Contextual batting and bowling in limited overs cricket
Why this work is in the frame
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Bibliographic record
Abstract
Cricket is a sport for which many batting and bowling statistics have been proposed. However, a feature of cricket is that the level of aggressiveness adopted by batsmen is dependent on match circumstances. It is therefore relevant to consider these circumstances when evaluating batting and bowling performances. This paper considers batting performance in the second innings of limited overs cricket when a target has been set. The runs required, the number of overs completed and the wickets taken are relevant in assessing the batting performance. We produce a visualization for second innings batting which describes how a batsman performs under different circumstances. The visualization is then reduced to a single statistic “clutch batting” which can be used to compare batsmen. An analogous approach is then provided for bowlers based on the symmetry between batting and bowling, and we define the statistic “clutch bowling”.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 it