Bibliographic record
Abstract
Dynamic balance is a key component required to be successful in many sports yet the importance of dynamic balance in elite level sports has not been identified. The aim of this programme of doctoral study was to determine whether sport specific measures of dynamic balance could differentiate for skill level in footballers. Initially a literature review was performed to identify any gaps in the literature and to inform the research. A scoping review was then performed to provide an in-depth investigation into the understanding of the term dynamic balance and associated terms. More encompassing definitions of dynamic balance, postural control and postural stability were developed as well as a taxonomy to classify movements and existing balance tests. Following this, an investigation into important movements in football was conducted and those movements identified as important were classified using the taxonomy and aligned with existing dynamic balance tests to provide specificity. Finally, sport-specific measures of dynamic balance, along with a common balance measure used in football, were investigated to identify whether they had the ability to differentiate for skill level in footballers. Definitions of dynamic balance and related terms demonstrated disparity, overlap and they fail to cover the full range of dynamic balance situations. There are numerous dynamic balance tests available, they lack specificity, and test selection is difficult due to the complex and multi-factorial nature of balance. The taxonomy provided an approach for differentiating dynamic balance components, comprehensive profile of existing dynamic balance tests and a tool to identify strengths and limitations of existing tests and identify sport specific tests. Important movements in football were identified as shielding the ball, a shoulder barge whilst running, jostling to win the ball and shielding the ball whilst jostling, accelerating and braking, and a single leg kick or standing volley. Investigations identified that no existing dynamic balance tests aligned with the important movements in football. The external forces test shows promise at being a measure that can differentiate for skill level in football. Time to stabilisation was lowest for elite players (1.33 s) followed by recreational players (1.91). A large effect size was observed between elite and recreational players (g = -1.3) and recreational and non-football players (g = 0.82). There was a small effect size between elite and non-football players (g = -0.43). The mSEBT, kicking task and deceleration task were not considered a good measure of performance nor are they able to differentiate for skill level in football. This programme of research identifies that previous research has not identified the components of balance that should be tested for in football, and previous research has not made use of sport specific tests to assess dynamic balance in football. It is recommended that future research in this field refers to the newly proposed definitions. Additionally, further work should investigate other outcome measures of dynamic balance and whether they provide a better indication of dynamic postural control strategies. Finally, future directions could focus on whether participant variability exists at different skill levels.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".