The school is organized within the activities of the BDsports and is part of the initiatives promoted by CLADAG (Classification and Data Analysis Group of the Italian Statistical Society).
The aim of the school is to provide an overview of statistical methods and models useful for the analysis of sports data. Topics will include a variety of methodological approaches and applications, in line with the research interests and expertise of the lecturers.
The school is intended for PhD students, young researchers, and practitioners with an interest in statistical methods applied to sports. The program will include both theoretical lectures and hands-on sessions, fostering interaction and discussion.
Participation in the CLADAG School is limited to a maximum of 28 participants. Should the number of applications exceed this limit, a selection process will be carried out by the Scientific Committee based on the applicants’ CVs, which must be submitted together with the registration form.
Coordinators: Marica Manisera and Paola Zuccolotto, BDsports, University of Brescia, Italy
Eugenio Brentari, University of Brescia, Italy
Maurizio Carpita, University of Brescia, Italy
Leonardo Egidi, University of Trieste, Italy
Ambra Macis, University of Brescia, Italy
Andreas Groll, TU Dortmund University, Germany
Dimitris Karlis, Athens University of Economics and Business, Greece
Christophe Ley, University of Luxembourg, Luxembourg
Marica Manisera, University of Brescia, Italy
Ioannis Ntzoufras, Athens University of Economics and Business, Greece
Paola Zuccolotto, University of Brescia, Italy
Big Data Analytics in Sports (BDsports)
PRIN Project “SMARTSports”, Statistical Models and AlgoRiThms in sports. Applications in professional and amateur contexts, with able-bodied and disabled athletes, PRIN 2022
International Statistical Institute – Special Interest Group on Sports Statistics
| Monday, 16 March 2026 | ||
| 9:00 – 9:15 | M. Manisera, P. Zuccolotto | Welcome and opening remarks |
| 9:15 – 10:00 | P. Zuccolotto | Introduction to Sports Analytics |
| 10:00 – 13:00 | I. Ntzoufras | Current Issues in Sports Analytics and Double Poisson Model for Football |
| 14:30 – 17:30 | I. Ntzoufras | Current Issues in Sports Analytics and Double Poisson Model for Football |
| Tuesday, 17 March 2026 | ||
| 9:30 – 12:30 | D. Karlis | Ranking and Rating in Sports |
| 14:30 – 17:30 | D. Karlis | Tracking Data in Sports |
| Wednesday, 18 March 2026 | ||
| 9:30 – 12:30 | C. Ley | Statistically Enhanced Learning for Sports Analytics and Medicine |
| 14:30 – 17:30 | C. Ley | Statistically Enhanced Learning for Sports Analytics and Medicine |
| Thursday, 19 March 2026 | ||
| 9:30 – 12:30 | A. Groll | Modeling and Prediction of Match Outcomes |
| 14:30 – 17:30 | A. Groll | Modeling and Prediction of Match Outcomes |
| Friday, 20 March 2026 | ||
| 9:30 – 11:30 | Round Table | PRIN Project “SMARTSports” (PI: L. Egidi, University of Trieste) |
| 11:30 – 12:15 | M. Manisera | Conclusions and Discussion |
| 12:15 – 12:30 | M. Manisera, P. Zuccolotto | Closing remarks |
Prof. Ioannis Ntzoufras, Athens University of Economics and Business, Greece
Current Issues in Sports Analytics
The Double Poisson Model for Football
Prof. Dimitris Karlis, Athens University of Economics and Business, Greece
Rating and Ranking in Sports
Tracking data in Sports
Prof. Christophe Ley, University of Luxembourg, Luxembourg
Prof. Andreas Groll, TU Dortmund University, Germany
The course introduces the basics of modeling and predicting match outcomes. We will provide an introduction to standard modeling techniques such as Generalized Linear Models (GLMs), regression and classification trees (CARTs), and random forests. Moreover, the course will address different sports, such as football (soccer), handball, and tennis.
The lessons combine theoretical topics with practical examples using R software.
Egidi, L., Karlis, D., Ntzoufras, I. (2025). Predictive Modelling for Football Analytics. 1st Edition. Chapman & Hall. ISBN 9781032030630.
Aldous, D. (2017) Elo Ratings and the Sports Model: A Neglected Topic in Applied Probability? Statistical. Science 32, 616–629
Cattelan, M.; Varin, C.; Firth, D. (2013) Dynamic Bradley–Terry modelling of sports tournaments. Applied Statistics, 62, 135–150
Kovalchik, S. A. (2023). Player tracking data in sports. Annual Review of Statistics and Its Application, 10(1), 677-697.
Fahrmeir, L., Kneib, T., Lang, S., Marx, B. (2021). Regression – Models, Methods and Applications. New York: Springer.
Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning – Data Mining, Inference, and Prediction. New York: Springer
– Software: R (open source), RStudio, R packages: ranger, party, rpart, mlr3, WinBUGS/OpenBUGS, BradleyTerry2, elo, footBayes
– Course Material. All course materials, including the data and R scripts for the examples, will be made available for course participants.
