On the search for reliable performance indicators in game sports
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article addresses the reliability of performance indicators in game sports. In this context, reliability is invariably treated from a technical point of view as a question of observer agreement i.e. high levels of agreement between observations. That the measurement process itself should yield reliable data, as defined, for sports performance is given. Our considerations of reliability, however, extend from the process of measurement to include the trait (i.e., the performance) be-ing measured. From these considerations, we present the argument that the performance traits, as measured, are inherently unstable and that the performance indicators are therefore necessarily unreliable (or unstable). In this light, the ongoing search for reliable (or stable) measures of sports performance indicators is questioned. Instead, alternative approaches for performance analysis are offered that recognise the dynamic interactions that characterise game sports as key features of sport performance. This notion of dynamic interactions is compatible with sporting experiences and the way that sports practitioners think about sports performance. We conclude that perform-ance analysis for purposes of theoretical advancement should make use of mathematical model-ling and simulation techniques, and that performance analysis for practical purposes should in-
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.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it