Chapter 19: Further Reading - Lineup Optimization

Foundational Texts

Basketball on Paper: Rules and Tools for Performance Analysis

Author: Dean Oliver Year: 2004 Publisher: Potomac Books

Dean Oliver's seminal work introduced the Four Factors framework that underlies modern lineup evaluation. His chapter on lineup analysis established fundamental principles still used today.

Key Chapters: Chapter 7 (Lineup Analysis), Chapter 3 (The Four Factors)

The Midrange Theory: Basketball's Evolution in the Age of Analytics

Author: Seth Partnow Year: 2021 Publisher: Triumph Books

Former Milwaukee Bucks Director of Basketball Research provides practical insights into how NBA front offices use lineup data. Includes detailed discussion of sample size challenges and real-world application.

Key Sections: Chapters on lineup construction, player evaluation, roster building

SprawlBall: A Visual Tour of the New Era of the NBA

Author: Kirk Goldsberry Year: 2019 Publisher: Houghton Mifflin Harcourt

Goldsberry's visual approach illuminates how spacing and shot selection affect lineup construction. Excellent for understanding the spatial dimensions of lineup optimization.

Key Sections: Chapters on spacing, shot location, offensive evolution

Academic Papers

Lineup Analysis in the NBA

Authors: Keshri, S., et al. Venue: MIT Sloan Sports Analytics Conference, 2019

Comprehensive methodology for analyzing NBA lineups, addressing sample size issues through regularization. Introduces approaches for comparing lineups with limited data.

Key Contribution: Ridge regression framework for lineup evaluation

Optimal Lineup Selection in Basketball

Authors: Grassetti, L., Bellio, R., Di Gaspero, L., & Fonseca, G. Journal: Journal of Sports Analytics, 2021

Formal optimization approach to lineup selection using integer programming. Addresses constraints like player minutes and rest requirements.

Key Contribution: Mathematical formulation of lineup optimization problem

Characterizing the Spatial Structure of Defensive Skill in Professional Basketball

Authors: Alexander Franks, Andrew Miller, Luke Bornn, Kirk Goldsberry Journal: Annals of Applied Statistics, 2015

While focused on defense, this paper provides methodology applicable to lineup versatility analysis and defensive composition evaluation.

Key Contribution: Spatial analysis framework for player evaluation

A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes

Authors: Daniel Cervone, Alexander D'Amour, Luke Bornn, Kirk Goldsberry Journal: Journal of the American Statistical Association, 2016

Advanced methodology for evaluating possession outcomes that can be applied to lineup-level analysis.

Key Contribution: EPV framework applicable to lineup evaluation

Regularized Adjusted Plus-Minus

Author: Joseph Sill Venue: MIT Sloan Sports Analytics Conference, 2010

Foundation paper for regularized approaches to player and lineup evaluation, addressing the fundamental sample size challenge.

Key Contribution: Ridge regression for stable player ratings

Bayesian Variable Selection for Detecting Player Interactions in Basketball

Authors: Xuan Liu, et al. Journal: Journal of Sports Analytics, 2018

Bayesian approach to identifying player synergies within lineup combinations.

Key Contribution: Probabilistic framework for synergy detection

Technical Resources

NBA Stats API - Lineup Dashboard

URL: https://stats.nba.com/ Type: Data Source

Official source for lineup statistics including: - Five-man lineup Net Ratings - Two-man and three-man combination data - On/off splits - Clutch lineup performance

Key Endpoints: /leaguedashlineups, /leaguedashplayerptshot

Cleaning the Glass

URL: https://cleaningtheglass.com/ Author: Ben Falk Type: Subscription Service

Premium analytics site featuring: - Garbage-time filtered lineup data - Luck-adjusted shooting metrics - Opponent-adjusted ratings - Extensive lineup filtering options

Notable Features: Most rigorous publicly available lineup analysis

Basketball-Reference Lineup Data

URL: https://www.basketball-reference.com/ Type: Free Resource

Historical lineup statistics dating back to tracking era. Includes: - Season and playoff lineup data - Plus/minus tracking - Basic counting stats by lineup

Second Spectrum

URL: https://www.secondspectrum.com/ Type: Tracking Data Provider

The NBA's optical tracking partner provides advanced lineup data including: - Ball movement metrics - Spacing calculations - Defensive coverage patterns

PBP Stats

URL: https://www.pbpstats.com/ Type: Free Resource

Detailed play-by-play derived statistics including: - Possession-level lineup tracking - Transition vs. halfcourt splits - Shot quality by lineup

Scheme and Strategy Resources

The Lowe Post Podcast

Host: Zach Lowe Platform: ESPN/Spotify Type: Podcast

Regular discussion of lineup strategy, rotation patterns, and analytical approaches with NBA coaches and executives.

Recommended Episodes: Any featuring front office executives or coaching staff

Thinking Basketball

Author: Ben Taylor URL: https://www.youtube.com/thinkingbasketball Type: Video Series

Deep dives into lineup construction, historical analysis, and strategic considerations. Combines statistical analysis with film study.

Recommended Series: "Greatest Peaks" includes lineup analysis

The Athletic NBA Coverage

Various Authors: Including John Hollinger, Sam Vecenie Type: Journalism

Regular analysis of lineup decisions, rotation patterns, and team construction from analytical perspective.

Historical and Contextual Reading

The Book of Basketball: The NBA According to the Sports Guy

Author: Bill Simmons Year: 2009 Publisher: ESPN Books

Historical context for lineup construction and team building. Discussion of great teams and their player combinations.

Relevant Sections: Championship team analyses

Seven Seconds or Less

Author: Jack McCallum Year: 2006 Publisher: Touchstone

Inside look at the Phoenix Suns' revolutionary small-ball approach that presaged modern lineup optimization.

Key Value: Understanding strategic evolution of small-ball concepts

Golden: The Miraculous Rise of Steph Curry

Author: Marcus Thompson II Year: 2017 Publisher: Touchstone

Background on the Warriors' "Death Lineup" and the player development that enabled it.

Conference Proceedings

MIT Sloan Sports Analytics Conference

URL: https://www.sloansportsconference.com/ Type: Annual Conference

Archive includes numerous lineup and rotation papers: - "Quantifying the Impact of NBA Lineups" (2013) - "Optimal Lineup Construction" (various years) - "Plus-Minus Methodologies" (various years)

NESSIS (New England Symposium on Statistics in Sports)

URL: http://www.nessis.org/ Type: Academic Conference

Academic papers on statistical approaches to lineup analysis.

Practical Implementation

Python Resources

nba_api: Python client for NBA statistics

pip install nba_api

Includes lineup endpoint access for all tracked seasons.

basketball_reference_web_scraper: Historical data scraping

pip install basketball_reference_web_scraper

scipy.optimize: Optimization algorithms for rotation planning Built into standard scientific Python stack.

R Packages

hoopR: Comprehensive NBA data access nbastatR: Alternative R interface to NBA data ballr: Basketball statistics package

Specialized Topics

Small-Ball and Lineup Innovation

"The Death of the Big Man" Analysis Various Sources: The Ringer, ESPN, The Athletic

Series of articles examining the evolution from traditional lineups to small-ball configurations.

Rotation and Rest Management

Load Management Research Source: NBA Team Sports Science Publications

Research on optimal rest intervals, fatigue management, and rotation timing.

Clutch Lineup Performance

"Closing Time" Analysis Author: Ben Taylor Platform: Thinking Basketball

Systematic analysis of which lineups perform best in high-leverage situations.

Key Takeaway from Literature

The consensus from lineup optimization research:

Sample size dominates: Most lineup analysis is confounded by insufficient data
Regularization is essential: Bayesian or ridge approaches required for stability
Two-man is more reliable: Smaller combinations have better signal-to-noise
Context matters: Same players perform differently in different situations
Systems enable flexibility: Good offensive/defensive systems reduce lineup variance
Closing lineups need separate analysis: Clutch performance follows different patterns
Staggering optimizes total performance: Distributing stars maximizes overall impact
Spacing and versatility are premium: Modern NBA prioritizes these traits