Chapter 17: Further Reading - Introduction to Predictive Analytics

Academic Papers

Foundational Sports Prediction

  1. "Predicting the Outcome of NFL Games Using Machine Learning" - Purucker (1996) - Early application of neural networks to sports prediction - Historical perspective on the field's evolution - Available through academic databases

  2. "Football Game Outcome Prediction with Neural Networks" - Kahn (2003) - Neural network architectures for game prediction - Feature selection for sports modeling - IEEE publication

  3. "A Machine Learning Approach to March Madness" - Huang & Hsu - Ensemble methods for tournament prediction - Handling small sample sizes - MIT Sloan Sports Analytics Conference

  4. "Predicting the Winner of NFL Football Games" - Warner (2010) - Logistic regression for game outcomes - Point spread prediction - Journal of Quantitative Analysis in Sports

Advanced Methods

  1. "Deep Reinforcement Learning in Sports" - Silver et al. - Neural network approaches to decision making - Sequential decision problems in sports - Nature publication

  2. "Expected Possession Value in Basketball" - Cervone et al. (2014) - Spatial models for play value - Real-time prediction systems - MIT Sloan Sports Analytics Conference

Books

Machine Learning Fundamentals

  1. "An Introduction to Statistical Learning" - James, Witten, Hastie, Tibshirani - Excellent ML introduction with R examples - Free online at: statlearning.com - Covers all fundamental algorithms

  2. "Hands-On Machine Learning with Scikit-Learn" - Aurélien Géron - Python-focused practical guide - Industry-standard practices - O'Reilly publication

  3. "Pattern Recognition and Machine Learning" - Christopher Bishop - Deeper theoretical treatment - Mathematical foundations - Springer publication

  4. "The Elements of Statistical Learning" - Hastie, Tibshirani, Friedman - Graduate-level treatment - Comprehensive coverage - Free online at: stanford.edu

Sports Analytics

  1. "Mathletics" - Wayne Winston - Sports analytics fundamentals - Includes football examples - Covers prediction basics

  2. "The Signal and the Noise" - Nate Silver - Prediction philosophy and pitfalls - Sports prediction chapter - Accessible to general audience

  3. "Analyzing Baseball Data with R" - Marchi & Albert - While baseball-focused, prediction principles transfer - Excellent R code examples - Chapman & Hall/CRC publication

Online Courses

Machine Learning

  1. "Machine Learning" - Andrew Ng (Coursera/Stanford) - Industry-standard introduction - Theory and implementation - Free to audit

  2. "Applied Data Science with Python" - University of Michigan (Coursera) - Practical Python ML skills - Includes sports examples in assignments - Specialization with 5 courses

  3. "Fast.ai Practical Deep Learning" - Jeremy Howard - Modern deep learning practices - Top-down learning approach - Free at: fast.ai

  4. "Statistical Learning" - Stanford Online - Companion to ISL textbook - Free video lectures - edX platform

Sports Analytics Specific

  1. "Sports Performance Analytics" - University of Michigan (Coursera) - Motion analysis and prediction - Sports-specific applications - Python implementation

  2. "Moneyball: The Art of Winning an Unfair Game" - Various platforms - Business of analytics - Historical perspective - Case study format

Online Resources

Documentation & Tutorials

  1. Scikit-Learn Documentation - Official tutorials and examples - API reference - https://scikit-learn.org/stable/

  2. Kaggle Learn - Interactive ML tutorials - Practice competitions - https://www.kaggle.com/learn

  3. Towards Data Science - ML tutorials and articles - Sports analytics posts - https://towardsdatascience.com

  4. Machine Learning Mastery - Practical tutorials - Python examples - https://machinelearningmastery.com

Sports Analytics

  1. nflfastR Documentation - Play-by-play data access - R and Python interfaces - https://www.nflfastr.com/

  2. Open Source Football - Community tutorials - Code examples - https://www.opensourcefootball.com/

  3. Football Outsiders - Advanced metrics explained - Prediction methodology - https://www.footballoutsiders.com/

Tools and Libraries

Python ML Stack

  1. scikit-learn - https://scikit-learn.org - Core ML library - Preprocessing, modeling, evaluation - Industry standard

  2. XGBoost - https://xgboost.readthedocs.io - Gradient boosting implementation - Competition-winning models - Efficient and scalable

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

  4. CatBoost - https://catboost.ai - Handles categorical features - Yandex development - Good default parameters

Model Evaluation

  1. Yellowbrick - https://www.scikit-yb.org - ML visualization toolkit - Diagnostic plots - scikit-learn compatible

  2. MLflow - https://mlflow.org - Experiment tracking - Model management - Deployment tools

Data Tools

  1. nfl_data_py - Python NFL data access
  2. cfbd - College football data API
  3. sportsipy - Sports reference scraper

Competitions and Practice

Kaggle Competitions

  1. NFL Big Data Bowl - Annual tracking data competition
  2. March Machine Learning Mania - NCAA tournament prediction
  3. NFL 1st and Future - Player safety prediction

Practice Datasets

  1. Kaggle NFL Play-by-Play - Historical play data
  2. nflfastR data - Cleaned NFL data
  3. Sports Reference - Historical statistics

Blogs and Newsletters

Technical Blogs

  1. Ben Baldwin's Newsletter - nflfastR creator
  2. The Athletic Analytics Coverage - Sports journalism
  3. FiveThirtyEight - Prediction methodology

Research Groups

  1. CMU Stats Sports - Academic research
  2. MIT Sloan Sports Analytics - Conference papers
  3. Stanford Sports Analytics - Research group

Podcasts

  1. "Thinking Basketball" - Analytics discussion (basketball but transferable)
  2. "The Analytics Edge" - Sports analytics podcast
  3. "The Football Analytics Show" - Football-specific analytics

Suggested Learning Path

Week 1-2: Foundations

  • Complete scikit-learn tutorial
  • Read ISL Chapters 1-4
  • Practice train/test split exercises

Week 3-4: Core Algorithms

  • Study logistic regression in depth
  • Implement random forest models
  • Learn cross-validation properly

Week 5-6: Evaluation

  • Master evaluation metrics
  • Study calibration techniques
  • Practice model comparison

Week 7-8: Advanced Topics

  • Explore gradient boosting
  • Learn feature engineering
  • Study deployment considerations

Week 9+: Application

  • Build complete prediction pipeline
  • Participate in a Kaggle competition
  • Develop personal project

Citation Format

When citing predictive modeling work:

APA Format:

Author, A. A. (Year). Title of work. Publisher/Journal.

Example:

Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.

Community Resources

Forums and Discussion

  1. Reddit r/MachineLearning - General ML discussion
  2. Reddit r/NFLstatheads - Football analytics
  3. Cross Validated (Stack Exchange) - Statistics Q&A
  4. Kaggle Discussion Forums - Competition-specific

Professional Networks

  1. Sports Analytics Slack Communities
  2. LinkedIn Sports Analytics Groups
  3. Twitter/X Analytics Community

Conference Proceedings

Key Conferences

  1. MIT Sloan Sports Analytics Conference - Premier sports analytics event
  2. NESSIS - New England Symposium on Statistics in Sports
  3. Carnegie Mellon Sports Analytics Conference - Academic focus
  4. SABR Analytics Conference - Sports analytics research