What steroids promote strength gains?The second, Steroids for Strength Sports: We explored that relationship in this anabolic steroids gains. Which Weight Class is Best for You? On its face, anabloic seems like a pretty reasonable approach. The first critique is very straightforward.
4 Best Anabolic Steroids for Mass Gains • WikiStero • The Anabolic Steroids Bible
The second, Steroids for Strength Sports: We explored that relationship in this series:. Which Weight Class is Best for You? On its face, this seems like a pretty reasonable approach. The first critique is very straightforward. When the talent pool in a sport grows, freakier freaks come out of the woodwork. The top performers get results several standard deviations better than the average person.
The further you move from the mean, the lower your odds of finding someone with that level of talent in a given talent pool. Has Peterson had better training than van Buren?
This trend is clear across the board in the NFL. This trend is also true in essentially every sport. As sports grow in popularity and as incentives increase, more people compete, so the best of the best keep getting better and better. At the very least, they have to cycle off for international competitions, which impacts performance; yet performances keep improving. Training has likely improved, but most of the progress is attributable to larger athlete pools.
The more you do, the more muscle they help you gain. When you first start lifting, you gain muscle pretty quickly. Over time, the gains taper off. When you add drugs into the mix, you initially gain muscle very rapidly. The people who used test while also lifting gained about 6. With more drugs, unsurprisingly, you gain more muscle.
If people could just use mg of testosterone per week and keep gaining muscle indefinitely, there would be no reason for people to take more and more compounds at higher and higher doses. The effect is basically the same — a quick spurt, followed by leveling off. The second third, fourth, fifth, etc. The first two use the Fat Free Mass Index, which is a measure of lean mass relative to body size, calculated by dividing fat free mass by height 2.
This allows you to compare the muscularity of people who are different heights. By squaring the height component, you get a slightly better comparison. Lean mass is the only thing we have to go off of.
In it, the researchers compared a group of 74 non-users to 83 steroid users. Eric Helms sums it up well:. In this study non-users were determined by interview. Those stating they never used steroids took a urine test to confirm they were non-users. While plausible that some may have lied about their status and passed the urine test, it seems unlikely. Research participants do not gain recognition.
They are de-identified and represented as part of a group. Also, there were no consequences to reporting steroid use. Finally, participants would be told what the study would entail before inclusion, and those uncomfortable with interviews would not have likely volunteered.
Nonetheless, perhaps due to the generally negative perception of steroid use, some may have lied and went on to pass the urine test. In all likelihood, the means of both groups were a bit lower than would be expected comparing people approaching their muscular potential, both with and without drugs. Our second study is by Brennan. It likely did a better job of getting relatively well-trained athletes, since participants were required to bench at least to participate — not a huge feat, but certainly one that requires a fair amount of training for most folks.
Additionally, training ages were reported. Most participants had at least years of experience in the gym. The average FFMIs are only separated by 0. It only reports lean mass. All the drug-free participants were national-level powerlifters, and all of the users were strongman and bodybuilding competitors. Most of the users were on a pretty fair amount of anabolics. If we assume they were the same height, that would mean a difference of about 4. Using these average numbers, the non-users had an FFMI of However, this is a very naive way to approach the question, because:.
During the period before steroids were available at all, the biggest Mr. America winner had an FFMI of Averaging the data from Brennan and Kouri Four standard deviations has you at Of course, a human myostatin mutant, lacking any working copies for the allele that makes the hormone myostatin which limits human growth, could perhaps even blow that number out of the water. Without a much larger data set to establish kurtosis and skewness, I think the best course of action is just to assume that FFMIs are normally distributed.
Instead of looking for a limit, we should be more concerned with likelihood. This leads us to the next question:. These are the odds that someone will fall within a given FFMI range with or without drugs. The likelihood that someone is on steroids in an entire population will also influence the odds of someone with a given FFMI being on drugs or not.
With that in mind, I made a handy dandy calculator incorporating the FFMI distributions of users and non-users and the percentage of a given population you think is truly drug-free.
Then the form will do the rest of the calculations for you to tell you the likelihood that someone is drug-free based on their FFMI. If you think in percentages instead of decimal points, just shift the decimal point over 2 places so 0. Use the calculator below to see what that range means for you, based on your height. That still gives you a pretty decent shot of winding up bigger or unfortunately smaller.
The average untrained male has an FFMI of about With extreme usage, the gap gets dramatically larger. Olym pia, but the top IFBB pros are about 6x further from the average person than the typical drug-free lifter is. Naturally, the magnitude of that advantage that drugs provide will be larger or smaller based on the amount of drugs someone takes, their genetics, and how well they respond to drugs.
His passions are making complex information easily understandable for athletes, coaches, and fitness enthusiasts, helping people reach their strength and fitness goals, and drinking great beer. Facebook , Twitter , YouTube. Hate to tell you this, but now you have two photos of Eiferman, one as a young man, one older. Google has all these older guys confused for some reason. Would you mind helping me locate a super jacked pic of Grimek then? Very good article Greg. I agree FFMI is a more of an indicator than an immutable law.
The only critique I would have is that I think you underestimate selection effects in the early bodybuilding population. I have no data on that just seems likely to me. Keep up the great work. I wonder how much of that perception would be influenced by recency bias, though a negative bias in this case. The old-school bodybuilders we remember were the very very best of the bunch, but we see a ton of modern wanna-be BBers with poor development and bad physiques.
Not sure about that, though. Thank you for this. At least in the US, the untested divisions of both powerlifting and bodybuilding have always gotten WAY more exposure. So your FFMI will slide to under 25 rather easily.
I know people lose some lean mass getting super shredded, but not nearly that much. For example, in this case study http: The drug-free group in that study was at There are quite a few different areas where mass could be lost. I do want to draw attention to what I think are some issues with the likelihood-of-being-natty calculator.
If I understand it correctly, the calculator is relying on some significant assumptions in order to provide the percentage chance that a given individual is natty even taking the inputted value concerning the percentage of the population on steroids as a given.
That said, your knowledge of statistics far exceeds mine, so perhaps I am missing something. Anyway, your content is awesome; keep it coming! One tip for your graphs: The general Gaussian function: For example, to plot That helps a lot!
That helps with the calculator at the end as well. In one or two mouse studies, mice got back quite a lot of their testosterone-induced gains after going on gear and then coming off. But long-term human studies, or cross-sectional research with large enough samples to speak with a lot of confidence?