Composition Partnership
1. Università degli Studi di Napoli Federico II, Italy (Coordinator and Host Institution)
2. Instituto Politécnico de Viana do Castelo, Portugal (Co-host Institution)
3. IE University, Spain (sending institution)
4. International school for social and business studies, Slovenia (sending institution)
5. Technische Universität Dortmund, Germany (sending institution)
6. Technische Universität München, Germany(sending institution)
7. Athens University of Economics and Business, Greece (sending institution)
Topic of the program
This course offers an in-depth exploration of sports analytics, focusing on the application of data science and machine learning techniques to sports data. The programme aims to provide analytical skills to create, manage and interrogate large datasets applicable to or derived from the sports industry. Participants will learn to analyse and interpret complex data structures, develop predictive models and understand team and player performance, strategy and the business of sport. In addition a set of programming tools will facilitate the implementation of models and enable participants to analyse decision-making processes.
This course provides the analytical requirements of a sports management programme. It is aimed at all students interested in data science as well as sports analysts and stakeholders.
At the end of the BIP, participants will be able to:
-Improve their ability to build empirical models and implement them through specification, estimation and simulation using the R package.
- Gain an understanding of basic statistical concepts and their applications in the world of sport.
- Develop proficiency in data collection, cleaning, and analysis.
- Obtain a broad overview of the methods used in the acquisition, processing, analysis, visualisation and implementation of sports data.
- Understand the role of analytics in sports decision-making
Schedule description
a. Description of the physical component
The course consists of one week in presence (8 September - 12 September 2025) organised as described below.
Face-to-face; 5 days
September 8, 2025
- Full day
Lecture: Complex data structures in Sports Analytics
Main topics: Introduction. Big data in Sport Analytics. Applications and Technologies. History and evolution of sports analytics, Machine learning and AI in sports
September 9, 2025
- Morning
Lecture: Statistical Analysis in Sports
Main topics: Descriptive analysis. Inferential statistics; Regression analysis
- Evening
Laboratory lectures – supervised tutorial
September 10, 2025
- Morning
Lecture: Methods for dimension reduction and clustering in Sport Analytics
Main topics: Clustering mixed type nominal/ordinal/interval data, Joint dimension reduction and clustering. Supervised and unsupervised learning.
- Evening
Laboratory lectures – supervised tutorial, individual and team work
September 11, 2025
- Morning
Lecture: Latent variable models in sport surveys analytics
Main topics: Latent variables, Classical Test Theory, Item Response Theory, IRT models for polytomous items, Hierarchically Structured Modelling
- Evening
Lecture: Performance Analysis
Main topics: Game strategy and tactics analysis, Player performance evaluation, Team dynamics and performance metrics, Internal and external load quantification for Load Management
September 12, 2025
- Morning
Lecture: Injury Analytics
Main topic: Sports injury prevention, Risk injury modelling.
- Evening
Summary, feedback, alternative methods, wrap-up
b. Description of the virtual component
The course includes 4 virtual sessions (Live virtual sessions) organised as follows
September 3, 2025
Introduction to the topic of Statistical learning for Sport Analytics
Main topics: General introduction to the course with the participation of all the representatives of the partner Institutes/Universities
September 4, 2025
Presentation of the Data integration, Data Wrangling and Integration in Sports, pre-processing and data fusion system (with the support of a technical expert)
Main topics: Tidy data and the main different types of messy data; pivoting data, Rectangling data and Nesting data; Splitting and combining data; Joining data; Recode and manipulate variables; Basic treatment of missing variables; Cleaning dirty data; Data analysis will be performed on national and international surveys
October 9, 2025
Presentation of results using Dashboard (with the participation of a BIP Faculty expert in Data Science & Machine Learning).
Main topics: Dashboard creation; Customise the dashboard: layout, components, data visualisations and tables; Use of external source file to separate dashboard design and dashboard content; Parameterized markdown to customise and automate dashboards
October 20, 2025
Presentation of the results of an analysis project by participants (final meeting) and seminar by a keynote speaker on Exploring Frontiers in Sports Analytics
Practical information
- Level of students: Master and PhD students
- Number of ECTS: 3
- Main language of instruction/training: English
- Venue of Activities (City, Institution): Naples, Department of Political Sciences, University of Naples Federico II (Statistics Laboratory)
Open Badge has been associated with our program, providing a digital recognition of the skills and achievements gained through participation.
Further details at: https://bestr.it/badge/show/4685?ln=en
https://bestr.it/project/show/229?ln=en
Course information, main materials used by BIP Faculty and professionals will be available at: https://indico.unina.it/e/bip25
In addition, a tutor will be available for each participant during the training period (weekly online mentorship from Sport Analytics experts) and a dedicated programme manager provided by BIP Faculty will be available virtually. These figures will act as support in the learning and skills development phase.
