Chapter 26: Machine Learning in Basketball - Further Reading

Foundational Machine Learning Texts

Introductory

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning (2nd ed.). Springer. The essential introduction to statistical learning methods. Free at www.statlearning.com. Covers regression, classification, resampling, tree methods, and unsupervised learning with R examples.

Geron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O'Reilly Media. Practical implementation guide covering the complete ML workflow. Excellent for building working systems.

Muller, A. C., & Guido, S. (2016). Introduction to Machine Learning with Python. O'Reilly Media. Python-focused introduction using scikit-learn. Good for those new to ML implementation.

Advanced

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. Comprehensive mathematical treatment of machine learning. Free online. More rigorous than ISLR.

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. Classic ML text with Bayesian perspective. Mathematically rigorous.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. The definitive deep learning textbook. Free at www.deeplearningbook.org.

Sports Analytics Applications

Basketball-Specific

Goldsberry, K. (2019). Sprawlball: A Visual Tour of the New Era of the NBA. Mariner Books. Visual analytics approach to modern basketball. Demonstrates effective data visualization.

Oliver, D. (2004). Basketball on Paper. Potomac Books. Foundation for basketball analytics. Essential context for ML applications.

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). Academic foundation for basketball metrics.

Cross-Sport ML Applications

Albert, J., Glickman, M. E., Swartz, T. B., & Koning, R. H. (Eds.). (2017). Handbook of Statistical Methods and Analyses in Sports. Chapman and Hall/CRC. Comprehensive handbook covering ML applications across sports.

Lewis, M. (2003). Moneyball: The Art of Winning an Unfair Game. W. W. Norton. Classic on data-driven sports decisions. While baseball-focused, principles apply broadly.

Research Papers

Player Evaluation

Cervone, D., D'Amour, A., Bornn, L., & Goldsberry, K. (2016). "A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes." Journal of the American Statistical Association, 111(514), 585-599. Expected Possession Value (EPV) model. Cutting-edge ML application to basketball.

Engelmann, J. (2017). "Possession-Based Player Performance Analysis in Basketball." MIT Sloan Sports Analytics Conference. Academic treatment of player impact metrics.

Clustering and Classification

Wang, K. C., & Zemel, R. (2016). "Classifying NBA Offensive Plays Using Neural Networks." MIT Sloan Sports Analytics Conference. Neural network application to play classification.

Metulini, R., Manisera, M., & Zuccolotto, P. (2018). "Modelling the Dynamic Pattern of Surface Area in Basketball and its Effects on Team Performance." Journal of Quantitative Analysis in Sports, 14(2), 61-75. Tracking data analysis with ML methods.

Deep Learning Applications

Yue, Y., Lucey, P., Carr, P., Bialkowski, A., & Matthews, I. (2014). "Learning Fine-Grained Spatial Models for Dynamic Sports Play Prediction." IEEE International Conference on Data Mining. Deep learning for spatial pattern recognition in sports.

Online Courses

Machine Learning Foundations

Andrew Ng's Machine Learning (Coursera) - https://www.coursera.org/learn/machine-learning - Classic introduction to ML concepts

Fast.ai Practical Deep Learning - https://www.fast.ai/ - Practical, code-first approach to deep learning

Google Machine Learning Crash Course - https://developers.google.com/machine-learning/crash-course - Free, quick introduction with TensorFlow

Specialized Topics

Kaggle Learn - https://www.kaggle.com/learn - Micro-courses on specific ML topics

DataCamp - https://www.datacamp.com/ - Interactive courses including sports analytics tracks

Tools and Libraries

Core Libraries

scikit-learn - https://scikit-learn.org/ - Essential Python ML library - Comprehensive documentation and tutorials

XGBoost - https://xgboost.readthedocs.io/ - Gradient boosting implementation - Often best performance for tabular data

LightGBM - https://lightgbm.readthedocs.io/ - Fast gradient boosting from Microsoft - Good for large datasets

Deep Learning

TensorFlow - https://www.tensorflow.org/ - Google's deep learning framework - Good production support

PyTorch - https://pytorch.org/ - Facebook's deep learning framework - Preferred for research

Interpretation

SHAP - https://github.com/slundberg/shap - Model interpretation library - Essential for explaining predictions

LIME - https://github.com/marcotcr/lime - Local interpretable explanations

Data Sources

NBA Statistics

Basketball-Reference - https://www.basketball-reference.com/ - Comprehensive historical statistics

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

nba_api Python Package - https://github.com/swar/nba_api - Python access to NBA.com data

Tracking Data

NBA.com/stats Advanced - Player tracking metrics - Shot location data

Second Spectrum - https://www.secondspectrum.com/ - Official NBA tracking provider - Limited public access

Kaggle Datasets

NBA Players Stats - Various datasets on Kaggle - Search: "NBA statistics kaggle"

Blogs and Websites

Technical ML

Towards Data Science - https://towardsdatascience.com/ - ML tutorials and applications

Machine Learning Mastery - https://machinelearningmastery.com/ - Practical tutorials by Jason Brownlee

Sports Analytics

Nylon Calculus - https://fansided.com/nba/nylon-calculus/ - Basketball analytics writing

Cleaning the Glass - https://cleaningtheglass.com/ - Advanced basketball analysis

FiveThirtyEight - https://fivethirtyeight.com/sports/nba/ - Statistical sports journalism

For ML Beginners

  1. ISLR (James et al.) - Chapters 1-6
  2. Andrew Ng's Coursera course
  3. Geron's Hands-On ML - Part I
  4. Practice on Kaggle competitions

For Basketball Analysts New to ML

  1. Oliver - Basketball on Paper
  2. ISLR - Chapters 1-4
  3. scikit-learn tutorials
  4. Apply to basketball datasets

For Experienced ML Practitioners

  1. ESL (Hastie et al.) - selected chapters
  2. Academic papers (Cervone, etc.)
  3. Advanced techniques (deep learning, tracking data)
  4. Original research projects

Conference Resources

MIT Sloan Sports Analytics Conference

  • https://www.sloansportsconference.com/
  • Annual conference with research papers
  • Archives available online

CVPR/NeurIPS Sports Workshops

  • Computer vision and deep learning conferences
  • Increasingly include sports analytics tracks

KDD Sports Analytics Workshop

  • https://www.kdd.org/
  • Data mining applications in sports