Powerlifting is a sport that has widely expanded in popularity over the past 10 years. The increase in interest has lead to an increase in the number of cash prizes available for lifters. With a higher stake, some have called into question the validity of the method for determining who is the "Best Lifter" across weight classes.
I began powerlifting in 2015. It was a great way for me to push myself to do things I never thought my body could do. I went into the weight room knowing nothing at first, but I taught myself the motions. Since I began I have competed in a powerlifting meet, and learned enough to take full charge of my training program. This project served as a great way for me to combine my interests in powerlifting and in data science
In order to score a powerlifting meet, the lifter's total weight lifted is multiplied by a coefficient that measures relative strength. (how much a lifter lifts relative to their bodyweight, instead of how much a lifter lifts overall). I learned a lot about that coefficient and how it was created, I also learned that it is flawed.
My project aims to come up with a new way to score powerlifting meets that utilizes what I learned in my course. The project showed success in various metrics and served as a great learning opportunity.
A full technical report is available under "Technical Report" and the code written is available under parts 1-3
This page uses data from the OpenPowerlifting project, http://www.openpowerlifting.org.
You may download a copy of the data at https://github.com/sstangl/openpowerlifting.