What you’re getting yourself into:
3,300 words, 11-22 minute read time
1) Drug-free muscular potential is influenced by the size of your frame.
2) Strength is a function of neural factors and muscular factors. Once you’ve hit a point of diminishing returns for the neural factors, your strength potential will be determined by how much muscle you can build.
3) Based on a few simple calculations, you can get a pretty good idea of your muscular and strength potential.
I think almost every lifter has had this thought: “I used to be able to get bigger and stronger so easily! But lately the gains have been slowing down dramatically, and I’ve barely made any progress in the past year. How much bigger and stronger can I reasonably get?”
That’s a hard question to answer objectively, but I’m going to try. This article explains the basics of the models I’m using and makes some rough predictions. Part 2 refines those predictions and lets you know how you can use that information to make training decisions.
First, just a smidgen of background information:
- How much muscle you can gain largely depends on the size of your frame.
- How strong you can get is a product of how much muscle you have, and how proficient you are with the lifts you’re using to demonstrate your strength.
So, first and foremost, I’ll discuss the model you can use to predict how much muscle you can put on your frame. Then, based on that information, I’ll discuss the model you can use to predict how much more strength you can gain, and what proportion of that strength potential comes from neural/technical factors, and what proportion comes from muscular factors.
This may sound complicated and confusing, but bear with me; it’s really pretty simple conceptually, and you don’t need to worry about all the math behind it.
What’s my muscular potential?
There have been a variety of ways proposed to predict someone’s muscular potential. They all give reasonably similar predictions, but there are two that I think are notably better than the others, and only one of them is user-friendly. I’ll touch on them all so that you can see the strengths and weaknesses of each.
This one’s probably the simplest. Martin Berkhan (the guy who runs Leangains.com) developed this model based on his observations concerning the stage weights of drug-free bodybuilders.
Weight (kg, in shredded, stage-ready condition) = height in cm – 100
To convert that to fat-free mass, you’d multiply the result by .95 (to account for 5% body fat in stage-ready condition).
So, if you’re 160cm (5’3″), you should expect to be about 60kg, with 57kg of lean mass.
You can use this calculator to see where you’d fall using this formula:
The Fat Free Mass Index (FFMI) is probably the most well-researched of these models.
It’s also relatively simple to calculate: Fat Free Mass (in kg) divided by height (in meters) squared. It can then be normalized by adding 6.1x(1.8-height in m) so that height doesn’t throw off the calculation.
You can find out your FFMI using this calculator:
A lot of people have gotten the idea that an FFMI of 25 is the maximum possible without drugs, largely based off of this study by Kouri et Al. You can see how big you’d be with a normalized FFMI of 25 using this calculator.
The study compared the FFMIs of 74 nonusers to those of 83 steroid users. None of the nonusers (which included some successful drug-free bodybuilders and strength athletes) had an adjusted FFMI over 25, whereas around half of the steroid users did.
Case closed? An FFMI over 25 automatically means someone is on drugs, right?
Not so fast.
Kouri’s study also reports the FFMIs of top pre-steroid era bodybuilders who won the Mr. America show.
“With certainty we know the majority of anabolic steroid use in the US lifting community began in the late 50s once superior synthetic steroids were produced. However, we also know top American lifters and bodybuilders experimented with testosterone as of 1954. Finally, we know testosterone use likely began as early as the late 1940s among Soviet lifters, considering the institutionalized use by their weightlifting team at the 1952 Olympics.
“These are the confirmed records of when steroid use began. However, just because something was not recorded or corroborated does not mean it couldn’t have occurred. The Mexican hormone industry began producing more affordable testosterone in 1945, coinciding with increased public awareness of testosterone’s potential with the publication of ‘The Male Hormone.’ Thus, depending on the level of skepticism applied, one can view the 1939-1959 Mr. America FFMI’s from two perspectives. The highly skeptical can view only the 1939-1944 winners as being almost certainly drug-free, while the moderately skeptical can view the 1939-1953 winners as being almost certainly drug free…
“…If we accept the 1939-1944 winners as natural, the average FFMI is 24.9, with the highest reported at 27.3. Applying moderate skepticism and accepting the 1939-1953 winners as natural, the average FFMI is 25.6, with the highest reported at 28.0. These means are not much different from the 1939-1959 group mean. In fact, the authors analyzed the FFMIs to determine if they were increasing over time. They noted: ‘There was no significant trend towards increased FFMI among the Mr. America winners over a 20-year span from 1939 to 1959 (slope = 0.044 FFMI units/yr; p = 0.44 by regression analysis).’ Thus if drug use was occurring, perhaps it wasn’t frequent or effective enough to significantly affect the aggregate FFMI.”
- The possibility that some of the nonusers in their sample were actually using. However, in their words, “It is impossible that all of the individuals in this cluster (just below 25) were lying.” Obviously this wasn’t an issue for the Mr. America winners competing before steroids existed.
- Their sample of 74 nonusers, in their words, “Might not have been large enough to exhibit fully the upper limits of muscularity attainable naturally.” This is a major limitation: A sample size of 74 is great for a study in this field, but certainly not enough to encapsulate the entire range of human potential. They refer to the FFMIs of the Mr. America winners from 1939-1959, which averaged 25.4, with three people exceeding 27, and one with an FFMI of 28.
- It is likely that the measured and predicted body fat percentages were off by a bit (which would affect fat free mass estimates, and thus FFMI). However, they go on to explain that the reasonable error range wouldn’t affect the results in any meaningful way.
- FFMIs are likely higher for fatter individuals. Adipose tissue isn’t 100% fat; there’s also some water, connective tissue, organelles, vascular networks, etc. For leaner people, adipose tissue is about 50% fat by volume, and for fatter people, it’s about 85% fat by volume. Interestingly, the highest fat free mass reported in the literature was from a sumo wrestler. This wasn’t noted in the study, but dehydrating and glycogen depleting would have the opposite effect, reducing FFMI a bit (by about 2 points or so).
- The last limitation isn’t overly relevant for our purposes here: The FFMI boost from steroids would likely be different for aerobic athletes.
So, what can we get from this study?
- Steroids seemed to give people an average increase of about 3 FFMI points (the average for the 83 steroid users was 24.8, and the average for the 74 nonusers was 21.8). That corresponds to about 7.5-10kg of lean mass. Of course, this doesn’t take megadosing into account (top pro bodybuilders today can have FFMIs around 40 – I highly doubt someone will hit an FFMI of 37 without drugs), but it’s likely a useful rule of thumb for most people on reasonable doses.
- You definitely have to pick the right set of parents to reach an FFMI of 25; however, it’s not a hard ceiling like many people believe. We know an FFMI of at least 27.3 is achievable without steroids; there was no realistic way Stanko could have been pinning in 1944. In all likelihood, Delinger (FFMI of 28) was drug-free as well in 1949, but if you want to be super skeptical, you can disregard him.
On the whole, FFMI and the Berkhan/Leangains models have three major drawbacks.
- An FFMI cutoff of 25, or a cutoff of height in cm minus 100 would imply that some highly gifted drug-free athletes were, in fact, on drugs. Multiple people exceeded those “limits” before steroids even existed.
- A cutoff of 25 also doesn’t “catch” a hefty number of people who are on steroids. In Kouri’s study, about half of the steroid users had FFMIs below 25.
- The most relevant drawback for the purposes of this article: They’re not predictive. Even IF 25 was a true “limit” for what’s achievable without drugs (though it’s not), that wouldn’t mean that most people could reach 25, or even get particularly close to it. This is also the drawback of the Berkhan/Leangains model: It predicts what generally gifted people can achieve, not what you can achieve.
So, FFMI and the Berkhan/Leangains are both useful, but they both have some major drawbacks. In that case, what are the better options?
Muscle to Bone Ratio
This is the brainchild of anthropometrics researcher Francis Holway. He’s a big enough hotshot to be quoted in David Epstein’s The Sports Gene, and he’s patient enough to put up with my pestering.
His idea is that the amount of muscle you can gain is limited by how much bone you have. To quote The Sports Gene:
“One bookcase that is four inches wider than another will weigh only slightly more. But fill both cases with books and suddenly the little bit of extra width on the broader bookcase translates to a considerable amount of weight. Such is the case with the human skeleton.
“In measurements of thousands of elite athletes from soccer to weight lifting, judo, rugby, and more, Holway has found that each kilogram (2.2 pounds) of bone supports a maximum of five kilograms (11 pounds) of muscle. Five-to-one, then, is a general limit of the human muscle bookcase. The limit for women is closer to 4.1 to 1.
“Holway experimented on himself, spending years in heavy weight training with a diet high in protein and supplemented by creatine. But as he closed in on five-to-one, inhaling more steaks and shakes only added fat, not muscle.”
This idea has a lot of intuitive appeal.
Muscle and bone are intrinsically linked. For example: They arise from the same stem cell line, they grow and strengthen in response to very similar hormonal signals in puberty, osteopenia (bone weakening) and sarcopenia (muscle weakening) with age are very closely associated, muscle contractions strengthen bone, there’s evidence that muscle and bone can “talk” directly to each other via paracrine hormones, and muscles even have receptors for hormones that were once thought to affect bone almost exclusively (here’s a great review if you’d like to read more about the interactions between muscle and bone). The exact details of their molecular interactions are still being worked out, but it’s becoming more apparent every day that muscle and bone affect each other at a very basic level.
This notion also has empirical support. The size of someone’s frame correlates with how much fat free mass they have, and it’s been found that people who are more solidly-built prior to starting a training program gain more muscle and strength (assuming pre-training FFM corresponded to frame size in that study – a pretty safe assumption). Bone mass has also been used as a predictor for meat yield (aka muscle mass) from livestock since the ’30s.
Much like an FFMI of 25, however, a muscle:bone ratio above 5:1 doesn’t guarantee someone’s on steroids. Francis has drug-free athletes in his database with muscle:bone ratios nearing 5.5:1.
Muscle:bone ratio is a much better indicator of someone’s ability to gain muscle because it doesn’t only consider someone’s height (like FFMI and the Berkhan/Leangains model do); bone mass also reflects how wide and thick someone’s frame is. The guy who walks into the gym for the first time as wide as he is tall likely has more potential to gain muscle than the guy who’s the same height, but looks like a zipper when he turns sideways and sticks out his tongue. FFMI only takes those two peoples’ heights into account, whereas muscle:bone ratio reflects the sizes of their entire frames.
Since Francis has thousands of athletes in his database, it’s less likely that a muscle:bone ratio above 5.5:1 will produce a “false positive” than an FFMI of 25 – indicating someone’s on drugs when they really aren’t.
Likewise, remember that ~50% of the steroid-users in the Kouri study had FFMIs below 25. A muscle:bone ratio of 5:1 also produces far fewer “false negatives” for people on drugs, but whose frames are too small to reach an FFMI of 25, even with the assistance of drugs.
Muscle:bone ratio sounds great, right? We could predict your muscular limits based on muscle to bone ratio, with 4:1 for women and 5:1 for men being “general” standards that most people should be able to approach; athletes in sports that don’t require a ton of heavy strength training (like swimmers, divers, etc.) approach those numbers, so most people focusing solely on getting jacked should be able to get pretty close to those figures. Then we could pad those predictions a little by bumping that limit up to 4.5:1 for very gifted women and 5.5:1 for very gifted men, give you a prediction of muscular potential with a range of just a few kilos, and call it a day.
Easy peasy, right?
Think again. Unfortunately, the method for measuring muscle and bone mass required by this model requires 22 separate measurements by a trained anthropometrist.
Couldn’t you just get your bone mass measured via DEXA?
Nope. The bone mass reading you get from DEXA is the bone’s “dry weight” – just the mineral content, rather than the entire weight of the bone.
So, while muscle:bone ratio is promising, and perhaps the best method for predicting your muscular potential, it’s not very user-friendly, and basically impossible to assess on your own.
That leaves us with one final option:
Dr. Butt analyzed a metric crap ton of data from drug-free bodybuilders, relying heavily on data from the pre-steroid era to find a simple way to predict your maximum muscular potential.
He found a very strong relationship between peoples’ wrist circumference, their ankle circumference, and their muscular potential. I’d strongly recommend his book that discusses his methods in much more depth.
The study previously linked concerning the correlation between frame size and fat free mass also supports the use of the wrist as a predictor of fat free mass:
“Wrist breadth is potentially the best discriminator of an association between frame size and amounts of fat and muscle, independent of stature. Broad wrists are negatively associated with total body fat and positively associated with fat free mass and vice versa. … in the multiple regression analyses, wrist breadth was significantly and negatively associated with TBF and significantly and positively associated with FFM in both the men and the women after adjustment for stature. This suggests that wrist breadth contributes information regarding the size of the upper appendages that is associated with amounts of fat and lean tissue.”
And another study found that, “The variables which correlated most highly with actual frame size were body mass, ankle breadth, hand length and chest breadth, respectively. These variables were also positively correlated (P < 0.01) with fat-free mass (FFM)”
So, there is empirical evidence lending support to Dr. Butt’s predictions based on ankle and wrist measurements. Incidentally, this matches my own observations. I’ve been thinking that thick joints were the best predictor of potential in powerlifting for a while now, based on my experiences training athletes and meeting a ton of other lifters and meets and seminars.
I don’t think Dr. Butt’s model is quite as good as muscle:bone ratio, but it has the advantage of being much more user-friendly, and it’s also based on a ton of data. Like muscle:bone ratio, it also has an advantage over FFMI and the Berkhan/Leangains model because it takes frame size into account, instead of just height.
All you need to do to get a reasonably accurate prediction of your muscular potential is measure your wrist circumference (between your hand and the bony protrusions at the side of your wrist, with your hand open) and your ankle circumference at its smallest point.
You can use the calculator below, or check out Dr. Butt’s on his site if you’d prefer to use pounds and inches instead of centimeters and kilos:
Note that his formulas were based only on men. However, we can do some back-of-the-napkin math and get a reasonable estimate for women as well. In Olympic sports, female competitors have about 85% as much lean mass as their male counterparts, and Francis Holway found that women generally top out at around 4kg of muscle per kilo of bone, versus 5kg for men (20% less muscle relative to their frame size). Furthermore, since women have more essential fat than men do, they tend to perform the best at around 20% body fat instead of 12%. So, working with those two assumptions, the calculator above likely still gives women reasonable estimates as well.
Keep in mind that these predictions are by no means perfect (perfectly predicting the end result of a process as multi-factorial as muscle growth based on a handful of simple measurements is, quite frankly, impossible). Dr. Butt even says that some people will be doing very well to reach 95% of the maximum predicted by his model, and that it IS possible for a rare freak or two to exceed his predictions.
However, at this point, it’s the best we’ve got.
Moving on to strength
Now we’ve got a pretty decent idea about how much muscle you can build, so it’s time to turn our sights to strength.
Remember, strength is a product of two factors: muscle size relative to your height, and mastery of the lifts you’re using to demonstrate your strength.
Strength being related to muscle mass is obvious; the more muscle you have, the more strength potential you have.
Regarding neural factors, there are a few ways your nervous system can help you lift more. The most obvious is simply technical mastery of the lifts you’re using to demonstrate strength. Also, with training, not only does your technique improve, but your nervous system gets better at activating your muscles (both recruiting more motor units, and helping those motor units contract and relax faster for greater total force output).
In elite powerlifters, there’s a very strong relationship between strength and fat free mass per unit of height.
For these lifters, all of whom were competitive at the national or international level, we can assume they’d attained a high degree of mastery for the squat, bench, and deadlift. The neural component of strength was, if not maxed out, at least comfortably to the point of diminishing returns, so muscle mass was the main thing that determined how much they could lift.
Now, it should be noted that this is data from the 1997 USPF nationals, which allowed bench shirts and squat suits. However, that’s not a major issue because the gear that existed in the mid-90s didn’t give nearly as large of a performance boost as modern gear does. The squat and bench would be the only two lifts affected, and, most importantly, the gear used in the ’90s didn’t have a steep learning curve or fundamentally change the lift the way modern gear does; most people would throw on gear for 2-3 weeks before a meet, get a ~10% boost out of it, and go back to training without gear after the meet. So, for my model, I’m taking that into account by decreasing the slopes of the lines for squat and bench by 10%.
Here’s a really basic calculator based on this data. The next article will have a few that are much more in-depth:
So, those are the basics. Based on frame size, we can get a reasonable estimate of how much muscle you can build, and based on how much muscle you have per unit of height, we can get a reasonable estimate of how much you should be able to lift.
Part 2 will put these models into action so you can get a good idea of your muscle and strength potential.
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