Can an inertial measurement unit (IMU) in combination with machine learning measure fast bowling speed and perceived intensity in cricket?

ABSTRACT

This study examined whether an inertial measurement unit (IMU), in combination with machine learning, could accurately predict two indirect measures of bowling intensity through ball release speed (BRS) and perceived intensity zone (PIZ). One IMU was attached to the thoracic back of 44 fast bowlers. Each participant bowled 36 deliveries at two different PIZ zones (Zone 1 = 24 deliveries at 70% to 85% of maximum perceived bowling effort; Zone 2 = 12 deliveries at 100% of maximum perceived bowling effort) in a random order. IMU data (sampling rate = 250 Hz) were downsampled to 125 Hz, 50 Hz, and 25 Hz to determine if model accuracy was affected by the sampling frequency. Data were analysed using four machine learning models. A two-way repeated-measures ANOVA was used to compare the mean absolute error (MAE) and accuracy scores (separately) across the four models and four sampling frequencies. Gradient boosting models were shown to be the most consistent at measuring BRS (MAE = 3.61 km/h) and PIZ (F-score = 88%) across all sampling frequencies. This method could be used to measure BRS and PIZ which may contribute to a better understanding of overall bowling load which may help to reduce injuries.

KEYWORDS:

• Bowling workload
• artificial intelligence
• ball release speed
• wearable device
• injury prevention

Acknowledgments

The authors would like to thank all participants for their involvement in the study.

Disclosure statement

The authors declare that they have no competing interests. No funding was received for this study.