Collision detection in rugby union using MEMS and machine learning: Comparison of collision intensity between senior professional and elite U20 players
Dorian Le Moan, Michael Phomsoupha
Rugby union is a high intensity contact sport where collision events are key determinants of match outcomes, yet the intensity profiles during the transition from elite under-20 (U20) to Senior professional (SP) levels remain poorly described. This study aimed to (i) characterise collision intensity profiles using wearable microelectromechanical systems across sixteen Elite U20 (U20) and twenty-six Senior professional matches, (ii) compare collision frequency and intensity between SP and U20 players, and (iii) examine positional differences in collision demands within each competition level, employing a Random Forest algorithm to classify collisions into six intensity zones (IZ) and determine Mean Maximal Collision Intensity (MMCI). The results reveal that SP players are exposed to a significantly greater collision frequency than U20 players in nearly all intensity zones (p < 0.05), with the most pronounced differences occurring in high-intensity zones IZ 5 (8.01–10.0 g; +17%) and IZ 6 (> 10.01 g; +36%). Furthermore, SP players experienced a significantly higher MMCI than U20 players (11.3 ± 1.2 g