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General vs Individualised models for RIR in VBT

By William James · 9 September 2026

Estimated one rep max (e1RM) and Velocity Based Training (VBT) Reps in Reserve (RIR) charts are traditionally based on group averages. But you're not Mr(s) Median, and neither are your athletes. The evidence says your own numbers understand your lifting way better than the standard charts.

The trouble with a one-size-fits-all curve

Most online e1RM calculators work off decades-old charts like Epley or Brzycki, and were fitted to whatever group of lifters happened into those studies all those years ago. It's built to average out the grindy super heavyweight who always has a surprise extra rep and the hyper-technical super lightweight: the opposite of individualisation.

What the research actually shows

So what does the modern research say? Jukić et al. (2024) compared how lifters trained with a generic, generalised Velocity-RIR model to a bespoke, individualised model. The 46 men and women in the trial (all of whom had a resistance training background) completed an e1RM test, followed by two sessions. In these sessions, they did three AMRAP sets (at 90%, 80%, & 70%) to build their individualised Velocity-RIR curves.

They found that the individual curves were much better predictors of RIR than the general curves across sessions, and better results were seen in stronger individuals with a longer training background.

One curve, everyone pooled bar speed → RIR → the same three lifters, scattered wide A curve fit per lifter bar speed → RIR → each lifter's own points hug their own line
Schematic, not the paper's actual data, exaggerated for clarity. The same three lifters (colour-coded), shown two ways: fit with one line across everyone, their points scatter widely around it; each given their own line, their points hug it closely. Adapted from Jukić et al. (2024), Physiological Reports.

Same story, different lift

Caven et al. (2020) focused on trained female athletes in the bench and squat, and found similar results. Individual velocity-RIR curves were better at predicting top-end strength than general ones, albeit without reaching significance.

How much to trust this

Greig et al. (2023) conducted a systematic review and meta-analysis, which found that generalised models tended to overestimate one rep maxes and only had "moderate predictive validity", and that "predictions tended to be overestimations (4.5, 95% CI 1.5 to 7.4 kg)".

Bringing this into your training

And that's where Auriga comes in. By recording RPEs and velocities, our analysis tool (called The Hierophant) learns your personal velocity-RIR relationship, and understands how different absolute loads affect your bar speed. It adapts and continuously learns on your training data, to keep giving you relevant, actionable information to help guide and monitor your coaching.

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Sources

  • Jukić, I., Prnjak, K., Helms, E. R., & McGuigan, M. R. (2024). Modeling the repetitions-in-reserve-velocity relationship: a valid method for resistance training monitoring and prescription, and fatigue management. Physiological Reports. https://pmc.ncbi.nlm.nih.gov/articles/PMC10901726/
  • Caven, E. J. G., et al. (2020). Group versus Individualised Minimum Velocity Thresholds in the Prediction of Maximal Strength in Trained Female Athletes. International Journal of Environmental Research and Public Health. https://pmc.ncbi.nlm.nih.gov/articles/PMC7662485/
  • Greig, L., Aspe, R. R., Hall, A., Comfort, P., Cooper, K., & Swinton, P. A. (2023). The Predictive Validity of Individualised Load-Velocity Relationships for Predicting 1RM: A Systematic Review and Individual Participant Data Meta-analysis. Sports Medicine. https://pmc.ncbi.nlm.nih.gov/articles/PMC10432349/

Auriga logs RPE and bar speed on every set, so decisions like these don't rely on memory.

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