Further Reading: Introduction to Prediction Models
Foundational Books
"The Signal and the Noise" by Nate Silver Comprehensive look at prediction across domains, including sports. Essential reading for understanding what makes predictions succeed or fail.
"Superforecasting" by Philip Tetlock How the best forecasters think about uncertainty and probability. Applicable to NFL prediction thinking.
"Mathletics" by Wayne Winston Sports analytics fundamentals with NFL prediction sections.
"Scorecasting" by Moskowitz & Wertheim Debunks common sports assumptions, informing better models.
NFL-Specific Prediction Resources
FiveThirtyEight NFL Methodology Detailed explanation of their Elo-based prediction system. https://fivethirtyeight.com/methodology/how-our-nfl-predictions-work/
Football Outsiders DVOA Efficiency-based prediction approach. https://www.footballoutsiders.com/info/methods
Pro Football Reference Historical data for backtesting models. https://www.pro-football-reference.com/
nflfastR / nfl_data_py Play-by-play data for building advanced models. https://github.com/nflverse/nfl_data_py
Statistical Foundations
"An Introduction to Statistical Learning" by James et al. Free textbook on machine learning fundamentals. Essential for understanding model evaluation.
"Forecasting: Principles and Practice" by Hyndman & Athanasopoulos Comprehensive forecasting methodology. https://otexts.com/fpp3/
"Probability and Statistics" by DeGroot & Schervish Rigorous probability foundations.
Model Evaluation
"Proper Scoring Rules" by various authors Academic treatment of Brier score and calibration.
"The Case Against Accuracy" by various data scientists Why accuracy alone is insufficient for model evaluation.
"Calibration in Modern Machine Learning" Research on probability calibration techniques.
Betting Market Analysis
"Sharp Sports Betting" by Stanford Wong How betting markets work and incorporating market information.
"Trading Bases" by Joe Peta Wall Street approach to sports prediction.
"Weighing the Odds in Sports Betting" by King Yao Mathematical approach to sports gambling.
Time Series and Forecasting
"Time Series Analysis" by Hamilton Rigorous treatment of temporal prediction.
"Dynamic Models for Panel Data" by various Handling panel data like team performance over time.
Machine Learning for Sports
"Machine Learning for Sports Analytics" Papers from major ML conferences on sports prediction.
Kaggle NFL Competitions Past NFL prediction competitions with winning solutions. https://www.kaggle.com/competitions
MIT Sloan Sports Analytics Conference Annual presentations on sports prediction research. http://www.sloansportsconference.com/
Uncertainty Quantification
"Thinking, Fast and Slow" by Daniel Kahneman Cognitive biases affecting prediction.
"Against the Gods" by Peter Bernstein History of risk and probability thinking.
"The Black Swan" by Nassim Taleb Understanding rare events and model limitations.
Programming Resources
Python for Data Analysis by Wes McKinney Pandas and data manipulation for model building.
Scikit-learn Documentation Machine learning library used in NFL prediction. https://scikit-learn.org/
StatsModels Documentation Statistical modeling in Python. https://www.statsmodels.org/
Academic Papers
"Predicting Football Results" by Maher Early academic treatment of football prediction.
"Beating the NFL Spread" by various Academic research on sports betting markets.
"Elo Ratings" original paper by Arpad Elo Foundation of many sports rating systems.
Blogs and Online Resources
The Power Rank NFL prediction modeling insights. https://thepowerrank.com/
Advanced Football Analytics (archived) Brian Burke's pioneering work.
The Athletic Analytics Coverage Regular NFL prediction analysis. https://theathletic.com/
ESPN Analytics QBR and other proprietary metrics.
Podcasts
PFF NFL Podcast Data-driven NFL analysis.
The Ringer NFL Show Includes analytical segments.
Sharp Football Analysis Betting and prediction focus.
Tools and Software
Python + Jupyter Primary environment for model development.
R + RStudio Alternative statistical environment.
SQL Data management for large datasets.
Git/GitHub Version control for models.
Video Content
StatQuest with Josh Starmer Clear explanations of statistical concepts. https://www.youtube.com/c/joshstarmer
3Blue1Brown Visual math explanations relevant to prediction.
Coursera/edX Courses Machine learning and statistics courses.
Community Forums
r/NFLstatheads Reddit community for NFL analytics.
r/sportsbook Betting community with prediction discussions.
Sports Analytics Slack/Discord Communities for sports data scientists.
Conferences and Events
MIT Sloan Sports Analytics Conference Premier sports analytics gathering.
SABR Analytics Conference Originally baseball, expanding to other sports.
NESSIS New England Symposium on Statistics in Sports.
Key Papers to Read
- Elo's original chess rating paper - Foundation of many systems
- FiveThirtyEight methodology posts - Modern implementation
- Brier's scoring rule paper - Proper probability evaluation
- Recent NFL ML papers - Current state of the art
- Market efficiency papers - Understanding betting lines
These resources provide depth for both theoretical understanding and practical implementation of NFL prediction models.