Further Reading: Traditional Football Statistics

Foundational Texts

Books

  1. "The Hidden Game of Football" by Bob Carroll, Pete Palmer, and John Thorn - Pioneering work on football analytics - Introduces many statistical concepts still used today - Excellent historical perspective on football metrics

  2. "Football Outsiders Almanac" (Annual) - Comprehensive statistical analysis of NFL teams - Introduces DVOA and other advanced metrics - Good bridge between traditional and advanced stats

  3. "Statistical Sports Models in Excel" by Andrew Mack - Practical application of statistics to sports - Excel-based but concepts transfer to Python - Good for building intuition about sports data

  4. "Mathletics" by Wayne Winston - Chapter on football analytics - Mathematical foundations of sports statistics - Accessible to undergraduates

Academic Papers

  1. "A State-Space Model for National Football League Scores" by Mark Glickman and Hal Stern - Statistical modeling of football outcomes - Foundation for many predictive models - [Available through JSTOR]

  2. "The Passing Premium Puzzle" by David Romer - Economic analysis of football decision-making - Introduces expected value concepts - Classic paper in sports analytics

  3. "Consistency and Change in National Football League Standings" - Year-to-year predictability of team performance - Understanding variance in football statistics - Relevant to season-long analysis

Online Resources

Data Sources

  1. Sports Reference / College Football Reference - https://www.sports-reference.com/cfb/ - Comprehensive historical statistics - Box scores, season totals, career data - Free access to most statistics

  2. NCAA Statistics - https://stats.ncaa.org/ - Official NCAA statistical database - Division I, II, and III data - Historical records and rankings

  3. ESPN Statistics - https://www.espn.com/college-football/statistics - Current season statistics - Player and team leaderboards - Good for real-time data

  4. CFBStats.com - Detailed college football statistics - Historical data and trends - Conference-specific breakdowns

Learning Platforms

  1. Sports Analytics Courses on Coursera - University of Michigan Sports Analytics specialization - Covers statistical foundations - Programming components included

  2. DataCamp Sports Analytics Track - Python-based sports analysis courses - Practical coding exercises - Subscription required

  3. Khan Academy Statistics - https://www.khanacademy.org/math/statistics-probability - Foundation for statistical concepts - Free and comprehensive

Websites and Blogs

Analytics Websites

  1. Football Outsiders - https://www.footballoutsiders.com/ - Pioneering football analytics site - DVOA metric and analysis - Primarily NFL but concepts apply to college

  2. ESPN's FPI (Football Power Index) - College football predictive model - Methodology explanations available - Good example of applied statistics

  3. Bill Connelly's Work - Advanced college football analytics - SP+ rating system creator - Excellent explanatory writing

  4. The Athletic (Subscription) - Quality sports analytics journalism - Team-specific deep dives - Statistical explainers

Community Forums

  1. r/CFBAnalysis (Reddit) - Community discussion of college football analytics - Shared datasets and analysis - Good for questions and feedback

  2. SABR Analytics Conference Materials - Annual sports analytics conference - Research presentations available online - Cutting-edge methodology

Statistical Background

Statistics Fundamentals

  1. "Statistics" by David Freedman, Robert Pisani, and Roger Purves - Excellent introductory statistics text - Clear explanations of core concepts - Foundation for sports application

  2. "OpenIntro Statistics" (Free) - https://www.openintro.org/book/os/ - Free statistics textbook - R examples but concepts are universal

  3. "Naked Statistics" by Charles Wheelan - Accessible introduction to statistics - Real-world examples throughout - Good for building intuition

Python for Data Analysis

  1. "Python for Data Analysis" by Wes McKinney - Pandas creator's guide - Essential for data manipulation - Sports examples included

  2. "Hands-On Data Analysis with Pandas" by Stefanie Molin - Practical pandas techniques - Real-world data examples - Good reference for sports data

  3. Pandas Documentation - https://pandas.pydata.org/docs/ - Official documentation - Comprehensive and well-organized

Historical Context

Football History and Statistics

  1. Pro Football Reference Historical Data - Historical statistics going back decades - Understanding era context - Record progression over time

  2. "Total Football II" by Bob Carroll - Comprehensive football statistics encyclopedia - Historical context for metrics - Understanding how the game evolved

  3. NFL Record and Fact Book (Annual) - Official league records - Historical perspective - Standard for statistical definitions

Video Resources

YouTube Channels

  1. Thinking Basketball (NBA but applicable concepts) - Statistical thinking in sports - Visual explanations of metrics - Transferable analytical frameworks

  2. Secret Base / SB Nation - Jon Bois football statistics videos - Engaging statistical storytelling - Historical analysis

  3. The QB School - Film analysis with statistics - Understanding context behind numbers - Quarterback-specific analysis

Conference Presentations

  1. MIT Sloan Sports Analytics Conference - Annual conference recordings - Research presentations - Industry practitioner talks

  2. SABR Analytics Conference - Research presentations online - Mix of baseball and football - Methodological discussions

Tools and Software

Statistical Software

  1. Python (pandas, numpy, scipy) - Primary tools for this textbook - Extensive documentation - Large community support

  2. R (tidyverse, nflscrapR) - Alternative statistical language - Strong sports analytics community - Good for statistical modeling

  3. Excel/Google Sheets - Quick analysis and exploration - Good for learning concepts - Limited for large datasets

Visualization Tools

  1. Matplotlib/Seaborn (Python) - Standard Python visualization - Covered extensively in Part 3 - Sports-specific styling available

  2. Plotly (Interactive) - Interactive visualizations - Dashboard capabilities - Web deployment options

  3. Tableau Public - Free version available - Good for exploratory analysis - Professional visualization tool

Industry Perspectives

Career Resources

  1. Sports Analytics Career Guide by Ben Baumer - Academic and industry paths - Required skills and experience - Interview preparation

  2. SABR Career Development Resources - Networking opportunities - Industry connections - Conference presentations

Team Analytics Departments

  • Most NFL and FBS teams now have analytics departments
  • Follow team analytics hires and publications
  • LinkedIn connections in sports analytics

Podcasts

  1. Effectively Wild (Baseball but transferable concepts) - Statistical discussion format - Analytical thinking modeled - Historical episodes on methodology

  2. The Solid Verbal (College Football) - Statistical discussion included - College football specific - Weekly analysis during season

  3. PFF College (Subscription content) - Detailed statistical analysis - Player grades discussion - Draft and recruiting analysis

Progression Path

  1. Master this chapter's fundamentals - Counting vs. rate statistics - Efficiency calculations - Era adjustments

  2. Build Python proficiency - Pandas for data manipulation - Basic visualization - Statistical calculations

  3. Move to advanced metrics (Chapters 7-11) - EPA and success rate - Advanced passing metrics - Defensive analytics

  4. Apply through projects - Case studies in this book - Personal analysis projects - Kaggle competitions

  5. Engage with community - Share analysis online - Participate in discussions - Attend conferences (virtual or in-person)

Next Steps After This Chapter

After mastering traditional statistics, you're ready to explore:

  • Chapter 7: Advanced Passing Metrics - Building on passer rating
  • Chapter 8: Rushing Analytics - Beyond yards per carry
  • Chapter 9: Defensive Metrics - Measuring defense with numbers
  • Chapter 11: Efficiency Metrics - EPA and success rate fundamentals

The traditional statistics covered in this chapter provide the foundation for all advanced metrics in subsequent chapters.