Monitoring external load during real competition in male handball players through big data analytics

Abstract

The present study aimed to analyze the external load put on elite male handball players during the 2020 European Championship differentiated by playing positions. A system based on three phases was designed: 1) information capture of game events through sensor networks, LPS system and WebScraping techniques; 2) information processing based on Big Data Analytics; 3) extraction of results based on a descriptive analytics approach. Results showed that wings (Ws) and center backs (CBs) performed more accelerations and decelerations than the players in other positions in the entire match and attack. In defense, wings showed higher values than the rest of the players, followed by line players (LPs). In regard to body contacts, the positions that received more average number during the whole match were the CBs and LPs, with the CBs presenting the highest values in offense and the LPs in defense. Finally, backs were the ones performing more total jumps per game and in offense. In defense, LPs and left backs presented the highest values. It is necessary to monitor individual high intensity events to develop individual training programmes for different playing positions. High-intensity decelerations should be specially considered since they enlarge injury risks.