Chapter 25: Game Outcome Prediction - Further Reading

Foundational Texts

Sports Prediction and Modeling

Silver, N. (2012). The Signal and the Noise. Penguin Press. Nate Silver's exploration of prediction across domains includes excellent chapters on sports forecasting. His discussion of baseball projections (PECOTA) and political modeling provides frameworks applicable to basketball prediction.

Winston, W. L. (2009). Mathletics. Princeton University Press. Accessible introduction to sports analytics with chapters on rating systems, game prediction, and betting markets. Provides mathematical foundations with practical applications.

Albert, J., Bennett, J., & Cochran, J. J. (Eds.). (2017). Anthology of Statistics in Sports. SIAM. Academic collection covering statistical methods in sports including rating systems, prediction models, and market efficiency studies.

Rating Systems

Elo, A. (1978). The Rating of Chessplayers, Past and Present. Arco Publishing. Original source for Elo rating methodology. While chess-focused, the principles apply directly to team sports ratings.

Glickman, M. E. (1999). "Parameter Estimation in Large Dynamic Paired Comparison Experiments." Applied Statistics, 48(3), 377-394. Academic treatment of rating systems including Glicko (Elo variant with uncertainty). Important for understanding rating precision.

Stefani, R. T. (2011). "The Methodology of Officially Recognized International Sports Rating Systems." Journal of Quantitative Analysis in Sports, 7(4). Comprehensive overview of rating systems across sports with practical implementation guidance.

Academic Research

Prediction Methodology

Kvam, P., & Sokol, J. S. (2006). "A Logistic Regression/Markov Chain Model for NCAA Basketball." Naval Research Logistics, 53(8), 788-803. Academic model for college basketball prediction with methodology applicable to NBA. Demonstrates Markov chain approaches.

Lopez, M. J., & Matthews, G. J. (2015). "Building an NCAA Men's Basketball Predictive Model and Quantifying Its Success." Journal of Quantitative Analysis in Sports, 11(1), 5-12. Rigorous treatment of prediction model evaluation in basketball context.

Manner, H. (2016). "Modeling and Forecasting the Outcomes of NBA Basketball Games." Journal of Quantitative Analysis in Sports, 12(1), 31-41. Academic study of NBA prediction comparing various modeling approaches.

Market Efficiency

Levitt, S. D. (2004). "Why Are Gambling Markets Organised So Differently from Financial Markets?" The Economic Journal, 114(495), 223-246. Economic analysis of betting market structure by Freakonomics author. Essential for understanding market efficiency.

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. Study of specific inefficiencies in NBA betting markets.

Woodland, L. M., & Woodland, B. M. (1994). "Market Efficiency and the Favorite-Longshot Bias: The Baseball Betting Market." The Journal of Finance, 49(1), 269-279. Classic study of favorite-longshot bias with methodology applicable to NBA markets.

Evaluation Methods

Brier, G. W. (1950). "Verification of Forecasts Expressed in Terms of Probability." Monthly Weather Review, 78(1), 1-3. Original paper introducing the Brier score. Foundation for probability calibration assessment.

Gneiting, T., & Raftery, A. E. (2007). "Strictly Proper Scoring Rules, Prediction, and Estimation." Journal of the American Statistical Association, 102(477), 359-378. Comprehensive treatment of scoring rules for probability predictions. Technical but essential for evaluation methodology.

Applied Analytics Resources

FiveThirtyEight

Silver, N. et al. "How Our NBA Predictions Work." - https://fivethirtyeight.com/methodology/how-our-nba-predictions-work/ - Documentation of FiveThirtyEight's NBA prediction methodology - Combines Elo, RAPTOR ratings, and travel/rest factors

RAPTOR Documentation - https://fivethirtyeight.com/features/how-our-raptor-metric-works/ - Player-level ratings that feed into game predictions

ESPN

ESPN BPI (Basketball Power Index) - https://www.espn.com/nba/bpi - Team strength ratings used for prediction - Methodology periodically updated

Betting Market Analysis

Pinnacle Sports Blog - https://www.pinnacle.com/en/betting-resources/ - Educational content on betting markets, line movement, market efficiency

Unabated - https://unabated.com/ - Advanced betting market analysis tools and educational content

Technical Implementation

Python Libraries

scikit-learn Documentation - https://scikit-learn.org/stable/documentation.html - Essential for implementing prediction models - Regression, classification, cross-validation

statsmodels - https://www.statsmodels.org/ - Statistical modeling including logistic regression

Prophet (Facebook) - https://facebook.github.io/prophet/ - Time series forecasting applicable to season predictions

Code Examples

Basketball Reference API/Scraping - https://www.basketball-reference.com/ - Historical game data for model training

NBA API (nba_api) - https://github.com/swar/nba_api - Python library for NBA statistics access

NBA Analytics (Open Source) - Various GitHub repositories with NBA prediction examples - Search: "NBA prediction model Python"

Books on Betting and Markets

Market Dynamics

Thaler, R. H., & Sunstein, C. R. (2008). Nudge. Penguin Books. Behavioral economics with implications for understanding bettor behavior and market inefficiencies.

Surowiecki, J. (2005). The Wisdom of Crowds. Anchor. How collective intelligence works, directly applicable to understanding why betting markets are efficient.

Practical Betting

Miller, E. (2016). The Logic of Sports Betting. Amazon. Practical guide to understanding betting markets from professional perspective.

Konik, M. (2006). The Smart Money. Simon & Schuster. Inside look at professional sports betting operations.

Data Sources

Historical Game Data

Basketball-Reference - https://www.basketball-reference.com/ - Complete historical game logs and box scores

NBA Stats Official - https://www.nba.com/stats/ - Official NBA statistics API

Kaggle NBA Datasets - https://www.kaggle.com/datasets - Search for NBA historical data

Betting Market Data

Sports Odds History - https://www.sportsbookreview.com/betting-odds/ - Historical betting lines (limited free access)

Odds Portal - https://www.oddsportal.com/ - Historical odds comparison

Conference and Journal Resources

Academic Conferences

MIT Sloan Sports Analytics Conference - https://www.sloansportsconference.com/ - Annual conference with prediction-related papers

SABR Analytics Conference - Baseball-focused but methodology applicable to basketball

Journals

Journal of Quantitative Analysis in Sports - Academic journal for sports analytics research

Journal of Sports Economics - Economic analysis of sports including betting markets

For Beginners

  1. Winston (2009) - Mathletics (Chapters 1-5)
  2. Silver (2012) - Signal and the Noise (Chapter 3)
  3. FiveThirtyEight methodology articles
  4. Basic Elo implementation tutorial

For Intermediate Analysts

  1. Glickman (1999) - Rating systems paper
  2. Academic prediction papers (Kvam, Lopez)
  3. Market efficiency studies
  4. Implement multi-factor prediction model

For Advanced Practitioners

  1. Gneiting & Raftery (2007) - Scoring rules
  2. Original Elo (1978)
  3. Betting market literature (Levitt)
  4. Develop novel prediction approaches

Staying Current

Follow These Sources

  • FiveThirtyEight NBA section
  • ESPN analytics content
  • Twitter: Sports analytics community
  • Academic journal alerts (JQAS)

Practice and Competition

  • Kaggle competitions (when available)
  • Fantasy sports prediction leagues
  • Paper trading against closing lines

Community

  • Reddit: r/sportsbook, r/nba analytics discussions
  • Discord: Sports analytics communities
  • Academic conferences and meetups