England and Wales Cricket Board, Loughborough University, Loughborough, UK.
Catapult Sports, Loughborough University, Loughborough, UK.
J Sports Sci. 2020 Apr;38(7):767-772. doi: 10.1080/02640414.2020.1734308. Epub 2020 Feb 26.
Cricket fast bowlers are at a high risk of injury occurrence, which has previously been shown to be correlated to bowling workloads. This study aimed to develop and test an algorithm that can automatically, reliably and accurately detect bowling deliveries. Inertial sensor data from a Catapult OptimEye S5 wearable device was collected from both national and international level fast bowlers (n = 35) in both training and matches, at various intensities. A machine-learning based approach was used to develop the algorithm. Outputs were compared with over 20,000 manually recorded events. A high Matthews correlation coefficient ( showed very good agreement between the automatically detected bowling deliveries and manually recorded ones. The algorithm was found to be both sensitive and specific in training (96.3%, 98.3%) and matches (99.6%, 96.9%), respectively. Rare falsely classified events were typically warm-up deliveries or throws preceded by a run. Inertial sensors data processed by a machine-learning based algorithm provide a valid tool to automatically detect bowling events, whilst also providing the opportunity to look at performance metrics associated with fast bowling. This offers the possibility to better monitor bowling workloads across a range of intensities to mitigate injury risk potential and maximise performance.
板球快速投球手受伤的风险很高，这已经被证明与投球工作量有关。本研究旨在开发和测试一种能够自动、可靠和准确地检测投球的算法。从 Catapult OptimEye S5 可穿戴设备收集了来自国家级和国际级快速投球手（n=35）在训练和比赛中的惯性传感器数据，强度各异。基于机器学习的方法被用于开发算法。输出结果与超过 20000 次手动记录的事件进行了比较。高马修斯相关系数（ 表明自动检测的投球与手动记录的投球之间非常吻合。该算法在训练（96.3%，98.3%）和比赛（99.6%，96.9%）中均具有较高的灵敏度和特异性。罕见的错误分类事件通常是热身投球或投球前的跑动。由基于机器学习的算法处理的惯性传感器数据提供了一种有效的工具来自动检测投球事件，同时也提供了机会来查看与快速投球相关的绩效指标。这提供了一种可能性，可以在不同强度范围内更好地监测投球工作量，以减轻受伤风险，并最大限度地提高表现。
