Chapter 28: Building a Basketball Analytics Career - Further Reading

General Career Guidance

Newport, C. (2012). So Good They Can't Ignore You: Why Skills Trump Passion in the Quest for Work You Love. Grand Central Publishing. Challenges the "follow your passion" advice and argues for building rare and valuable skills. Highly relevant for those building technical expertise in a competitive field like sports analytics.

Newport, C. (2016). Deep Work: Rules for Focused Success in a Distracted World. Grand Central Publishing. Strategies for developing the concentrated focus needed for serious analytical work. Essential reading for developing technical skills while managing modern distractions.

Bolles, R. N. (Updated annually). What Color Is Your Parachute? Ten Speed Press. Classic job search guide with practical advice on self-assessment, resume writing, interviewing, and networking. Applicable across industries.

Sports Industry Specific

Karcher, R. (2021). "Careers in Sports Analytics." Journal of Sports Analytics, 7(2). Academic overview of the sports analytics job market, including survey data on salaries, backgrounds, and career paths.

Alamar, B. (2013). Sports Analytics: A Guide for Coaches, Managers, and Other Decision Makers. Columbia University Press. While focused on decision-making, provides useful context about how analytics is used in sports organizations.

Technical Skill Development

Programming

McKinney, W. (2022). Python for Data Analysis (3rd ed.). O'Reilly Media. Comprehensive guide to Python data analysis by the creator of pandas. Essential for building Python proficiency.

Sweigart, A. (2019). Automate the Boring Stuff with Python (2nd ed.). No Starch Press. Practical Python programming for automation. Good for those new to programming. Free online version available.

Beaulieu, A. (2020). Learning SQL (3rd ed.). O'Reilly Media. Clear introduction to SQL for data analysis. Covers the concepts needed for most analytics work.

Chacon, S., & Straub, B. (2014). Pro Git (2nd ed.). Apress. Comprehensive guide to Git version control. Free online at git-scm.com. Essential for collaborative development.

Statistics and Machine Learning

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

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

McElreath, R. (2020). Statistical Rethinking (2nd ed.). CRC Press. Modern approach to Bayesian statistics with practical applications. Lectures available free on YouTube.

Data Visualization

Knaflic, C. N. (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley. Excellent guide to creating clear, effective visualizations. Focuses on communication rather than technical implementation.

Tufte, E. (2001). The Visual Display of Quantitative Information (2nd ed.). Graphics Press. Classic text on data visualization principles. Essential for developing visual design sensibility.

Wilke, C. O. (2019). Fundamentals of Data Visualization. O'Reilly Media. Modern guide to visualization with R examples. Free online at clauswilke.com/dataviz/.

Basketball Knowledge

Foundational Basketball Analytics

Oliver, D. (2004). Basketball on Paper: Rules and Tools for Performance Analysis. Potomac Books. The foundational text for basketball analytics. Introduces Four Factors, possessions-based thinking, and player evaluation frameworks that remain central to the field.

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 while explaining strategic evolution.

Hollinger, J. (2005). Pro Basketball Forecast. Potomac Books. While dated, introduces PER and other metrics that influenced the field's development. Useful historical context.

Basketball Strategy and Tactics

Wissel, H. (2011). Basketball: Steps to Success (3rd ed.). Human Kinetics. Comprehensive guide to basketball fundamentals and strategy. Good for developing understanding of what coaches consider.

Wootten, M., & Gilbert, D. (2012). Coaching Basketball Successfully (3rd ed.). Human Kinetics. Perspective from legendary high school coach. Provides insight into coaching mindset and decision-making.

NBA Coaches Playbook (various). Human Kinetics. Collection of offensive and defensive sets from NBA coaches. Useful for developing tactical vocabulary.

Basketball History

Simmons, B. (2009). The Book of Basketball: The NBA According to The Sports Guy. ESPN Books. Opinionated but comprehensive NBA history. Provides cultural and historical context for modern analysis.

Shea, S., & Baker, C. (2013). Basketball Analytics: Objective and Efficient Strategies for Understanding How Teams Win. CreateSpace. Accessible introduction to basketball analytics concepts with historical analysis.

Communication and Writing

Technical Communication

Strunk, W., & White, E. B. (2000). The Elements of Style (4th ed.). Longman. Essential guide to clear, concise writing. Required reading for anyone communicating analytical findings.

Pinker, S. (2014). The Sense of Style: The Thinking Person's Guide to Writing in the 21st Century. Viking. Modern writing guide from a cognitive scientist. Excellent for academic and technical communication.

Duarte, N. (2010). Resonate: Present Visual Stories that Transform Audiences. Wiley. Guide to presentation design and delivery. Useful for presenting analytical findings to decision-makers.

Data Communication

Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. Understanding cognitive biases is essential for communicating uncertainty and statistical concepts to non-experts.

Cairo, A. (2016). The Truthful Art: Data, Charts, and Maps for Communication. New Riders. Combines visualization principles with communication strategy. Excellent for data journalists and public-facing analysts.

Networking and Professional Development

Networking

Ferrazzi, K. (2014). Never Eat Alone: And Other Secrets to Success, One Relationship at a Time (Expanded ed.). Currency. Comprehensive guide to relationship building. Emphasizes generosity and authenticity in professional relationships.

Grant, A. (2013). Give and Take: Why Helping Others Drives Our Success. Viking. Research on how different interaction styles (givers, takers, matchers) affect career success. Makes the case for generous networking.

Professional Growth

Dweck, C. (2006). Mindset: The New Psychology of Success. Random House. Research on fixed vs. growth mindsets. Essential perspective for continuous learning in a rapidly evolving field.

Clear, J. (2018). Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones. Avery. Practical framework for building sustainable habits. Applicable to skill development and career building.

Online Courses and Tutorials

Programming and Data Science

Coursera - Python for Everybody Specialization (University of Michigan) - https://www.coursera.org/specializations/python - Excellent introduction to Python for beginners

DataCamp - https://www.datacamp.com/ - Interactive courses in Python, R, SQL, and data science - Some free content, subscription for full access

Fast.ai - Practical Deep Learning for Coders - https://www.fast.ai/ - Free, practical deep learning course - Top-down teaching approach

Machine Learning

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

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

Statistics

Khan Academy - Statistics and Probability - https://www.khanacademy.org/math/statistics-probability - Free, comprehensive statistics fundamentals

Stat Quest (YouTube) - https://www.youtube.com/c/joshstarmer - Clear explanations of statistical concepts

Basketball Analytics Content

Websites and Blogs

FiveThirtyEight - NBA - https://fivethirtyeight.com/sports/nba/ - Data-driven NBA analysis and prediction models

Cleaning the Glass - https://cleaningtheglass.com/ - Advanced NBA analytics (subscription)

Thinking Basketball - https://www.youtube.com/c/ThinkingBasketball - Video analysis combining stats and film

PBP Stats - https://www.pbpstats.com/ - Free play-by-play statistics and analysis

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

Podcasts

Thinking Basketball Podcast - In-depth analytical discussions

Dunc'd On - Combines analytics with salary cap and league coverage

The Lowe Post - ESPN's Zach Lowe with analytical perspective

Hollinger & Duncan NBA Show - Two veteran analysts discuss league topics

Data Sources

Free Sources

Basketball-Reference - https://www.basketball-reference.com/ - Historical statistics, play-by-play, game logs

NBA Stats API - https://www.nba.com/stats/ - Official NBA statistics (various endpoints)

nba_api Python Package - https://github.com/swar/nba_api - Python wrapper for NBA.com data

PBP Stats - https://www.pbpstats.com/ - Play-by-play data and analysis

Paid/Licensed Sources

Second Spectrum - Tracking data (available to NBA teams)

Synergy Sports - Play-type data and video (subscription)

Sports Info Solutions - College and NBA data (subscription)

BigDataBall - Play-by-play and tracking data products

Conference and Event Resources

Major Conferences

MIT Sloan Sports Analytics Conference - https://www.sloansportsconference.com/ - Annual in Boston (February/March) - Paper submissions, networking, panels

SABR Analytics Conference - https://sabr.org/analytics - Annual in Phoenix (March) - Baseball-focused but cross-sport networking

Sports Innovation Lab Summit - Various sports technology and analytics topics

Academic Venues

KDD Sports Analytics Workshop - At ACM KDD conference - Academic focus on sports ML/data mining

CVPR Sports Workshop - Computer vision in sports

Journal of Quantitative Analysis in Sports - Academic journal with basketball content

Community Resources

Online Communities

Reddit - r/nba - https://www.reddit.com/r/nba/ - General NBA discussion, occasional analytics content

Twitter/X Basketball Analytics Community - Follow prominent analysts - Engage with analysis discussions

Discord Servers - Various basketball analytics communities - Search for "basketball analytics discord"

Professional Organizations

Sports Analytics World - Community and resources for sports analytics professionals

SABR (Society for American Baseball Research) - Despite name, includes basketball content - Basketball research committee

Local Groups

Sports Analytics Meetups - meetup.com has groups in many cities - Search "sports analytics [your city]"

Job Search Resources

Job Boards

TeamWork Online - https://www.teamworkonline.com/ - Primary sports industry job board

LinkedIn - https://www.linkedin.com/jobs/ - General job board with sports listings

Indeed - https://www.indeed.com/ - Aggregates many job postings

Salary Research

Glassdoor - https://www.glassdoor.com/ - Salary reports and company reviews

Levels.fyi - https://www.levels.fyi/ - Tech company compensation data

Payscale - https://www.payscale.com/ - Salary benchmarking

For Technical Beginners

  1. Python for Everybody (Coursera)
  2. Learning SQL (Beaulieu)
  3. Basketball on Paper (Oliver)
  4. Introduction to Statistical Learning
  5. Hands-On Machine Learning (Geron)

For Basketball Enthusiasts New to Analytics

  1. Basketball on Paper (Oliver)
  2. Sprawlball (Goldsberry)
  3. Python for Data Analysis (McKinney)
  4. Introduction to Statistical Learning
  5. Practice with Basketball-Reference data

For Experienced Analysts Entering Sports

  1. Basketball on Paper (Oliver)
  2. Sprawlball (Goldsberry)
  3. Watch 50+ hours of NBA games analytically
  4. Read FiveThirtyEight and Cleaning the Glass archives
  5. Build basketball-specific portfolio projects

For Career Advancement

  1. So Good They Can't Ignore You (Newport)
  2. Never Eat Alone (Ferrazzi)
  3. Storytelling with Data (Knaflic)
  4. Deep Work (Newport)
  5. Attend MIT Sloan Conference

Quick Reference: Essential Resources

Must-Read Books

  • Basketball on Paper (Oliver)
  • Introduction to Statistical Learning (James et al.)
  • Python for Data Analysis (McKinney)

Must-Know Websites

  • Basketball-Reference
  • NBA.com/stats
  • TeamWork Online

Must-Follow Content

  • FiveThirtyEight NBA
  • Cleaning the Glass
  • Thinking Basketball

Must-Attend Events

  • MIT Sloan Sports Analytics Conference