The Impact of COVID-19 Related Disruption on Injury Rates in Elite Men's Domestic Cricket.

This study aimed to investigate the impact of COVID-19 enforced prolonged training disruption and shortened competitive season, on in-season injury and illness rates. Injury incidence and percent proportion was calculated for the 2020 elite men's senior domestic cricket season and compared to a historical average from five previous regular seasons (2015 to 2019 inclusive). The injury profile for the shortened 2020 season was generally equivalent to what would be expected in a regular season, except for a significant increase in medical illness as a proportion of time loss (17% compared to historic average of 6%) and in-season days lost (9% compared to historic average of 3%) due to COVID-19 related instances (most notably precautionary isolation due to contact with a confirmed or suspected COVID-19 case). There was a significant increase in the proportion of in-season days lost to thigh injuries (24% compared to 9%) and a significant decrease in the proportion of days lost to hand (4% compared to 12%) and lumbar spine (7% compared to 21%) injuries. These findings enhance understanding of the impact prolonged period of training disruption and shortened season can have on cricket injuries and the challenges faced by practitioners under such circumstances.

Variability and physical demands of international seam bowlers in one-day and Twenty20 international matches across five years.

OBJECTIVES: To quantify and compare the match demands and variability of international One-Day (ODI) with Twenty20 (T20) cricket matches and to compare ODI match demands when competing home and away. DESIGN: Single cohort, longitudinal observation. METHODS: Thirteen international male seam bowlers across 204matches (ODI=160; T20=44) were investigated over five-years (2015-2019). Using global positioning sensors and accelerometers, physical demands were quantified using distance covered at different velocities and the number of entries into high and low intensity acceleration and deceleration bands. Variability was quantified using coefficient of variation (CV) and smallest worthwhile change. RESULTS: Significantly greater (p<0.05) match demands were found for all physical variables relative to minutes played for T20 against ODI matches, except for distance covered 20-25kmh-1 which was greater for ODI. Distance covered between 0-7km∙h-1 showed no significance difference (p=0.60). The number of moderate decelerations (2-4m∙s2) were greater (p=0.04) away compared to home in ODI. All other variables showed no significance. Relative to minutes played, decelerations ≤4m∙s2 (within-player ODI CV=75.5%. T20=72.0%) accelerations >4m∙s2 (within-player ODI CV=79.2%. T20 CV=77.2%. Between-player ODI CV=84.7%. T20=38.8%) and distance covered >25kmh-1 (within-player ODI CV=65.5%. T20=64.1%) showed the greatest variability. CONCLUSIONS: Players are exposed to different physical demands in ODI Vs T20 matches, but not for home Vs away ODI matches. Practitioners should be aware of the large variability in high-speed/intensity accelerations and decelerations across matches.

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

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.