School of Science and Technology, University of New England, Armidale, NSW, Australia.
Carnegie Applied Rugby Research (CARR) Center, Carnegie School of Sport, Leeds Beckett University, Leeds, United Kingdom.
J Strength Cond Res. 2022 Jul 1;36(7):1951-1955. doi: 10.1519/JSC.0000000000003745. Epub 2020 Sep 17.
Cummins, C, Charlton, G, Naughton, M, Jones, B, Minahan, C, and Murphy, A. The validity of automated tackle detection in women's rugby league. J Strength Cond Res 36(7): 1951-1955, 2022-This study assessed the validity of microtechnology devices to automatically detect and differentiate tackles in elite women's rugby league match-play. Elite female players (n = 17) wore a microtechnology device (OptimEye S5 device; Catapult Group International) during a representative match, which involved a total of 512 tackles of which 365 were defensive and 147 were attacking. Tackles automatically detected by Catapult's tackle detection algorithm and video-coded tackles were time synchronized. True positive, false negative and false positive events were utilized to calculate sensitivity (i.e., when a tackle occurred, did the algorithm correctly detect this event) and precision (i.e., when the algorithm reported a tackle, was this a true event based on video-coding). Of the 512 video-derived attacking and defensive tackle events, the algorithm was able to detect 389 tackles. The algorithm also produced 81 false positives and 123 false negatives. As such when a tackle occurred, the algorithm correctly identified 76.0% of these events. When the algorithm reported that a tackle occurred, this was an actual event in 82.8% of circumstances. Across all players, the algorithm was more sensitive to the detection of an attacking event (sensitivity: 78.2%) as opposed to a defensive event (sensitivity: 75.1%). The sensitivity and precision of the algorithm was higher for forwards (sensitivity: 81.8%; precision: 92.1%) when compared with backs (sensitivity: 64.5%; precision: 66.1%). Given that understanding the tackle demands of rugby league is imperative from both an injury-prevention and physical-conditioning perspective there is an opportunity to develop a specific algorithm for the detection of tackles within women's rugby league.
库明斯、C、查尔顿、G、诺顿、M、琼斯、B、米纳汉、C 和墨菲、A. 自动擒抱检测在女子英式橄榄球联盟中的有效性。J 强实力研 36(7):1951-1955,2022-本研究评估了微技术设备自动检测和区分精英女子英式橄榄球联盟比赛中擒抱的有效性。17 名精英女球员在一场具有代表性的比赛中佩戴了微技术设备（Catapult Group International 的 OptimEye S5 设备），总共有 512 次擒抱，其中 365 次是防守性的，147 次是进攻性的。Catapult 的擒抱检测算法自动检测到的擒抱和视频编码的擒抱进行了时间同步。真阳性、假阴性和假阳性事件被用来计算敏感性（即，当发生擒抱时，算法是否正确检测到该事件）和精度（即，当算法报告擒抱时，根据视频编码，这是否是一个真实事件）。在 512 次视频衍生的进攻性和防守性擒抱事件中，算法能够检测到 389 次擒抱。该算法还产生了 81 个假阳性和 123 个假阴性。因此，当发生擒抱时，算法正确识别了这些事件的 76.0%。当算法报告发生擒抱时，在 82.8%的情况下，这是一个实际事件。在所有球员中，该算法对攻击事件的检测更为敏感（敏感性：78.2%），而不是防守事件（敏感性：75.1%）。与后卫（敏感性：64.5%；精度：66.1%）相比，前锋（敏感性：81.8%；精度：92.1%）的算法敏感性和精度更高。鉴于从伤病预防和体能训练的角度理解橄榄球联盟的擒抱需求至关重要，因此有机会为女子橄榄球联盟的擒抱检测开发特定的算法。
