Appendix H: Bibliography
This bibliography provides a comprehensive list of references organized by topic. It includes foundational texts, seminal papers, and contemporary resources essential for basketball analytics practitioners and researchers.
H.1 Foundational Texts in Basketball Analytics
Books
Oliver, D. (2004). Basketball on Paper: Rules and Tools for Performance Analysis. Potomac Books. ISBN: 978-1574886887.
The foundational text of modern basketball analytics. Introduces the "Four Factors" framework, offensive/defensive rating calculations, and possession-based analysis. Essential reading for anyone entering the field.
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), Article 1.
Seminal paper establishing standardized basketball analytics terminology and methodology. Introduces rate statistics and pace adjustments.
Hollinger, J. (2005). Pro Basketball Forecast. Potomac Books. ISBN: 978-1574889611.
Introduces Player Efficiency Rating (PER) and provides detailed methodology for player evaluation. Includes projection systems and historical analysis.
Alamar, B. (2013). Sports Analytics: A Guide for Coaches, Managers, and Other Decision Makers. Columbia University Press. ISBN: 978-0231162920.
Broader sports analytics text with significant basketball content. Excellent introduction to decision-making frameworks and analytical thinking.
Lewis, M. (2003). Moneyball: The Art of Winning an Unfair Game. W. W. Norton & Company. ISBN: 978-0393324815.
While focused on baseball, this book popularized sports analytics and influenced basketball's analytical revolution. Essential cultural context.
Goldsberry, K. (2019). SprawlBall: A Visual Tour of the New Era of the NBA. Houghton Mifflin Harcourt. ISBN: 978-1328767516.
Excellent visual analytics approach to understanding modern basketball strategy, particularly the three-point revolution.
H.2 Statistical and Mathematical Foundations
Statistics Textbooks
Casella, G., & Berger, R. L. (2002). Statistical Inference (2nd ed.). Duxbury Press. ISBN: 978-0534243128.
Comprehensive graduate-level statistics text covering probability theory, estimation, and hypothesis testing.
Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. Springer. ISBN: 978-0387402727.
More accessible introduction to mathematical statistics, suitable for those with strong calculus background.
DeGroot, M. H., & Schervish, M. J. (2012). Probability and Statistics (4th ed.). Pearson. ISBN: 978-0321500465.
Balanced coverage of probability and statistics fundamentals.
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. ISBN: 978-1439840955.
The definitive text on Bayesian methods, increasingly important in sports analytics.
Regression and Statistical Learning
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning (2nd ed.). Springer. ISBN: 978-1071614174.
Excellent introduction to statistical learning methods with R applications. Free PDF available from authors.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. ISBN: 978-0387848570.
More advanced treatment of statistical learning. Essential reference for machine learning in sports.
Faraway, J. J. (2014). Linear Models with R (2nd ed.). Chapman and Hall/CRC. ISBN: 978-1439887332.
Practical guide to linear modeling with excellent R code examples.
Linear Algebra
Strang, G. (2016). Introduction to Linear Algebra (5th ed.). Wellesley-Cambridge Press. ISBN: 978-0980232776.
Clear presentation of linear algebra fundamentals essential for understanding RAPM and other regression-based metrics.
H.3 Machine Learning and Data Science
General Machine Learning
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. ISBN: 978-0387310732.
Comprehensive machine learning text with strong Bayesian perspective.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. ISBN: 978-0262035613.
Definitive text on neural networks and deep learning. Free online version available.
Murphy, K. P. (2022). Probabilistic Machine Learning: An Introduction. MIT Press. ISBN: 978-0262046824.
Modern treatment of machine learning with probabilistic foundations.
Python for Data Science
McKinney, W. (2022). Python for Data Analysis (3rd ed.). O'Reilly Media. ISBN: 978-1098104030.
Essential guide to pandas and data manipulation in Python, by the creator of pandas.
VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. ISBN: 978-1491912058.
Comprehensive coverage of NumPy, pandas, matplotlib, and scikit-learn. Free online version available.
Grus, J. (2019). Data Science from Scratch (2nd ed.). O'Reilly Media. ISBN: 978-1492041139.
Implements data science algorithms from first principles in Python.
H.4 Player Evaluation and Metrics
Plus-Minus and Impact Metrics
Rosenbaum, D. T. (2004). Measuring how NBA players help their teams win. 82games.com.
Introduces Adjusted Plus-Minus methodology.
Engelmann, J. (2017). Possession-based player performance analysis in basketball. MIT Sloan Sports Analytics Conference.
Overview of modern plus-minus approaches including RAPM.
Sill, J. (2010). Improved NBA adjusted +/- using regularization and out-of-sample testing. MIT Sloan Sports Analytics Conference.
Introduces regularization techniques to plus-minus estimation, foundational for modern RAPM.
Myers, D. (2011). About Box Plus/Minus (BPM). Basketball Reference.
Documentation of BPM methodology, explaining the box score estimate of plus-minus.
Ilardi, S., & Barzilai, A. (2008). Adjusted plus-minus ratings: New and improved for 2007-2008. 82games.com.
Important methodological improvements to plus-minus estimation.
Win Shares and Value Metrics
Oliver, D. (2010). Win Shares. Basketball Reference.
Official documentation of Win Shares calculation methodology.
Berri, D. J., Schmidt, M. B., & Brook, S. L. (2006). The Wages of Wins: Taking Measure of the Many Myths in Modern Sport. Stanford Business Books. ISBN: 978-0804754279.
Introduces Wins Produced metric and critiques of PER.
Shot Quality and Efficiency
Skinner, B. (2012). The problem of shot selection in basketball. PLoS ONE, 7(1), e30776.
Analytical framework for understanding shot selection decisions.
Cervone, D., D'Amour, A., Bornn, L., & Goldsberry, K. (2014). A multiresolution stochastic process model for predicting basketball possession outcomes. Journal of the American Statistical Association, 111(514), 585-599.
Introduces Expected Possession Value (EPV) using tracking data.
Chang, Y. H., Maheswaran, R., Su, J., Kwok, S., Levy, T., Wexler, A., & Squire, K. (2014). Quantifying shot quality in the NBA. MIT Sloan Sports Analytics Conference.
Framework for evaluating shot difficulty and shot-making skill.
H.5 Player Tracking and Spatial Analytics
Tracking Data Analysis
Bornn, L., Cervone, D., & Fernandez, J. (2017). Soccer analytics: Unravelling the complexity of "the beautiful game." Significance, 14(3), 26-29.
While focused on soccer, introduces spatial analysis concepts applicable to basketball.
Lucey, P., Bialkowski, A., Carr, P., Morgan, S., Matthews, I., & Sheikh, Y. (2013). Representing and discovering adversarial team behaviors using player roles. IEEE Conference on Computer Vision and Pattern Recognition.
Methods for understanding player movement and roles from tracking data.
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.
Pioneering work on spatial defensive analysis using tracking data.
Shot Charts and Visualization
Goldsberry, K. (2012). CourtVision: New visual and spatial analytics for the NBA. MIT Sloan Sports Analytics Conference.
Introduces spatial shot analysis and visualization techniques.
Reich, B. J., Hodges, J. S., Carlin, B. P., & Reich, A. M. (2006). A spatial analysis of basketball shot chart data. The American Statistician, 60(1), 3-12.
Statistical methods for analyzing shot location data.
H.6 Team Strategy and Game Theory
Team Analysis
Skinner, B., & Goldman, M. (2017). Optimal strategy in basketball. In J. Albert, M. E. Glickman, T. B. Swartz, & R. H. Koning (Eds.), Handbook of Statistical Methods and Analyses in Sports. Chapman and Hall/CRC.
Game-theoretic approach to basketball strategy.
Maheswaran, R., Chang, Y. H., Henehan, A., & Danesis, S. (2012). Deconstructing the rebound with optical tracking data. MIT Sloan Sports Analytics Conference.
Analysis of rebounding using tracking data.
Zuccolotto, P., Manisera, M., & Sandri, M. (2018). Big data analytics for modeling scoring probability in basketball: The effect of shooting under high-pressure conditions. International Journal of Sports Science & Coaching, 13(4), 569-589.
Situational analysis of basketball performance.
Lineup Optimization
Maymin, P. Z., Maymin, P. J., & Shen, E. (2012). NBA chemistry: Positive and negative synergies in basketball. MIT Sloan Sports Analytics Conference.
Analysis of lineup synergies and chemistry effects.
Manner, H. (2016). Modeling and forecasting the outcomes of NBA basketball games. Journal of Quantitative Analysis in Sports, 12(1), 31-41.
Methods for game prediction incorporating lineup effects.
H.7 Draft Analysis and Player Projection
Draft Modeling
Canzano, J. (2020). Predicting the NBA draft with machine learning. MIT Sloan Sports Analytics Conference.
Modern machine learning approaches to draft prediction.
Yang, Y., & Shi, W. (2020). Predicting NBA player performance using machine learning. Journal of Sports Analytics, 6(4), 297-311.
Player projection systems using statistical learning.
Aging Curves
Bradbury, J. C. (2009). Peak athletic performance and ageing: Evidence from baseball. Journal of Sports Sciences, 27(6), 599-610.
Methodology for aging curve estimation applicable to basketball.
Wakim, A., & Jin, J. (2014). Functional data analysis of aging curves in sports. arXiv preprint arXiv:1403.7548.
Advanced statistical methods for modeling athlete aging.
H.8 Historical and Contextual Resources
Basketball History
Simmons, B. (2009). The Book of Basketball: The NBA According to the Sports Guy. Ballantine Books. ISBN: 978-0345511768.
Comprehensive basketball history with analytical perspective.
Haberstroh, T. (2020). The analytics revolution in the NBA. ESPN.
Chronicles the adoption of analytics in professional basketball.
Era Adjustments
Kubatko, J. (2009). Comparing statistics across eras. Basketball Reference Blog.
Methods for comparing players across different eras.
Silver, N. (2015). CARMELO player projections. FiveThirtyEight.
Documentation of projection system accounting for era effects.
H.9 Research Journals and Conferences
Academic Journals
Journal of Quantitative Analysis in Sports (De Gruyter)
Primary academic journal for sports analytics research. Peer-reviewed quantitative analysis.
Journal of Sports Analytics (IOS Press)
Dedicated to statistical and analytical methods in sports.
International Journal of Sports Science & Coaching (SAGE)
Includes practical applications of analytics to coaching.
Journal of the American Statistical Association (Taylor & Francis)
Occasionally publishes sports analytics research.
The American Statistician (Taylor & Francis)
Publishes accessible sports statistics articles.
Conferences
MIT Sloan Sports Analytics Conference (SSAC)
Premier sports analytics conference. Research papers and presentations available online at sloansportsconference.com.
NESSIS (New England Symposium on Statistics in Sports)
Academic conference focused on statistical methods in sports.
Carnegie Mellon Sports Analytics Conference
Academic conference with strong methodological focus.
H.10 Online Resources and Data Sources
Websites and Blogs
Basketball Reference (basketball-reference.com)
Comprehensive historical statistics database. Essential research resource.
Cleaning the Glass (cleaningtheglass.com)
Advanced team and player statistics with emphasis on four factors.
NBA Stats (stats.nba.com)
Official NBA statistics including tracking data.
FiveThirtyEight (fivethirtyeight.com/sports/nba)
Data journalism with detailed methodology documentation.
The Ringer (theringer.com)
Sports analysis including basketball analytics content.
Thinking Basketball (thinkingbasketball.net)
Video analysis combining film and statistics.
Podcasts
Thinking Basketball Podcast (Ben Taylor)
In-depth basketball analysis combining film and statistics.
Nylon Calculus (various hosts)
Analytics-focused basketball discussion.
Dunc'd On Basketball Podcast (Nate Duncan, Danny Leroux)
Salary cap and analytical basketball coverage.
The Lowe Post (Zach Lowe)
High-level basketball analysis with analytical perspective.
H.11 Python and R Resources
Python Libraries Documentation
- pandas: pandas.pydata.org/docs/
- NumPy: numpy.org/doc/
- scikit-learn: scikit-learn.org/stable/documentation.html
- matplotlib: matplotlib.org/stable/contents.html
- seaborn: seaborn.pydata.org/
- nba_api: github.com/swar/nba_api
R Resources
Torgo, L. (2010). Data Mining with R: Learning with Case Studies. Chapman and Hall/CRC. ISBN: 978-1439810187.
R for Data Science (r4ds.had.co.nz) - Wickham, H., & Grolemund, G.
H.12 Additional Academic References
Methodology Papers
Piette, J., Anand, S., & Zhang, K. (2010). Scoring and shooting abilities of NBA players. Journal of Quantitative Analysis in Sports, 6(1), Article 1.
Fearnhead, P., & Taylor, B. M. (2011). On estimating the ability of NBA players. Journal of Quantitative Analysis in Sports, 7(3), Article 11.
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.
Grassetti, L., Bellio, R., Di Gaspero, L., Fonseca, G., & Vidoni, P. (2021). An extended regularized adjusted plus-minus analysis for lineup management in basketball using play-by-play data. IMA Journal of Management Mathematics, 32(4), 385-409.
Defensive Analysis
Franks, A., Miller, A., Bornn, L., Goldsberry, K., & Wu, D. (2016). Meta-analytics: tools for understanding the statistical properties of sports metrics. Journal of Quantitative Analysis in Sports, 12(4), 151-165.
Network and Passing Analysis
Fewell, J. H., Armbruster, D., Ingraham, J., Petersen, A., & Waters, J. S. (2012). Basketball teams as strategic networks. PLoS ONE, 7(11), e47445.
Clemente, F. M., Martins, F. M. L., Wong, D. P., Kalamaras, D., & Mendes, R. S. (2015). Midfielder as the prominent participant in the building attack: A network analysis of national teams in FIFA World Cup 2014. International Journal of Performance Analysis in Sport, 15(2), 704-722.
Citation Guidelines
When citing sources in academic work:
Journal Articles (APA 7th Edition)
Author, A. A., & Author, B. B. (Year). Title of article. Journal Name, Volume(Issue), Page-Page. DOI
Books
Author, A. A. (Year). Title of work: Capital letter also for subtitle. Publisher. ISBN
Conference Papers
Author, A. A. (Year). Title of paper. In Conference Name. Location.
Websites
Author or Organization. (Year, Month Day). Title of page. Website Name. URL
This bibliography is not exhaustive but provides a comprehensive starting point for further study in basketball analytics. Students are encouraged to follow citation trails from these sources to discover additional relevant research.