Efficiency of the Wilks and IPF Formulas at Comparing Maximal Strength Regardless of Bodyweight through Analysis of the Open Powerlifting Database
Bibliographic record
Abstract
The purpose of this study was to measure the efficiency of the Wilks and International Powerlifting Federation (IPF) Formulas at comparing powerlifting performance (total) between weight classes of the same sex (men and women) and division (classic and equipped) in order to determine the champion of champions. The Open Powerlifting database was downloaded (June 21st, 2019), filtered (Python) and analyzed (IBM SPSS). Single factor ANOVA revealed that the total (kg) was able to compare 3 out of the 98 weight class comparison possibilities (3.1%), the total ratio was able to compare 5 of the 98 weight class comparison possibilities (5.1%), the Wilks Formula was able to compare 53 of the 98 weight class comparison possibilities (54.1%) and that the IPF formula was able to compare 51 of the 98 weight class comparison possibilities (52%). Making the Wilks slightly more efficient than the IPF Formula (54.1% > 52%) at determining the champion of champions. Results also show that the IPF Formula is more efficient at comparing women's weight classes and that the Wilks Formula is more efficient at comparing men's weight classes, for both divisions. Results could not validate the IPF's decision to replace the Wilks by the IPF Formula. Subjects' performances (kg, ratio, % of the event on the total, Wilks and IPF points) presented for each weight class per sex and division coming from a total of 26,472 open powerlifters could be utilized by practitioners. Further research should be directed towards updating the constants of both formulas.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".