Projects Using Our Data
The following is a partial list of projects, articles, and anything else using OpenPowerlifting data.
Did you use the data for something interesting? Contact us and we'll add your work to the list!
Peer-Reviewed Publications
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Machek, Steven B., et al. (2020). Myosin Heavy Chain Composition, Creatine Analogues, and the Relationship of Muscle Creatine Content and Fast-Twitch Proportion to Wilks Coefficient in Powerlifters. The Journal of Strength & Conditioning Research, 34(11), 3022-3030.
Fast-twitch muscle fiber prevalence and muscle total creatine do not solely determine powerlifting skill variation.
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Ferland, Pierre-Marc, et al. (2020). Efficiency of the Wilks and IPF Formulas at Comparing Maximal Strength Regardless of Bodyweight through Analysis of the Open Powerlifting Database. International Journal of Exercise Science, 13(4), 567-582.
Compares the Wilks and IPF formulas, concluding that the design of Wilks is sound and it just needs updated constants.
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Machek, Steven B., et al. (2019). Skeletal Muscle Fiber Type and Morphology in a Middle-Aged Elite Male Powerlifter Using Anabolic Steroids. Journal of Science in Sport and Exercise, 1-8.
Extends the known physiological limits of human muscle size and strength.
Pre-Print Publications
- Vigotsky, Andrew D., et al. (2020). Improbable data patterns in the work of Barbalho et al. SportRxiv.
Uses the OpenPowerlifting dataset to illustrate statistical concepts used for the work in the white-paper.
Academic Presentations
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Steven Machek, MS, CSCS. (2019). Total Creatine and Creatine-Associated Markers in Relation to Wilks Coefficient. International Society of Sports Nutrition, 16th Annual Conference.
Total muscle creatine content is not a significant predictor of powerlifting performance.
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Marisa Brooks. (2019). Powerlifting in the USA. UIUC Library and Information Sciences.
Investigates participation trends across USA regions from 2016 through 2018.
Articles
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Stronger By Science. (2018). How Sex, Strength, and Age Affect Strength Gains in Powerlifters.
Factors assumed to predict rate of strength increase are less well-correlated than commonly believed.
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Peidi Wu. (2018). A Better Wilks Formula.
Proposes a new ranking formula based on Z-scores.
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Elias Oziolor, Ph.D. (2018). Getting old? You can still lift!
Examines the relationship between age and total, concluding that age is less of a factor than commonly believed — "detraining" with age is mostly due to bodyweight loss.
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Stronger By Science. (2017). How to Get Strong: What is Strong?
Powerlifters, on the whole, don't seem to be meaningfully improving. However, the number of competitors has increased almost 5-fold, which accounts for the increases we've seen in world records and top-level competition. USAPL strength analytics.
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Greg Nuckols. (2017). Group Data Don't Tell You Much About Individuals.
Very clear group trends in "strength gained per day" mask the tremendous variability between individuals.
Projects
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Christoph Liebender. (2022). PowerliftingSharp.
Lightweight C# scraper for the OpenPowerlifting webserver.
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Pablo Bandinopla. (2022). Weight for Reps: SBD World Rank.
Calculates percentiles for competition lifts.