Predicting a Successful Attempt in Raw Powerlifting. A Nonlinear Mixed Logistic Regression Analysis

Darragh, IAJ, Egan, B, Nolan, D, and Bennett, KE. Predicting a successful attempt in raw powerlifting. A nonlinear mixed logistic regression analysis. J Strength Cond Res 40(2): e138-e146, 2026-This study aimed to predict successful lift attempts in raw powerlifting by examining the influence of jump size (the absolute difference in weight between consecutive attempts) and other factors, using a logistic regression model with nonlinear splines. Data from 93,333 lifters (62,679 males, 30,654 females) across 6,979 International Powerlifting Federation competitions were analyzed. The data set was partitioned into training (80%) and test (20%) sets, with sex-specific models developed to account for class imbalance. Predictors included jump size, lift type (squat, bench press, deadlift), attempt transition (first-to-second or second-to-third), and covariates such as body mass, age, and opening attempt weight. Random effects for lifter identity were included to control for individual performance variability. Significance was set at p < 0.05. Results revealed that jump size significantly influenced success rates, with effects varying by lift type, attempt transition, and sex. For male lifters, moderate jump sizes (5-20 kg) improved squat and deadlift success, but success declined with jumps >20 kg, especially on third attempts. Bench press success increased slightly with jump sizes <10 kg but dropped sharply beyond that-more so in third attempts. For female lifters, squat success improved only with small jumps (≤8 kg); jumps >10 kg significantly reduced success, particularly on third attempts. In the bench press, female lifters showed an almost linear decline in success with increasing jump size on second attempts, with third-attempt success decreasing sharply beyond 4 kg. Female deadlift success followed a similar pattern to squats but showed slightly greater tolerance, with optimal jumps approximately 9-11 kg. In both sexes, third attempts consistently had lower success rates than second attempts. These findings highlight the nuanced role of jump size in attempt success and provide actionable insights for competition strategy. Coaches and lifters can use these results to optimize attempt selection, particularly in balancing risk and reward across attempts.

Authors

Institutions

Publication Details

Journal
The Journal of Strength and Conditioning Research
Published
2025-10-29
DOI
https://doi.org/10.1519/jsc.0000000000005276
Primary Topic
Sports Performance and Training
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predicting a Successful Attempt in Raw Powerlifting. A Nonlinear Mixed Logistic Regression Analysis

Kathleen Bennett, David A Nolan, Ian A. J. Darragh, Brendan Egan
The Journal of Strength and Conditioning Research
Sports Performance and Training
article

Predicting a Successful Attempt in Raw Powerlifting. A Nonlinear Mixed Logistic Regression Analysis

Kathleen Bennett, David A Nolan, Ian A. J. Darragh, Brendan Egan
article en

Abstract

Darragh, IAJ, Egan, B, Nolan, D, and Bennett, KE. Predicting a successful attempt in raw powerlifting. A nonlinear mixed logistic regression analysis. J Strength Cond Res 40(2): e138-e146, 2026-This study aimed to predict successful lift attempts in raw powerlifting by examining the influence of jump size (the absolute difference in weight between consecutive attempts) and other factors, using a logistic regression model with nonlinear splines. Data from 93,333 lifters (62,679 males, 30,654 females) across 6,979 International Powerlifting Federation competitions were analyzed. The data set was partitioned into training (80%) and test (20%) sets, with sex-specific models developed to account for class imbalance. Predictors included jump size, lift type (squat, bench press, deadlift), attempt transition (first-to-second or second-to-third), and covariates such as body mass, age, and opening attempt weight. Random effects for lifter identity were included to control for individual performance variability. Significance was set at p < 0.05. Results revealed that jump size significantly influenced success rates, with effects varying by lift type, attempt transition, and sex. For male lifters, moderate jump sizes (5-20 kg) improved squat and deadlift success, but success declined with jumps >20 kg, especially on third attempts. Bench press success increased slightly with jump sizes <10 kg but dropped sharply beyond that-more so in third attempts. For female lifters, squat success improved only with small jumps (≤8 kg); jumps >10 kg significantly reduced success, particularly on third attempts. In the bench press, female lifters showed an almost linear decline in success with increasing jump size on second attempts, with third-attempt success decreasing sharply beyond 4 kg. Female deadlift success followed a similar pattern to squats but showed slightly greater tolerance, with optimal jumps approximately 9-11 kg. In both sexes, third attempts consistently had lower success rates than second attempts. These findings highlight the nuanced role of jump size in attempt success and provide actionable insights for competition strategy. Coaches and lifters can use these results to optimize attempt selection, particularly in balancing risk and reward across attempts.

The Journal of Strength and Conditioning ResearchVol. 40(2)
University College Dublin (IE), Florida Institute for Human and Machine Cognition (US), Dublin City University (IE)
Openalex Percentile: Top 59%
Sports Performance and Training
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.