Auto Detecting Deliveries in Elite Cricket Fast Bowlers Using Microsensors and Machine Learning

Jowitt, H.K., Durussel, J., Brandon, R., King, M.

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 (r = 0.945) 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 sensor data processed by a machine-learning-based algorithm provide a valid tool to automatically detect bowling events, while 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 maximize performance.

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