Prieto-Lage Iván, Prieto M A, Curran Thomas P, Gutiérrez-Santiago Alfonso
Faculty of Education and Sports Sciences, University of Vigo, Vigo, Spain.
Nutrition and Bromatology Group, Faculty of Food Science and Technology, University of Vigo, Ourense Campus, Vigo, Spain.
J Hum Kinet. 2018 Jun 13;62:199-212. doi: 10.1515/hukin-2017-0170. eCollection 2018 Jun.
The goal of this study was to present an accurate and rapid detection system to identify patterns in tennis, based on t-pattern analysis. As a case study, the break point situations in the final matches of the clay court tournaments played during the seasons 2011 and 2012 between the tennis players Novak Djokovic and Rafael Nadal were chosen. The results show that Nadal achieves a higher conversion rate with respect to Djokovic in the break point situations, independent of the outcome of the match. Some repetitive patterns of both players were revealed in break point circumstances. In long rally sequences (higher than seven hits), the Spanish player won more break points, both serving and receiving, as a result of unforced errors of his opponent's backhand. In medium rally sequences (between four and seven hits), other factors such as the type, direction or serve location have shown to play an important role in the outcome of the point. The study also reveals that Djokovic frequently commits double faults in these critical situations of the match. This is the first time that t-patterns have been used to analyze the sport of tennis. The technique is based on computer vision algorithms and video recording material to detect particular relationships between events and helps to discover the hidden mechanistic sequences of tennis players.
本研究的目的是基于t模式分析,提出一种准确、快速的检测系统,以识别网球运动中的模式。作为一个案例研究,选取了网球运动员诺瓦克·德约科维奇和拉斐尔·纳达尔在2011赛季和2012赛季之间进行的红土场地锦标赛决赛中的破发点情况。结果表明,在破发点情况下,纳达尔相对于德约科维奇实现了更高的转化率,与比赛结果无关。在破发点情况下,揭示了两位球员的一些重复模式。在长回合序列(超过七次击球)中,由于对手反手的非受迫性失误,这位西班牙球员在发球和接发球时赢得了更多的破发点。在中等回合序列(四到七次击球)中,其他因素,如类型、方向或发球位置,已证明在得分结果中起着重要作用。该研究还表明,德约科维奇在比赛的这些关键情况下经常双发失误。这是首次使用t模式来分析网球运动。该技术基于计算机视觉算法和视频记录材料,以检测事件之间的特定关系,并有助于发现网球运动员隐藏的机制序列。