Data Science for Sports Analytics
The Data Science for Sports Analytics group has a multidisciplinary background, i.e. machine learning technique, big data analysis and sports science. The main topics of this group involve the development of models in sports and in particular in soccer that permits to deeply assess the sport performance both in physical demands and technical-tactical performances.
SoBigData++ strives to deliver a distributed, Pan-European, multi-disciplinary research infrastructure for big social data analytics, coupled with the consolidation of a cross-disciplinary European research community, aimed at using social mining and big data to understand the complexity of our contemporary, globally-interconnected society. SoBigData++ will move forward from a starting community of pioneers to a wide and diverse scientific movement, capable of empowering the next generation of responsible social data scientists, engaged in the grand societal challenges laid out in its exploratories: Societal Debates and Online Misinformation, Sustainable Cities for Citizens, Demography, Economics & Finance 2.0, Migration Studies, Sports Data Science, Social Impact of Artificial Intelligence and Explainable Machine Learning. In particular, the exploratory on Sports Data Science focuses on research related to sports and health analytics.
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PLOS ONE, 13 (7), pp. e0201264, 2018.