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.

Version 3 of the package:

Summer of 2022 we incremented to version 3.0 of the package. There are several things worth highlighting:

Breaking changes:

• add_risk() is no longer exported and is superseded by add_uncertainty()
• Several helper functions are no-longer exported
• When loading ffanalytics, no other packages load with it (i.e., we removed all packages from the "Depends" field). Previously, calling library(ffanalytics) also loaded dplyr, tidyr, and several other packages.
• We no longer use the projection_sources R6 object internally

Updates:

• Individual scrapes are now self-contained internally (e.g. ffanalytics:::scrape_cbs())
• Rate limits have been added to all scrapes (typically waiting 2 seconds between pages)
• The projections_table function has a new argument: avg_type = c("average", "robust", "weighted"). By default the projections_table function will compute all average types, but one or two can be specified.

Installation

Installation of the ffanalytics package can be done directly from github:

install.packages("remotes")
remotes::install_github("FantasyFootballAnalytics/ffanalytics")

Projection sources

The following sources are available for scraping:

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

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

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 specified. To pull data for QBs, RBs, WRs, TEs and DSTs from CBS, NFL and NumberFire for the 2022 season the user would run:

my_scrape <- scrape_data(src = c("CBS", "NFL", "NumberFire"), 
                         pos = c("QB", "RB", "WR", "TE", "DST"),
                         season = NULL, # NULL grabs the current season
                         week = NULL) # NULL grabs the current week

my_scrape will be a list of tibbles, one for each position scraped, which contains the data for each source for that position. In the tibble the data_src column specifies 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_uncertainty (superseding add_risk), add_adp, and add_aav functions:

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

Note that add_ecr will need to be called before add_uncertainty to ensure that the ECR data is available for the uncertainty 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()