Chapter 16 Further Reading: Shot Quality Models

Key Resources

Goldsberry, K. (2019). SprawlBall: A Visual Tour of the New Era of the NBA.

Summary: Accessible exploration of how shot selection has transformed modern basketball. Excellent visualizations of shot charts and spatial analysis. Essential reading for understanding the analytics revolution in shot selection.

Chang, Y.H. et al. (2014). "Quantifying Shot Quality in the NBA." MIT Sloan Sports Analytics Conference.

Summary: Foundational paper on shot quality modeling using spatial and contextual features. Introduces the framework for expected field goal percentage based on shot location and defender positioning.

Cervone, D. et al. (2014). "POINTWISE: Predicting Points and Valuing Decisions in Real Time." MIT Sloan Sports Analytics Conference.

Summary: Introduces expected possession value (EPV) which extends shot quality to full possession evaluation. Shows how shot decisions can be valued in context of alternatives.

Skinner, B. (2012). "The Price of Anarchy in Basketball." Journal of Quantitative Analysis in Sports.

Summary: Game-theoretic analysis of shot selection. Explores optimal shot allocation and how individual incentives may not align with team optimization.

Academic Papers

Shot Probability Models

Reich, B.J. et al. (2006). "A Spatial Analysis of Basketball Shot Chart Data." The American Statistician.
Early spatial modeling of shot locations
Gaussian process approaches
Franks, A. et al. (2015). "Characterizing the Spatial Structure of Defensive Skill in Professional Basketball." Annals of Applied Statistics.
Defensive impact on shot quality
Spatial defensive metrics
Sandholtz, N. & Bornn, L. (2020). "Markov Decision Processes with Dynamic Transition Probabilities: An Analysis of Shooting Strategies in Basketball." Annals of Applied Statistics.
Decision-making framework for shot selection
Dynamic programming approaches

Machine Learning Applications

Lucey, P. et al. (2014). "Quality vs Quantity: Improved Shot Prediction in Soccer using Strategic Features from Spatiotemporal Data." MIT Sloan Sports Analytics Conference.
Transfer learning from soccer to basketball
Feature engineering for shot models
Sicilia, A. et al. (2019). "DeepHoops: Evaluating Micro-Actions in Basketball Using Deep Feature Representations of Spatio-Temporal Data." KDD Sports Analytics Workshop.
Deep learning for shot evaluation
Convolutional neural networks on tracking data
Yue, Y. et al. (2014). "Learning Fine-Grained Spatial Models for Dynamic Sports Play Prediction." ICDM.
Recurrent models for possession outcomes
Temporal sequence modeling

Online Resources

NBA Stats - Shot Dashboard

Link: https://www.nba.com/stats/players/shooting/ Description: Official NBA shooting statistics including shot zones, defender distance, touch time, and dribbles. Essential data source for shot quality analysis.

Basketball-Reference Advanced Shooting

Link: https://www.basketball-reference.com/leagues/NBA_2024_shooting.html Description: Comprehensive shooting statistics by distance and zone. Good for historical analysis and league-wide trends.

Cleaning the Glass

Link: https://cleaningtheglass.com/ Description: Premium analytics site with shot location data, expected efficiency, and player shooting profiles. Excellent for applied analysis.

NBA Advanced Stats - Player Tracking

Link: https://www.nba.com/stats/players/shots-closest-defender/ Description: Official tracking statistics including closest defender distance and shot clock data.

Books

Statistics and Probability

James, G. et al. (2021). "An Introduction to Statistical Learning." Springer.
Chapter on logistic regression essential for shot models
Classification methodology overview
Hastie, T. et al. (2009). "The Elements of Statistical Learning." Springer.
Advanced treatment of regularization
Model evaluation and calibration

Basketball Analytics

Oliver, D. (2004). "Basketball on Paper." Potomac Books.
Foundation of basketball analytics
Efficiency metrics context
Alamar, B. (2013). "Sports Analytics: A Guide for Coaches, Managers, and Other Decision Makers." Columbia University Press.
General sports analytics framework
Decision-making applications

Technical References

Logistic Regression and Classification

Hosmer, D.W. & Lemeshow, S. (2013). "Applied Logistic Regression." Wiley.
Comprehensive logistic regression treatment
Calibration and goodness-of-fit
Platt, J. (1999). "Probabilistic Outputs for Support Vector Machines." Advances in Large Margin Classifiers.
Probability calibration methods
Platt scaling for well-calibrated probabilities

Spatial Statistics

Cressie, N. & Wikle, C.K. (2011). "Statistics for Spatio-Temporal Data." Wiley.
Spatial modeling fundamentals
Kriging and Gaussian processes

Video Resources

Conference Presentations

MIT Sloan Sports Analytics Conference (various years)
Annual presentations on shot quality research
Available on YouTube and conference website
NESSIS (New England Symposium on Statistics in Sports)
Academic sports statistics presentations
Shot modeling methodologies

Tutorial Content

StatQuest with Josh Starmer - Logistic Regression
Accessible introduction to logistic regression
Machine learning fundamentals

Data Sources

Public Data

NBA Stats API (shooting data)
Basketball-Reference (historical data)
Kaggle NBA datasets (various)

Commercial Data

Second Spectrum (tracking data)
Synergy Sports (play-by-play video)
Sportradar (comprehensive feeds)

Software and Tools

Python Libraries

nba_api - Access to NBA Stats
scikit-learn - Machine learning models
statsmodels - Statistical modeling
matplotlib/seaborn - Visualization

R Packages

hoopR - NBA data access
glmnet - Regularized regression
caret - Machine learning framework

Historical Context

Evolution of Shot Selection Analytics

2004: Dean Oliver's work establishes efficiency concepts
2006: First spatial shot models published
2012-14: Tracking data enables defender distance metrics
2014-16: Expected points models become standard
2017-present: Machine learning and deep learning applications