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Sports analytics is defined as the process that identifies and acquires knowledge and insight about players and teams’ performances. To do so, analysts use a wide variety of data sources coming from matches and individual players’ performances (O'Donoghue & Holmes 2014; Jayal, McRobert, Oatley & O’Donoghue, 2018). Nowadays, detailed data from different nature including technical skills, individual physiological performances, team formations, or injuries are analysed on a daily basis by the analytics departments belonging to sports clubs and professional franchises. Even private companies like STATS or OPTA generate important revenues offering their movement tracking values and advanced metrics to media and fans. In the emerging field of Sports Analytics, as in many others, analysts must be aware of spurious correlations. These can come up due to the size (not nature) of data, a common-causal variable or just due to serendipity. For this reason, we always must keep in mind the lessons of the statistician Stephen John Senn and his sharp quote on the matter: “Statistics is not just about merely warning that correlation is not causation. Sometimes correlation isn’t even correlation”. Thus, we will explain an example of how climate change can be affecting, or not, on the FIFA World Cup performance statistics.
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RICYDE. Revista Internacional de Ciencias del Deporte
Publisher: Ramón Cantó Alcaraz
ISSN:1885-3137 - Periodicidad Trimestral / Quarterly