Introduction

We are continuously looking to provide users ways to replicate our analyses and improve their performance in fantasy football. To that aim, we are introducing the ffanalytics R package that includes a streamlined version of the scripts used to scrape projections from multiple sources and calculate projected points using the wisdom of the crowd.

First, Learn the Basics of R

We have released the ffanalytics package for fantasy football data analysis.  It is a package for R, a piece of software for statistical analysis that has a steep learning curve.  Before trying to use the ffanalytics package, we highly recommend first learning the basics of R, so you know about loading packages and the different object types such as lists, vectors, data frames, etc.  For more info how to learn the basics of R, see here.  We will not be troubleshooting R basics with users.  For troubleshooting the basics of R, please post to the R mailing list or forums if you have questions.

Familiarize Yourself with tidyverse

The latest version of the package relies heavily on the vocabulary of tidyverse, so it would be beneficial to familiarize yourself with the concepts and notation from that envrionment. For documentation on tidyverse, please visit https://www.tidyverse.org.

What Software You Need

We recommend the latest versions of the following software:

• R (for running the scripts)
• RStudio (best text editor for viewing, editing, and running R scripts)

Installation

The package is available through our GitHub repository, and installation requires that you have already installed the devtools and rstudioapi packages. You can install the devtools and rstudioapi packages in R/RStudio via:

install.packages(c("devtools","rstudioapi"), dependencies=TRUE, repos=c("http://rstudio.org/_packages", "http://cran.rstudio.com"))

You can then install the ffanalytics R package via:

devtools::install_github(repo = "FantasyFootballAnalytics/ffanalytics", build_vignettes = TRUE)

If you you receive errors that a package is missing or a non-zero exit status error, the ffanalytics package may not have installed the packages it depends on (or the dependencies of these packages).  Here is how to install the packages manually (along with their dependencies):

install.packages(c("tidyverse","rvest","httr","readxl","janitor","glue","Hmisc"), dependencies=TRUE, repos=c("http://rstudio.org/_packages", "http://cran.rstudio.com"))

Loading the Package

After the ffanalytics package is installed, load the package via:

library("ffanalytics")

my_scrape <- scrape_data(src = c("CBS", "ESPN", "Yahoo"), 
                         pos = c("QB", "RB", "WR", "TE", "DST"),
                         season = 2018, week = 0)

my_projections <- projections_table(my_scrape)

my_projections <- my_projections %>% add_ecr() %>% add_risk() %>%
  add_adp() %>% add_aav()

my_projections <- my_projections %>% add_player_info()

Viewing/Saving the Projections

If you have run the scrape and saved the scraped data into an object, i.e.:

my_scrape <- scrape_data(src = c("CBS", "ESPN", "Yahoo"), 
                         pos = c("QB", "RB", "WR", "TE", "DST"),
                         season = 2018, week = 0)

And if you have calculated the projections using the scraped data, i.e.:

my_projections <- projections_table(my_scrape)

Package Manual

There is documentation included within the package that can be explored directly in R/RStudio, but we have also made that documentation available as a pkgdown site here.

Troubleshooting

If you run into errors or issues, feel free to let us know in the comments or to open a GitHub issue on our GitHub repo.  Please note the version of R, RStudio, and the ffanalytics package that you’re using, along with the specific error you received and the code that caused the error.  If you are able to find and fix an issue, please share the fix with the community (for more info how to share your scripts with the community, see here).

Downloading Our R Scripts

In addition to the ffanalytics R Package, we also have R scripts that accompany posts on the site.  To download and run our R scripts, see here.  If you’d rather calculate projections using our webapps without the R package, see here.  We encourage people to use and improve our scripts, and to share them with the community so everyone benefits.  For info on how to share your scripts with the community, see here.