Appendix H: Bibliography

This bibliography collects the principal references cited throughout the textbook, along with additional recommended reading for advanced study. Entries are organized by topic. Within each section, sources are listed alphabetically by first author's last name.

H.1 Betting Mathematics and Theory

  1. Barnett, T. (2011). How Much Am I Giving Up by Using the Kelly Criterion? Working paper. An analysis of the cost of imprecise probability estimation in Kelly betting.

  2. Browne, S. (2000). "Stochastic Differential Portfolio Games." Journal of Applied Probability, 37(1), 126-147. Theoretical treatment of competing portfolio strategies.

  3. Cover, T. M. and Thomas, J. A. (2006). Elements of Information Theory (2nd ed.). Wiley-Interscience. Chapters on gambling and data compression provide the information-theoretic foundation for the Kelly criterion.

  4. Epstein, R. A. (2009). The Theory of Gambling and Statistical Logic (2nd ed.). Academic Press. Comprehensive mathematical treatment of gambling from probability theory through game theory.

  5. Ethier, S. N. (2010). The Doctrine of Chances: Probabilistic Aspects of Gambling. Springer. Rigorous mathematical analysis of various gambling games and strategies.

  6. Haigh, J. (2003). Taking Chances: Winning with Probability (2nd ed.). Oxford University Press. Accessible introduction to probability with gambling applications.

  7. Kelly, J. L. (1956). "A New Interpretation of Information Rate." Bell System Technical Journal, 35(4), 917-926. The original Kelly criterion paper.

  8. MacLean, L. C., Thorp, E. O., and Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific. Definitive collection of papers on Kelly criterion applications.

  9. Poundstone, W. (2005). Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street. Hill and Wang. Popular history of the Kelly criterion and its applications.

  10. Thorp, E. O. (1966). Beat the Dealer: A Winning Strategy for the Game of Twenty-One. Vintage. The classic that launched quantitative gambling.

  11. Thorp, E. O. (2006). "The Kelly Criterion in Blackjack, Sports Betting, and the Stock Market." In Handbook of Asset and Liability Management, Volume 1, 385-428. Elsevier. The definitive practical guide to Kelly betting across domains.

  12. Ziemba, W. T. and Hausch, D. B. (1986). Betting at the Racetrack. Dr. Z Investments. Pioneering application of Kelly criterion to horse racing markets.

H.2 Market Efficiency and Odds Analysis

  1. Borghesi, R. (2007). "The Home Team Weather Advantage and Biases in the NFL Betting Market." Journal of Economics and Business, 59(4), 340-354.

  2. Dare, W. H. and MacDonald, S. S. (1996). "A Generalized Model for Testing the Home and Favorite Team Advantage in Point Spread Markets." Journal of Financial Economics, 40(2), 295-318.

  3. Gandar, J., Zuber, R., O'Brien, T., and Russo, B. (1988). "Testing Rationality in the Point Spread Betting Market." Journal of Finance, 43(4), 995-1008. Early examination of NFL betting market efficiency.

  4. Gil, R. and Levitt, S. D. (2007). "Testing the Efficiency of Markets in the 2002 World Cup." Journal of Prediction Markets, 1(3), 255-270.

  5. Golec, J. and Tamarkin, M. (1991). "The Degree of Inefficiency in the Football Betting Market." Journal of Financial Economics, 30(2), 311-323.

  6. Gray, P. K. and Gray, S. F. (1997). "Testing Market Efficiency: Evidence from the NFL Sports Betting Market." Journal of Finance, 52(4), 1725-1737.

  7. Kuypers, T. (2000). "Information and Efficiency: An Empirical Study of a Fixed Odds Betting Market." Applied Economics, 32(11), 1353-1363. Soccer betting market efficiency study.

  8. Levitt, S. D. (2004). "Why Are Gambling Markets Organised So Differently from Financial Markets?" Economic Journal, 114(495), 223-246. Influential paper on bookmaker incentives and market structure.

  9. Moskowitz, T. (2021). "Asset Pricing and Sports Betting." Journal of Finance, 76(6), 3153-3209. Landmark paper connecting financial asset pricing to sports betting.

  10. Paul, R. J. and Weinbach, A. P. (2007). "Does Sportsbook.com Set Pointspreads to Maximize Profits? Tests of the Levitt Model of Sportsbook Behavior." Journal of Prediction Markets, 1(3), 209-218.

  11. Sauer, R. D. (1998). "The Economics of Wagering Markets." Journal of Economic Literature, 36(4), 2021-2064. Comprehensive survey of the economics of betting markets.

  12. Shin, H. S. (1993). "Measuring the Incidence of Insider Trading in a Market for State-Contingent Claims." Economic Journal, 103(420), 1141-1153. The Shin model for devigging odds.

  13. Snowberg, E. and Wolfers, J. (2010). "Explaining the Favorite-Long Shot Bias: Is it Risk-Love or Misperceptions?" Journal of Political Economy, 118(4), 723-746.

  14. Stern, H. (1991). "On the Probability of Winning a Football Game." American Statistician, 45(3), 179-183. Foundational paper on NFL scoring distributions.

  15. Vaughan Williams, L. (Ed.) (2003). The Economics of Gambling. Routledge. Collected academic papers on betting market economics.

  16. Vaughan Williams, L. and Stekler, H. O. (2010). "Sports Forecasting." International Journal of Forecasting, 26(3), 445-447. Introduction to a special issue on sports prediction.

  17. Wolfers, J. and Zitzewitz, E. (2004). "Prediction Markets." Journal of Economic Perspectives, 18(2), 107-126. Overview of how prediction markets (including sports betting) aggregate information.

H.3 Football (NFL/College) Analytics

  1. Burke, B. (2010-2019). Advanced NFL Analytics, advancednflstats.com (archived). Pioneering blog on expected points and win probability models.

  2. Carroll, B., Palmer, P., and Thorn, J. (1988). The Hidden Game of Football. Total Sports. Early application of analytics to football.

  3. Eager, E. and Erickson, G. (2020). "Comparing NFL Quarterback Evaluation Metrics." Journal of Quantitative Analysis in Sports, 16(3), 211-227.

  4. Fernandez, J. and Bornn, L. (2018). "Wide Open Spaces: A Statistical Technique for Measuring Space Creation in Professional Soccer." MIT Sloan Sports Analytics Conference. Adapted for football spatial analysis.

  5. Lopez, M. J. (2020). "Bigger Data, Better Questions, and a Return to Fourth Down Decision-Making." Football Outsiders Almanac, 2020 edition.

  6. Lopez, M. J., Thompson, G., and Bliss, A. (2018). "nflfastR: Functions to Efficiently Access NFL Play by Play Data." R package documentation and methodology.

  7. Morris, B. (2016). "How Our NFL Predictions Work." FiveThirtyEight, September 2016. Description of the Elo-based NFL prediction model.

  8. Romer, D. (2006). "Do Firms Maximize? Evidence from Professional Football." Journal of Political Economy, 114(2), 340-365. The classic fourth-down decision-making paper.

  9. Schatz, A. (Ed.) (2003-present). Football Outsiders Almanac (annual). The annual DVOA and advanced stats reference.

H.4 Basketball (NBA/College) Analytics

  1. Berri, D. J. (2012). "Measuring Performance in the National Basketball Association." In The Oxford Handbook of Sports Economics, Volume 2.

  2. Cervone, D., D'Amour, A., Bornn, L., and Goldsberry, K. (2016). "A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes." Journal of the American Statistical Association, 111(514), 585-599. The expected possession value framework.

  3. Engelmann, J. (2017). "Regularized Adjusted Plus-Minus." Working paper. Key methodology for player evaluation.

  4. Franks, A., Miller, A., Bornn, L., and Goldsberry, K. (2015). "Counterpoints: Advanced Defensive Metrics for NBA Basketball." MIT Sloan Sports Analytics Conference.

  5. Goldsberry, K. (2019). Sprawlball: A Visual Tour of the New Era of the NBA. Houghton Mifflin Harcourt. Spatial analysis and shot selection in modern basketball.

  6. Kubatko, J., Oliver, D., Pelton, K., and Rosenbaum, D. T. (2007). "A Starting Point for Analyzing Basketball Statistics." Journal of Quantitative Analysis in Sports, 3(3).

  7. Oliver, D. (2004). Basketball on Paper: Rules and Tools for Performance Analysis. Potomac Books. The foundational text on basketball analytics, introducing the four factors.

  8. Pomeroy, K. (2004-present). kenpom.com methodology. The standard for college basketball efficiency ratings.

  9. Silver, N. (2015). "How Our RAPTOR Metric Works." FiveThirtyEight. Description of the RAPTOR player evaluation system.

  10. Zimmerman, A. and Moorthy, S. (2020). "Predicting College Basketball Match Outcomes Using Machine Learning Techniques." Journal of Quantitative Analysis in Sports, 16(1), 79-94.

H.5 Baseball (MLB) Analytics

  1. Albert, J. and Bennett, J. (2001). Curve Ball: Baseball, Statistics, and the Role of Chance in the Game. Copernicus. Excellent introduction to baseball probability.

  2. Albert, J., Glickman, M. E., Swartz, T. B., and Koning, R. H. (Eds.) (2017). Handbook of Statistical Methods and Analyses in Sports. Chapman and Hall/CRC.

  3. Baumer, B. and Zimbalist, A. (2014). The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball. University of Pennsylvania Press.

  4. Costa, G. B., Huber, M. R., and Saccoman, J. T. (2019). Understanding Sabermetrics: An Introduction to the Science of Baseball Statistics (2nd ed.). McFarland.

  5. James, B. (1982-present). The Bill James Baseball Abstracts and subsequent publications. The founding works of sabermetrics.

  6. Lewis, M. (2003). Moneyball: The Art of Winning an Unfair Game. W.W. Norton. The popular account of the Oakland A's analytics revolution.

  7. Lichtman, M. (2014). "UZR (Ultimate Zone Rating)." FanGraphs methodology document. The standard fielding metric.

  8. Marchi, M., Albert, J., and Baumer, B. S. (2018). Analyzing Baseball Data with R (2nd ed.). CRC Press. Practical guide to baseball analytics in R.

  9. Tango, T. M., Lichtman, M. G., and Dolphin, A. E. (2007). The Book: Playing the Percentages in Baseball. Potomac Books. The definitive analytical treatment of in-game baseball strategy.

  10. Thorn, J. and Palmer, P. (1984). The Hidden Game of Baseball. Doubleday. Pioneering work introducing linear weights and earned run analysis.

H.6 Hockey (NHL) Analytics

  1. Desjardins, G. (2010-present). BehindTheNet.ca and subsequent work on NHL shot-based metrics. Foundational hockey analytics.

  2. Gramacy, R. B., Jensen, S. T., and Taddy, M. (2013). "Estimating Player Contribution in Hockey with Regularized Logistic Regression." Journal of Quantitative Analysis in Sports, 9(1), 97-111.

  3. Macdonald, B. (2011). "A Regression-Based Adjusted Plus-Minus Statistic for NHL Players." Journal of Quantitative Analysis in Sports, 7(3).

  4. Pettigrew, S. (2015). "Assessing the Offensive Productivity of NHL Players Using In-Game Win Probabilities." MIT Sloan Sports Analytics Conference.

  5. Schuckers, M. and Curro, J. (2013). "Total Hockey Rating (THoR): A Comprehensive Statistical Rating of National Hockey League Forwards and Defensemen Based Upon All On-Ice Events." MIT Sloan Sports Analytics Conference.

  6. Thomas, A. C., Ventura, S. L., Jensen, S. T., and Ma, S. (2013). "Competing Process Hazard Function Models for Player Ratings in Ice Hockey." Annals of Applied Statistics, 7(3), 1497-1524.

H.7 Soccer (Association Football) Analytics

  1. Anderson, C. and Sally, D. (2013). The Numbers Game: Why Everything You Know About Soccer Is Wrong. Penguin. Popular introduction to soccer analytics.

  2. Brechot, M. and Flepp, R. (2020). "Dealing with Randomness in Match Outcomes: How to Rethink Performance Evaluation in European Club Football." Journal of Sports Economics, 21(4), 363-389.

  3. Caley, M. (2014-2019). "Premier League Projections and New Expected Goals." Cartilage Free Captain blog. Influential expected goals modeling work.

  4. Dixon, M. J. and Coles, S. G. (1997). "Modelling Association Football Scores and Inefficiencies in the Football Betting Market." Journal of the Royal Statistical Society: Series C, 46(2), 265-280. The foundational paper on Poisson-based soccer scoring models.

  5. Eggels, H., van Elk, R., and Pechenizkiy, M. (2016). "Expected Goals in Soccer: Explaining Match Results Using Predictive Analytics." The Machine Learning and Data Mining for Sports Analytics Workshop.

  6. Goddard, J. and Asimakopoulos, I. (2004). "Forecasting Football Results and the Efficiency of Fixed-Odds Betting." Journal of Forecasting, 23(1), 51-66.

  7. Karlis, D. and Ntzoufras, I. (2003). "Analysis of Sports Data by Using Bivariate Poisson Models." Journal of the Royal Statistical Society: Series D, 52(3), 381-393. Extension of the Poisson model to handle score correlation.

  8. Maher, M. J. (1982). "Modelling Association Football Scores." Statistica Neerlandica, 36(3), 109-118. Early Poisson model for soccer.

  9. Palacios-Huerta, I. (2004). "Structural Changes During a Century of the World's Most Popular Sport." Statistical Methods and Applications, 13(2), 241-258.

  10. Rue, H. and Salvesen, O. (2000). "Prediction and Retrospective Analysis of Soccer Matches in a League." Journal of the Royal Statistical Society: Series D, 49(3), 399-418.

  11. Spann, M. and Skiera, B. (2009). "Sports Forecasting: A Comparison of the Forecast Accuracy of Prediction Markets, Betting Odds, and Tipsters." Journal of Forecasting, 28(1), 55-72.

H.8 Machine Learning and Statistical Prediction

  1. Berrar, D. (2019). "Confidence Curves for Sports Prediction." International Journal of Forecasting, 35(3), 1067-1076.

  2. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. Graduate-level textbook on ML fundamentals.

  3. Breiman, L. (2001). "Random Forests." Machine Learning, 45(1), 5-32. The foundational random forest paper.

  4. Chen, T. and Guestrin, C. (2016). "XGBoost: A Scalable Tree Boosting System." Proceedings of the 22nd ACM SIGKDD International Conference, 785-794. The XGBoost paper.

  5. Constantinou, A. C. and Fenton, N. E. (2012). "Solving the Problem of Inadequate Scoring Rules for Assessing Probabilistic Football Forecast Models." Journal of Quantitative Analysis in Sports, 8(1).

  6. Friedman, J. H. (2001). "Greedy Function Approximation: A Gradient Boosting Machine." Annals of Statistics, 29(5), 1189-1232.

  7. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). Chapman and Hall/CRC.

  8. Glickman, M. E. (1999). "Parameter Estimation in Large Dynamic Paired Comparison Experiments." Journal of the Royal Statistical Society: Series C, 48(3), 377-394. The Glicko rating system paper.

  9. Glickman, M. E. and Stern, H. S. (1998). "A State-Space Model for National Football League Scores." Journal of the American Statistical Association, 93(441), 25-35.

  10. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press. The standard deep learning textbook.

  11. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. The essential reference for statistical learning methods.

  12. Hubacek, O., Sourek, G., and Zelezny, F. (2019). "Exploiting Sports-Betting Market Using Machine Learning." International Journal of Forecasting, 35(2), 783-796.

  13. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2021). An Introduction to Statistical Learning (2nd ed.). Springer. The accessible companion to Elements of Statistical Learning.

  14. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). "LightGBM: A Highly Efficient Gradient Boosting Decision Tree." Advances in Neural Information Processing Systems, 30.

  15. Lundberg, S. M. and Lee, S.-I. (2017). "A Unified Approach to Interpreting Model Predictions." Advances in Neural Information Processing Systems, 30. The SHAP paper.

  16. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.

  17. Prasetio, D. and Harlili, D. (2016). "Predicting Football Match Results with Logistic Regression." International Conference on Advanced Informatics.

  18. Raschka, S. and Mirjalili, V. (2019). Python Machine Learning (3rd ed.). Packt. Practical ML with scikit-learn and TensorFlow.

  19. Rue, H., Martino, S., and Chopin, N. (2009). "Approximate Bayesian Inference for Latent Gaussian Models by Using Integrated Nested Laplace Approximations." Journal of the Royal Statistical Society: Series B, 71(2), 319-392.

H.9 Rating Systems and Power Rankings

  1. Annis, D. H. and Craig, B. A. (2005). "Hybrid Paired Comparison Analysis, with Applications to the Ranking of College Football Teams." Journal of Quantitative Analysis in Sports, 1(1).

  2. Cattelan, M., Varin, C., and Firth, D. (2013). "Dynamic Bradley-Terry Modelling of Sports Tournaments." Journal of the Royal Statistical Society: Series C, 62(1), 135-150.

  3. Elo, A. E. (1978). The Rating of Chessplayers, Past and Present. Arco. The original description of the Elo rating system.

  4. Massey, K. (1997). "Statistical Models Applied to the Rating of Sports Teams." Honors thesis, Bluefield College. The Massey rating system.

  5. Sagarin, J. (1985-present). Sagarin ratings published in USA Today. Long-running computer ranking system.

  6. Silver, N. (2014). "How We Calculate NBA Elo Ratings." FiveThirtyEight. Methodology description.

  7. Stefani, R. T. (2011). "The Methodology of Officially Recognized International Sports Rating Systems." Journal of Quantitative Analysis in Sports, 7(4).

H.10 Psychology of Gambling and Behavioral Economics

  1. Barberis, N. (2012). "A Model of Casino Gambling." Management Science, 58(1), 35-51.

  2. Conlisk, J. (1993). "The Utility of Gambling." Journal of Risk and Uncertainty, 6(3), 255-275.

  3. Croson, R. and Sundali, J. (2005). "The Gambler's Fallacy and the Hot Hand: Empirical Data from Casinos." Journal of Risk and Uncertainty, 30(3), 195-209.

  4. Gilovich, T., Vallone, R., and Tversky, A. (1985). "The Hot Hand in Basketball: On the Misperception of Random Sequences." Cognitive Psychology, 17(3), 295-314.

  5. Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. Essential reading on cognitive biases.

  6. Kahneman, D. and Tversky, A. (1979). "Prospect Theory: An Analysis of Decision under Risk." Econometrica, 47(2), 263-292. The foundational paper on prospect theory.

  7. Miller, J. B. and Sanjurjo, A. (2018). "Surprised by the Hot Hand Fallacy? A Truth in the Law of Small Numbers." Econometrica, 86(6), 2019-2047. Important correction showing the hot hand may be real.

  8. Rabin, M. (2000). "Risk Aversion and Expected-Utility Theory: A Calibration Theorem." Econometrica, 68(5), 1281-1292.

  9. Simmons, J. P., Nelson, L. D., and Simonsohn, U. (2011). "False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant." Psychological Science, 22(11), 1359-1366.

  10. Thaler, R. H. (2015). Misbehaving: The Making of Behavioral Economics. W.W. Norton.

  11. Thaler, R. H. and Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. Penguin.

  12. Tversky, A. and Kahneman, D. (1974). "Judgment Under Uncertainty: Heuristics and Biases." Science, 185(4157), 1124-1131.

H.11 Industry, Regulation, and Responsible Gambling

  1. American Gaming Association. (2023). State of the States Report. Annual survey of US gaming industry.

  2. Blaszczynski, A. and Nower, L. (2002). "A Pathways Model of Problem and Pathological Gambling." Addiction, 97(5), 487-499.

  3. Forrest, D. and Simmons, R. (2003). "Sport and Gambling." Oxford Review of Economic Policy, 19(4), 598-611.

  4. Gainsbury, S. M. (2015). "Online Gambling Addiction: The Relationship Between Internet Gambling and Disordered Gambling." Current Addiction Reports, 2(2), 185-193.

  5. Griffiths, M. D. (2003). "Internet Gambling: Issues, Concerns, and Recommendations." CyberPsychology and Behavior, 6(6), 557-568.

  6. Humphreys, B. R. and Perez, L. (2012). "Cellar Dwellers, Favorites, and Longshots: A New Market-Based Assessment of the Efficiency of Sports Betting Markets." Working paper.

  7. National Council on Problem Gambling. (2021). National Survey on Gambling Attitudes and Gambling Experiences. Washington, DC.

  8. Nower, L. and Blaszczynski, A. (2010). "Gambling Motivations, Money-Limiting Strategies, and Precommitment Preferences of Problem Versus Non-Problem Gamblers." Journal of Gambling Studies, 26(3), 361-372.

  9. Stewart, D. (2022). The Big Gamble: The State-by-State Fight Over Legal Sports Betting. University of Michigan Press.

  10. Wood, R. T. and Williams, R. J. (2007). "Problem Gambling on the Internet: Implications for Internet Gambling Policy in North America." New Media and Society, 9(3), 520-542.

H.12 General Statistics and Mathematics

  1. Casella, G. and Berger, R. L. (2002). Statistical Inference (2nd ed.). Cengage. Graduate-level statistics textbook.

  2. DeGroot, M. H. and Schervish, M. J. (2012). Probability and Statistics (4th ed.). Pearson.

  3. Devore, J. L. (2015). Probability and Statistics for Engineering and the Sciences (9th ed.). Cengage.

  4. Grinstead, C. M. and Snell, J. L. (1997). Introduction to Probability. American Mathematical Society. Available free online.

  5. Kolmogorov, A. N. (1933). Foundations of the Theory of Probability. Chelsea (English translation 1956).

  6. Rice, J. A. (2006). Mathematical Statistics and Data Analysis (3rd ed.). Cengage.

  7. Ross, S. M. (2014). A First Course in Probability (9th ed.). Pearson.

  8. Strang, G. (2016). Introduction to Linear Algebra (5th ed.). Wellesley-Cambridge Press.

  9. Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. Springer. Excellent bridge between intro and graduate statistics.

H.13 Python Programming and Data Science

  1. Grus, J. (2019). Data Science from Scratch (2nd ed.). O'Reilly Media.

  2. McKinney, W. (2022). Python for Data Analysis (3rd ed.). O'Reilly Media. The definitive pandas reference by the creator of the library.

  3. Muller, A. C. and Guido, S. (2016). Introduction to Machine Learning with Python. O'Reilly Media. Practical scikit-learn guide.

  4. Paszke, A., Gross, S., Massa, F., et al. (2019). "PyTorch: An Imperative Style, High-Performance Deep Learning Library." Advances in Neural Information Processing Systems, 32.

  5. Pedregosa, F., et al. (2011). "Scikit-learn: Machine Learning in Python." Journal of Machine Learning Research, 12, 2825-2830.

  6. VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. Comprehensive reference for NumPy, pandas, Matplotlib, and scikit-learn.

  7. Virtanen, P., et al. (2020). "SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python." Nature Methods, 17, 261-272.

H.14 Websites and Online Resources

  1. Baseball Savant (baseballsavant.mlb.com). MLB Statcast data and visualizations.

  2. FanGraphs (fangraphs.com). Advanced baseball statistics and analysis.

  3. FBref.com (fbref.com). Soccer statistics powered by StatsBomb data.

  4. FiveThirtyEight Sports (fivethirtyeight.com/sports). Now part of ABC News. Elo ratings and prediction models for major sports.

  5. Football Outsiders (footballoutsiders.com). DVOA and advanced NFL metrics.

  6. Football-Data.co.uk (football-data.co.uk). Historical soccer results and betting odds.

  7. KenPom (kenpom.com). College basketball efficiency ratings.

  8. MoneyPuck (moneypuck.com). NHL expected goals and advanced analytics.

  9. nflfastR (github.com/nflverse/nflfastR). NFL play-by-play data with EPA and WPA.

  10. The Odds API (the-odds-api.com). Multi-bookmaker odds data via REST API.

  11. Pro Football Reference (pro-football-reference.com). Comprehensive NFL historical statistics.

  12. Sports Reference (sports-reference.com). Family of sites covering NFL, NBA, MLB, NHL, soccer, and college sports.

  13. Pinnacle Betting Resources (pinnacle.com/betting-resources). Educational articles on betting strategy from the sharpest bookmaker.

  14. Kaggle Sports Analytics (kaggle.com). Community datasets and competitions.

  15. Betfair Developer Program (developer.betfair.com). API documentation for exchange data.

  16. Transfermarkt (transfermarkt.com). Soccer player valuations and transfer data.

H.15 Selected Dissertations and Theses

  1. Constantinou, A. C. (2013). Bayesian Network Models for the Prediction of Association Football Match Outcomes. PhD thesis, Queen Mary, University of London.

  2. Manner, H. (2016). Forecasting Football Match Results. PhD thesis, University of Cologne.

  3. Wheatcroft, E. (2019). Evaluating Probabilistic Forecasts in the Presence of Bias: Calibrated Forecasts, Proper Scoring Rules, and Pointwise Testing. PhD thesis, London School of Economics.

Readers pursuing further study are encouraged to search Google Scholar, SSRN, and arXiv for the latest research. The field evolves rapidly, and the most current work appears online well before formal publication. The companion website maintains an updated version of this bibliography.