Using Actual Betting Percentages to Analyze Sportsbook Behavior: The Canadian and Arena Football Leagues
Bibliographic record
Abstract
Sportsbook behavior is tested for the Canadian and Arena Football Leagues using real sportsbook betting percentages from on-line sportsbooks. The balanced book hypothesis of the traditional sportsbook models does not appear to hold for these leagues, as favorites and overs attract more than 50 percent of the betting dollars. Although there is some slight evidence toward shading the line in these directions, there is also no overwhelming evidence supporting the Levitt (2004) hypothesis, as sportsbooks do not appear to be actively pricing to maximize profits. In general, the results seem more consistent with the sportsbook pricing as a forecast, content with earning their commission on losing bets as simple strategies win about 50 percent of the time. A study by Levitt (2004) in The Economic Journal challenged the traditional view of sportsbook behavior. In the Levitt hypothesis, sportsbooks set prices to maximize profits, not to balance the sports betting action. This model differs substantially from the traditional models of sportsbook behavior, such as Pankoff (1968), Zuber, et al. (1985), and Sauer, et al. (1988), where sportsbooks set prices to balance the book. They achieve this by setting a price that attracts equal dollars on each side of the betting proposition. Under this model, using sports betting data to test the efficient markets hypothesis is straightforward. Under the assumptions of the traditional models, the efficient markets hypothesis could
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".