The use of GPS and inertial devices for player monitoring in team sports: A review of current and future applications
Bibliographic record
Abstract
Player-worn devices, combining global positioning system and inertial monitors, are being used increasingly by professional sports teams. Recent interest focusses on using the data generated to track trainingload and whether this may lead to more effective training prescription with better management of injury risk. The aim of this review is to summarize the development and current use of this technology alongside proposed future applications. PubMed and Medline searches (2000-2017) identified all relevant studies involving use in team sports or comparative studies with other accepted methods. Our review determined that the latest devices are valid and reliably track activity levels. This technology is both accurate and more efficient than previous methods. Furthermore, recent research has shown that measurable changes in trainingload (the acute-to-chronic load ratio) are related to injury risk. However, results remain very sport specific and generalization must be done with caution. Future uses may include injury-prevention strategies and return-to-play judgement.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".