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https://github.com/FantasyFootballAnalytics/ffanalytics

ffanalytics R package
https://github.com/FantasyFootballAnalytics/ffanalytics

fantasy-football fantasy-football-analytics fantasy-football-database fantasy-football-projections football football-analytics football-data football-prediction r

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ffanalytics R package

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README 

        
# 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* `add_risk()` is no longer exported and is superseded by `add_uncertainty()`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()

```