---
name: SportsAnalytics
topic: Sports Analytics
maintainer: Benjamin S. Baumer, Quang Nguyen, Gregory J. Matthews
email: ben.baumer@gmail.com
version: 2024-09-19
source: https://github.com/cran-task-views/SportsAnalytics/
---

This CRAN Task View contains a list of packages useful for sports analytics. 
Most of the packages are sport-specific and are grouped as such.  However, we
also include a **General** section for packages that provide ancillary
functionality relevant to sports analytics (e.g., team-themed color palettes),
and a **Modeling** section for packages useful for statistical modeling.
Throughout the task view, and collected in the **Related links** section at the
end, we have included a list of selected books and articles that use some of
these packages in substantive ways.  Our goal in compiling this list is to help
researchers find the tools they need to complete their work in R.

To be considered for inclusion, the package must be useful for conducting sports analytics. 
Most packages provide functionality for some combination of:

#. acquiring data for a specific sport or league
#. performing common computations on sport-specific data

Esports and sports betting packages are within scope.

The list of packages is aspirationally comprehensive. 
If there is a sports analytics package on CRAN that we have missed, please let us know. 
Contributions are always welcome, and encouraged -- please see the linked GitHub repository for details. 

### Sport-Specific Packages

#### American Football 🏈

-   `r pkg("nflverse", priority = "core")` is a collection of packages for obtaining and analyzing NFL data. The core `r pkg("nflverse")` includes `r pkg("nflfastR")`, `r pkg("nflseedR")`, `r pkg("nfl4th")`, `r pkg("nflreadr")`, and `r pkg("nflplotR")`.
-   `r pkg("nflfastR")` contains functions to efficiently scrape NFL play-by-play data from 1999 to present. It is similar to [nflscrapR](https://github.com/maksimhorowitz/nflscrapR), but much faster. All models required by `r pkg("nflfastR")` are hosted in `r pkg("fastrmodels")`.
-   `r pkg("nflreadr")` efficiently downloads data from **GitHub** repositories of the [nflverse](https://github.com/nflverse) project, including pre-computed `r pkg("nflfastR")` data frames.
-   `r pkg("nfl4th")` consists of functions to calculate optimal Fourth Down decisions in the National Football League. Data on 4th downs is collected from [NFL](https://www.nfl.com/) and [ESPN](https://www.espn.com/).
-   `r pkg("nflseedR")` contains functions for ranking NFL teams based on the complex NFL tie breaking rules. It includes division ranking, playoff seeding, and draft order.
-   `r pkg("nflplotR")` includes functions for making NFL data visualization in **ggplot2** easier.
-   `r pkg("NFLSimulatoR")` consists of tools for simulating plays and drives, and furthermore evaluating in-game strategies in the NFL.
-   `r pkg("ffscrapr")` helps access various fantasy football APIs including MFL, Sleeper, ESPN, and Fleaflicker with a consistent interface and built-in authentication, rate-limiting, and caching.
-   `r pkg("gsisdecoder")` contains functions to decode NFL Player IDs for use in conjunction with the `r pkg("nflfastR")` package.

#### Association Football (Soccer) ⚽

-   `r pkg("worldfootballR")` provides clean and tidy football data from a number of popular sites, including [FBref](https://fbref.com/en/), transfer and valuations data from [Transfermarkt](https://www.transfermarkt.com/) and shooting location data from [Understat](https://understat.com/) and [fotmob](https://www.fotmob.com/).
-   `r pkg("socceR")` provides functions for evaluating soccer predictions and simulating results from soccer matches and tournament.
-   `r pkg("ggsoccer")` provides functions for visualizing soccer event data in **ggplot2**.
-   `r pkg("footballpenaltiesBL")` contains data and plotting functions for analyzing penalty kicks in the [German Men's Bundesliga](https://www.bundesliga.com/) from 1963-64 to 2016-17.
-   `r pkg("footBayes")` consists of functions for fitting widely known soccer models (double Poisson, bivariate Poisson, Skellam, Student's t) through Hamiltonian Monte Carlo and Maximum Likelihood estimation approaches using Stan. The package also provides tools for visualizing team strengths and predicting match outcomes.
-   `r pkg("itscalledsoccer")` enables access to American soccer (MLS, NWSL, and USL) data through the [American Soccer Analysis app API](https://app.americansocceranalysis.com/).
-   `r pkg("FPLdata")` contains functions for retrieving player attributes on [Fantasy Premier League](https://fantasy.premierleague.com/).
-   `r pkg("EUfootball")` provides European football match results for top leagues in England, France, Germany, Italy, Spain, Netherlands, and Turkey from 2010-2011 to 2019-2020.
-   `r pkg("bundesligR")` contains all final standings of the Bundesliga in Germany from 1964 to 2016.

#### Australian Rules Football 🏉

-   `r pkg("fitzRoy")` is a package for scraping and processing Australian Football League (AFL) data. `r pkg("fitzRoy")` provides access to publicly data sources such as [AFL Tables](https://afltables.com/afl/afl_index.html), [Footy Wire](https://www.footywire.com), and [The Squiggle](https://squiggle.com.au).

#### Baseball ⚾

-   Historical baseball data is available through the `r pkg("Lahman", priority = "core")` package, which contains season-level data for Major League Baseball going back to 1871.
- `r pkg("baseballr", priority = "core")` consists of functions for extracting and analyzing baseball data from various sources such as [Baseball Reference](https://www.baseball-reference.com/), [FanGraphs](https://www.fangraphs.com/), and [Baseball Savant](https://baseballsavant.mlb.com/). The package is featured prominently in the 3rd edition of Albert, J., Marchi, M., and Baumer, B. S. (2024). [*Analyzing Baseball Data with R*](https://beanumber.github.io/abdwr3e/) (`r doi("10.1201/9781351107099")`) and largely replaces the now-defunct `pitchRx` package. 
-   `r pkg("retrosheet")` facilitates downloading game log, team IDs, rosters, and play-by-play and other files from [Retrosheet.org](http://wwws.retrosheet.org/), and returning the results as data frames. Local caching can be employed to improve efficiency. Note that the play-by-play data returned comes directly from the event files and is not parsed (i.e., [Chadwick](https://github.com/chadwickbureau/chadwick) is not bundled).
-   `r pkg("mlbstats")` provides functions for vector-based computation of many baseball statistics, both traditional and sabermetric.
-   `r pkg("mlbplotR")` contains tools for visualizing MLB analysis with **ggplot2** and **gt**.

#### Basketball 🏀

-   `r pkg("BAwiR", priority = "core")` is a collection of tools to analyze basketball data, with focus on data scraping and visualization.
-   `r pkg("AdvancedBasketballStats")` provides functions to calculate and analyze basketball statistics for players, teams, lineups (quintets), and plays.
-   `r pkg("uncmbb")` contains data on University of North Carolina (at Chapel Hill) Men's Basketball Results since the 1949-50 season.
-   `r pkg("BasketballAnalyzeR")` accompanies the book [*Basketball Data Science With Applications in R*](https://www.routledge.com/Basketball-Data-Science-With-Applications-in-R/Zuccolotto-Manisera/p/book/9781138600799). This package includes data and functions to analyze and visualize basketball data.
-   `r pkg("NBAloveR")` is an R interface to access basketball data from [Basketball Reference](https://www.basketball-reference.com) API. This package also contains functions to help users with analyzing basketball data.
-   `r pkg("wehoop")` provides functions for accessing women's college basketball and WNBA data from the [ESPN](https://www.espn.com) API.
-   `r pkg("hoopR")` consists of functions for accessing men's college basketball and NBA data from various sources, including [ESPN](https://www.espn.com), [NBA Stats API](https://www.nba.com/stats), and [Ken Pomeroy's college basketball ratings](https://www.kenpom.com).
-   `r pkg("euroleaguer")` provides functions for retrieving data from EuroLeague and EuroCup API.
-   `r pkg("nblR")` allows users to obtain play-by-play, shooting locations, and box score statistics for the [National Basketball League](https://nbl.com.au/) in Australia.

#### Chess ♟

-   `r pkg("chess")` is an opinionated wrapper for R around [python-chess](https://github.com/niklasf/python-chess). It reads and writes [PGN files](https://en.wikipedia.org/wiki/Portable_Game_Notation) and SVGs of game boards.
-   Like `r pkg("chess")`, `r pkg("bigchess")` reads and writes PGN files. `r pkg("bigchess")` provides an API to the UCI chess engines. `r pkg("bigchess")` is also able to read multiple game files at once without copying to RAM.
-   `r pkg("ChessGmooG")` contains FIDE Ratings for chess players for 2015 and 2020.

#### Cricket 🏏

-   `r pkg("yorkr")` provides functions for analyzing statistics of cricket players and teams based on [Cricsheet](https://cricsheet.org) data.
-   `r pkg("cricketr")` is a collection of tools for analyzing cricket performances of players and teams based on [ESPN Cricinfo Statsguru](https://stats.espncricinfo.com/ci/engine/stats) data.
-   `r pkg("cricketdata")` includes functions to obtain international cricket data from two major sources, [ESPNCricinfo](https://www.espncricinfo.com/) and [Cricsheet](https://cricsheet.org/).
-   `r pkg("howzatR")` consists of functions for calculating various cricket statistics.

#### Esports 🎮

-   `r pkg("CSGo")` collects Counter-Strike Global Offensive data from the [Steam API](https://developer.valvesoftware.com/wiki/Steam_Web_API).
-   `r pkg("rbedrock")` supports data analysis and management of Minecraft (Bedrock Edition).
-   `r pkg("ROpenDota")`, `r pkg("opendotaR")`, and `r pkg("RDota2")` extract Dota2 data from the [OpenDota](https://www.opendota.com/) and [Steam](https://developer.valvesoftware.com/wiki/Steam_Web_API) APIs.

#### GPS Tracking 📍

-   `r pkg("trackeR")` and `r pkg("trackeRapp")` provide tools for analyzing running, cycling and swimming data from GPS-enabled tracking devices within R. These two packages allow users to tidy and explore data from workouts and competitions.
-   `r pkg("rStrava")` contains functions to access [Strava](https://www.strava.com/) activity data from the [Strava API](https://developers.strava.com/).
-   A detailed overview of tools for processing and analyzing tracking data can be found in the `r view("Tracking")` CRAN Task View.

#### Hockey 🏒

-   `r pkg("NHLData")` contains scores from NHL games dating back to 1917. Data are stored one season at a time and contains scores for every game during a particular season.
-   Access to data exposed by the [NHL API](https://gitlab.com/dword4/nhlapi) is provided by the `r pkg("nhlapi")` package.
-   `r pkg("fastRhockey")` provides API wrappers for the NHL and Premier Hockey Federation (PHF), formerly known as the National Women's Hockey League (NWHL).

#### Softball 🥎

-   `r pkg("runexp")` provides methods for estimating runs scored in softball. In particular, `r pkg("runexp")` centers around theoretical expectation using discrete Markov chains and empirical distribution using multinomial random simulation.

#### Swimming 🏊

-   `r pkg("SwimmeR")` reads swimming results in a variety of formats and returns results in tidy data frame. It also includes functions for converting times between short-course yards (SCY), short-course meters (SCM), and long-course meters (LCM).

#### Track and Field 🏃

-   `r pkg("combinedevents")` contains functions for calculating scores and marks for combined events competitions in track and field, based on the [International Association of Athletics Federation](https://www.worldathletics.org/) scoring tables.

#### Volleyball 🏐

-   `r pkg("volleystat")` contains match statistics from the [German Volleyball Bundesliga](https://www.volleyball-bundesliga.de/) from 2013-14 to 2018-19. Data were extracted from the league [homepage](www.volleyball-bundesliga.de).

### General

-   `r pkg("teamcolors")` provides color palettes, **ggplot2** themes, **xaringan** themes, and logos for professional teams across a variety of sports and leagues. `r pkg("teamcolors")` was originally designed to create the data graphics in [Lopez, et al. (2018)](https://projecteuclid.org/journals/annals-of-applied-statistics/volume-12/issue-4/How-often-does-the-best-team-win-A-unified-approach/10.1214/18-AOAS1165) (`r doi("10.1214/18-AOAS1165")`).
-   `r pkg("colorr")` contains color palettes for professional sports teams in the EPL, MLB, NBA, NHL, and NFL.
-   `r pkg("nbapalettes")` contains color palettes inspired by NBA team jersey colors.
-   `r pkg("gameR")` contains color palettes inspired by video games.
-   `r pkg("sleeperapi")` offers functions for gathering data from the [Sleeper API](https://api.sleeper.app/) for fantasy sports.
-   `r pkg("sportyR")` contains functions for creating **ggplot2** representations of sports playing surfaces pursuant to rule-book specifications. This is particularly useful for plotting player tracking data.
-   `r pkg("SportsTour")` provides functions for displaying tournament fixtures using knock-out and round robin methods.
-   `r pkg("injurytools")` provides functionality for analyzing, visualizing, and modeling sports injuries. 
-   `r pkg("ISAR")` contains datasets used in the textbook [*Introduction to Sports Analytics using R*](https://www.prospectpressvt.com/textbooks/elmore-sports-analytics).

###  Modeling

A wide array of functions for modeling in sports analytics are available in the R base package (e.g. `lm()` and `glm()`). 
In addition, other CRAN Task Views such as `r view("Bayesian")`, `r view("MachineLearning")`, `r view("Robust")`, `r view("Spatial")`, and `r view("SpatioTemporal")` may contain appropriate packages for applying statistical methods to sports.

#### Betting

-   `r pkg("oddsapiR")` provides tools for accessing sports odds from [The Odds API](https://the-odds-api.com).
-   `r pkg("odds.converter")` contributes functions for converting common sports betting odds types, including US odds, Hong Kong odds, Decimal odds, Indonesian odds, Malaysian odds, and raw probability.
-   `r pkg("implied")` is a collection of functions that convert between bookmaker odds and probabilities, based on various algorithms.
-   `r pkg("pinnacle.data")` contains [Pinnacle](https://www.pinnacle.com/) market odds, highlighted by a dataset of all wagering lines for the 2016 MLB season.
-   `r pkg("RKelly")` computes the [Kelly criterion](https://doi.org/10.1002/j.1538-7305.1956.tb03809.x) for betting and provides functions to calculate outcome probabilities for multi-leg contests.

#### Ratings

-   `r pkg("BradleyTerry2", priority = "core")` provides functions and examples for fitting Bradley-Terry models (`r doi("10.2307/2334029")`) to paired comparison data. See `r doi("10.18637/jss.v012.i01")` for background on this package. 
-   Methods for estimating the Elo rating in sports can be found in the `r pkg("elo")`, `r pkg("welo")`, `r pkg("EloOptimized")`, `r pkg("EloChoice")`, and `r pkg("EloRating")` packages. `r pkg("PlayerRatings")` also offers implementations to other rating systems, including Glicko, Glicko-2, and Stephenson, in addition to Elo.
-   `r pkg("piratings")` computes pi-ratings for determining team ability in association football, as described in [Constantinou and Fenton (2013)](https://www.degruyter.com/document/doi/10.1515/jqas-2012-0036/html) (`r doi("10.1016/j.knosys.2013.05.008")`).
-   `r pkg("mvglmmRank")` provides functions for building multivariate generalized mixed models for ranking teams in sports.

### Links

-   Lopez, M. J., Matthews, G. J., and Baumer, B. S. (2018). [How often does the best team win? A unified approach to understanding randomness in North American sport](https://projecteuclid.org/journals/annals-of-applied-statistics/volume-12/issue-4/How-often-does-the-best-team-win-A-unified-approach/10.1214/18-AOAS1165.full). *The Annals of Applied Statistics*, 12(4), 2483-2516.
-   Constantinou, A. C., Fenton, N. E., and Neil, M. (2013). [Profiting from an inefficient Association Football gambling market: Prediction, Risk and Uncertainty using Bayesian networks](https://www.sciencedirect.com/science/article/pii/S095070511300169X). *Knowledge-Based Systems*, 50, 60-86.
-   Zuccolotto, P., and Manisera, M. (2020). [*Basketball data science: with applications in R*](https://www.routledge.com/Basketball-Data-Science-With-Applications-in-R/Zuccolotto-Manisera/p/book/9780429470615). CRC Press.
-   Marchi, M., Albert, J., and Baumer, B. S. (2018). [*Analyzing baseball data with R*](https://www.taylorfrancis.com/books/mono/10.1201/9781351107099/analyzing-baseball-data-max-marchi-jim-albert-benjamin-baumer). 2nd edition. Chapman and Hall/CRC.
-   Bradley, R. A., & Terry, M. E. (1952). [Rank analysis of incomplete block designs: I. The method of paired comparisons.](https://www.jstor.org/stable/2334029?seq=1) *Biometrika*, 39(3/4), 324-345.