Chapter 24: Further Reading

Academic Papers

Foundational Works

  1. "Tracking Data Analysis in Football" - Bornn, L., Cervone, D., & Fernandez, J. (2018) - Comprehensive overview of tracking data applications in sports - Framework for spatial analysis

  2. "Expected Threat in Soccer" - Karun Singh (2019) - Introduces spatial value models applicable to football - Methodology for position-based analysis

  3. "Wide Open Spaces" - Fernandez, J. & Bornn, L. (2018) - Pitch control models - Space occupation metrics

Route Analysis

  1. "Quantifying Route Quality" - Deshpande, S. & Evans, K. (2020) - NFL Big Data Bowl winning approach - Route running evaluation framework

  2. "Separation Analysis in the NFL" - MIT Sloan Sports Analytics Conference - Correlation between separation and completion - Value of receiver separation

Computer Vision Applications

  1. "Automatic Player Detection and Tracking" - Lu, W.L. et al. (2013) - Computer vision techniques for sports - Object detection and tracking methods

  2. "Deep Learning for Sports Analytics" - Decroos, T. et al. (2019) - Neural network approaches to tracking data - Sequence modeling for play prediction

Books

Technical References

  • "Handbook of Statistical Methods for Sports Analytics" - Albert, J., Glickman, M., Swartz, T., & Koning, R. (2017)
  • Comprehensive statistics reference

  • Chapter on spatial analysis

  • "Analyzing Baseball Data with R" - Marchi, M., Albert, J., & Baumer, B.

  • Applicable concepts for tracking data
  • Visualization techniques

General Sports Analytics

  • "Mathletics" - Winston, W.
  • Foundation in sports analytics

  • Decision-making frameworks

  • "The Book: Playing the Percentages in Baseball" - Tango, T., Lichtman, M., & Dolphin, A.

  • Statistical thinking in sports
  • Expected value concepts

Online Resources

Competitions and Datasets

  • NFL Big Data Bowl (Kaggle)
  • Annual competition with tracking data
  • Past solutions and approaches

  • https://www.kaggle.com/c/nfl-big-data-bowl-2024

  • NFL Next Gen Stats

  • Official tracking data portal
  • Player tracking metrics

  • https://nextgenstats.nfl.com/

  • Statsbomb Open Data

  • Free soccer tracking data
  • Similar concepts applicable to football

Tutorials and Courses

  • NFL Big Data Bowl Starter Notebooks
  • Official competition notebooks

  • Data loading and visualization examples

  • Analytics with Sports Data (Coursera)

  • General sports analytics course

  • Python implementation

  • Tracking Data Analysis Workshop

  • MIT Sloan Conference workshops
  • Advanced analysis techniques

Blogs and Articles

  • Open Source Football
  • NFL analytics tutorials

  • nflfastR package documentation

  • PFF (Pro Football Focus)

  • Advanced football metrics articles

  • Tracking data insights

  • Football Outsiders

  • Historical football analytics
  • Efficiency metrics

Software and Libraries

Python Packages

# Essential packages for tracking analysis
packages = {
    'pandas': 'Data manipulation',
    'numpy': 'Numerical operations',
    'scipy': 'Scientific computing, spatial analysis',
    'matplotlib': 'Visualization',
    'seaborn': 'Statistical visualization',
    'plotly': 'Interactive visualizations',
    'scikit-learn': 'Machine learning',
    'networkx': 'Network analysis',
    'opencv-python': 'Computer vision',
    'shapely': 'Geometric operations'
}

Specialized Tools

  • nflfastR (R package)
  • NFL play-by-play data

  • EPA calculations

  • nfl-data-py (Python)

  • Python interface to NFL data

  • Tracking data access

  • mplsoccer (Python)

  • Football visualization
  • Adaptable for American football

Video Resources

Conference Presentations

  • MIT Sloan Sports Analytics Conference
  • Annual research presentations

  • Industry practitioner talks

  • NESSIS (New England Symposium on Statistics in Sports)

  • Academic sports statistics
  • Methodological advances

Tutorial Videos

  • NFL Big Data Bowl Winner Presentations
  • Competition winning approaches

  • Implementation details

  • StatsBomb Conference Talks

  • Tracking data analysis
  • Industry best practices

Industry Resources

Team Analytics

  • NFL Team Analytics Departments
  • Job postings and requirements
  • Industry standards

Vendor Solutions

  • STATS Perform
  • Tracking data provider

  • Analytics products

  • Second Spectrum

  • Player tracking technology
  • ML-based analysis

Learning Path

Beginner

  1. Start with NFL Big Data Bowl starter notebooks
  2. Learn pandas basics for data manipulation
  3. Practice simple visualizations
  4. Understand basic metrics (speed, distance)

Intermediate

  1. Implement separation analysis
  2. Build route classification models
  3. Create animated visualizations
  4. Study formation recognition

Advanced

  1. Develop expected value models
  2. Implement real-time processing
  3. Build computer vision pipelines
  4. Create production-ready systems

Practice Projects

  1. Separation Analysis: Analyze receiver separation for one game
  2. Route Classifier: Build ML model for route type classification
  3. Play Animator: Create smooth animations of tracking data
  4. Coverage Detector: Classify defensive coverage from alignments
  5. xYAC Model: Predict expected yards after catch

Community

  • #NFLBigDataBowl on Twitter/X
  • r/NFLstatheads on Reddit
  • Sports Analytics Slack communities
  • Kaggle discussion forums