mfl_basics

Tan Ho

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

suppressPackageStartupMessages({
  library(ffscrapr)
  library(dplyr)
  library(tidyr)
})

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    25.9
#> 3 0001         Team Pikachu   11680     Landry, Ja~ WR    CLE    27.7
#> 4 0001         Team Pikachu   13290     Cohen, Tar~ RB    CHI    25  
#> 5 0001         Team Pikachu   13155     Ross, John  WR    CIN    24.7
#> 6 0001         Team Pikachu   13158     Westbrook,~ WR    JAC    26.7
#> # ... 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    21.3
#> 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    25.9
#> 5 0001         Team Pikachu   14777     Burrow, Joe QB    CIN    23.7
#> 6 0001         Team Pikachu   14838     Shenault, ~ WR    JAC    21.8
#> # ... 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        41952   567 19728  2014 19643
#>  2 0009         Team Link                38954  2852 11086  2187 22829
#>  3 0006         Team King Dedede         36827  6122  7649  1680 21376
#>  4 0007         Team Kirby               35358  3367 24013  2608  5370
#>  5 0014         Team Luigi               34025  2150   357   973 30545
#>  6 0003         Team Captain Falcon      33577  2083 10109  6223 15162
#>  7 0010         Team Yoshi               33383  1745  7596  6440 17602
#>  8 0012         Team Mewtwo              28507  1023 17510  1309  8665
#>  9 0002         Team Simon Belmont       28030   381 10792    89 16768
#> 10 0011         Team Diddy Kong          28006  1807 13287  1593 11319
#> 11 0008         Team Fox                 23803  7565  9873   391  5974
#> 12 0005         Team Dr. Mario           22406    13  1355  3512 17526
#> 13 0013         Team Ness                20221   469 15726  1504  2522
#> 14 0001         Team Pikachu             19698  1303 11584  2290  4521

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        0.099 0.018 0.123 0.061 0.098
#>  2 0009         Team Link                0.092 0.091 0.069 0.067 0.114
#>  3 0006         Team King Dedede         0.087 0.195 0.048 0.051 0.107
#>  4 0007         Team Kirby               0.083 0.107 0.149 0.079 0.027
#>  5 0014         Team Luigi               0.08  0.068 0.002 0.03  0.153
#>  6 0003         Team Captain Falcon      0.079 0.066 0.063 0.19  0.076
#>  7 0010         Team Yoshi               0.079 0.055 0.047 0.196 0.088
#>  8 0012         Team Mewtwo              0.067 0.033 0.109 0.04  0.043
#>  9 0002         Team Simon Belmont       0.066 0.012 0.067 0.003 0.084
#> 10 0011         Team Diddy Kong          0.066 0.057 0.083 0.049 0.057
#> 11 0008         Team Fox                 0.056 0.241 0.061 0.012 0.03 
#> 12 0005         Team Dr. Mario           0.053 0     0.008 0.107 0.088
#> 13 0013         Team Ness                0.048 0.015 0.098 0.046 0.013
#> 14 0001         Team Pikachu             0.046 0.041 0.072 0.07  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     24.2   23.0   25.9   26.4        6        6
#>  2 0002         Team Simon Be~   34.4   23.9   24.1   24.3        7       10
#>  3 0003         Team Captain ~   24.7   23.7   30.1   26.4        6       11
#>  4 0004         Team Ice Clim~   27.6   24.6   26.1   27.8        5        7
#>  5 0005         Team Dr. Mario   37.7   19.4   23.9   24.4        3        7
#>  6 0006         Team King Ded~   24.8   25.5   25.8   24.8        3       10
#>  7 0007         Team Kirby       23.9   24.1   29.4   26.0        3       11
#>  8 0008         Team Fox         24.7   26.1   26.0   26.7        3       11
#>  9 0009         Team Link        25.8   25.8   27.1   27.4        2        9
#> 10 0010         Team Yoshi       28.9   21.4   26.8   24.2        4        4
#> 11 0011         Team Diddy Ko~   30.7   25.4   23.6   25.4        2       13
#> 12 0012         Team Mewtwo      29.3   24.1   24.5   23.5        4        6
#> 13 0013         Team Ness        30.6   23.1   22.8   26.1        5        9
#> 14 0014         Team Luigi       32.0   28.2   26.9   26.5        5        9
#> # ... with 2 more variables: count_TE <int>, count_WR <int>