Chapter 34: Further Reading -- Prop Bets and Player Markets

Foundational References

Winston, W. L. (2012). Mathletics: How Gamblers, Managers, and Sports Enthusiasts Use Mathematics in Baseball, Basketball, and Football. Princeton University Press. Provides the statistical foundations for player performance modeling in sports, including regression-based projection systems and the use of per-possession and per-minute rates as stabilized estimators of player ability.
Oliver, D. (2004). Basketball on Paper: Rules and Tools for Performance Analysis. Potomac Books. The definitive reference for basketball analytics, introducing usage rate, offensive and defensive rating, and the four-factors model. Essential background for anyone building NBA player prop projection systems.
Silver, N. (2012). The Signal and the Noise: Why So Many Predictions Fail -- but Some Don't. Penguin Press. Discusses the general framework of probabilistic forecasting, Bayesian reasoning, and the balance between signal and noise that directly applies to player stat projection and the stabilization of small-sample estimates.

Nelsen, R. B. (2006). An Introduction to Copulas. 2nd ed. Springer. The standard textbook on copula theory, covering the Gaussian copula, Archimedean copulas, and their application to modeling multivariate dependence structures. Provides the mathematical foundation for the SGP correlation analysis presented in this chapter.
McNeil, A. J., Frey, R., & Embrechts, P. (2015). Quantitative Risk Management: Concepts, Techniques and Tools. Revised ed. Princeton University Press. Comprehensive treatment of dependence modeling in finance, including copulas, tail dependence, and portfolio risk. The techniques transfer directly to modeling correlated outcomes in same-game parlays.
Joe, H. (2014). Dependence Modeling with Copulas. Chapman and Hall/CRC. Advanced reference on copula selection, estimation, and goodness-of-fit testing. Relevant for bettors seeking to move beyond the Gaussian copula to capture tail dependence in extreme game outcomes.

Kubatko, J., Oliver, D., Pelton, K., & Rosenbaum, D. T. (2007). "A Starting Point for Analyzing Basketball Statistics." Journal of Quantitative Analysis in Sports, 3(3). Foundational paper on advanced basketball statistics, including the derivation and interpretation of usage rate, true shooting percentage, and per-possession metrics that form the basis of player prop projections.
Franks, A., Miller, A., Bornn, L., & Goldsberry, K. (2015). "Characterizing the Spatial Structure of Defensive Skill in Professional Basketball." Annals of Applied Statistics, 9(1), 94-121. Introduces spatial modeling of defensive ability in basketball, providing the analytical basis for position-specific and location-specific opponent defense adjustments used in prop modeling.
Deshpande, S. K. & Jensen, S. T. (2016). "Estimating an NBA Player's Impact on His Team's Chances of Winning." Journal of Quantitative Analysis in Sports, 12(2), 51-72. Develops Bayesian methods for estimating player impact, including hierarchical models that naturally handle the stabilization of small-sample estimates by shrinking toward population priors.

Levitt, S. D. (2004). "Why Are Gambling Markets Organised So Differently from Financial Markets?" The Economic Journal, 114(495), 223-246. Examines the market structure of sports betting, including how sportsbooks set and shade lines in response to public action. Directly relevant to understanding the "public bias toward overs" phenomenon in player prop markets.
Humphreys, B. R., Paul, R. J., & Weinbach, A. P. (2013). "Consumption Benefits and Gambling: Evidence from the NCAA Basketball Betting Market." Journal of Economic Psychology, 36, 27-36. Provides evidence that recreational bettors derive consumption utility from betting on favorites and overs, explaining the systematic bias toward over bets in player prop markets that creates contrarian value.
Woodland, B. M. & Woodland, L. M. (2001). "Market Efficiency and Profitable Wagering in the National Hockey League: Can Bettors Score on Totals?" Southern Economic Journal, 67(4), 983-995. Examines the efficiency of totals markets across sports, with methodology applicable to evaluating whether prop line totals are efficiently priced.

Paul, R. J. & Weinbach, A. P. (2005). "Bettor Misperceptions in the NBA: The Overbetting of Large Favorites and the 'Hot Hand'." Journal of Sports Economics, 6(4), 390-400. Documents systematic betting biases in basketball markets that extend to prop betting, where star player performance is overbet in favorable matchups.
Kovalchik, S. A. (2016). "Searching for the GOAT of Tennis Win Prediction." Journal of Quantitative Analysis in Sports, 12(3), 127-138. Demonstrates the application of calibrated probability models for individual player outcomes, with methodology transferable to player prop projection and evaluation.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis. 3rd ed. Chapman and Hall/CRC. The standard reference for Bayesian statistical methods, covering hierarchical models, prior specification, and posterior inference. Essential for the Bayesian stabilization technique used in player prop projections with limited sample sizes.
Hyndman, R. J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice. 3rd ed. OTexts. Comprehensive treatment of time series forecasting methods, including exponentially weighted moving averages and forecast combination. Available free online. Directly applicable to modeling player performance trends over a season.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. 2nd ed. Springer. Covers regularization, cross-validation, and ensemble methods relevant to building robust player projection systems that avoid overfitting to small samples. Chapter 7 on model assessment is particularly relevant for evaluating prop model accuracy.

Konik, M. (2006). The Smart Money: How the World's Best Sports Bettors Beat the Bookies Out of Millions. Simon & Schuster. Provides practical insight into how professional sports bettors identify and exploit market inefficiencies, including niche markets like player props where modeling edges are larger than in mainstream lines.