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is no longer exported and is superseded byadd_uncertainty()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, callinglibrary(ffanalytics)
also loadeddplyr
,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 theprojections_table
function will compute all average types, but one or two can be specified.
Installation of the ffanalytics
package can be done directly from github:
install.packages("remotes")
remotes::install_github("FantasyFootballAnalytics/ffanalytics")
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.
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, 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 = 2022, week = 0)
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.
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.
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 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()