BasketballAnalyzeR

BasketballAnalyzeR is an R package that accompanies the book:

P. Zuccolotto and M. Manisera (2020) Basketball Data Science – With Applications in R, Chapman and Hall/CRC. ISBN 9781138600799.

It has been developed by Marco Sandri, Paola Zuccolotto, Marica Manisera (Big&Open Data Innovation Laboratory BODaI-Lab, University of Brescia) and provides functions for analysis and visualization of Basketball Data.

This web page gives details and information about the package.

Supplementary material for the book

Codes for reproducing the case studies presented throughout the book with BasketballAnalyzeR – R code checked under R-3.5.3 for Windows (64 bit) and for Mac OS X 10.11

pdf format

R format

Overview of the book (Chapter Abstracts)

Authors have invested a lot of time and effort in creating this package. Please give credit and cite BasketballAnalyzeR when you use it for data analysis.

Suggested citations:

Paola Zuccolotto and Marica Manisera (2020), Basketball Data Science – with Applications in R. Chapman and Hall/CRC. ISBN 9781138600799.
Marco Sandri, Paola Zuccolotto, and Marica Manisera (2020).
BasketballAnalyzeR: analysis and visualization of basketball data.
https://CRAN.R-project.org/package=BasketballAnalyzeR
Marco Sandri (2020), The R package BasketballAnalyzeR, in: Zuccolotto P. and Manisera M., Basketball Data Science – with Applications in R. Chapman and Hall/CRC, Chapter 6.
Marica Manisera, Marco Sandri and Paola Zuccolotto (2019), BasketballAnalyzeR: the R package for basketball analytics. Proceedings of the Conference Smart Statistics for Smart Applications (SIS 2019), Università Cattolica del Sacro Cuore, Milan, 19st-21st June 2019, 395 – 402.

Download BibTeX citations for the book and the R package BasketballAnalyzeR

How to install BasketballAnalyzeR

BasketballAnalyzeR is on CRAN and github.

Step 1 – Installing R

Download the latest version of R from CRAN
Install R following the instructions of the installer (you can safely use the default settings and just keep clicking Next until R starts installing)
If you have experienced problems with the installation, read for example here or here

Step 2 – Installing BasketballAnalyzeR

There are three alternative procedures, that can be optionally chosen by the user.

PROCEDURE 1 – STANDARD INSTALLATION FROM CRAN

Open R
Write install.packages("BasketballAnalyzeR") and then press Enter
Wait until the package is installed
To load the BasketballAnalyzeR package, write library(BasketballAnalyzeR) and then press Enter
To test the package, write example(shotchart) and then press Enter

PROCEDURE 2 – INSTALLATION FROM LOCAL ZIP FILE

Download the package’s zip file
Install it from local zip file (from the R menu: Packages -> Install package(s) from local files -> BasketballAnalyzeR_0.5.0.tar.gz)
Wait until the package is installed (it can take several minutes)
To load the BasketballAnalyzeR package, write library(BasketballAnalyzeR) and then press Enter
To test the package, write example(shotchart) and then press Enter

PROCEDURE 3 – INSTALLATION OF THE DEVELOPMENT VERSION

With this procedure the user can install the latest version of the package, with the most recent updates in development version, not yet implemented in the CRAN version of the package.

Open R
Write install.packages("devtools") and then press Enter
To download and install the BasketballAnalyzeR package, write devtools::install_github("sndmrc/BasketballAnalyzeR") and then press Enter
Wait until the package is installed (it can take several minutes)
To load the BasketballAnalyzeR package, write library(BasketballAnalyzeR) and then press Enter
To test the package, write example(shotchart) and then press Enter

With BasketballAnalyzeR the following packages are automatically installed: ggplot2, hexbin, plyr, dplyr, tidyr, rlang, magrittr, ggrepel, gridExtra, scales, MASS, directlabels, corrplot, ggplotify, network, sna, dendextend, circlize, PBSmapping, sp, operators, stringr, GGally, statnet, common, ggnetwork, readr.

Data for exercises

All the data are in .Rdata format and can be used to reproduce the analyses presented in the book on other datasets

Boxscores NBA Regular Season 2018/2019 (containing the data frames Pbox1819, Tbox1819, Obox1819, Tadd1819 having the same structure of Pbox, Tbox, Obox, Tadd)
Play-by-play data of all the 82 games played by Cleveland Cavaliers in the Regular Season 2017/2018 (containing the data frame PbP.BDB.CLE having the same structure of PbP.BDB; note that the data frame has to be manipulated with PbP.CLE <- PbPmanipulation(PbP.BDB.CLE) in order to obtain a file with the same structure of PbP, as explained in Section 2.1 of the book). Data have been kindly made available by BigDataBall, a data provider which leverages computer-vision technologies to richen and extend sports datasets with lots of unique metrics. Since its establishment, BigDataBall has also supported many academic studies and is referred as a reliable source of validated and verified stats for NBA, MLB, NFL and WNBA.

Testimonials

Statistics et al. (Jack Davis)

“My overall impression of “Basketball Data Science” is that it’s exactly the sort of book I would recommend to an instructor or able student of statistics in sport.”

“On a spectrum from “all methodology”, like Casella and Berger’s famous textbook, to “all results” like a typical sports almanac, I would put “Basketball Data Science” between Casella-Berger and Jim Albert’s “Visualizing Baseball”. “Basketball Data Science” is intended to show how the data analysis is done, rather than revealing many sports-related insights directly.”

Sweep Sports Analytics

“Latest academic research in the field of basketball analytics stress the importance of metrics such as pace, ratings, and the four factors. Inspired by the great book Basketball Data Science with Applications in R, we had a look at how the Milwaukee Bucks and the Atlanta Hawks performed as a team. Anyone interested in basketball analytics should definitely get their hands on a copy. The BasketballAnalyzeR R package is simply amazing.”

Related work

Fox, A., Manisera, M., Sandri, M., & Zuccolotto, P. (2022). Analyzing Basketball Data with BasketballAnalyzeR. CHANCE, 35(3), 42-56.

Blanco, A. T. (2022). Implementación del paquete de R BasketballAnalyzeR, Máster en Estadística Aplicada, Escuela Internacional de Posgrado, Granada, Spain.

Zuccolotto, P., Manisera, M., & Sandri, M. (2021). Alley‐oop! Basketball analytics in R. Significance, 18(2), 26-31.

FAQ

See the most frequently asked questions

ERRATA CORRIGE of the book “Basketball Data Science”

See the errata corrige

Send your questions to basketballanalyzer.help@unibs.it