Chapter 13: Further Reading - Play-by-Play Visualization

Essential Resources

Play-by-Play Data Sources

College Football Data API https://collegefootballdata.com/ Free API providing comprehensive play-by-play data for college football including: - Historical data back to 2000 - Drive and play-level information - Pre-computed EPA values - Team and player statistics

nflfastR https://www.nflfastr.com/ While NFL-focused, this R package demonstrates excellent play-by-play data structures and EPA implementations that transfer to college football.

CFBDataAPI Python Package https://pypi.org/project/cfbd/ Python wrapper for the College Football Data API, making data access straightforward:

pip install cfbd

Win Probability Models

Academic Papers

A Bayesian Method for Evaluating and Discovering NFL Game Plans Goldner, K. (2012), MIT Sloan Sports Analytics Conference Early work on win probability models with applications to game strategy.

Win Probabilities in Football Lock, D. & Nettleton, D. (2014), Journal of Quantitative Analysis in Sports Statistical foundations for in-game win probability estimation.

Expected Points and EPA Explained Burke, B. (2014), Advanced Football Analytics Foundational explanation of the expected points framework that underlies modern play-by-play analysis.

Online Resources

Introduction to NFL Analytics with R Lee Sharpe (2020) Comprehensive guide to working with play-by-play data: https://github.com/leesharpe/nfldata

Ben Baldwin's EPA Primer Accessible explanation of EPA concepts: https://www.opensourcefootball.com/posts/

Visualization Techniques

Sports-Specific Visualization

Visualizing Basketball: The Art and Science of Basketball Charts Goldsberry, K. (2012) While basketball-focused, demonstrates excellent principles for spatial sports visualization that apply to football field visualizations.

Football Analytics Visualizations Sharpe, L. - nflverse/nflfastR Open-source visualization examples for football analytics: https://github.com/nflverse/nflfastR

General Visualization

The Grammar of Graphics Wilkinson, L. (2005) Theoretical foundation for building complex visualizations from simple components.

ggplot2: Elegant Graphics for Data Analysis Wickham, H. (2016) While R-focused, the concepts of layered visualization apply to matplotlib.

Animation and Interactivity

Animation Resources

Matplotlib Animation Tutorial https://matplotlib.org/stable/gallery/animation/ Official documentation for creating animated visualizations.

Animate Your Data Analysis with Python Real Python - Tutorial series on creating animated charts.

Interactive Visualization

Plotly for Python https://plotly.com/python/ Interactive visualization library excellent for game flow exploration.

Bokeh https://bokeh.org/ Web-based interactive visualization suitable for dashboards.

Streamlit https://streamlit.io/ Rapid dashboard development for play-by-play analysis tools.

Professional Examples

Broadcast Graphics

ESPN's Playoff Predictor Example of win probability visualization in broadcast: https://www.espn.com/college-football/fpi

The Athletic's Game Recap Graphics Sophisticated play-by-play visualizations in sports journalism.

Research Implementations

Open Source Football https://www.opensourcefootball.com/ Community-driven football analytics with visualization examples.

FiveThirtyEight Sports https://fivethirtyeight.com/sports/ Data journalism with excellent sports visualization practices.

Python Libraries

Core Visualization

matplotlib https://matplotlib.org/

pip install matplotlib

The foundation for all visualizations in this chapter.

seaborn https://seaborn.pydata.org/

pip install seaborn

Statistical visualization built on matplotlib.

Specialized Tools

mplsoccer https://mplsoccer.readthedocs.io/

pip install mplsoccer

Soccer visualization library with field drawing utilities adaptable to football.

nfl-data-py https://pypi.org/project/nfl-data-py/

pip install nfl-data-py

Python wrapper for NFL play-by-play data.

Animation

celluloid https://github.com/jwkvam/celluloid

pip install celluloid

Simplified matplotlib animations.

imageio https://imageio.readthedocs.io/

pip install imageio

For creating GIF exports of animations.

Online Courses

Free Resources

Data Visualization with Python (Coursera) IBM's introduction to visualization with matplotlib and seaborn.

Sports Analytics MOOC (University of Michigan) Covers sports data analysis including visualization approaches.

DataCamp: Data Visualization with Python Comprehensive Python visualization training.

Udemy: Matplotlib for Python Developers Detailed matplotlib course for creating publication-quality figures.

Communities and Forums

Sports Analytics Communities

r/CFBAnalysis https://www.reddit.com/r/CFBAnalysis/ Reddit community focused on college football analytics.

Sports Analytics Twitter Key accounts to follow: - @CFBDataAnalysis - @CFB_Data - @benikielsen (college football EPA)

MIT Sloan Sports Analytics Conference https://www.sloansportsconference.com/ Annual conference featuring cutting-edge sports analytics.

Visualization Communities

Data Visualization Society https://www.datavisualizationsociety.org/ Community of visualization practitioners.

r/dataisbeautiful https://www.reddit.com/r/dataisbeautiful/ Active community for sharing and critiquing visualizations.

Practice Datasets

Historical Game Data

College Football Reference https://www.sports-reference.com/cfb/ Historical statistics and game logs.

CFBData Play-by-Play Archives Historical play-by-play data via API for practice analysis.

Sample Data Files

nflfastR Data https://github.com/nflverse/nflverse-data Ready-to-use play-by-play data files (NFL, but structure applicable to CFB).

Books

Sports Analytics

The Hidden Game of Football Carroll, B., Palmer, P., & Thorn, J. (1988) Classic text on football analytics methodology.

Mathletics: A Scientist Explains 100 Amazing Things About Sports Winston, W.L. (2012) Accessible introduction to sports analytics with football examples.

Data Visualization

The Visual Display of Quantitative Information Tufte, E.R. (2001) Essential reading for anyone creating data visualizations.

Storytelling with Data Knaflic, C.N. (2015) Practical guide to effective data communication.

Fundamentals of Data Visualization Wilke, C.O. (2019) Modern, comprehensive guide available free online: https://clauswilke.com/dataviz/

Week 1-2: Foundations

  1. Access play-by-play data via CFBData API
  2. Understand data structure and key fields
  3. Calculate basic EPA from raw data
  4. Create first drive chart

Week 3-4: Win Probability

  1. Study win probability model concepts
  2. Implement basic WP visualization
  3. Add annotations and context
  4. Create multi-game comparisons

Week 5-6: Advanced Techniques

  1. Build situational heatmaps
  2. Create animated drive replays
  3. Design broadcast-ready graphics
  4. Integrate with dashboard framework

Week 7-8: Projects

  1. Visualize a full game with all techniques
  2. Create a team season summary
  3. Build an interactive game explorer
  4. Present analysis to a target audience

Citation Format

For Play-by-Play Data

College Football Data API. (Year). [Play-by-play data for TEAM vs. OPPONENT, DATE].
Retrieved from https://collegefootballdata.com/

For Visualizations

Author. (Year). Title of Visualization [Chart type].
Source/Publication. Retrieved from URL