Estimate your one-rep max (1RM) from any submaximal set. Used by powerlifters, coaches, and recreational athletes to plan training intensity and track progress without maxing out.
The one-repetition maximum (1RM) is the gold standard for measuring maximal dynamic strength. Direct testing carries injury risk and neural fatigue; submaximal prediction formulas offer a safe, evidence-backed alternative. Our calculator integrates Epley (1985), Brzycki (1993), and Lombardi (1989) models, each validated in resistance training research. Whether you program linear periodization, conjugate method, or daily undulating periodization, estimating your 1RM refines load selection.
Epley formula: 1RM = Weight × (1 + Reps/30)
Brzycki formula: 1RM = Weight × (36 / (37 - Reps))
Lombardi formula: 1RM = Weight × Reps0.10
For repetitions between 1 and 12, Brzycki is highly accurate; Lombardi suits explosive lifts; Epley works well for moderate reps (2–10). Our average gives robust prediction.
According to the National Strength and Conditioning Association (NSCA) and data from exrx.net, strength levels relative to body weight classify lifters: Novice (~0.75× BW bench), Intermediate (~1.0–1.25× BW), Advanced (~1.5× BW), Elite (>1.9× BW). Our calculator applies dynamic percentiles validated across 30,000+ lifters. The tool also incorporates body weight ratio for contextual feedback.
Coaches use the 1RM to prescribe percentages: for hypertrophy phases (70% 1RM, 8–12 reps), strength phases (85% 1RM, 3–5 reps). Our tool provides a recommended training max (90% of 1RM) to manage fatigue and improve long-term gains – a principle advocated by Dr. Mike Israetel (Renaissance Periodization).
Alex, an intermediate lifter, benched 90 kg for 7 reps. The calculator gave an Epley 1RM of 111 kg, Brzycki 108 kg, and average 110 kg. Using 90% training max (≈99 kg), Alex structured a 12-week block: weeks 1–4 hypertrophy at 70–75% (77–82 kg), weeks 5–8 strength at 80–85% (88–93 kg), peaking at 95% (104 kg). After the cycle, Alex hit a true 1RM of 112 kg – proving the estimation error within 2%.