Further Reading: Machine Learning for NFL Prediction
Foundational ML Books
"An Introduction to Statistical Learning" (ISLR) by James et al. Free online textbook covering ML fundamentals. Essential foundation for sports prediction. https://www.statlearning.com/
"The Elements of Statistical Learning" by Hastie, Tibshirani, Friedman More advanced treatment of ML algorithms. https://hastie.su.domains/ElemStatLearn/
"Hands-On Machine Learning" by Aurélien Géron Practical ML with Python and scikit-learn. Excellent for implementation.
"Pattern Recognition and Machine Learning" by Bishop Rigorous mathematical treatment of ML.
Gradient Boosting Resources
XGBoost Documentation Official documentation and tutorials. https://xgboost.readthedocs.io/
LightGBM Documentation Microsoft's fast gradient boosting library. https://lightgbm.readthedocs.io/
CatBoost Documentation Yandex's gradient boosting with categorical features. https://catboost.ai/
"XGBoost: A Scalable Tree Boosting System" (original paper) Chen & Guestrin, 2016. Understanding the algorithm that dominates tabular data.
Neural Networks for Sports
"Deep Learning" by Goodfellow, Bengio, Courville Comprehensive deep learning textbook. https://www.deeplearningbook.org/
PyTorch Documentation Modern deep learning framework. https://pytorch.org/docs/
"Neural Network Methods for Sports Prediction" Academic papers on applying deep learning to sports.
Feature Engineering
"Feature Engineering for Machine Learning" by Zheng & Casari Practical guide to creating predictive features.
"Automated Feature Engineering" papers Research on automating feature creation.
"Football Analytics with Python" tutorials NFL-specific feature engineering examples.
Cross-Validation and Evaluation
"Time Series Cross-Validation" by various authors Proper validation for temporal data.
"Proper Scoring Rules" by Gneiting & Raftery Mathematical treatment of evaluation metrics.
"Calibration in Machine Learning" Ensuring predicted probabilities are accurate.
Sports Analytics ML Papers
MIT Sloan Sports Analytics Conference Papers Annual research presentations. http://www.sloansportsconference.com/
Kaggle NFL Competition Solutions Winning approaches from ML competitions. https://www.kaggle.com/competitions
"Predicting NFL Games" academic papers Search Google Scholar for recent research.
NFL-Specific Data Sources
nflfastR / nfl_data_py Play-by-play data for feature engineering. https://github.com/nflverse/nfl_data_py
Pro-Football-Reference Historical statistics for model training. https://www.pro-football-reference.com/
ESPN API / NFL API Real-time data sources.
Model Interpretation
"SHAP Values" by Lundberg & Lee Explaining ML predictions. https://shap.readthedocs.io/
"Interpretable Machine Learning" by Molnar Free online book on model explainability. https://christophm.github.io/interpretable-ml-book/
LIME (Local Interpretable Model-Agnostic Explanations) Alternative explanation method.
Ensemble Methods
"Ensemble Methods" by Zhou Comprehensive treatment of model combination.
"Stacking and Blending" tutorials Practical ensemble techniques.
"Kaggle Ensembling Guide" Competition-proven ensemble strategies.
Regularization and Overfitting
"Dropout: A Simple Way to Prevent Neural Networks from Overfitting" Srivastava et al., 2014.
"Ridge Regression and LASSO" tutorials Understanding regularization techniques.
"Early Stopping as Regularization" Prechelt, 1998.
AutoML and Hyperparameter Tuning
"Optuna: A Next-generation Hyperparameter Optimization Framework" Modern hyperparameter tuning. https://optuna.org/
"AutoML: A Survey of the State-of-the-Art" He et al., 2021.
"Hyperopt Documentation" Bayesian hyperparameter optimization.
Production ML Systems
"Designing Machine Learning Systems" by Chip Huyen ML systems in production.
MLflow Documentation ML experiment tracking and deployment. https://mlflow.org/
"Feature Store" resources Managing features for production.
Bayesian Approaches
"Bayesian Data Analysis" by Gelman et al. Foundation for Bayesian methods.
"Probabilistic Programming" tutorials PyMC, Stan, and similar tools.
"Bayesian Neural Networks" papers Uncertainty quantification in deep learning.
Online Learning
"Online Learning and Online Convex Optimization" Theory of learning from streaming data.
"Continuous Training" tutorials Keeping models updated in production.
Market Efficiency and Betting
"Efficient Market Hypothesis in Sports Betting" Academic research on beating markets.
"Sharp Sports Betting" by Stanford Wong Market dynamics and model validation.
"Beating the Spread" papers Research on sports betting prediction.
Python Libraries
scikit-learn Documentation Core ML library. https://scikit-learn.org/
pandas Documentation Data manipulation. https://pandas.pydata.org/
NumPy Documentation Numerical computing. https://numpy.org/
Matplotlib/Seaborn Visualization libraries.
Video Courses
Andrew Ng's Machine Learning Course (Coursera) Foundational ML course.
Fast.ai Practical Deep Learning Hands-on deep learning course. https://www.fast.ai/
StatQuest with Josh Starmer Clear ML concept explanations. https://www.youtube.com/c/joshstarmer
Blogs and Online Resources
Towards Data Science ML tutorials and case studies. https://towardsdatascience.com/
Machine Learning Mastery Practical ML tutorials. https://machinelearningmastery.com/
Distill.pub Interactive ML research articles. https://distill.pub/
Conferences
NeurIPS (Neural Information Processing Systems) Premier ML research conference.
ICML (International Conference on Machine Learning) Major ML conference.
KDD (Knowledge Discovery and Data Mining) Applied ML conference with sports analytics track.
Key Papers to Start With
- XGBoost paper - Chen & Guestrin, 2016
- Random Forests - Breiman, 2001
- SHAP values - Lundberg & Lee, 2017
- Dropout - Srivastava et al., 2014
- Batch Normalization - Ioffe & Szegedy, 2015
These provide foundation for understanding modern ML techniques applied to NFL prediction.