The conference will feature 5 high-profiles keynote speakers:
Paola Zuccolotto is a prominent Italian academic and statistician who currently serves as a Full Professor of Statistics at the University of Brescia's Department of Economics and Management.
She earned her Master's degree in Economics and Commerce with highest honors from the University of Verona in 1995, followed by a PhD in Methodological Statistics from the University of Trento in 1998.
Her research interest range from data mining and data analysis, time series analysis and sports analytics.
The application of Data Science tools to sports data has been gaining significant popularity recently, capturing the attention of sports managers, coaches, athletes, fans, and enthusiasts. Scientific research in this area is expanding, with innovative methods being developed and new application areas being investigated. Findings are increasingly shared through a growing number of books and scientific articles, published in both specialized journals and dedicated issues focused on specific topics. In 2016, the Big and Open Data Innovation Laboratory at the University of Brescia, Italy (BODaI-Lab, bodai.unibs.it), launched the international scientific network Big Data Analytics in Sports (BDsports, bdsports.unibs.it), coordinated by Paola Zuccolotto and Marica Manisera. The primary goal of BDsports is to establish a broad network of individuals interested in sports analytics, fostering connections between scientists and sports professionals. The project is built on four main pillars: scientific research, practical applications, dissemination, and education, with the latter two focusing on spreading interest in these topics among students and the public. In late 2018, the International Statistical Institute (ISI) entrusted BDsports with creating a new Special Interest Group (SIG) on Sports Statistics. This initiative aimed to further expand the use of Data Science in sports, operating under the official umbrella of ISI, the leading global organization in the field of Statistics. Researchers within BDsports are committed to the analysis of several different sports; however, in this talk, we will focus specifically on basketball. We will summarize the main activities carried out by BDsports across its four pillars, with particular emphasis on scientific research and education. For scientific research, we will outline the main topics explored by BDsports, including the analysis of performance in high-pressure game situations, the identification of new player roles, and the estimation of scoring probabilities from different areas of the court. Regarding the education pillar, we will briefly introduce our book Basketball Data Science and its upcoming second volume, which focuses on advanced topics.
Frits Spieksma is a distinguished academic in the field of Operations Research and Combinatorial Optimization.
He currently serves as Full Professor and Chair of Combinatorial Optimization in the Department of Mathematics and Computer Science at Eindhoven University of Technology.
His research topics include logistics optimization, Kidney exchange models scheduling and routing problems and sport scheduling systems.
At first sight the schedule of any particular league or competition may look like a mundane matter.
Indeed, it is obvious that one has to pick certain dates and times for a set of matches to be played.
However, that is not all there is to it.
There is a, sometimes subtle, relation between the schedule and the outcome of the tournament.
When one accepts this, it is clear that the schedule should be made with care, ensuring that, among the interests of all other stakeholders, fairness among players or teams, receives the attention it deserves.
We will review examples that shed light on the relation between the schedule on the one hand, and the outcome on the other hand.
From these examples, we extract analytical insights that allow us to construct fairer schedules.
We illustrate our findings by considering, among others, the Champions League in football, the TATA Steel Chess tournament, and the Premier League of Darts.
Romain Seil is a full professor and specialist in orthopaedic surgery and sports medicine.
He currently serves as President of the Luxembourg Institute for Research in Orthopaedics, Sports Medicine and Science (LIROMS) and works as an orthopaedic surgeon at the Centre Hospitalier de Luxembourg since 2004.
Prof. Seil earned his doctoral thesis on meniscal repair in 2001 and his professorial thesis on pediatric anterior cruciate ligament injuries in 2003.
He was appointed as an extraordinary professor in orthopaedic surgery at the University of Saarland in 2007.
His research interests and clinical activities focus on orthopaedic sports medicine, osteotomies of the knee, and primary and revision knee arthroplasty.
In today's world of sports and health, we collect more data than ever—from motion sensors, GPS trackers, strength tests, and medical records reaching from individual diagnostic and intraoperative imaging tools to large scale registries. But how can we turn all that information into better care for athletes and patients? And which are the barriers to overcome for data acquisition in sports medicine? In this talk, we'll explore data mining—the process of finding patterns and meaning in large datasets — from the clinical perspective and illustrate how it can support injury prevention, better diagnosis, personalized treatment, and safer return-to-play decisions. Using practical examples, we'll show how research helps us understand how the knee is loaded during movement and injury, how data collection can help understand injuries, and how combining test results can guide recovery after surgery. Whether you're a researcher, a clinician, or just curious about how data is changing medicine, this session offers a clear view of how numbers, science, and care can come together to keep athletes healthier and performing at their best.
Tim Pawlowski is a Full Professor of Sport Economics at the University of Tübingen. He also serves as the Vice Dean for Research since 2019. His research focuses on sports economics, particularly in areas of competitive balance, sports participation, and the economic aspects of professional football.
Even though it is a popular claim that emotions play an important role in human behavior [1], robust empirical evidence from the field is scarce. In this keynote I will present a series of studies, where we consider sporting events as an 'emotions lab' to explore how emotions influence the behavior of fans during games. More specifically, we closely follow the work by Ely, Frankel, and Kamenica [2] to operationalize reference point-dependent emotions (Surprise and Suspense) and explore their effects on TV viewing behavior [3], Twitter activity [4], and alcohol use [5]. The modelling process in these studies requires the use of different data mining, simulation and machine learning techniques. For instance, to avoid endogeneity concerns when constructing Surprise and Suspense, Study 1 and Study 2 rely on in-play outcome probabilities from simulations. In contrast, Study 3 uses actual in-play odds from an Asian bookmaker minimizing any endogeneity concerns. However, these odds exhibit missing values requiring imputations using Neural Networks. To detect fans, haters, and neutrals from sentiment released in Tweets, Study 2 relies on Natural Language Processing tools. Finally, because of the complex hierarchical nature of our model in Study 3, we implement a recursive algorithm based on Hamiltonian mechanics.
References:
Université du Luxembourg
Department of Mathematics (DMATH)
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L-4364 Esch-sur-Alzette, Luxembourg
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