Chapter 11: Further Reading and Resources

Foundational Papers and Articles

Expected Points Development

  1. "Expected Points and Expected Points Added" - Brian Burke - Original framework development - Foundational concepts for modern EPA - https://www.advancedfootballanalytics.com/

  2. "The Hidden Game of Football" - Carroll, Palmer & Thorn (1988) - Pioneer work on expected points - Historical context for modern analytics - Still relevant methodology discussions

  3. "Building the Expected Points Model" - nflfastR Documentation - Technical implementation details - Model calibration and validation - https://www.nflfastr.com/articles/nflfastR.html

Success Rate Analysis

  1. "What is Football Success Rate?" - Football Outsiders - Original success rate definition - Application to team evaluation - Historical benchmarks

  2. "Success Rate vs. EPA" - Ben Baldwin - Comparison of metrics - When to use each - Complementary analysis

Technical Resources

Model Implementation

  1. cfbfastR Package Documentation - https://cfbfastR.sportsdataverse.org/ - College football EPA implementation - R code examples

  2. nflfastR Package Documentation - https://www.nflfastr.com/ - Professional-grade EPA models - Open-source reference implementation

  3. nfl-data-py Python Library - https://github.com/nflverse/nfl_data_py - Python interface for EPA data - Easy data access

Statistical Methods

  1. "Regression to the Mean in Sports Analytics" - Sample size considerations - When metrics stabilize - Proper inference techniques

  2. "Causal Inference in Sports"

    • Attribution challenges
    • Separating skill from luck
    • Methodology for player evaluation

Books

Sports Analytics General

  1. "Mathletics" - Wayne Winston

    • Football chapters on EP/EPA
    • Mathematical foundations
    • Practical examples

  2. "Football Analytics with Python and R" - Eric Eager & George Chahrouri

    • Modern computational approaches
    • Code-heavy implementation
    • Real-world applications

  3. "The Signal and the Noise" - Nate Silver

    • Chapter on sports prediction
    • Probabilistic thinking
    • Model evaluation

Football-Specific

  1. "Take Your Eye Off the Ball 2.0" - Pat Kirwan

    • Tactical context for analytics
    • Understanding what metrics measure
    • Complementary to statistical analysis

  2. "The Art of Smart Football" - Chris B. Brown

    • Strategic foundations
    • Scheme understanding
    • Context for efficiency analysis

Online Courses

  1. Sports Analytics Certificate Programs

    • Various universities offer online courses
    • Comprehensive coverage of EPA methods
    • Project-based learning

  2. Coursera: Sports Analytics

    • University-level instruction
    • Statistical foundations
    • Programming exercises

  3. DataCamp: Sports Analytics Track

    • R and Python courses
    • Hands-on coding
    • Football-specific projects

Websites and Blogs

Regular Analysis

  1. The Athletic - College Football Analytics

    • https://theathletic.com/
    • Weekly EPA analysis
    • Premium content

  2. Football Outsiders

    • https://www.footballoutsiders.com/
    • DVOA methodology (related to EPA)
    • Historical data

  3. Open Source Football

    • https://www.opensourcefootball.com/
    • Community-driven analysis
    • Code sharing

  4. Ben Baldwin's Analytics

    • https://rbsdm.com/
    • EPA methodology discussions
    • Fourth-down analysis
    • R code and visualizations

  5. ESPN Stats & Information

    • Expected points explanations
    • Weekly applications
    • Mainstream integration

Data Sources

Free/Open Data

  1. collegefootballdata.com

    • https://collegefootballdata.com/
    • API access
    • Play-by-play data

  2. cfbfastR Data

    • EPA-enriched play-by-play
    • Free to access
    • Well-documented

  3. Sports Reference - College Football

    • https://www.sports-reference.com/cfb/
    • Historical statistics
    • Basic play-by-play

Premium Data

  1. Pro Football Focus

    • Player-level grades
    • Tracking data
    • Premium analytics

  2. Sports Info Solutions

    • Charting data
    • Player tracking
    • Research partnership options

Research Tools

Visualization

  1. ggplot2 / Matplotlib / Seaborn

    • Creating EPA visualizations
    • Publication-quality graphics
    • Extensive documentation

  2. Plotly / Altair

    • Interactive visualizations
    • Web-ready charts
    • Dashboard integration

Statistical Analysis

  1. scikit-learn

    • Machine learning integration
    • EPA modeling
    • Prediction applications

  2. statsmodels

    • Regression analysis
    • Time series
    • Statistical testing

Conference Presentations

  1. MIT Sloan Sports Analytics Conference

    • Annual EPA-related presentations
    • Cutting-edge research
    • Networking opportunities

  2. SABR Analytics Conference

    • Multi-sport focus
    • Methodology discussions
    • Academic presentations

Community Resources

Online Communities

  1. r/NFLstatheads (Reddit)

    • Community discussion
    • Code sharing
    • Methodology debates

  2. Analytics Twitter

    • Real-time discussion
    • Research sharing
    • Key accounts:
    • @benaborowitz
    • @SethWalder
    • @tejsethi
    • @CedGriffinNFL

  3. Discord: Football Analytics

    • Live chat
    • Project collaboration
    • Help and support

Academic Journals

  1. Journal of Quantitative Analysis in Sports

    • Peer-reviewed research
    • Methodological advances
    • Academic rigor

  2. Big Data (Journal)

    • Sports analytics section
    • Data science applications
    • Interdisciplinary work

Learning Path Recommendations

Beginner

  1. Start with Chapter 11 content
  2. Read Burke's original EPA articles
  3. Explore cfbfastR documentation
  4. Practice basic calculations

Intermediate

  1. Implement EPA models from scratch
  2. Study nflfastR methodology
  3. Work through Football Analytics book
  4. Build visualization portfolio

Advanced

  1. Read academic papers
  2. Contribute to open-source projects
  3. Attend analytics conferences
  4. Develop novel methodologies

Project Ideas

Skill Development

  1. Replicate cfbfastR EPA model
  2. Build success rate dashboard
  3. Create player comparison tool
  4. Analyze historical trends

Research Projects

  1. EPA stability analysis
  2. Opponent adjustment methods
  3. Play-caller evaluation
  4. Fourth-down decision optimization

Citation Guidelines

For Academic Work:

Burke, B. (2014). Expected Points and Expected Points Added.
Advanced Football Analytics.

Yurko, R., Ventura, S., & Horowitz, M. (2019). nflWAR: A
Reproducible Method for Offensive Player Evaluation in
Football. Journal of Quantitative Analysis in Sports, 15(3).

For Data Sources:

cfbfastR R package (version X.X). (2024). Retrieved from
https://cfbfastr.sportsdataverse.org/

College Football Data API. (2024). Retrieved from
https://collegefootballdata.com/

Staying Current

  • Follow key analysts on social media
  • Subscribe to analytics newsletters
  • Attend virtual/in-person conferences
  • Participate in community discussions
  • Regularly check package documentation for updates

The field of football analytics evolves rapidly. Regular engagement with the community and ongoing learning are essential for staying at the cutting edge.