Single Value Decomposition (SVD): A Golfer’s Tutorial Single Value Decomposition (SVD) is one of my favorite tools for factorizing data, but it can be a rather hard concept to wrap one’s brain around, especially if you don’t have a strong mathematical background. In order to gain a more practical understanding of how SVD are performed and their practical applications, many resort to Googling terms like “Single Value Decomposition tutorial” and “Single Value Decomposition practical example,” only to be disappointed by the results. Alas, here is a tutorial that is both easy to understand, while applying a practical example that more can related to: Golf Score Prediction Using SVD.

This tutorial breaks down the SVD process by looking at the golf scores of three players – Phil, Tiger, and Vijay. By starting with a simple, naive example, the author builds a complete understanding of not only practical mechanics of SVD, but the mathematical background as well. Overall, a simple and elegant example.

Based on the tutorial work, here are a few R scripts I used to recreate the results:  Then, one can compute the SVD: Resulting in, Graphically, the singular values can be visualized as, This means that first left and right singular values (\$u, \$v) represent almost 98.9% of the variance in the matrix. In R, we can approximate the result with, Resulting in, SaveSave