Tactical behaviour of winning athletes in major championship 1500‐m and 5000‐m track finals
Bibliographic record
Abstract
This article analyses the tactics employed by middle-distance (1500-m) and long-distance (5000-m) runners from an observational methodology perspective. The subject of investigation has received little attention from specialists in the field of athletics, with most research focusing on physiological studies of athlete performance. Using an ad hoc observation tool and a database containing systematically recorded data we detected time patterns (T-patterns) within the data recorded using the Theme software program (version 5.0), and analysed the tactics employed by winners of the men's 1500-m and 5000-m finals of the World Championships in Athletics [Edmonton 2001, Paris 2003, Helsinki 2005 (1500-m final only), Osaka 2007 (1500-m final only), Berlin 2009 and Daegu 2011], the European Athletics Championships (Munich 2002, Göteborg 2006, and Barcelona 2010) and the Olympic Games (Sydney 2000, Athens 2004, Beijing 2008 and London 2012). T-pattern detection and investigation of the relationship between category systems corresponding to the criteria comprising the observation tool revealed both similarities (starting lane and lane used during race, runner's position during race and sprint zone and lane) and differences (variations in pace, zones in which changes of pace occur, sprint initiation zone and winner's position at the start of the sprint) between the two disciplines.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".