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)
library(tidyr)
Set up the connection to the league:
<- mfl_connect(season = 2020,
ssb 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.
<- ff_league(ssb)
ssb_summary
str(ssb_summary)
#> tibble [1 × 17] (S3: tbl_df/tbl/data.frame)
#> $ league_id : chr "54040"
#> $ league_name : chr "The Super Smash Bros Dynasty League"
#> $ season : int 2020
#> $ league_type : chr NA
#> $ 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
#> $ draft_type : chr " email draft"
#> $ draft_player_pool: chr "Both"
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.
<- ff_rosters(ssb)
ssb_rosters
head(ssb_rosters)
#> # A tibble: 6 × 11
#> franchise_id franc…¹ playe…² playe…³ pos team age roste…⁴ drafted draft…⁵
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 0001 Team P… 13189 Engram… TE NYG 28.4 ROSTER 3.04 2017
#> 2 0001 Team P… 11680 Landry… WR CLE 30.2 ROSTER 4.02 2014
#> 3 0001 Team P… 13645 Smith,… WR NOS 27.1 ROSTER 18.02 2018
#> 4 0001 Team P… 12110 Brate,… TE TBB 31.6 ROSTER 19.04 2014
#> 5 0001 Team P… 13168 Reynol… WR LAR 28 ROSTER 20.02 2017
#> 6 0001 Team P… 13793 Valdes… WR GBP 28.3 ROSTER 21.04 2018
#> # … with 1 more variable: draft_round <chr>, and abbreviated variable names
#> # ¹franchise_name, ²player_id, ³player_name, ⁴roster_status, ⁵draft_year
#> # ℹ Use `colnames()` to see all variable names
Cool! Let’s pull in some additional context by adding DynastyProcess player values.
<- dp_values("values-players.csv")
player_values
# 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.
<- dp_playerids() %>%
player_ids 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_rosters %>%
ssb_values 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 franc…¹ playe…² playe…³ pos team age roste…⁴ drafted draft…⁵
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 0001 Team P… 14803 Edward… RB KCC 23.8 ROSTER 1.01 2020
#> 2 0001 Team P… 14835 Higgin… WR CIN 24 ROSTER Trade 2020
#> 3 0001 Team P… 14779 Herber… QB LAC 24.9 ROSTER 2.11 2020
#> 4 0001 Team P… 14777 Burrow… QB CIN 26.2 INJURE… 1.14 2020
#> 5 0001 Team P… 14838 Shenau… WR JAC 24.4 ROSTER 2.02 2020
#> 6 0001 Team P… 11680 Landry… WR CLE 30.2 ROSTER 4.02 2014
#> # … with 4 more variables: draft_round <chr>, ecr_1qb <dbl>, ecr_pos <dbl>,
#> # value_1qb <int>, and abbreviated variable names ¹franchise_name,
#> # ²player_id, ³player_name, ⁴roster_status, ⁵draft_year
#> # ℹ Use `colnames()` to see all variable names
Let’s do some team summaries now!
<- ssb_values %>%
value_summary 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 %>%
value_summary_pct 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.
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!
<- ssb_values %>%
age_summary 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]
#> franchi…¹ franc…² age_QB age_RB age_TE age_WR count…³ count…⁴ count…⁵ count…⁶
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int> <int> <int> <int>
#> 1 0001 Team P… 25.5 24.3 27.7 24.9 4 8 7 14
#> 2 0002 Team S… 26.5 27.1 26.3 26.1 8 11 3 8
#> 3 0003 Team D… 26.5 25.3 33.3 28.7 5 8 7 13
#> 4 0004 Team I… 30.3 27.1 28.3 28.6 5 9 9 13
#> 5 0005 Team D… 37.4 24.8 26.5 26.3 2 7 3 19
#> 6 0006 Team K… 27.4 27.4 28.2 26.8 3 10 7 10
#> 7 0007 Team K… 26.1 26.8 30.5 30.4 4 9 4 17
#> 8 0008 Team B… 27.8 28.4 34.7 29.9 4 11 4 11
#> 9 0009 Team L… 29.0 27.8 30.1 29.9 3 11 5 7
#> 10 0010 Team Y… 29.4 23.9 29.4 27.7 2 6 6 16
#> 11 0011 Team D… 33.1 28.5 25.7 25.1 4 12 6 11
#> 12 0012 Team M… 34.7 25.7 26.2 25.5 5 7 6 13
#> 13 0013 Team N… 34.4 25.3 25.2 27.0 5 9 5 12
#> 14 0014 Team L… 34.2 27.0 25.3 28.8 3 10 7 14
#> # … with abbreviated variable names ¹franchise_id, ²franchise_name, ³count_QB,
#> # ⁴count_RB, ⁵count_TE, ⁶count_WR
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?