Data Science for Sports Analytics

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Sports analytics have evolved in recent years in an amazing way, thanks to automated or semi-automated sensing technologies that provide high-fidelity data streams extracted from every game. In the lab we are investigating data-driven approaches to boost the understanding of sports performance, in two main context: cycling and football..
Manager: 
Cintia, P., L. Pappalardo, D. Pedreschi, F. Giannotti, and M. Malvaldi, "The harsh rule of the goals: data-driven performance indicators for football teams", IEEE International Conference on Data Science and Advanced Analytics, 2015.
Cintia, P., L. Pappalardo, and D. Pedreschi, "Mining efficient training patterns of non-professional cyclists", 22nd Italian Symposium on Advanced Database Systems, {SEBD} 2014, Sorrento Coast, Italy, June 16-18, 2014., 2014.
Cintia, P., L. Pappalardo, and D. Pedreschi, ""Engine Matters": {A} First Large Scale Data Driven Study on Cyclists' Performance", 13th {IEEE} International Conference on Data Mining Workshops, {ICDM} Workshops, TX, USA, December 7-10, 2013, 2013.

Paolo Cintia and Luca Pappalardo have joined the conversation on sports at Radio Aula 40 talking about sports analytics.

Big Data applied to soccer: Paolo Cintia and Luca Pappalardo are interviewed by Zona Cesarini, a sport program on Rai Radio 1, the Italian national radio broadcasting network.

Paolo Cintia and Luca Pappalardo interviewed by Radio 3 Scienza on the italian national broadcasting network Rai Radio 3.

Un computer a disposizione di un allenatore era un’utopia, nel 1973. La storia dei dati e del calcolo applicato al calcio non poteva che cominciare in un posto dove di utopia, nel 1973, se ne intendevano: l’Unione Sovietica.

I passaggi tra giocatori sono relazioni in una rete: volume e imprevedibilità delle connessioni determinano il risultato

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Image by Charis Tsevis CC NC-ND 2.0, via Flickr
Acronym
DSSA
Start Date
1 June 2013
Type
Area
Affiliation
Department of Computer Science, University of Pisa (DI-UNIPI)