The influence of training, posture, nutrition or psychological attitudes on an athlete’s career is well described in literature. An additional factor of success that is widely recognised as crucial is the network of matches that an athlete plays during a season. The hypothesis is that the quality of a player’s opponents affects her long-term ranking and performance. Even though the relevance of these factors is widely recognised as important, a quantitative characterisation is missing. In this paper, we try to fill this gap combining network analysis and machine learning to estimate the contribution of the network of matches in predicting an athlete’s success. We consider all the official games played by the Italian table tennis players between 2011 and 2016. We observe that the matches network shows scale-free behaviour, typical of several real-world systems, and that different structural properties are positively 15 correlated with the athletes’ performance (Spearman ρ ¼ 0:88, p-value <0:01). Using these findings, we implement three different tasks, such as talent identification, performance and ranking prediction. Results shows consistently that machine learning approaches are able to predict players’ success and that the topological features play an effective role in increasing their predictive power.

The role of the network of matches on predicting success in table tennis

Lai, Mirko;Meo, Rosa;Schifanella, Rossano;Sulis, Emilio
2018-01-01

Abstract

The influence of training, posture, nutrition or psychological attitudes on an athlete’s career is well described in literature. An additional factor of success that is widely recognised as crucial is the network of matches that an athlete plays during a season. The hypothesis is that the quality of a player’s opponents affects her long-term ranking and performance. Even though the relevance of these factors is widely recognised as important, a quantitative characterisation is missing. In this paper, we try to fill this gap combining network analysis and machine learning to estimate the contribution of the network of matches in predicting an athlete’s success. We consider all the official games played by the Italian table tennis players between 2011 and 2016. We observe that the matches network shows scale-free behaviour, typical of several real-world systems, and that different structural properties are positively 15 correlated with the athletes’ performance (Spearman ρ ¼ 0:88, p-value <0:01). Using these findings, we implement three different tasks, such as talent identification, performance and ranking prediction. Results shows consistently that machine learning approaches are able to predict players’ success and that the topological features play an effective role in increasing their predictive power.
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http://dx.doi.org/10.1080/02640414.2018.1482813
Social network analysis; machine learning; performance analysis; talent identification; ranking prediction
Lai, Mirko; Meo, Rosa; Schifanella, Rossano; Sulis, Emilio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1669672
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