What Is a Good FFMI? Ranges for Men and Women

What is a good FFMI? There is no universal cutoff, but context can make the number useful. In a large general-population sample, the median FFMI among young adults was approximately 18.9 for men and 15.4 for women. Trained athletic groups often score higher, although results vary substantially by sex, sport, height and body-composition method.

The practical answer is to treat FFMI as a reference and a trend—not as a grade, a measure of pure muscle or a test of whether someone is natural.

A Good FFMI Compared With Whom?

The first question many people ask after calculating FFMI is: “Is my result good?”

The more useful question is: Good compared with whom—and measured how?

The same score might be high relative to the general population, fairly ordinary among strength athletes and based on a body-fat estimate that is wrong by several percentage points. That is why a good interpretation needs more than a color-coded chart.

If you have not calculated your score yet, you can use the FFMI calculator to calculate both raw and normalized FFMI and see how the formulas work.

Quick FFMI Reference Table

The following ranges are a practical synthesis of general-population, athletic and physique-sport research. They are not validated biological categories.

Important: This is a THG practical synthesis, not a universal grading system. The underlying studies used different populations, equipment and methods. Do not interpret a boundary such as 21.9 versus 22.0 as a meaningful biological transition.

What Is the Average FFMI?

One of the largest frequently cited reference datasets included 5,635 apparently healthy Swiss adults. Among adults aged 18–34, the median FFMI was approximately:

• 18.9 for men
• 15.4 for women

In a related analysis, people within the conventional “normal” BMI range had FFMI values of approximately 16.7–19.8 for men and 14.6–16.8 for women.

These figures are useful population references, but they are not strength-training targets. The samples were not designed to represent experienced natural lifters, and body composition was estimated with bioelectrical impedance analysis rather than measured directly.

Age matters as well. FFMI can change across adulthood, particularly as physical activity, body weight and age-related losses of lean tissue change. A young lifter should therefore not treat an all-age population average as a personalized ideal.

What Is a Good FFMI for Men?

For a man, an FFMI around 19 is close to the young-adult general-population median reported above. A score in the low 20s is above that reference and is common among many trained men, while scores around 23–24 overlap with several muscular athletic cohorts.

For example:

• A 2024 sample of 1,961 collegiate athletes reported an average FFMI of 21.5 ± 1.9 for men and 17.9 ± 1.8 for women, with clear differences between sports.
• Male NCAA Division III athletes averaged 23.37 ± 2.41 in one study using air-displacement plethysmography.
• NCAA Division I and II American football players averaged 23.7 ± 2.1 using DXA-based body-composition estimates.
• A separate group of Division III football players averaged 23.50 ± 2.04, with values ranging from 18.1 to 27.7.
• Placed male natural bodybuilding competitors with usable skinfold data averaged approximately 22.74 ± 2.55 in a small observational sample.

Those numbers do not mean that every man with an FFMI of 23 is a bodybuilder—or that a result below 20 is poor. Football linemen, endurance athletes, physique competitors and recreational lifters have different performance demands, body-fat levels and muscle distributions.

A reasonable interpretation is:

• Around 19 is close to a general-population reference.
• Around 20–22 is above that reference and can reflect a visibly trained physique, depending on body fat and proportions.
• Around 22–24 is common in muscular male athletic samples.
• Around 25 or higher is unusual in most groups, but it is not an automatic natural limit or doping verdict.

What Is a Good FFMI for Women?

Female FFMI ranges deserve their own evidence. They should not simply be reverse-engineered from male values.

The general-population median among young women in the Swiss reference sample was approximately 15.4. Athletic datasets tend to be higher, but the sport differences are substantial:

• A study of 266 collegiate female athletes reported a mean FFMI of 16.9 ± 1.7, with values from 13.3 to 25.5.
• Median values in that study ranged from 15.1 among cross-country runners to 18.0 among football players.
• Another study of 372 collegiate female athletes reported an average of 18.82 ± 2.08. Rugby players averaged 20.09, while cross-country runners averaged 16.56.
• Female natural bodybuilding competitors with usable skinfold data averaged approximately 18.1 ± 1.95, although this estimate came from a small subset.

For women, an FFMI around 15–16 is therefore close to a general-population reference, while approximately 17–19 overlaps with many trained athletic groups. A score around 20 or above is high relative to most samples, but it can occur in some strength- and power-oriented sports.

There is not enough evidence to defend one precise female “natural limit.” The original study behind the famous FFMI 25 claim included men only.

How Athletes and Bodybuilders Compare

FFMI differs not only between trained and untrained people, but also between sports.

The overlap is more informative than any single cutoff. A football lineman may carry much more fat-free mass relative to height than a competitive runner. That does not make one athlete universally “better”; it reflects different physiques and sporting demands.

The methods are also not interchangeable. A number produced by DXA should not be treated as perfectly equivalent to one produced by an InBody device, skinfolds or air-displacement plethysmography.

Why Training Years Do Not Map Cleanly to FFMI

It is tempting to create a chart saying that a beginner should have one FFMI, an intermediate another and an advanced lifter a third. Real training histories are rarely that tidy.

I have trained consistently for many years, but I would not describe every one of those years as an equally focused muscle-building phase. During the last year, for example, my main goal has been maintaining strength. I have generally trained with an upper/lower structure, covered all major muscle groups and often performed two hard sets per exercise. I have still become slightly stronger, but I have not always followed a tightly structured programme designed to maximize hypertrophy.

That does not mean two sets are ineffective. A small number of hard, well-executed sets can be productive. The point is that calendar training age does not reveal the goal, effort, progression, volume, consistency or quality of those years.

FFMI only sees your current estimated fat-free mass and height. It cannot tell whether ten years included ten deliberate gaining phases, several maintenance periods, repeated cuts, injuries or inconsistent programming.

For that reason, use yearly muscle-gain ranges as broad planning context and lifetime natural-muscle potential as a separate, more uncertain question. Neither can be read directly from one FFMI score.

Why the Same FFMI Can Mean Different Things

Two people with the same FFMI can look and perform very differently. FFMI does not show:

• how muscle is distributed across the body;
• body-fat percentage and fat distribution;
• bone structure, limb lengths or shoulder and hip width;
• whether fat-free mass comes from muscle, water, bone or other tissues;
• strength, athletic performance or muscle quality.

Fat-free mass is not the same as skeletal muscle mass. It includes body water, bone mineral, organs, connective tissue and skeletal muscle. A higher FFMI usually indicates more fat-free mass relative to height, but it cannot isolate the amount of contractile muscle tissue.

This is also why illustrations claiming to show exactly what FFMI 18, 20, 22 or 25 “looks like” are misleading. Leanness, proportions and muscle distribution can change the appearance dramatically.

How Body-Fat Measurement Changes Your FFMI

FFMI is calculated from estimated fat-free mass. If the body-fat estimate changes, the FFMI changes—even when the person has not gained or lost any muscle.

Here is my own current example. At a morning body weight of 86.7 kg and a height of 1.86 m, an InBody-based body-fat estimate of 14–15% produces the following results:

Even this narrow one-percentage-point range changes raw FFMI by about 0.25 points. If the same weight were entered with hypothetical estimates of 12% and 18%, the calculated raw FFMI would change from approximately 22.1 to 20.6—a difference of about 1.5 points with no change in the body itself.

InBody devices use bioelectrical impedance analysis. Results can be affected by hydration, recent food and fluid intake, glycogen, exercise and the device’s prediction equation. Skinfolds depend on the technician and equation. DXA is often more repeatable under controlled conditions, but it also estimates body composition and can vary with equipment, software and testing conditions.

If you are choosing a method or comparing past readings, use the full DEXA vs InBody comparison before interpreting a difference as new muscle or fat.

The practical lesson is not that body-composition testing is useless. It is that categories separated by one FFMI point may be less distinct than they appear.

Raw FFMI vs Normalized FFMI

Raw FFMI is calculated as:

Normalized FFMI applies an additional height adjustment:

The adjustment was introduced because FFMI was still related to height in the sample used by Kouri and colleagues. It increases the score of people shorter than 1.80 m and reduces the score of taller people.

At 1.86 m, for example, the formula subtracts approximately 0.38 points from my raw FFMI.

Normalized FFMI can make height comparisons somewhat fairer, but it is not a perfect correction. The coefficient came from an older male athlete sample and should not be assumed to remove every effect of height across sexes, ethnicities or sporting populations.

When comparing your result with a study or reference table, first check whether both numbers are raw or normalized. Do not mix them silently.

Does a High FFMI Mean Someone Is Enhanced?

No. FFMI cannot determine whether a person uses anabolic drugs.

The popular “FFMI 25 natural limit” comes largely from a 1995 study of 157 male athletes. The nonuser group extended to a normalized FFMI of approximately 25, while many anabolic-steroid users scored higher. The authors described the findings as preliminary; they did not establish a universal biological ceiling or a diagnostic drug test.

Later athletic studies have reported individuals above 25, including collegiate football players. Small natural-bodybuilding samples have also included competitors above 25. These observations do not verify anyone’s lifetime drug-use history, but they do show why 25 cannot function as an absolute cutoff.

For the original study, later athletic evidence and the limits of the famous cutoff, read Is FFMI 25 the Natural Limit?.

How to Use FFMI to Track Your Progress

FFMI is most useful when you stop treating it as a score to win and start treating it as one part of a repeated assessment.

1. Establish a baseline. Record body weight, body-fat method, raw or normalized FFMI and the testing conditions.
2. Repeat the same method. Use the same device or technician where possible.
3. Standardize the conditions. Measure at a similar time of day and under similar hydration, food and exercise conditions.
4. Look for a trend over months. Small week-to-week changes are often noise.
5. Use other evidence. Compare FFMI with gym performance, circumference measurements, body weight and consistent progress photos.

When I evaluate progress, I would rather see a repeatable upward trend in performance, measurements, photos and consistently estimated FFMI than one isolated “excellent” result. A category can describe a comparison; it cannot tell me whether a programme is currently working.

For muscle growth, the more actionable questions concern training quality, progression, recovery and nutrition. See what causes muscle growth and the guide to progressive overload for those mechanisms.

Limitations of FFMI

Keep these limitations in mind whenever you interpret a result:

• Body-fat error: FFMI inherits the error in the body-fat or fat-free-mass estimate.
• Not pure muscle: Fat-free mass includes water, bone, organs and connective tissue.
• Height: Raw FFMI is not completely independent of height, while normalized FFMI relies on an imperfect correction.
• Sex: Male reference values cannot simply be applied to women.
• Population: General adults, endurance athletes, football players and bodybuilders require different comparisons.
• Frame and proportions: Bone structure and the distribution of muscle affect both the score and appearance.
• Age: Reference values and the meaning of a score can differ across the lifespan.
• Cross-sectional data: Most reference studies describe groups at one point in time; they do not tell you how much muscle a specific person can still gain.
• Drug-use inference: FFMI cannot confirm or exclude anabolic-drug use.

Frequently Asked Questions

Final Takeaway

A good FFMI is one that you interpret against a relevant population, with a clear understanding of how it was measured.

General-population references sit around 19 for young men and 15–16 for young women, while many muscular athletic groups average higher. Those figures can provide context, but they are not universal biological categories.

Use FFMI to follow a consistently measured trend alongside strength, body weight, measurements and photos. Do not use it as a verdict on your physique, your remaining potential or another person’s natural status.

Sources

1. Schutz Y, Kyle UUG, Pichard C. Fat-free mass index and fat mass index percentiles in Caucasians aged 18–98 years. International Journal of Obesity. 2002.
2. Kyle UG et al. Body composition interpretation: contributions of the fat-free mass index and the body fat mass index. Nutrition. 2003.
3. Brandner CF et al. Sport differences in fat-free mass index among a diverse sample of NCAA Division III collegiate athletes. Journal of Strength and Conditioning Research. 2022.
4. Harty PS et al. Normative fat-free mass index values for a diverse sample of collegiate female athletes. 2019.
5. Blue MNM et al. Upper and lower fat-free mass index thresholds in female collegiate athletes. 2019.
6. Trexler ET et al. Fat-Free Mass Index in NCAA Division I and II Collegiate American Football Players. Journal of Strength and Conditioning Research. 2017.
7. Fat-free mass index in a large sample of collegiate American football athletes. 2024.
8. Chappell AJ et al. Nutritional strategies of high-level natural bodybuilders during competition preparation. Journal of the International Society of Sports Nutrition. 2018.
9. Do muscle mass and body fat differ between elite and amateur natural physique athletes? 2024.
10. Kouri EM et al. Fat-free mass index in users and nonusers of anabolic-androgenic steroids. Clinical Journal of Sport Medicine. 1995.
11. Fat-Free Mass Index in a Large Sample of National Collegiate Athletic Association Men and Women Athletes From a Variety of Sports. Journal of Strength and Conditioning Research. 2024.