Factors Affecting Cyclists’ Chances of Success in Match-Sprint Tournaments

Abstract

Purpose: To further the understanding of elite athlete performance in complex race environments by examining the changes in cyclists’ performance between solo time trials and head-to-head racing in match-sprint tournaments. Methods: Analyses were derived from official results of cyclists in 61 elite international sprint tournaments (2000–2016), incorporating the results of 2060 male and 1969 female head-to-head match races. Linear mixed modeling of log-transformed qualification and finish ranks was used to determine estimates of performance predictability as intraclass correlation coefficients. Correlations between qualifying performance and final tournament rank were also calculated. Chances of winning head-to-head races were estimated adjusting for the difference in the cyclists’ qualifying times. All effects were evaluated using magnitude-based inference. Results: Minor differences in predictability between qualification time trial and final tournament rank were suggestive of more competitiveness among men in the overall tournament. Performance in the qualification time trial was strongly correlated with, but not fully indicative of, performance in the overall tournament. Correspondingly, being the faster qualifier had a large positive effect on the chances of winning a head-to-head race, but small substantial differences between riders remained after adjustment for time-trial differentials. Conclusions: The present study provides further insight into how real-world competition data can be used to investigate elite athlete performance in sports where athletes must directly interact with their opponents. For elite match-sprint cyclists, qualifying time-trial performance largely determines success in the overall tournament, but there is evidence of a consistent match-race ability that modifies the chances of winning head-to-head races.

Determinants of Cycling Performance: a Review of the Dimensions and Features Regulating Performance in Elite Cycling Competitions

“…Chidley and colleagues [26] found that most of the variance in finish times of downhill mountain bikers was explained by skill (r 2 = − 0.76), with only a small proportion attributable to physiological variables (anaerobic capacity, r 2 = 0.0; VO 2 max, r 2 = − 0.3). Phillips and Hopkins [42,81] demonstrated the tenuous links between an individual's physiological features and elite competitive performance, finding that factors determining consistent individual time-trial performance transferred minimally to performance in mass-start or one-versus-one racing. Cyclists must have high levels of physiological fitness to reach the elite level, but at this level there appear to be little differences in their physiological characteristics, and therefore other factors contribute to race outcomes [25,80].…”

“…In competitions where opponents are able to interact, riders continually adapt to the actions of opponents and the changing race environment [81,[89][90][91]. The tactical dimension refers to features arising from this interaction between competitors and the actions they take in response to what is occurring.…”

Maximal muscular power: lessons from sprint cycling

“…Assuming technical/tactical competency and access to modern (i.e. aerodynamic) equipment [ 11 ], the sprint cyclist that exhibits the highest levels of maximum and sustained power relative to their body mass and C D A will be the fastest [ 23 ], and generally the most successful [ 24 ].…”

Reply to: Comment on: “Using Field Based Data to Model Sprint Track Cycling Performance”

“…Dorel et al [4] focused on the flying 200-m component of the sprint event, which is one race of 9-12 races over a 1-2-day period. Phillips and Hopkins [5] focused on the relationships between the flying 200-m time and the overall outcomes, which ignores the tactical components of the sprint event. We fail to see how 74% of PPO explains 30 s Wingate test performance, when only correlation analysis was used [6], and compared to data showing counter movement jump, seated Wingate PPO and seated Wingate average power for 30 s only explained 41-66% of performance of the starting straight in BMX [7].…”