Assessing criterion validity and test-retest reliability of a computer vision-based system for tracking player location in cricket
Year: 2026
Type: article
Abstract: Player tracking is widely utilised to quantify physical and tactical performance of elite athletes, with wearable technologies being the most commonly used systems. However, wearable devices are limited in their ability to capture location data of an opposing team, thereby limiting the collection of complete match information. Alternatively, computer vision-based systems can track all players on the field without the need for wearable devices. This study aimed to evaluate the criterion validity and test-retest reliability of a computer vision-based system for tracking player locations in cricket. Validity was assessed as error in 1) measured distance from a player to a reference location and 2) distance between two players. The reliability of these measurements was evaluated along with system's ability to measure location of a player returning to a specific location multiple times. Validity and reliability were evaluated separately for near-end and far-end of the field relative to camera position. The root mean square error (RMSE) for distance measurements ranged 0.28m–0.49 m (near-end) and 0.82m–1.06 m (far-end). The system marginally underestimated (mean bias: −0.13 m to −0.32 m; homogenous distribution of errors) and overestimated (mean bias: 0.72 m to 0.97 m; heterogenous distribution of errors) measured distances at near-end and far-end, respectively. The system demonstrated excellent reliability, with interclass correlation coefficients (ICC) of 1.00 at near-end and far-end. The technical error of measurement (TEM) ranged 0.05m–0.31 m (near-end) and 0.26m–0.34 m (far-end). These findings support the use of specific computer vision-based system as a valid and reliable tool for capturing player location data to analyse tactical performance in cricket.
Institution: Deakin University
Language: English
Subfield: Orthopedics and Sports Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,650
OpenAlex ID: W7123713758