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Louis Passfield and James G. Hopker

This paper explores the notion that the availability and analysis of large data sets have the capacity to improve practice and change the nature of science in the sport and exercise setting. The increasing use of data and information technology in sport is giving rise to this change. Web sites hold large data repositories, and the development of wearable technology, mobile phone applications, and related instruments for monitoring physical activity, training, and competition provide large data sets of extensive and detailed measurements. Innovative approaches conceived to more fully exploit these large data sets could provide a basis for more objective evaluation of coaching strategies and new approaches to how science is conducted. An emerging discipline, sports analytics, could help overcome some of the challenges involved in obtaining knowledge and wisdom from these large data sets. Examples of where large data sets have been analyzed, to evaluate the career development of elite cyclists and to characterize and optimize the training load of well-trained runners, are discussed. Careful verification of large data sets is time consuming and imperative before useful conclusions can be drawn. Consequently, it is recommended that prospective studies be preferred over retrospective analyses of data. It is concluded that rigorous analysis of large data sets could enhance our knowledge in the sport and exercise sciences, inform competitive strategies, and allow innovative new research and findings.

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Daniel J. Madigan, Joachim Stoeber and Louis Passfield

Perfectionism in sports has been shown to predict longitudinal changes in athlete burnout. What mediates these changes over time, however, is still unclear. Adopting a self-determination theory perspective and using a three-wave longitudinal design, the current study examined perfectionistic strivings, perfectionistic concerns, autonomous motivation, controlled motivation, and athlete burnout in 141 junior athletes (mean age = 17.3 years) over 6 months of active training. When multilevel structural equation modeling was employed to test a mediational model, a differential pattern of between- and within-person relationships emerged. Whereas autonomous motivation mediated the negative relationship that perfectionistic strivings had with burnout at the between- and within-person level, controlled motivation mediated the positive relationship that perfectionistic concerns had with burnout at the between-persons level only. The present findings suggest that differences in autonomous and controlled motivation explain why perfectionism predicts changes in athlete burnout over time.

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Richard Ebreo, Louis Passfield and James Hopker

Purpose:

To evaluate the reliability of calculating gross efficiency (GE) conventionally and using a back extrapolation (BE) method during high intensity exercise (HIE).

Methods:

12 trained participants completed two HIE bouts (P1 = 4-min 80% Maximal Aerobic Power (MAP); P2 = 4-min at 100%MAP). GE was calculated conventionally in the last 3 min of submaximal (50%MAP) cycling bouts performed before and after HIE (Pre50%MAP and Post 50%MAP). To calculate GE using BE (BGE), a linear regression of GE submaximal values post-HIE were back extrapolated to the end of the HIE bout.

Results:

BGE was significantly correlated with Post50%MAP GE in P1 (r= 0.64; P = 0.01), and in P2 (r = 0.85; P = 0.002). Reliability data for P1 and P2 BGE demonstrate a mean CV of 7.8% and 9.8% with limits of agreement of 4.3% and 4.5% in relative GE units respectively. P2 BGE was significantly lower than P2 Post50%MAP GE (18.1 ± 1.6% vs 20.3 ± 1.7%; P= 0.01). Using a declining GE from the BE method, there was a 44% greater anaerobic contribution compared to assuming a constant GE during 4 min HIE at 100%MAP.

Conclusion:

HIE acutely reduced BGE at 100%MAP. A greater anaerobic contribution to exercise as well as excess post oxygen consumption at 100%MAP may contribute to this decline in efficiency. The BE method may be a reliable and valid tool in both estimating GE during HIE and calculating aerobic and anaerobic contributions.

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Daniel J. Madigan, Joachim Stoeber and Louis Passfield

Perfectionism in sports has been shown to be associated with burnout in athletes. Whether perfectionism predicts longitudinal changes in athlete burnout, however, is still unclear. Using a two-wave cross-lagged panel design, the current study examined perfectionistic strivings, perfectionistic concerns, and athlete burnout in 101 junior athletes (mean age 17.7 years) over 3 months of active training. When structural equation modeling was employed to test a series of competing models, the best-fitting model showed opposite patterns for perfectionistic strivings and perfectionistic concerns. Whereas perfectionistic concerns predicted increases in athlete burnout over the 3 mon ths, perfectionistic strivings predicted decreases. The present findings suggest that perfectionistic concerns are a risk factor for junior athletes contributing to the development of athlete burnout whereas perfectionistic strivings appear to be a protective factor.

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Marco Arkesteijn, Simon Jobson, James Hopker and Louis Passfield

Background:

Previous research has shown that cycling in a standing position reduces cycling economy compared with seated cycling. It is unknown whether the cycling intensity moderates the reduction in cycling economy while standing.

Purpose:

The aim was to determine whether the negative effect of standing on cycling economy would be decreased at a higher intensity.

Methods:

Ten cyclists cycled in 8 different conditions. Each condition was either at an intensity of 50% or 70% of maximal aerobic power at a gradient of 4% or 8% and in the seated or standing cycling position. Cycling economy and muscle activation level of 8 leg muscles were recorded.

Results:

There was an interaction between cycling intensity and position for cycling economy (P = .03), the overall activation of the leg muscles (P = .02), and the activation of the lower leg muscles (P = .05). The interaction showed decreased cycling economy when standing compared with seated cycling, but the difference was reduced at higher intensity. The overall activation of the leg muscles and the lower leg muscles, respectively, increased and decreased, but the differences between standing and seated cycling were reduced at higher intensity.

Conclusions:

Cycling economy was lower during standing cycling than seated cycling, but the difference in economy diminishes when cycling intensity increases. Activation of the lower leg muscles did not explain the lower cycling economy while standing. The increased overall activation, therefore, suggests that increased activation of the upper leg muscles explains part of the lower cycling economy while standing.

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Andy Galbraith, James Hopker, Marco Cardinale, Brian Cunniffe and Louis Passfield

Purpose:

To examine the training and concomitant changes in laboratory- and field-test performance of highly trained endurance runners.

Methods:

Fourteen highly trained male endurance runners (mean ± SD maximal oxygen uptake [VO2max] 69.8 ± 6.3 mL · kg−1 · min−1) completed this 1-y training study commencing in April. During the study the runners undertook 5 laboratory tests of VO2max, lactate threshold (LT), and running economy and 9 field tests to determine critical speed (CS) and the modeled maximum distance performed above CS (D′). The data for different periods of the year were compared using repeated-measures ANOVA. The influence of training on laboratory- and field-test changes was analyzed by multiple regression.

Results:

Total training distance varied during the year and was lower in May–July (333 ± 206 km, P = .01) and July–August (339 ± 206 km, P = .02) than in the subsequent January–February period (474 ± 188 km). VO2max increased from the April baseline (4.7 ± 0.4 L/min) in October and January periods (5.0 ± 0.4 L/min, P ≤ .01). Other laboratory measures did not change. Runners’ CS was lowest in August (4.90 ± 0.32 m/s) and highest in February (4.99 ± 0.30 m/s, P = .02). Total training distance and the percentage of training time spent above LT velocity explained 33% of the variation in CS.

Conclusion:

Highly trained endurance runners achieve small but significant changes in VO2max and CS in a year. Increases in training distance and time above LT velocity were related to increases in CS.

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Andy Galbraith, James Hopker, Stephen Lelliott, Louise Diddams and Louis Passfield

Purpose:

To compare critical speed (CS) measured from a single-visit field test of the distance–time relationship with the “traditional” treadmill time-to-exhaustion multivisit protocol.

Methods:

Ten male distance runners completed treadmill and field tests to calculate CS and the maximum distance performed above CS (D′). The field test involved 3 runs on a single visit to an outdoor athletics track over 3600, 2400, and 1200 m. Two field-test protocols were evaluated using either a 30-min recovery or a 60-min recovery between runs. The treadmill test involved runs to exhaustion at 100%, 105%, and 110% of velocity at VO2max, with 24 h recovery between runs.

Results:

There was no difference in CS measured with the treadmill and 30-min- and 60-minrecovery field tests (P < .05). CS from the treadmill test was highly correlated with CS from the 30- and 60-min-recovery field tests (r = .89, r = .82; P < .05). However there was a difference and no correlation in D′ between the treadmill test and the 30 and 60-min-recovery field tests (r = .13; r = .33, P > .05). A typical error of the estimate of 0.14 m/s (95% confidence limits 0.09–0.26 m/s) was seen for CS and 88 m (95% confidence limits 60–169 m) for D′. A coefficient of variation of 0.4% (95% confidence limits: 0.3–0.8%) was found for repeat tests of CS and 13% (95% confidence limits 10–27%) for D′.

Conclusion:

The single-visit method provides a useful alternative for assessing CS in the field.

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Arthur H. Bossi, Ciaran O’Grady, Richard Ebreo, Louis Passfield and James G. Hopker

Purpose : To describe pacing strategy and competitive behavior in elite-level cyclo-cross races. Methods: Data from 329 men and women competing in 5 editions (2012–2016) of Union Cycliste Internationale Cyclo-Cross World Championships were compiled. Individual mean racing speeds from each lap were normalized to the mean speeds of the whole race. Lap and overall rankings were also explored. Pacing strategy was compared between sexes and between top- and bottom-placed cyclists. Results: A significant main effect of laps was found in 8 out of 10 races (4 positive, 3 variable, 2 even, and 1 negative pacing strategies), and an interaction effect of ranking-based groups was found in 2 (2016, male and female races). Kendall tau-b correlations revealed an increasingly positive relationship between intermediate and overall rankings throughout the races. The number of overtakes during races decreased from start to finish, as suggested by significant Friedman tests. In the first lap, normalized cycling speeds were different in 3 out of 5 editions—men were faster in 1 and slower in 2 editions. In the last lap, however, normalized cycling speeds of men were lower than those of women in 4 editions. Conclusions : Elite cyclo-cross competitors adopt slightly distinct pacing strategies in each race, but positive pacing strategies are highly probable in most events, with more changes in rankings during the first laps. Sporadically, top- and bottom-placed groups might adopt different pacing strategies during either men’s or women’s races. Men and women seem to distribute their efforts differently, but this effect is of small magnitude.

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Daniel J. Madigan, Thomas Curran, Joachim Stoeber, Andrew P. Hill, Martin M. Smith and Louis Passfield

Perfectionism predicts cognitions, emotions, and behaviors in sport. Nonetheless, our understanding of the factors that influence its development is limited. The authors sought to address this issue by examining the role of coach and parental pressure in the development of perfectionism in sport. Using 3 samples of junior athletes (16–19 years; cross-sectional n = 212, 3-month longitudinal n = 101, and 6-month longitudinal n = 110), the authors examined relations between coach pressure to be perfect, parental pressure to be perfect, perfectionistic strivings, and perfectionistic concerns. Mini meta-analysis of the combined cross-sectional data (N = 423) showed that both coach pressure and parental pressure were positively correlated with perfectionistic strivings and perfectionistic concerns. In contrast, longitudinal analyses showed that only coach pressure predicted increased perfectionistic strivings and perfectionistic concerns over time. Overall, our findings provide preliminary evidence that coaches may play a more important role in the development of junior athletes’ perfectionism than parents.