knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) options(dplyr.summarise.inform = FALSE, rmarkdown.html_vignette.check_title = FALSE) eval <- TRUE tryCatch(expr = { download.file("https://github.com/ffverse/ffscrapr-tests/archive/1.4.7.zip","f.zip") unzip('f.zip', exdir = ".") httptest::.mockPaths(new = "ffscrapr-tests-1.4.7")}, warning = function(e) eval <<- FALSE, error = function(e) eval <<- FALSE) httptest::use_mock_api()
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:
ssb <- mfl_connect(season = 2020, league_id = 54040, # from the URL of your league rate_limit_number = 3, rate_limit_seconds = 6) ssb
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) str(ssb_summary)
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)
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)
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
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
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!
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
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?
httptest::stop_mocking() unlink(c("ffscrapr-tests-1.4.7","f.zip"), recursive = TRUE, force = TRUE)
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