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

Journal of sports sciences

PubMedID: 28103737

Novak AR, Bennett KJ, Fransen J, Dascombe BJ. A multidimensional approach to performance prediction in Olympic distance cross-country mountain bikers. J Sports Sci. 2017;1-8.
This 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 R(2) = 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 R(2): 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.