A multidimensional approach to performance prediction in Olympic distance cross-country mountain bikers

Abstract

ABSTRACTThis study adopted a multidimensional approach to performance prediction within Olympic distance cross-country mountain biking (XCO-MTB). Twelve competitive XCO-MTB cyclists (VO2max 60.8 ± 6.7 ml · kg−1 · min−1) completed an incremental cycling test, maximal hand grip strength test, cycling power profile (maximal efforts lasting 6–600 s), decision-making test and an individual XCO-MTB time-trial (34.25 km). A hierarchical approach using multiple linear regression analyses was used to develop predictive models of performance across 10 circuit subsections and the total time-trial. The strongest model to predict overall time-trial performance achieved prediction accuracy of 127.1 s across 6246.8 ± 452.0 s (adjusted R2 = 0.92; P < 0.01). This model included VO2max relative to total cycling mass, maximal mean power across 5 and 30 s, peak left hand grip strength, and response time for correct decisions in the decision-making task. A range of factors contributed to the models for each individual subsection of the circuit with varying predictive strength (adjusted R2: 0.62–0.97; P < 0.05). The high prediction accuracy for the total time-trial supports that a multidimensional approach should be taken to develop XCO-MTB performance. Additionally, individual models for circuit subsections may help guide training practices relative to the specific trail characteristics of various XCO-MTB circuits.