442  1702 10937
#> 10 0002         Team Simon Belmont       27004   415  9816    90 16683
#> 11 0008         Team Fox                 24055  7283 10027   625  6120
#> 12 0005         Team Dr. Mario           23073     8  1763  3804 17498
#> 13 0013         Team Ness                20714   449 16255  1508  2502
#> 14 0001         Team Pikachu             19728  1391 11575  2275  4487

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 0.016 0.122 0.06  0.097
#>  2 0009         Team Link                0.092 0.091 0.069 0.065 0.115
#>  3 0006         Team King Dedede         0.086 0.19  0.047 0.053 0.106
#>  4 0007         Team Kirby               0.082 0.106 0.147 0.079 0.026
#>  5 0003         Team Captain Falcon      0.081 0.075 0.063 0.189 0.077
#>  6 0010         Team Yoshi               0.08  0.053 0.049 0.195 0.089
#>  7 0014         Team Luigi               0.078 0.067 0.002 0.023 0.152
#>  8 0012         Team Mewtwo              0.068 0.034 0.11  0.041 0.044
#>  9 0011         Team Diddy Kong          0.066 0.061 0.083 0.05  0.055
#> 10 0002         Team Simon Belmont       0.063 0.013 0.061 0.003 0.084
#> 11 0008         Team Fox                 0.057 0.234 0.062 0.018 0.031
#> 12 0005         Team Dr. Mario           0.054 0     0.011 0.112 0.088
#> 13 0013         Team Ness                0.049 0.014 0.101 0.045 0.013
#> 14 0001         Team Pikachu             0.046 0.045 0.072 0.067 0.023

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))
#> `summarise()` regrouping output by 'franchise_id', 'franchise_name' (override with `.groups` argument)

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     24.1   23.0   25.9   26.3        6        9
#>  2 0002         Team Simon Be~   32.2   24.4   24.0   24.2        7       10
#>  3 0003         Team Captain ~   24.7   23.7   30.2   26.4        5        9
#>  4 0004         Team Ice Clim~   27.7   24.6   26.0   27.9        5        7
#>  5 0005         Team Dr. Mario   37.8   23.6   24     24.4        3        9
#>  6 0006         Team King Ded~   24.8   25.5   25.7   24.8        3       11
#>  7 0007         Team Kirby       23.9   24.2   29.5   25.9        4       10
#>  8 0008         Team Fox         24.8   26.2   28.4   26.8        3       11
#>  9 0009         Team Link        25.8   25.8   27.2   27.5        2        9
#> 10 0010         Team Yoshi       28.7   21.5   26.9   24.2        4        4
#> 11 0011         Team Diddy Ko~   30.8   25.5   23.6   25.5        3       14
#> 12 0012         Team Mewtwo      29.3   24.0   24.4   23.5        4        7
#> 13 0013         Team Ness        30.8   23.1   23.0   25.8        6       12
#> 14 0014         Team Luigi       31.9   28.0   24.7   26.5        3        9
#> # ... with 2 more variables: count_TE <int>, count_WR <int>