Identifying Pacing Profiles in 2000 Metre World Championship Rowing
Bibliographic record
Abstract
The pacing strategy adopted by athletes is a major determinants of success during timed competition. Various pacing profiles are reported in the literature and its importance depends on the mode of sport. However, in 2000 metre rowing, the definition of these pacing profiles has been limited by the minimal availability of data. Purpose: Our aim is to objectively identify pacing profiles used in World Championship 2000 metre rowing races using reproducible methods. Methods: We use the average speed for each 50 metre split for each available boat in every race of the Rowing World Championships from 2010-2017. This data was scraped from www.worldrowing.com. This data set is publicly available (https://github.com/danichusfu/rowing_pacing_profiles) to help the field of rowing research. Pacing profiles are determined by using k-shape clustering, a time series clustering method. A multinomial logistic regression is then fit to test whether variables such as boat size, gender, round, or rank are associated with pacing profiles. Results: Four pacing strategies (Even, Positive, Reverse J-Shaped, and U-Shaped) are identified from the clustering process. Boat size, round (Heat vs Finals), rank, gender, and weight class are all found to affect pacing profiles. Conclusion: We use an objective methodology with more granular data to identify four pacing strategies. We identify important associations between these pacing profiles and race factors. Finally, we make the full data set public to further rowing research and to replicate our results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".