Winning the Coin Toss and the Home Team Advantage in One-Day International Cricket Matches
Bibliographic record
Abstract
This paper provides a statistical analysis of 427 one-day international cricket matches played during the 1990's. Two general conclusions are obtained: (1) contrary to widespread opinion, winning the coin toss at the outset of a match provides no competitive advantage and (2) the advantage of playing on one's home field increases the log-odds of the probability of winning by approximately .5. Keywords : ODI cricket matches, statistical modelling. B de Silva is Senior Lecturer, Department of Statistics and Operations Research, RMIT University, GPO Box 2476V, Melbourne Victoria, Australia 3001. T Swartz is Associate Professor, Department of Mathematics and Statistics, Simon Fraser University, Burnaby BC, Canada V5A1S6. T. Swartz was partially supported by a grant from the Natural Sciences and Engineering Research Council of Canada. The authors wish to thank all 3 referees for insightful comments that led to an improved paper. 1. INTRODUCTION The game of cricket has world-wide appeal ...
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".