THE IMPACT OF SENTIMENT ON POINT SPREADS IN THE COLLEGE FOOTBALL WAGERING MARKET
Bibliographic record
Abstract
This paper investigates whether sentimental bettors affect the point-spread-formation process in the college football wagering market, as well as whether this market is efficient. Following Avery and Chevalier (1999), this study focuses solely on sources of sentiment that have natural analogues in the stock market, such as predictions of so-called experts, momentum in teams’ performances, teams that are highly visible, and teams with high fan avidity. Sentiment relating to expert picks, even though the picks are uninformative, nonetheless is related to intraweek changes in spreads. Also, bettors’ sentiment for recent performance against the spread, for teams in major conferences, and for teams with high fan avidity appears to be related to spreads moving during the week. However, betting strategies designed to exploit any anticipated sentimental betting are marginally profitable at best, suggesting that rational arbitrageurs serve to correct most biases in spreads.The authors express thanks to Bruno Deschamps, Michael Hertzel, Michael Lemmon, J. Spencer Martin, Federico Nardari, Rodney Paul, and Mukunthan Santhanakrishnan for careful feedback and thoughtful suggestions. The authors are also grateful for comments from seminar participants at the UC Riverside Conference on Growth of Gambling and Prediction Markets, at the Financial Management Association meetings in Toronto, at Arizona State University, and at Montana State University. Greg Durham expresses a special thanks to Jim Harmon for providing historical data related to the Harmon Forecast. (The authors are responsible for any outstanding errors in this paper.)
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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 | 0.008 | 0.000 |
| 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) | 0.000 | 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 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".