Chapter 35: Further Reading -- Futures and Season-Long Markets

Foundational References

James, B. (1983). The Bill James Baseball Abstract. Ballantine Books. Introduced the Pythagorean expectation formula that estimates win percentage from points scored and points allowed. This foundational concept, adapted with sport-specific exponents, remains one of the most powerful tools for projecting season win totals across all major sports.
Silver, N. & Fischer, E. (2003-present). "PECOTA and FiveThirtyEight Projection Systems." Nate Silver's projection systems for baseball (PECOTA) and general elections pioneered the use of Monte Carlo simulation for generating probability distributions of outcomes. The principles of aging curves, regression to the mean, and similarity scoring translate directly to sports futures modeling.
Winston, W. L. (2012). Mathletics: How Gamblers, Managers, and Sports Enthusiasts Use Mathematics in Baseball, Basketball, and Football. Princeton University Press. Provides accessible derivations of Pythagorean win formulas across sports and demonstrates how to use them for prediction. Includes discussion of regression toward the mean and its application to projecting future performance from past results.

Glickman, M. E. & Jones, A. C. (1999). "Rating the Chess Rating System." Chance, 12(2), 21-28. Describes the Glicko rating system, which extends Elo by incorporating rating uncertainty. This concept of maintaining and updating uncertainty around team ratings is essential for realistic season simulation.
Elo, A. E. (1978). The Rating of Chessplayers, Past and Present. Arco Publishing. The original text on the Elo rating system, which provides the mathematical foundation for the win probability formula used in game-by-game season simulation. Understanding Elo's assumptions about the distribution of performance is crucial for calibrating the scale parameter.
Stern, H. S. (1991). "On the Probability of Winning a Football Game." The American Statistician, 45(3), 179-183. Develops a probabilistic model for football game outcomes, analyzing the relationship between point spreads and win probabilities. This framework extends naturally to season simulation by providing calibrated single-game probabilities.

Shin, H. S. (1993). "Measuring the Incidence of Insider Trading in a Market for State-Contingent Claims." The Economic Journal, 103(420), 1141-1153. Introduces the Shin method for extracting fair probabilities from bookmaker prices, accounting for the presence of informed bettors. This is the most theoretically grounded approach to de-vigging multi-outcome futures markets.
Levitt, S. D. (2004). "Why Are Gambling Markets Organised So Differently from Financial Markets?" The Economic Journal, 114(495), 223-246. Analyzes the economic structure of sports betting markets, explaining why sportsbooks profit by exploiting bettor biases rather than by acting as pure market-makers. The insights about long-shot bias and favorite-longshot bias directly apply to championship futures pricing.
Snowberg, E. & Wolfers, J. (2010). "Explaining the Favorite-Long Shot Bias: Is It Risk-Love or Misperceptions?" Journal of Political Economy, 118(4), 723-746. Provides rigorous empirical evidence on the favorite-longshot bias across multiple betting markets, demonstrating that bettors systematically overpay for long shots. This finding is especially pronounced in championship futures markets with many selections.

Kelly, J. L. (1956). "A New Interpretation of Information Rate." Bell System Technical Journal, 35(4), 917-926. The original Kelly criterion paper, which derives the optimal fraction of capital to wager given a known edge and odds. The framework is the foundation for bet sizing in futures portfolios, though practical implementation requires modification for estimation error and capital lockup.
Thorp, E. O. (2006). "The Kelly Criterion in Blackjack Sports Betting, and the Stock Market." In Handbook of Asset and Liability Management. Edward Thorp's comprehensive treatment of the Kelly criterion across domains, including sports betting. Discusses practical modifications (fractional Kelly, multiple simultaneous bets, correlated outcomes) that are essential for futures portfolio management.
Markowitz, H. (1952). "Portfolio Selection." The Journal of Finance, 7(1), 77-91. The foundational paper on mean-variance portfolio optimization. While originally developed for financial securities, the framework applies directly to managing a portfolio of correlated futures bets, where the goal is to maximize expected return for a given level of risk.

Oliver, D. (2004). Basketball on Paper: Rules and Tools for Performance Analysis. Potomac Books. Provides the four-factors model for evaluating basketball team quality, which is one of the best inputs for constructing team ratings used in NBA season simulation. Also covers the concept of diminishing returns from dominant strategies.
Schatz, A. (ed.) (2003-present). "Football Outsiders Almanac." Annual publication that demonstrates applied season projection methodology for the NFL, including DVOA (Defense-adjusted Value Over Average) team ratings, schedule-adjusted projections, and Monte Carlo season simulations for playoff probability estimation.
Tango, T. M., Lichtman, M. G., & Dolphin, A. E. (2007). The Book: Playing the Percentages in Baseball. Potomac Books. A rigorous treatment of probabilistic reasoning in baseball that demonstrates how to build projection systems from components. The approach of decomposing player value into components and combining them multiplicatively is the same method used to build team ratings for season simulation.

Thorp, E. O. & Kassouf, S. T. (1967). Beat the Market: A Scientific Stock Market System. Random House. Though focused on financial markets, this book provides the intellectual framework for hedging strategies. The concepts of delta hedging and risk management transfer directly to the problem of dynamically hedging a futures position as probabilities evolve.
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 Bayesian statistics reference, essential for understanding how to update team ratings and win probabilities as new games are played during the season. The Bayesian updating framework is the correct way to combine preseason priors with in-season observations.
Kahneman, D. & Tversky, A. (1979). "Prospect Theory: An Analysis of Decision under Risk." Econometrica, 47(2), 263-291. Understanding the behavioral biases described in prospect theory (loss aversion, the certainty effect, probability weighting) is essential for understanding why futures markets are mispriced and why bettors make suboptimal hedging decisions. The certainty effect in particular explains why many bettors over-hedge by choosing guaranteed profit over higher expected value.