Auto detecting deliveries in elite cricket fast bowlers using microsensors and machine learning

Abstract

Cricket fast bowlers are at a high risk of injury occurrence, and this 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.

RETRACTED: Sports highlight recognition and event detection using rule inference system

“…The Spatial feature contains information on the location points of the frame where the activity of the functional object (Jowitt et al, 2020) is high. For example, consider a ball in a cricket sport, its low-level features like the color (white) and size (small) are extracted for further processing.…”

Biomechanical risk factors of lower back pain in cricket fast bowlers using inertial measurement units: a prospective and retrospective investigation

“…[24][25][26][27] Portable miniature sensors like those used in this study may offer a viable alternative. Indeed the application of sensors for cricket bowling analysis is beginning to find its place in the literature, [28][29][30][31][32] however this is the first time they have been used to explore LBP, spinal kinematics and limb impacts.…”

Misuse of the term ‘load’ in sport and exercise science

“…In spite of the publication "Workload"time to abandon?' 6 some studies have still slipped through the cracks and have been published using incorrect terminology (e.g., [11][12][13][14] ). In some cases, the term 'workload' is even used in the article title (e.g., 8,15,16 ).…”