In this series, I aim to create a repository of useful information and analytical thinking about the sport of powerlifting. This will be done through short analytical articles that are published from time to time with fresh content. I hope you enjoy!
Analytics are the process of collecting, cleaning, and analyzing data in order to gain new insights. This field is growing in popularity across many different disciplines and is becoming increasingly important in powerlifting as well. Analytics can be utilized in any number of ways such as identifying trends, optimizing strength training, and more. This series will touch on each of these topics and more as we continue to grow this resource.
Collecting and analyzing data is important for two main reasons. First, it allows you to quantify the performance of lifters which has the potential to help identify trends and predict future outcomes. Moreover, it allows for lifters to track their progress over time which can help optimize training. This leads to a more effective strength program which will result in more consistent gains. Although there are a number of ways to track progress in powerlifting including tracking one rep maxes, recording lifting volume, and more, my analysis will be more focused on data from previous competitions.
This page uses data from the OpenPowerlifting project, https://www.openpowerlifting.org. You may download a copy of the data at https://data.openpowerlifting.org.
OpenPowerlifting is a community service project to create a permanent, open archive of the world’s powerlifting data.
I would like to thank all contributors for this project and would encourage any reader to help with their project. If you would like to support their effort, please consider donating to the project.
All competition data available on this website are contributed to the Public Domain.
The main source for the analysis of this series is the Open Powerlifting. In case a different source of data is used, it will be presented.
On this series, we try to answer what is happening in the sport, understand why is it happening and find some interesting insights on what to expect for the future, and if so, what we should do.
To do it, I’ll be exploring the data that is available on Open Powerlifting. On the day I’m writing this, the data has over 4 millions rows and 42 columns, that’s way too much to handle in Excel. That is why I’ll be using Rstudio, which is also a great opportunity to improve my skills with it. I encourage you to give me your feedback and suggestions on the findings and the overall work (content and structure).