Chapter 23: Draft Modeling and Prospect Evaluation - Further Reading

Foundational Texts

Draft Analysis and Prospect Evaluation

Pelton, K. (2012). "Draft Grades and Evaluation Methods." Basketball Prospectus. Kevin Pelton's work on draft evaluation methodology provides practical frameworks for prospect analysis. His historical draft grades and evaluation systems influenced modern approaches to translation and projection.

Hollinger, J. (2007-2012). "Draft Rater." ESPN.com. John Hollinger's draft rating system was among the first public attempts to systematically project draft prospects using statistical methods. While methodologically dated, the conceptual approach remains relevant.

Silver, N. (2011). "CARMELO Draft Projections Methodology." FiveThirtyEight. Nate Silver's CARMELO system extended to draft prospects provides insight into similarity-based projection methods. Documentation discusses challenges specific to projecting young players.

Statistical Foundations

Berri, D. J., Schmidt, M. B., & Brook, S. L. (2006). The Wages of Wins. Stanford Business Books. While covering broader sports economics, Chapter 4 discusses player valuation methods applicable to draft evaluation. Provides academic grounding for win-based metrics.

Oliver, D. (2004). Basketball on Paper. Potomac Books. Essential basketball analytics foundation. Understanding Four Factors and possession-based statistics is prerequisite to draft modeling. Chapter 8 on player evaluation informs projection methodology.

Winston, W. L. (2009). Mathletics: How Gamblers, Managers, and Sports Enthusiasts Use Mathematics in Baseball, Basketball, and Football. Princeton University Press. Chapter on basketball includes discussion of draft value and player evaluation with accessible mathematical treatment.

Academic Research

Draft Efficiency and Value

Massey, C., & Thaler, R. H. (2013). "The Loser's Curse: Decision Making and Market Efficiency in the National Football League Draft." Management Science, 59(7), 1479-1495. While focused on NFL, this paper's methodology for analyzing draft efficiency applies directly to NBA analysis. Demonstrates how to measure market efficiency in player selection.

Groothuis, P. A., & Hill, J. R. (2004). "Exit Discrimination in the NBA: A Duration Analysis of Career Length." Economic Inquiry, 42(2), 341-349. Academic treatment of career length determinants, relevant to projecting draft bust probability and career duration.

Berri, D. J., Brook, S. L., & Fenn, A. J. (2011). "From College to the Pros: Predicting the NBA Amateur Player Draft." Journal of Productivity Analysis, 35(1), 25-35. Academic study of draft prediction using college statistics. Methodologically rigorous treatment of translation challenges.

Statistical Translation

Kubatko, J. (2010). "College Statistics as Predictors of NBA Success." APBR Conference Proceedings. Technical presentation on translation coefficients and their derivation. Provides statistical foundation for college-to-NBA projections.

Chetty, V., & Hendricks, W. (2000). "Draft Position and NBA Success." Statistical Science, 15(4), 293-309. Early academic work on relationship between draft position and career outcomes. Foundation for pick value curves.

Physical Measurements and Combine

Teramoto, M., Cross, C. L., Rieger, R. H., Maak, T. G., & Willick, S. E. (2018). "Predictive Validity of NBA Draft Combine on Future Performance." Journal of Strength and Conditioning Research, 32(2), 396-408. Academic study of combine measurement predictive validity. Quantifies which measurements predict NBA success.

McGee, K. J., & Burkett, L. N. (2003). "The National Basketball Association Combine: Anthropometric and Performance Characteristics of Draft Picks." Journal of Strength and Conditioning Research, 17(2), 322-328. Analysis of combine data and its relationship to career outcomes. Provides historical context for physical measurement analysis.

Technical Resources

Machine Learning for Draft Prediction

Cao, C. (2012). "Sports Data Mining Technology Used in Basketball Outcome Prediction." Master's thesis, Dublin Institute of Technology. Comprehensive treatment of machine learning applications in basketball, including draft prediction models.

Ganguly, S., & Frank, N. (2018). "The Problem of Shot Selection in Basketball." MIT Sloan Sports Analytics Conference. While focused on shot selection, methodology for feature engineering and model validation applies to draft contexts.

Manner, H. (2016). "Modeling and Forecasting the Outcomes of NBA Basketball Games." Journal of Quantitative Analysis in Sports, 12(1), 31-41. Model building methodology applicable to draft prediction, including validation approaches.

Programming Implementation

McKinney, W. (2017). Python for Data Analysis (2nd ed.). O'Reilly Media. Essential reference for implementing draft models in Python. Chapters on data manipulation and feature engineering are directly applicable.

Geron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O'Reilly Media. Practical machine learning implementation guide. Chapters on ensemble methods and model evaluation relevant to draft modeling.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning (2nd ed.). Springer. Statistical learning foundations with R implementations. Free at www.statlearning.com.

Data Sources

Public Statistics

Sports Reference / Basketball-Reference - https://www.basketball-reference.com/ - Historical NBA statistics - College basketball statistics (Sports-Reference) - Draft combine measurements

NBA Stats Official - https://www.nba.com/stats/ - Official combine results - Draft history

ESPN / CBB - https://www.espn.com/mens-college-basketball/ - College basketball statistics - Recruiting rankings

Specialized Draft Resources

NBADraft.net - https://www.nbadraft.net/ - Mock draft history - Prospect profiles and measurements

The Stepien - https://www.thestepien.com/ - Draft analysis and projections - Historical draft data

Tankathon - https://tankathon.com/ - Draft order and pick value - Trade simulator

International Statistics

RealGM International - https://basketball.realgm.com/international/ - International league statistics - European prospect tracking

Euroleague Stats - https://www.euroleague.net/ - Official EuroLeague statistics

FIBA Basketball - https://www.fiba.basketball/ - International competition statistics

Applied Analytics and Media

Draft Analysis Content

The Ringer Draft Coverage - Kevin O'Connor's prospect analysis - Draft guides and big boards - Methodology discussions

ESPN Draft Analysis - Mike Schmitz scouting reports - Jonathan Givony analysis (DraftExpress legacy) - Statistical breakdowns

The Athletic Draft Coverage - Sam Vecenie's draft analysis - Detailed prospect reports - Statistical projections

Podcasts and Video

Thinking Basketball Draft Episodes - Ben Taylor's analytical approach to prospects - Integration of statistical and film analysis

The Lowe Post Draft Shows - Zach Lowe's interviews with evaluators - Industry perspective on draft process

Cleaning the Glass Podcast - Ben Falk's analytical draft discussions - Methodology insights

Historical and Contextual

Draft History

DraftExpress.com (Archive) - Historical prospect reports - Pre-draft analysis archive - Methodology documentation

Hollinger's Draft Review (ESPN Archive) - Historical draft grades - Retrospective analysis

Era Analysis

Goldsberry, K. (2019). Sprawlball: A Visual Tour of the New Era of the NBA. Mariner Books. Context for how the modern game affects prospect evaluation. Understanding three-point revolution's impact on draft priorities.

Taylor, B. (2021). Thinking Basketball: How to Watch and Think About the Game. Framework for qualitative evaluation that complements statistical analysis.

Conference Proceedings

MIT Sloan Sports Analytics Conference

Annual proceedings include draft-related research: - https://www.sloansportsconference.com/

Notable papers: - Various years feature draft prediction models - Research competition submissions often address prospect evaluation

SABR Analytics Conference

While baseball-focused, methodology papers apply to basketball: - Projection system comparisons - Validation methodology - Uncertainty quantification

For Beginners

  1. Oliver (2004) - Basketball on Paper
  2. FiveThirtyEight methodology articles
  3. Basketball-Reference glossary and tutorials
  4. James et al. (2021) - Statistical Learning (Chapters 1-6)

For Intermediate Analysts

  1. Academic papers on draft efficiency
  2. Combine measurement studies
  3. McKinney (2017) for implementation
  4. Historical draft retrospectives

For Advanced Practitioners

  1. Primary research papers
  2. Machine learning texts (Geron 2019)
  3. Bayesian methods for small samples
  4. Original model development and validation

Staying Current

Follow These Sources

  • FiveThirtyEight sports section
  • The Ringer NBA coverage
  • The Athletic NBA draft analysis
  • Academic journal alerts (JQAS, JSE)

Twitter/Social Media

  • Draft analysts and evaluators
  • Basketball analytics researchers
  • NBA front office staff (when public)

Annual Events

  • NBA Draft Combine (May)
  • NBA Draft (June)
  • Summer League (July)
  • MIT Sloan Conference (March)