BookmarkSubscribeRSS Feed

Ranking Teams with Matrix Computations

Started ‎09-02-2014 by
Modified ‎04-04-2022 by
Views 3,334

This entry contains the code from a 2008 SAS Global Forum paper, Generalizing Google’s PageRank to Rank National Football League Teams by Govan, Meyer and Albright.. The paper explores various ways to represent  the outcomes of games as a matrix and then uses a solver or an Eigen Decomposition to rank the teams. There is one IML source file attached. In the file are three different IML modules and each one performs a slightly different formulation of the "season" as a matrix and then computes a ranking. In addition to the three modules, the file contains the main IML code that drives an example. The IML modules and program are further described in the SAS Global Forum paper.

Version history
Last update:
‎04-04-2022 03:14 PM
Updated by:

hackathon24-white-horiz.png

The 2025 SAS Hackathon Kicks Off on June 11!

Watch the live Hackathon Kickoff to get all the essential information about the SAS Hackathon—including how to join, how to participate, and expert tips for success.

YouTube LinkedIn

SAS AI and Machine Learning Courses

The rapid growth of AI technologies is driving an AI skills gap and demand for AI talent. Ready to grow your AI literacy? SAS offers free ways to get started for beginners, business leaders, and analytics professionals of all skill levels. Your future self will thank you.

Get started

Article Tags