Pacing and predictors of performance during cross-country skiing races: A systematic review
Bibliographic record
Abstract
BACKGROUND: Cross-country skiing (XCS) racing, a popular international winter sport, is complex and challenging from physical, technical, and tactical perspectives. Despite the vast amount of research focusing on this sport, no review has yet addressed the pacing strategies of elite XCS racers or the factors that influence their performance. The aim was to review the scientific literature in an attempt to determine the effects of pacing strategy on the performance of elite XCS racers. METHODS: Four electronic databases were searched using relevant subject headings and keywords. Only original research articles published in peer-reviewed journals and the English language and addressing performance, biomechanics, physiology, and anthropometry of XCS racers were reviewed. RESULTS: All 27 included articles applied correlative designs to study the effectiveness of different pacing strategies. None of the articles involved the use of an experimental design. Furthermore, potential changes in external conditions (e.g., weather, ski properties) were not taken into consideration. A comparable number of studies focused on the skating or classical technique. In most cases, positive pacing was observed, with certain indications that higher-level athletes and those with more endurance and strength utilized a more even pacing strategy. The ability to achieve and maintain a long cycle length on all types of terrain was an important determinant of performance in all of the included studies, which was not the case for cycle rate. In general, uphill performance was closely related to overall race performance, with uphill performance being most closely correlated to the success of female skiers and performance on flat terrain being more important for male skiers. Moreover, pacing was coupled to the selection and distribution of technique during a race, with faster skiers employing more double poling and kick double poling, less diagonal stride, and more V2 (double dance) than V1 (single dance) skating across a race. CONCLUSION: We propose that skiers at all levels can improve their performance with more specific training in techniques (i.e., maintaining long cycles without compromising cycle rate and selecting appropriate techniques) in combination with training for endurance and more strength. Furthermore, we would advise less experienced skiers and/or those with lower levels of performance to apply a more even pacing strategy rather than a positive one (i.e., starting the race too fast).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".