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.
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/
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")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 namesYou 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"
ffopportunity data is automated with GitHub Actions and can be manually downloaded in RDS, parquet, and csv formats from the releases page.
The best places to get help on this package are:
- the nflverse discord (for both this package as well as anything R/NFL related)
- opening an issue
Many hands make light work! Here are some ways you can contribute to this project:
-
You can open an issue if you’d like to request specific data or report a bug/error.
-
If you’d like to contribute code, please check out the contribution guidelines.
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
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.