Key result
Machine-learning algorithm using inertial sensors detects bowling deliveries with ~95% agreement versus manual recording.
Why the study?
Cricket fast bowlers face high injury risk correlated with bowling workloads, requiring an algorithm to automatically, reliably, and accurately detect bowling deliveries.
Effect estimate: r = 0.945
A machine-learning algorithm using wearable inertial sensors can accurately and reliably detect bowling deliveries in elite cricket fast bowlers, enabling better workload monitoring.
May enable automated workload monitoring in cricket; leaves open prospective validation before injury prevention use.
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 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.
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Jowitt et al. (2020) studied Elite cricket fast bowlers (n=35). Machine-learning based algorithm using inertial sensor data vs. Manually recorded events was evaluated on Agreement between automatically detected bowling deliveries and manually recorded ones (r = 0.945). A machine-learning algorithm using inertial sensor data accurately detected bowling deliveries compared to manual recording (Matthews correlation coefficient r = 0.945).
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