Bibliography

Books

Burke, B. (2020). The Hidden Game of Football. Football Outsiders.

Epstein, D. (2019). Range: Why Generalists Triumph in a Specialized World. Riverhead Books.

Goldsberry, K. (2019). Sprawlball: A Visual Tour of the New Era of the NBA. Houghton Mifflin Harcourt.

James, B. (1982). The Bill James Baseball Abstract. Ballantine Books.

Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley.

Kleppmann, M. (2017). Designing Data-Intensive Applications. O'Reilly Media.

Lewis, M. (2003). Moneyball: The Art of Winning an Unfair Game. W.W. Norton & Company.

Lindbergh, B., & Sawchik, T. (2019). The MVP Machine: How Baseball's New Nonconformists Are Using Data to Build Better Players. Basic Books.

McKinney, W. (2022). Python for Data Analysis (3rd ed.). O'Reilly Media.

Newport, C. (2016). So Good They Can't Ignore You. Grand Central Publishing.

Reiter, B. (2018). Astroball: The New Way to Win It All. Crown.

Silver, N. (2012). The Signal and the Noise: Why So Many Predictions Fail—but Some Don't. Penguin Press.

Winston, W. L. (2012). Mathletics: How Gamblers, Managers, and Sports Enthusiasts Use Mathematics in Baseball, Basketball, and Football. Princeton University Press.

Academic Papers and Research

Baldwin, B. (2019). nflfastR: Functions to efficiently scrape NFL play by play data. R package.

Burke, B. (2019). DeepQB: Deep Learning for Real-Time Quarterback Evaluation. MIT Sloan Sports Analytics Conference.

Cervone, D., D'Amour, A., Bornn, L., & Goldsberry, K. (2016). A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes. Journal of the American Statistical Association, 111(514), 585-599.

Fernández, J., & Bornn, L. (2018). Wide Open Spaces: A Statistical Technique for Measuring Space Creation in Professional Soccer. MIT Sloan Sports Analytics Conference.

Lock, D., & Nettleton, D. (2014). Using Random Forests to Estimate Win Probability Before Each Play of an NFL Game. Journal of Quantitative Analysis in Sports, 10(2), 197-205.

Lopez, M., Matthews, G., & Baumer, B. (2018). How Often Does the Best Team Win? A Unified Approach to Understanding Randomness in North American Sport. Annals of Applied Statistics, 12(4), 2483-2516.

Romer, D. (2006). Do Firms Maximize? Evidence from Professional Football. Journal of Political Economy, 114(2), 340-365.

Yam, D., & Lopez, M. (2019). What Was Lost? A Causal Estimate of Fourth Down Decision Making in the NFL. Journal of Sports Analytics, 5(3), 153-167.

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), 163-183.

Technical Documentation

Apache Kafka Documentation. https://kafka.apache.org/documentation/

Docker Documentation. https://docs.docker.com/

FastAPI Documentation. https://fastapi.tiangolo.com/

Kubernetes Documentation. https://kubernetes.io/docs/

pandas Documentation. https://pandas.pydata.org/docs/

PostgreSQL Documentation. https://www.postgresql.org/docs/

Prometheus Documentation. https://prometheus.io/docs/

Redis Documentation. https://redis.io/documentation

scikit-learn Documentation. https://scikit-learn.org/stable/documentation.html

Online Resources and Websites

College Football Data API. (n.d.). https://collegefootballdata.com/

ESPN Stats & Information. (n.d.). https://www.espn.com/

Football Outsiders. (n.d.). https://www.footballoutsiders.com/

Open Source Football. (n.d.). https://www.opensourcefootball.com/

Pro Football Focus. (n.d.). https://www.pff.com/

Pro Football Reference. (n.d.). https://www.pro-football-reference.com/

Sports Reference. (n.d.). https://www.sports-reference.com/

The Athletic. (n.d.). https://theathletic.com/

Conference Proceedings

MIT Sloan Sports Analytics Conference. Annual proceedings, 2007-present. https://www.sloansportsconference.com/

SABR Analytics Conference. Annual proceedings, 2012-present. https://sabr.org/analytics/

Datasets

College Football Data. Historical play-by-play and statistics. https://collegefootballdata.com/

Kaggle NFL Big Data Bowl. Player tracking data. https://www.kaggle.com/c/nfl-big-data-bowl-2024

nflfastR Data Repository. NFL play-by-play with EPA. https://github.com/nflverse/nflfastR-data

Software and Tools

Matplotlib Development Team. (2023). Matplotlib: Visualization with Python. https://matplotlib.org/

pandas Development Team. (2023). pandas: powerful Python data analysis toolkit. https://pandas.pydata.org/

Plotly Technologies Inc. (2023). Plotly Graphing Libraries. https://plotly.com/

scikit-learn Developers. (2023). scikit-learn: Machine Learning in Python. https://scikit-learn.org/

Seaborn Development Team. (2023). seaborn: statistical data visualization. https://seaborn.pydata.org/

How to Cite This Textbook

APA Format

Author. (Year). College Football Analytics and Visualization: A Data-Driven Approach to NCAA Football. Publisher.

MLA Format

Author. College Football Analytics and Visualization: A Data-Driven Approach to NCAA Football. Publisher, Year.

Chicago Format

Author. College Football Analytics and Visualization: A Data-Driven Approach to NCAA Football. Place: Publisher, Year.