ffanalytics
packageWe are continously trying to provide users ways to replicate results and provide insights to what we are doing in terms of providing data. To streamline this process we have now build an R package that includes a streamlined version of the scripts used to scrape data and calculate projected points. It is the intent for the pacakge to be used like other packages.
Right now the package is only available through our GitHub repository, and installation requires that you have already installed the devtools
package. You can then install the package via
devtools::install_github("dadrivr/FantasyFootballAnalyticsR", subdir = "RPackage/ffanalytics")
There is also a tarball available in the GitHub repository that you can download and install with install.packages
. After the package is installed you can then use library(ffanalytics)
to attach the package.
The package includes a reference manual in standard package format. You can also find this in the help pane in RStudio. The full PDF version of the manual is here.
The ffanalytics
package includes a couple of RStudio addins to help you processing with data. To be able to fully take advandtage of this you will need to run RStudio (v0.99.878 or later). The first addin Run Scrape
will just execute a data scrape while Run Projections
will execute the data scrape as well as calculate projected points. The addins will construct code and return them to the console in RStudio to be executed. Since the Run Projections
addin contains functionality of the Run Scrape
addin we will just focus on the projections addin here.
The Run Projection
addin consists of three tabs that you will need to review before running the projections.
When you start the projections addin you should see the tab where you can select which sources and positions you want to use. Note that this includes the ability to use subscription based sources but you will need to either download the data separately or provide user name and password for those sites. Also, set the period you want to scrape for. Note that historical data scrapes are nearly impossible to do as sites usually don’t store their historical projections.
You can review and set the scoring settings for the calculations on the scoring settings tab. There is a panel for each position with the names of the variables to score. These settings are similar to the ones we use in our projection tool. Just use the scoring setting that matches your league
On the calculations tab you can set which average you want to use for the calculations as well as select sources for the ADP values. It also allows you to select which MFL drafts types to use when getting the ADP data.
You can also execute the code to run directly from R or RStudio. If you want to run a data scrape then you can execute
runScrape(week = 0, season = 2016, analysts = c(-1, 5, 7, 18, 27), positions = c("QB", "RB", "WR", "TE", "K", "DST"))
This runs a data scrape for the 2016 season for 5 different analysts for the Non-IDP positions. Refer to the analysts
table in the package to identify the analysts you want to use.
If you want to run the projection calculation from R or RStudio you will need the output from the runScrape
function. Then you can execute:
getProjections(scrapeData, avgMethod = "average", leagueScoring = userScoring, teams = 12, format = "standard", mflMocks = -1, mflLeagues = -1, adpSources = c("CBS", "ESPN", "FFC", "MFL", "NFL"))
Where scrapeData
is the output from the runScrape
function and userScoring
is your defined scoring rules. See scoringRules
in the package for an example of defining scoring rules.