Predicting Future Performance in Powerlifting: A Machine Learning Approach
Bibliographic record
Abstract
BACKGROUND: Powerlifting is a discipline in which athletes aim to lift the maximum weight in 3 exercises: Squat, Bench Press, and Deadlift. Since the introduction of "Classic" powerlifting by the International Powerlifting Federation (IPF) in 2012, there has been an increase in popularity, athlete participation, and attention from sports science research. Previous studies have examined factors influencing the long-term longitudinal adaptation of upper- and lower-body strength, but no one used this information to develop predictive models of future classic powerlifting performances, especially considering the different age, sex, and weight categories, with the final aim of tailoring the medium- and long-term training goals. This study aims to develop and validate a machine learning-based linear regression model to predict single-lift and overall performance in classic powerlifters. The model considered variables such as sex, age, weight, initial strength levels, and competition history. The study also seeks to provide European normative powerlifting performance data across different categories to assist in talent identification and optimization of training. RESULTS: 0.90 to 0.94), with no differences between mean values (p 0.733 to 0.930), extremely large correlations (r 0.95 to 0.97), and no significant bias (z-score - 1.78 to - 0.64) between predicted and actual performance values across all lifts. CONCLUSIONS: The developed machine learning model provides valid and accurate predictions of individual powerlifting performance, by accounting for various individual characteristics. The model can assist coaches and athletes in setting realistic training goals and monitoring progress. Moreover, normative data for each lift and total performance were provided, stratified by sex, age, weight category, and initial strength levels, offering valuable benchmarks for athletes and coaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".