PROFESSIONAL BETTORS, ODDS-ARBITRAGE COMPETITION, AND BETTING MARKET EQUILIBRIUM
Bibliographic record
Abstract
A slew of empirical evidence on horse racetrack betting markets points to betting biases and market inefficiency. More recent empirical work has documented the absence of betting biases in racetrack betting markets characterized by a high volume of betting. This paper offers a competition-based model of betting behavior that is consistent with the pattern of betting biases reported in the literature. We postulate the existence of professional bettors who, being better informed and/or having different objectives than the general betting population, engage in odds arbitrage when doing so is profitable. We evaluate the case of a single odds-arbitraging bettor first in order to establish the fundamental properties of odds arbitrage. We then examine the effects of entry of professional bettors who play a Nash game in odds arbitrage; the results show that professionals' participation causes the final track odds to converge to the level implied by the horses' true win probabilities when there is a high volume of betting.
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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) | 0.001 | 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) | 0.011 | 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 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".