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ffopportunity

Models and Data for Expected Fantasy Points

ffopportunity builds a dataframe of Expected Fantasy Points by preprocessing and applying an xgboost model to nflverse play-by-play data. It also includes utilities to download precomputed data from automated GitHub releases.

About

Expected Fantasy Points are a measure of player opportunities in fantasy football - essentially aiming to quantify how many points the average player would score given a specific situation and opportunity. It uses xgboost and tidymodels trained on public nflverse data from 2006-2020 to do this.

For more on the modeling details, see the articles posted to this website: https://ffopportunity.ffverse.com/articles/

Installation

Install the development version from GitHub with:

install.packages("ffopportunity", repos = c("https://ffverse.r-universe.dev", getOption("repos")))

# or use remotes/devtools
# install.packages("remotes")
remotes::install_github("ffverse/ffopportunity")

Usage

The two main functions of {ffopportunity} are ep_load() and ep_build().

You can download the latest version of the EP data with ep_load() as follows:

library(ffopportunity)
#> Warning: package 'ffopportunity' was built under R version 4.2.1
ep_load(season = 2020:2021, type = "weekly")
#> → <ffopportunity predictions>
#> → Generated 2022-09-12 12:59:18 with ep model version "latest"
#> # A tibble: 11,769 × 159
#>    season posteam  week game_id  playe…¹ full_…² posit…³ pass_…⁴ rec_a…⁵ rush_…⁶
#>    <chr>  <chr>   <dbl> <chr>    <chr>   <chr>   <chr>     <dbl>   <dbl>   <dbl>
#>  1 2020   SF          1 2020_01… 00-003… Jimmy … QB           33       0       1
#>  2 2020   SF          1 2020_01… 00-003… George… TE            0       5       1
#>  3 2020   ARI         1 2020_01… 00-003… Kyler … QB           39       0      11
#>  4 2020   ARI         1 2020_01… 00-003… DeAndr… WR            0      16       0
#>  5 2020   ARI         1 2020_01… 00-002… Larry … WR            0       5       0
#>  6 2020   ARI         1 2020_01… <NA>    <NA>    <NA>          0       2       0
#>  7 2020   SF          1 2020_01… 00-003… Raheem… RB            0       5      15
#>  8 2020   ARI         1 2020_01… 00-003… Kenyan… RB            0       2      16
#>  9 2020   ARI         1 2020_01… 00-003… Christ… WR            0       5       0
#> 10 2020   SF          1 2020_01… 00-003… Trent … WR            0       5       0
#> # … with 11,759 more rows, 149 more variables: pass_air_yards <dbl>,
#> #   rec_air_yards <dbl>, pass_completions <dbl>, receptions <dbl>,
#> #   pass_completions_exp <dbl>, receptions_exp <dbl>, pass_yards_gained <dbl>,
#> #   rec_yards_gained <dbl>, rush_yards_gained <dbl>,
#> #   pass_yards_gained_exp <dbl>, rec_yards_gained_exp <dbl>,
#> #   rush_yards_gained_exp <dbl>, pass_touchdown <dbl>, rec_touchdown <dbl>,
#> #   rush_touchdown <dbl>, pass_touchdown_exp <dbl>, rec_touchdown_exp <dbl>, …
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

You can also build EP from base nflverse data with ep_build() as follows:

ep_build(season = 2021, version = "latest")
#> -- Starting ep build for 2021 season(s)! 2022-01-11 07:58:44 -------------------
#> > Loading pbp 2022-01-11 07:58:44
#> > Preprocessing pbp 2022-01-11 07:58:46
#> > Generating predictions 2022-01-11 07:58:54
#> > Summarizing data 2022-01-11 07:59:33
#> -- Finished building ep for 2021 season(s)! 2022-01-11 07:59:33 ----------------
#> > <ffopportunity predictions>
#> > Generated 2022-01-11 07:59:33 with model version latest
#> List of 5
#>  $ ep_weekly  : tibble [5,756 x 159] (S3: tbl_df/tbl/data.frame)
#>  $ ep_pbp_pass: tibble [18,747 x 57] (S3: tbl_df/tbl/data.table/data.frame)
#>  $ ep_pbp_rush: tibble [14,038 x 47] (S3: tbl_df/tbl/data.table/data.frame)
#>  $ ep_version : chr "latest"
#>  $ timestamp  : POSIXct[1:1], format: "2022-01-11 07:59:33"

Data

ffopportunity data is automated with GitHub Actions and can be manually downloaded in RDS, parquet, and csv formats from the releases page.

Getting help

The best places to get help on this package are:

Contributing

Many hands make light work! Here are some ways you can contribute to this project:

Terms of Use

The R code for this package is released as open source under the GPL v3 License. The models and expected points data included within this package’s are licensed under Creative Commons Attribution-ShareAlike 4.0 International License

Code of Conduct

Please note that the ffopportunity project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.