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Topic General/Theory

Instructors

Affiliated with University of Brescia, Italy

Prof. Marica Manisera

Marica Manisera is Associate Professor of Statistics at the University of Brescia (Italy) and carries out scientific research in Statistical Science, both with a methodological and applied approach. She is Chair of the ISI SIG in Sport Statistics, scientific co-coordinator of the project “Big Data Analytics in Sports” and Associate Editor of the Journal of Sports Analytics.

In the context of Sports Analytics, she has organized workshops and sessions at statistical conferences and guest edited Special Issues. She co-supervises the Statistics in sports section of the PhD program in Analytics for Economics and Management at the University of Brescia.

Prof. Paola Zuccolotto

Paola Zuccolotto is Full Professor of Statistics at the University of Brescia (Italy) and carries out scientific research in Statistical Science, both with a methodological and applied approach. She is in the Management Committee of the ISI SIG in Sport Statistics, scientific co-coordinator of the project “Big Data Analytics in Sports” and member of the Editorial Advisory Board of the Journal of Sports Sciences.

In the context of Sports Analytics, she has organized workshops and sessions at statistical conferences and guest edited Special Issues. She co-supervises the Statistics in sports section of the PhD program in Analytics for Economics and Management at the University of Brescia.

Course description

This short course offers instructor-led and hands-on training in basketball analytics for students, young statisticians, and sports professionals. It provides the understanding of the concepts of basketball data science, by covering basic statistics tools and advanced methods of data analysis, as discussed in the book Basketball Data Science – with Applications in R by P. Zuccolotto and M. Manisera (2020) and using the R package BasketballAnalyzeR. Real examples from NBA data are shown and small exercises are assigned to students.

Target audience

Young statisticians, young researchers in sport statistics, students with basic knowledge of statistics and R language, sports professionals with basic knowledge of statistics and R language. Prerequisites: Basic knowledge of statistics and R language.

Syllabus

The course is concerned with the description and discussion of some statistical tools useful to analyze basketball data, in order to make a valid support for technical experts in the field. Through the use of real cases and applications, the course aims at providing operational and practical guidance to data analysis useful to support decisions of technical experts.

In particular, the learners will gain the following skills:

  • Knowledge and understanding: learners will acquire the methodological and applied knowledge about the basic statistical concepts of basketball data analysis and will be able to apply such knowledge by means of appropriate software.
  • Applying knowledge and understanding: learners will be able to use some of the main exploratory methods of data analysis in order to analyse real basketball data.
  • Making judgements: learners will be able to analyse and interpret basketball data and organize results in order to draw conclusions and support basketball technical decisions.
  • Communication skills: learners will be able to communicate, to experts and non-experts, data information with the help of outputs from specific software of data analysis and visualization.
  • Learning skills: learners will learn how to use the R package to answer research and practical questions about basketball analytics. This can be a starting point to face subsequent research investigations.

Required software

R with the R package BasketballAnalyzer already installed.