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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 bookCodes for reproducing the case studies presented throughout the book with Overview of the book (Chapter Abstracts) |
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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.
BasketballAnalyzeR
: analysis and visualization of basketball data.BasketballAnalyzeR
, in: Zuccolotto P. and Manisera M., Basketball Data Science – with Applications in R. Chapman and Hall/CRC, Chapter 6.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
BasketballAnalyzeR
is on CRAN and github.
There are three alternative procedures, that can be optionally chosen by the user.
install.packages("BasketballAnalyzeR")
and then press EnterBasketballAnalyzeR
package, write library(BasketballAnalyzeR)
and then press Enterexample(shotchart)
and then press EnterBasketballAnalyzeR
package, write library(BasketballAnalyzeR)
and then press Enterexample(shotchart)
and then press EnterWith 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.
install.packages("devtools")
and then press Enterdevtools::install_github("sndmrc/BasketballAnalyzeR")
and then press EnterBasketballAnalyzeR
package, write library(BasketballAnalyzeR)
and then press Enterexample(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
.
All the data are in .Rdata
format and can be used to reproduce the analyses presented in the book on other datasets
Pbox1819, Tbox1819, Obox1819, Tadd1819
having the same structure of Pbox, Tbox, Obox, Tadd
)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.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.
See the most frequently asked questions
See the errata corrige
Send your questions to basketballanalyzer.help@unibs.it