How accurate are 1RM calculators?

1RM calculators are generally accurate within 5-10% when used with rep ranges under 10. The accuracy depends on factors like training experience, muscle fiber composition, and the specific formula used. Our calculator uses 7 different formulas to give you the most comprehensive estimate.

Which 1RM formula is most accurate?

The Epley formula is most widely used and accurate for most people, especially with rep ranges of 2-10. However, different formulas work better for different individuals. Brzycki tends to be conservative, while Lombardi works well for powerlifters. Our calculator shows all 7 formulas so you can compare.

Can I use this for any exercise?

Yes, you can use 1RM calculators for any compound exercise like bench press, squat, deadlift, overhead press, and rows. The formulas are based on general strength principles that apply across exercises, though they're most accurate for compound movements.

What rep range should I use for the most accurate calculation?

For best accuracy, use rep ranges between 2-10 reps. The sweet spot is 3-8 reps where most formulas perform optimally. Avoid using very high rep ranges (15+) as the formulas become less reliable due to muscular endurance factors.

How often should I recalculate my 1RM?

Recalculate your 1RM every 4-6 weeks or whenever you notice significant strength gains. For beginners, monthly calculations work well. Advanced lifters might recalculate every 6-8 weeks. Always use recent max effort sets for the most accurate results.

Why Different 1RM Formulas Give Different Results

If you've used multiple 1RM calculators, you've probably noticed they rarely give the same answer. The difference can be as much as 20-30 pounds on a 300-pound lift. This isn't a bug—it's a feature that reflects the complex reality of human strength.

Each formula was developed using different populations, methodologies, and assumptions about how strength drops off with increasing repetitions. Understanding these differences helps you choose the right formula for your situation.

The Core Problem: Strength Isn't Linear

The fundamental challenge all formulas face is that the relationship between weight and repetitions isn't perfectly predictable. Here's why:

Individual Variation Factors

Physiological Differences:

• Muscle fiber type distribution
• Nervous system efficiency
• Lactate threshold
• ATP-PC system capacity
• Muscle architecture

Training Factors:

• Training history and style
• Recent training emphasis
• Technical proficiency
• Mental approach to max efforts
• Competition experience

The Result: Someone who trains primarily with singles and doubles will have a different strength curve than someone who does sets of 8-12. This is why no single formula can be perfectly accurate for everyone.

Head-to-Head Formula Comparison

Let's see how different formulas estimate 1RM from the same input (225 lbs × 8 reps):

Formula	Calculation	Result	Variance
Epley	225 × (1 + 8/30)	284 lbs	+4 lbs
Brzycki	225 × (36/(37-8))	279 lbs	-1 lb
Lombardi	225 × 8^0.10	282 lbs	+2 lbs
O'Conner	225 × (1 + 8/40)	270 lbs	-10 lbs
Lander	100 × 225/(101.3-2.67×8)	281 lbs	+1 lb
Average	-	280 lbs	±5 lbs

Range: 270-284 lbs (14 lb spread or 5% variation)

Variance Increases with Rep Count

The disagreement between formulas grows as reps increase:

3 reps:

±2-3% variance

5 reps:

±3-5% variance

10 reps:

±5-8% variance

15 reps:

±10-15% variance

Why Each Formula Behaves Differently

Linear vs. Exponential Models

Linear Models (Epley, O'Conner):

Assume each rep adds/subtracts a fixed percentage of your max.

• Simple calculation
• Works well for 1-10 reps
• Overestimates at high reps
• Underestimates endurance athletes

Exponential Models (Mayhew, Wathan):

Account for non-linear strength decay with fatigue.

• Complex calculation
• Better at all rep ranges
• More accurate for 10+ reps
• Accounts for fatigue better

Population-Specific Biases

Epley - General Population

Developed using recreational lifters. Tends to be optimistic for average trainers. Most widely used due to simplicity and reasonable accuracy for most people.

Brzycki - College Athletes

Based on NCAA football players. Very accurate for explosive athletes with good technique. May underestimate for endurance-trained individuals.

Lombardi - Powerlifters

Developed using competitive powerlifters. Accounts for superior neural efficiency in experienced lifters. Often underestimates beginners.

Mayhew - Large Dataset

Based on hundreds of subjects across different training backgrounds. Most balanced but may not be optimal for specific populations.

Real-World Examples

Let's see how different athlete types might see formula variance:

Example 1: Powerlifter Profile

Athlete: 5 years training, primarily singles and doubles
Test: 405 lbs × 3 reps on squat
True 1RM: 450 lbs

Formula	Estimate	Error	Accuracy
Brzycki	441 lbs	-9 lbs	98%
Lombardi	448 lbs	-2 lbs	99.5%
Epley	446 lbs	-4 lbs	99%

Best fit: Lombardi (designed for powerlifters)

Example 2: CrossFit Athlete

Athlete: 3 years training, lots of metabolic conditioning
Test: 185 lbs × 12 reps on bench press
True 1RM: 245 lbs

Formula	Estimate	Error	Accuracy
Epley	259 lbs	+14 lbs	94%
Mayhew	248 lbs	+3 lbs	99%
O'Conner	241 lbs	-4 lbs	98%

Best fit: O'Conner (conservative for endurance athletes)

Which Formula Should You Trust?

Decision Framework

If you're testing with 1-3 reps:

Use Brzycki - most accurate at low reps

If you're testing with 4-6 reps:

Use Epley or average multiple formulas

If you're testing with 8-12 reps:

Use Mayhew or Wathan - exponential models handle fatigue better

If you're a beginner:

Use O'Conner or Lander - more conservative estimates

If you're experienced (3+ years):

Use Lombardi for low reps, Brzycki for general use

When in doubt:

Average all formulas - reduces individual formula bias

Important Considerations

• Track your own data: Over time, you'll learn which formula matches your strength curve best
• Exercise matters: Deadlifts often underperform predictions, bench press usually matches closely
• Use the same formula consistently: This allows accurate progress tracking even if the absolute number is off
• Consider fatigue state: All formulas assume you're relatively fresh

Practical Application Tips

Creating Your Personal Correction Factor

Here's how to calibrate formulas to your individual strength curve:

1. Test your actual 1RM (or very heavy single)
2. Within a week, test 3RM, 5RM, and 8RM on separate days
3. Calculate estimated 1RM from each test using different formulas
4. Find your correction factor:
Correction = Actual 1RM ÷ Formula Estimate
5. Apply this factor to future calculations with that formula

Example: If Epley estimates 300 but your actual max is 285, your correction factor is 0.95. Multiply all future Epley estimates by 0.95.

The Bottom Line

Formula differences aren't errors—they're features that reflect the complexity of human strength. No formula will be perfect for everyone because we all have different:

• Muscle fiber compositions
• Training histories
• Neural efficiencies
• Fatigue resistance
• Technical proficiency

The best approach? Use multiple formulas, understand their biases, and track which one best predicts your actual performance. Over time, you'll develop an intuitive sense of how to interpret the numbers for your body.

Remember: These are tools for training, not definitive measurements. Use them to guide programming, but always listen to your body and adjust based on actual performance.

Compare all 7 formulas with our calculator:

See Your Formula Comparison

Why Different Formulas Give Different Results