Chapter 18: Further Reading - Game Outcome Prediction

Academic Papers

Rating Systems

  1. "Elo Ratings: A System's Perspective" - Arpad Elo (1978) - Original Elo rating system paper - Mathematical foundations - Chess rating implementation

  2. "A Bradley-Terry Type Model for Forecasting Tennis Match Results" - Klaassen & Magnus - Extensions of paired comparison models - Application to sports prediction - Statistical inference methods

  3. "The Power of Elo: Applications to Sports Analytics" - Hvattum & Arntzen - Elo adaptations for team sports - Soccer and football applications - Comparison with other methods

  4. "Margin of Victory and NFL Game Predictions" - Journal of Quantitative Analysis in Sports - Incorporating margin into ratings - Optimal K-factor selection - Home field advantage estimation

Machine Learning in Sports

  1. "Machine Learning Methods for Predicting NFL Game Outcomes" - Comparison of ML algorithms - Feature engineering approaches - Cross-validation strategies

  2. "Deep Learning for Sports Prediction" - Recent arXiv preprints - Neural network architectures - Sequence modeling for sports - State-of-the-art methods

  3. "Ensemble Methods for Sports Forecasting" - Combining multiple models - Weight optimization - Stacking approaches

Books

Statistical Prediction

  1. "Statistical Sports Models in Excel" - Andrew Mack - Practical spreadsheet implementations - Step-by-step tutorials - Football-specific examples

  2. "The Signal and the Noise" - Nate Silver - Prediction philosophy - Sports prediction chapter - FiveThirtyEight methodology insights

  3. "Superforecasting" - Philip Tetlock - Probability calibration - Expert prediction improvement - Uncertainty quantification

Machine Learning

  1. "Hands-On Machine Learning with Scikit-Learn" - Aurélien Géron - Python ML implementation - Model evaluation techniques - Production deployment

  2. "Feature Engineering and Selection" - Max Kuhn & Kjell Johnson - Comprehensive feature engineering - Selection methods - Sports analytics examples

  3. "Probabilistic Machine Learning" - Kevin Murphy - Probabilistic prediction - Bayesian approaches - Uncertainty quantification

Online Resources

Rating Systems

  1. FiveThirtyEight NFL Elo Methodology - https://fivethirtyeight.com/methodology/how-our-nfl-predictions-work/ - Detailed Elo implementation - Home field and rest adjustments

  2. ESPN FPI Methodology - ESPN's Football Power Index explanation - Efficiency-based ratings - Preseason initialization

  3. SP+ Methodology (Bill Connelly) - Football-specific efficiency ratings - Play-by-play adjustments - Historical analysis

Data Sources

  1. nflfastR / cfbfastR - R packages for NFL/CFB data - Play-by-play statistics - https://www.nflfastr.com/

  2. College Football Data API - Free CFB data access - Historical game results - https://collegefootballdata.com/

  3. Sports Reference - Comprehensive historical data - Team and player statistics - https://www.sports-reference.com/cfb/

Tools and Libraries

Python Prediction Stack

  1. scikit-learn - https://scikit-learn.org - Core ML library - Model selection and evaluation - Cross-validation tools

  2. XGBoost - https://xgboost.readthedocs.io - Gradient boosting implementation - High performance - Feature importance

  3. LightGBM - https://lightgbm.readthedocs.io - Fast gradient boosting - Categorical feature handling - Large dataset support

  4. CatBoost - https://catboost.ai - Handles categorical data natively - Good default parameters - Robust to overfitting

Specialized Tools

  1. elopy - Python Elo implementation - Easy rating system setup - Custom K-factor support

  2. sportsipy - Sports reference scraping - Automated data collection - Multiple sports support

  3. betting-models - Betting analytics - Line comparison tools - ROI calculations

Industry Blogs and Sites

Analytics Sites

  1. Football Outsiders - https://www.footballoutsiders.com/ - DVOA methodology - Advanced statistics - Game previews

  2. The Athletic (Analytics Coverage) - In-depth analysis - Model explanations - Industry insights

  3. FiveThirtyEight Sports - Prediction methodology - Model performance tracking - Interactive tools

Technical Blogs

  1. Towards Data Science - Sports Analytics - Tutorial articles - Code examples - https://towardsdatascience.com/

  2. Pinnacle Betting Resources - Market efficiency analysis - Betting mathematics - Model evaluation

  3. SBNation (Football Study Hall) - Bill Connelly's work - SP+ explanations - Advanced stats

Competitions and Practice

Kaggle Competitions

  1. NFL Big Data Bowl - Annual competition - Tracking data - Novel analytics - Real NFL evaluation

  2. March Machine Learning Mania - NCAA Tournament - Game prediction - Probability calibration - Public leaderboard

  3. NFL 1st and Future - Player safety - Prediction challenges - Industry prizes

Practice Datasets

  1. Historical NFL/CFB Results - Available on Kaggle - Multiple decades - Clean format

  2. nflfastR Data Repository - Play-by-play data - Regular updates - Documentation

Podcasts and Videos

Podcasts

  1. "Bet The Process" - Sports betting analytics
  2. "PFF NFL Podcast" - Advanced statistics
  3. "The Analytics Edge" - Sports prediction
  4. "Thinking Basketball" - Analytics philosophy (transferable)

Video Courses

  1. "Sports Analytics" - Coursera - University of Michigan - Prediction fundamentals - Python implementation

  2. "Machine Learning A-Z" - Udemy - Comprehensive ML - Sports examples - Practical focus

Research Groups

Academic

  1. CMU Statistics in Sports - Carnegie Mellon research - Published papers - Student projects

  2. Stanford Sports Analytics - Research initiatives - Industry connections - Technical papers

  3. MIT Sloan Sports Analytics Conference - Annual conference - Research papers - Industry presentations

Industry

  1. ESPN Analytics - FPI development - QBR methodology - Win probability

  2. Pro Football Focus (PFF) - Grading systems - Expected points - Player projections

Suggested Learning Path

Week 1-2: Rating Systems

  • Implement basic Elo from scratch
  • Add home field advantage
  • Study FiveThirtyEight methodology

Week 3-4: Feature Engineering

  • Create team strength features
  • Build matchup differentials
  • Add situational features

Week 5-6: Model Building

  • Train multiple model types
  • Implement cross-validation
  • Build ensemble

Week 7-8: Evaluation and Calibration

  • Calculate all metrics
  • Plot calibration curves
  • Compare to baselines

Week 9-10: Production Systems

  • Build prediction pipeline
  • Generate weekly predictions
  • Track performance over time

Key Papers by Topic

Elo and Ratings

  • Elo, A. (1978). The Rating of Chessplayers
  • Glickman, M. (1999). Parameter estimation in large dynamic paired comparison experiments

Sports Prediction

  • Boulier, B. & Stekler, H. (1999). Are sports seedings good predictors?
  • Song, C. et al. (2007). Limits of predictability in human mobility

Calibration

  • Gneiting, T. & Raftery, A. (2007). Strictly proper scoring rules
  • Niculescu-Mizil, A. & Caruana, R. (2005). Predicting good probabilities

Ensemble Methods

  • Dietterich, T. (2000). Ensemble methods in machine learning
  • Caruana, R. et al. (2004). Ensemble selection from libraries of models

Citation Format

APA Format:

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

Example:

Silver, N. (2012). The Signal and the Noise: Why So Many
Predictions Fail—but Some Don't. Penguin Press.