ffanalytics

This package allows users to scrape projected stats from several sites that have publicly available projections. Once data is scraped the user can then use functions within the package to calculate projected points and produce rankings. The package relies heavily on the vocabulary from the tidyverse and users will better be able to use the package if they familiarize themselves with the tidyverse way of creating code.

Installation

Intallation of the ffanalytics package can be done directly from github: devtools::install_github("FantasyFootballAnalytics/ffanalytics")

Projection sources

The following sources are available for scraping:

• For seasonal data: CBS, ESPN, FantasyData, FantasyPros, FantasySharks, FFToday, FleaFlicker, NumberFire, Yahoo, FantasyFootballNerd, NFL, RTSports, Walterfootball
• For weekly data: CBS, ESPN, FantasyData, FantasyPros, FantasySharks, FFToday, FleaFlicker, NumberFire, Yahoo, FantasyFootballNerd, NFL

While the scrape functions allows the user to specify season and week, scraping historical periods will not be successful.

Projection sources are defined as R6 classes and the projection_sources object is a list containing the projection sources defined in the pacakge. Review the source_classes.R file to see how these classes are defined and the source_configs.R file in the data-raw directory has all the individual sources defined and running that script will re-create the projections_sources object for the package

Scraping data

The main function for scraping data is scrape_data. This function will pull data from the sources specified, for the positions specified in the season and week specificed. To pull data for QBs, RBs, WRs, TEs and DSTs from CBS, ESPN and Yahoo for the 2018 season the user would run:

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

my_scrape will be a list of tibbles, one for each positon scraped, which contains the data for each source for that position. In the tibble the data_src column speficies the source of the data.

Calculating projections

Once data is scraped the projected points can be calculated. this is done with the projections_table function:

my_projections <-  projections_table(my_scrape)

This will calculate projections using the default settings. You can provide additional parameters for the projections_table function to customize the calculations. See ?projections_table for details.

Adding additional information

To add rankings information, risk value and ADP/AAV data use the add_ecr, add_risk, add_adp, and add_aav functions:

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

Note that add_ecr will need to be called before add_risk to ensure that the ECR data is available for the risk calculation.

The add_adp and add_aav allows to specify sources for ADP and AAV. See ?add_adp, and ?add_aav for details.

Player data

Player data is pulled from MFL when the package loads and stored in the player_table object. To add player data to the projections table use add_player_info, which adds the player names, teams, positions, age, and experience to the data set.

my_projections <- my_projections %>% add_player_info()