Towards Sports Betting as a Financial Asset: An Investigative Analysis of Risk, Investment Potential, and Future Perspectives
Bibliographic record
Abstract
This paper explores the potential of sports betting as a financial asset class by integrating financial theory and artificial intelligence. We frame systematic sports wagering as a form of investment, highlighting its uncorrelated returns and potential diversification benefits. Financial models such as the Kelly criterion, Sharpe ratio, and Value at Risk (VaR) are applied to assess risk-adjusted performance and portfolio optimization. Additionally, we investigate how leverage can amplify both returns and risks in betting portfolios, and we introduce an uncertainty-adjusted Kelly criterion to account for prediction errors, validating these approaches via simulations. 1 Artificial intelligence techniques-including machine learning, deep learning, and reinforcement learning-are used to forecast outcomes, manage risk, and optimize betting strategies. Real-world use cases, such as artificial intelligence-driven betting funds, algorithmic arbitrage across bookmakers, and automated market-making on exchanges, illustrate practical implementations of these models. While high volatility, regulatory barriers, and liquidity constraints remain challenges, our findings suggest that with mature analytics and governance frameworks, sports betting could emerge as a credible alternative asset for institutional portfolios.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".