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Between the Lines: How Do We Measure Pressure?

University of Toronto Sports Analytics Student Group

Hassaan Inayatali, Engineering Science Undergraduate Student | Linkedin

Daniel Hocevar, Computer Science Undergraduate Student | Linkedin

Aaron White, Statistics Undergraduate Student | Linkedin

Submission for the 2023 NFL Big Data Bowl - Undergraduate Track

Repository Directory

Code

Our Code directory contains all code applied to complete our NFL Big Data Bowl Project. Under the R directory, we include the R file applied to complete our Exploratory Data Analysis (EDA). Under the Python directory, we include subdirectories with code for our visualizations, CPP data, and survival analysis.

Dataset

Our Dataset directory contains the csv we used to conduct our analysis of OPLE/DPLE by team. This dataset includes the playId, gameId, CPP data at every frame of the dataset, play data from the Plays Dataset as well as information from the PFF Scouting Dataset. This dataset includes only plays discussed in our Data Preparation section of the Kaggle submission.

Formulas

Our Formulas directory contains a pdf with the formulas we applied to develop our CPP model in addition to our OPLE/DPLE Survival Analysis Metric.

Figures

Our Figures directory contains all images used in our Kaggle submission for the 2023 NFL Big Data Bowl.

Bibliography

Bornn, L. & Fernandez, J. (2018). Wide Open Spaces: A statistical technique for measuring space creation in professional soccer. MIT Sloan Sports Analytics Conference. http://www.lukebornn.com/papers/fernandez_ssac_2018.pdf

Burris, K. (2019). A Trajectory Planning Algorithm for Quantifying Space Ownership in Professional Football. NFL Big Data Bowl 2019. https://operations.nfl.com/media/3665/big-data-bowl-burris.pdf

Cavan, E., Kumagai, B., Moreau, R., & Ritchie, R. (2022). Punt Returns: Using the Math to Find the Path. NFL Big Data Bowl 2022. https://www.kaggle.com/code/robynritchie/punt-returns-using-the-math-to-find-the-path/notebook#How-is-this-innovative-and-useful-to-the-NFL

Ibrahim, P. (2020). A Spatial Framework for Analyzing NFL Offensive Line Play. University of Chicago. https://www.stat.cmu.edu/cmsac/conference/2021/assets/pdf/PaulIbrahim.pdf

Stern, A. (2019). Practical Applications of Space Creation for the Modern NFL Franchise. NFL Big Data Bowl 2019. https://operations.nfl.com/media/4202/bdb_stern.pdf

The National Football League. (2022). NFL Big Data Bowl 2023 Dataset. Retreived October 6, 2022 from https://www.kaggle.com/competitions/nfl-big-data-bowl-2023/data

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