Nicholas Lopez and Elliot Chin
December 12, 2021
This project is part of a research report investating the use of Linear Algebra in Data Analytics. As a case study, the web application ranks NFL teams, though these processes can be applied to countless areas in any industry. The full report can be reviewed as a PDF, available in the repository, and the web application is linked below and can also be accessed by viewing the deployment of the active environment.
Massey Matrices use point differential to measure the strength of teams. However, point differential alone is not a perfect measure; often times, teams leading or trailing by a significant amount pull starters, inflating or deflating point differentials. Instead of simply using point differentials, we could combine point differentials with other differential variables, such as turnovers or rushing yards. First, each differential must be normalized to have a consistent mean and standard deviation. Then, differentials can be added together with custom weights- for example, Massey Matrices could be half dependent on point differential, and half dependent on interception differential. We have created a web application that allows users to use this strategy create custom NFL rankings. The application uses data across multiple fields, from record to penalty differential. The web application can be found here, and the source code can be found here. This application was made entirely from scratch. As far as we are aware, this has never been made before in such a fashion. The calculations are made real-time, following Massey’s Method exactly. No packages for matrix multiplication, data interpolation, or other shortcuts were used. All math is done entirely using matrices represented as nested lists in Python, and the data is displayed through HTML and JavaScript. The program allows users to set weightings for such a factor. As a default, the record is set to 100% of the weighting and must always have some non-zero weighting. Users may alter the weightings, which are applied to unique, individual y columns that contain data from every single game played through Week 12 of the NFL season. These y columns are added together to contain the resultant final y column. The rest of the calculations use Massey’s Method and the method of least squares to determine the ranking values for the unique parameters.
On January 27, 2022, the program was updated with the most recent data and added a recency factor, allowing users to set a new weighting determined by the recency of the data points. The program was then featured by the Harvard Sports Analytics Club in an article viewed here.
Information, ideas, and code presented throughout the project may be used. However, we would appreciate a citation and link back to the repository. Thank you!