Specialised courses

University of Caen, France – BIGSPORTSDATA Summer School

2022, 27th – 30th June

The digitization of human activities has recently extended to sports practices, making large amounts of data available to researchers. This data is useful to help refereeing, improve performance and monitoring of athletes or provide strategic analysis, but also develop the spectator experience.

While sports data analysis has in the past decades been more of a craft (practiced by coaches or sports journalists) than a science, this situation has recently changed. More and more mathematical and computational techniques have entered the field and are being used to support the work of movement and sport science experts, coaches and managers of professional teams, but also health actors.

Most of the major teams in American sports and European soccer already hire data analysts; data analysis has become a flourishing industry and the CNRS has recently launched a GDR “Sport and physical activity ” with the aim of facilitating interactions between researchers in different fields.

Today, large amounts of data are regularly generated, not only by professional athletes, but also, with the advent of low-cost, high-quality sensors (e.g. in smartphones) by private individuals.

Despite the existence of these data sources, techniques to analyze them and the interest to do so, the three groups involved – data owners, analysts and experts/users – are not necessarily in contact or in collaboration.

This summer school is supported by the CNRS and organized by François Rioult and Albrecht Zimmermann.

Module by Marica Manisera and Paola Zuccolotto (University of Brescia):

Marica Manisera – 60 min – Introduction to Basketball Data Science – This part provides the understanding of the basic concepts of basketball data science.
Paola Zuccolotto – 60 min – Basketball data and the R package BasketballAnalyzeR – Starting from a description of the various sources and forms of Basketball data, this part introduces the R package BasketballAnalyzeR and suggests how to use it with some basic and advanced methods of statistical data analysis.

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Athens University of Economics and Business – BASKETBALL DATA SCIENCE

Teachers: Marica Manisera and Paola Zuccolotto (University of Brescia)

2022, 23rd – 24th May

Exercises

Manisera M., Sandri M., Zuccolotto P. (2020), Advances in basketball statistics, in Ley C., Dominicy Y. (editors), Science meets Sports: when statistics are more than numbers, Cambridge Scholars Publishing, Newcastle upon Tyne (UK), ISBN 978-1-5275-5856-4, 19-53.

ISI 2021 Short Course – BASKETBALL DATA ANALYSIS

Teachers: Marica Manisera and Paola Zuccolotto (University of Brescia)

2021, 21st – 22nd June

This short course, organised by the Special Interest Group on Sports Statistics of the International Statistical Institute, 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.

Syllabus:

The course is concerned with the description and discussion of some statistical tools useful to analyse 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.

CORSO DI MATCH ANALYSIS PER LA PALLACANESTRO (in Italian)

First Edition: 2018, 21st – 22nd May and 10th – 11th September

Course for basketball coaches (CNA qualification required) coordinated by Prof. Raffaele Imbrogno

Teachers: Paola Zuccolotto e Marica Manisera (Università di Brescia), Dario dalla Vedova (Scienza dello Sport CONI), Paolo Raineri (CEO MYagonism), Giuseppe di Paolo (Reggio Emilia), Mario Fioretti (Olimpia Milano), Stefano Vanoncini (Poderosa Montegranaro), Alessandro Magro (Leonessa Brescia), Guido Corti (StatBasket).

Supplementary material and services available in the password protected page (reserved for the participants to the course)