Training Volume is the King of Girth Gains - Doing (Bro-)Science With Community Data!
Training Volume is the King of Girth Gains - Doing (Bro-)Science With Community Data!
TL:DR: After crunching data from dozens of community members (with major kudos to Pierre for the statistical heavy lifting), we found that total training volume—i.e., how many hours you actually put in at a solid intensity—is by far the most important predictor for girth gains. On average, it takes around 26 hours of decent girth training (pumping, clamping, or both) to add 0.1 inches, but there’s a fair bit of scatter around that average. Even so, routine specifics, fancy gadgets, or going all-out each session explain less of the variance in girth gains compared to the sheer amount of hours racked up. That said, technique and physiology obviously matter for why some folks gain faster or slower (looking at you, tri-layer tunica guys). Still, if you’re aiming for that extra inch, your best bet is to keep your sessions consistent, focused, and keep piling on the training volume. We will be trying to teach a bit of statistical method in this post, as well as carefully explain the many pitfalls and weaknesses inherent in collecting community data. Take our findings with a huge pinch of salt - they are by no means an exact science - more an inkling of what we would find if we could expand the study and collect better data in the spirit of TSoPE. Let’s dive in.
Introduction: The Big Question
What really drives girth gains in PE? Is it the type of routine you use, the fancy gadgets you buy, or how hard you’re willing to push yourself during each session? It turns out, the answer is none of these—at least not primarily. The single most important factor is something much simpler: training volume. Yep, just the total number of hours you put in (at a sufficient intensity).
Before you start pumping or clamping in frustration, let me assure you, there’s nuance here—we’ll get to that!
This article is the result of a collaboration between me and the brilliant Pierre u/Intelligent-Spell383 - a bona fide statistician and data scientist. Pierre is the one who did the heavy lifting with the numbers and diagrams, meticulously collecting and analysing data from PE enthusiasts. I know, I know, he didn’t want me to tell you about his credentials because he thinks the data should speak for itself—but hey, I insisted. On Reddit, a little appeal to authority never hurts.
Together, we found that training volume is the most significant predictor of girth gains. While other factors like technique and physiology probably play significant roles, the old saying that “consistency is key” couldn’t be truer. But we shall add nuance to that. Consistency with the wrong intensity or sessions of insufficient duration won’t do it. Total accumulated training volume is the king of girth gains as we shall show.
If you’ve ever wondered exactly how much effort it takes to gain an inch of girth, or how long you need to stick with a routine to see progress, this deep dive will give you answers—and maybe even save you some time. Let’s get started.
Some Notes on Techniques and Their Role in Volume
For the purposes of this article, training volume refers to the total time you spend on exercises aimed at girth growth. While training volume is the input—the effort you invest—its efficiency can be expressed as Hours to Gain 0.1” girth (HtG01), which reflects the time required to achieve measurable progress. Think of HtG01 as a performance metric: the fewer hours it takes to gain 0.1 inches, the more efficient your routine.
Whether you’re pumping, clamping, or using a hybrid method, your training volume contributes to your progress. That said, individual techniques and execution vary widely, which can certainly affect HtG01. For instance:
- Pumping pressures likely play a significant role in determining HtG01 but aren’t accounted for in our dataset. The same goes for things like the number and types of clamps used, etc.
- Static sets vs intervals vs rapid intervals likely also impact HtG01, but these variables were not isolated in this analysis. We also have too few data points to differentiate shorter more frequent sessions vs longer less frequent sessions.
- Hybrid methods, such as Pump-Assisted Clamping (PAC), combine approaches to maximize tissue expansion and may improve efficiency, but too few such data points are included to tell.
Finally, while supplements, recovery, and good nocturnal erections don’t directly factor into training volume, they can support tissue health and retention, potentially improving your HtG01. We’ll discuss these auxiliary factors later in the article.
Some Notes About Data Collection and Limitations Before We Start
The main potential error sources of this (bro-science) study compared to a proper scientific study are:
Measurement Challenges in Self-Reported Data
One of the primary limitations of this study is the reliance on self-reported data. Participants were responsible for reporting their hours and measurements, which introduces several potential sources of error:
- Temporary Gains:
- Pumping in particular, but also clamping, can cause temporary swelling that subsides after a few hours (or even days in extreme cases). There is an acute swelling in the form of edema, but also a longer temp gain that sticks around in the form of tunica fatigue. Without standardised pre-measurement waiting periods, these temporary changes could lead to overestimation of long-term progress.
- Measurement Inconsistencies:
- Users may measure gains inconsistently or under varying conditions. For example, poor erection quality can skew results. (To minimise this issue in case we do a follow-up study, we would recommend measuring girth progress by using a cock ring first thing in the morning, during a morning erection. Measurements should be taken within a few minutes, allowing the corpus spongiosum to fill completely but avoiding expansion beyond 100% EQ.)
- Memory Bias and Human Error:
- Participants may forget exact hours logged, leading to imprecise training volume estimates. People have a hard time recalling what they ate two days ago. Unless people keep a detailed PE log, the data they report will probably be very rough estimates.
- Deception (Intentional or Not):
- Some participants may report “best-case” measurements or exaggerate their results, either due to the social status attached to being bigger, an economic incentive in some cases, or simply through subconscious bias.
These challenges are inherent in community-driven data collection, and while we’ve accounted for them by excluding some outliers and using robust analysis methods, they remain a significant caveat to our findings.
Selection Bias:
The participants are mostly individuals who experienced noticeable gains, which means non-responders or those with negligible progress are likely underrepresented. Many quit after not seeing rapid gains. This potentially skews the dataset toward successful cases, inflating apparent effectiveness. To be fair, hard gainers might also over report their data to complain (I can't gain blablabla - we have all seen those posts). The point is: we can never be sure how significant the selection bias is, and in which direction it skews the data.
Small Sample Size:
The total number of data points collected is 41. Of these we have excluded 6 outliers. N=35. Although the dataset has grown over time, it’s still relatively small compared to what would be expected in a controlled scientific study (well, technically a rule of thumb for clinical experiment is to consider 30<n<100 as medium, n>100 as large). Outliers have a more significant impact on the results in smaller datasets, and trends may shift as more data is collected.
Lack of Controlled Variables:
While we’ve focused on training volume, other variables like intensity, routine specifics, recovery practices, individual physiological differences, and even genetic factors aren’t fully accounted for. These could influence results and add something called “omitted-variable bias” to the dataset. In an actual clinical experiment worth its mettle, you would use a single treatment protocol, or perhaps three protocols in a multi-pronged crossover study of Latin Square design (a rigorous experimental setup used to minimise bias). In a larger study where some or all of these variables were measured and controlled, they could have allowed us to explain the part of the variance in gains NOT explained by volume.
Despite these limitations, we think the dataset is a valuable snapshot of community-reported experiences. It offers insights that, while not definitive, provide useful guidelines for anyone pursuing girth gains. By highlighting these limitations up front, we aim to keep the analysis transparent and grounded. We have done outlier suppression with these error sources in mind and excluded some participants from some calculations (we will be clear about which and why).
The Need for Outlier Suppression
Here is how and why we decided to suppress outliers. See these participants marked in red in this rank-order bar chart? Those are the ones we do not include in the calculation of the average, the variance or the correlation. Note: Lower bar means faster gains (fewer hours spent to gain 0.1”). The red line is the average (outliers not included).
Why? Well, for the rightmost ones we find it likely that they overestimate how much they worked, or that they worked at insufficient intensity, or that they simply measured with poor erection quality. For the leftmost ones who showed exceptional gains rate, we find it likely that they do not wait sufficiently long after their last session before they measure (i.e. measure with temp-gains), or that they underestimate their amount of work, or that for some other reason they are reporting erroneous data. We can’t be sure of that, of course - perhaps it’s perfectly legitimate, and they simply perfected their respective techniques. The only way to know would be to expand the study and have 100+ data points instead of 41. (On a side note, I am pretty pleased to see that I am almost side by side with Hink and that my gains are coming in a little faster than the average of the study (i.e. below the red line, lower is faster).
On the image to the left you can see another visualization of the outliers and their effect on the bell curve.
Now, let’s move forward and explore the meat of the matter: how much training volume you actually need to achieve measurable progress.
Core Findings: How Much Time for 0.1 Inches?
This is called a “Scatter Plot.” Each of the 35 data points we kept (the ones that were not classified as outliers) is represented as a dot (we're sorry it's hard to see some user names). The dotted line running through the plot is called the regression line (or trendline). It represents the predicted relationship between training volume (on the x-axis) and girth gain (on the y-axis) based on the data.
What Does the Regression Line Tell Us?
The regression line shows the average trend: as training volume increases, girth gains also tend to increase. In simpler terms, it’s the best-fit line that minimises the overall distance between itself and all the individual data points. This line helps us visualise the general relationship between the two variables, even when individual points deviate from the line due to other factors.
Key Data Points:
- Mean Hours to Gain 0.1” (HtG01): 25.8 hours (rounded to 26 hours).
- Median HtG01: 25.8 hours.
- Standard deviation: 9.7 hours (rounded to 10), meaning most users fall within 10 hours above or below the mean. 68% to be precise.
- Explained variance: 0.53.
- Correlation coefficient: 0.73, indicating a moderately strong linear relationship between training volume and girth gains.
What Does This Mean in Practical Terms?
For most people, gaining 0.1 inches of girth is relatively predictable. Whether you’re pumping, clamping, or using a hybrid approach, the required time clusters around the mean of 26 hours. With a standard deviation of 9.7 hours, we expect about 68% of users to fall within the range of 16.1 to 35.5 hours. This range represents the majority of typical outcomes and provides a benchmark for what’s “normal.”
This estimation is in line with u/Hinkle_McKringlebry's prediction of 0.25" girth gain per year as a reasonable estimate (provided one's training volume is relatively low). A pumping routine of 3x7min per day, 6 days a week, amounts to 109h in the year. By using a conservative gain rate 1 sd below the average (36h per 0.1”), we have an estimated girth gain of 0.31” in a year. At the average gain rate it would be 0.4” in a year.
We will go into more detail about this later on in this article and return to Hink’s estimate and ours, as well as talk more about what could be an ideal workload, but first we want to teach some statistics in the spirit of TSoPE. The take-away will be your reward if you keep reading. ;)
Explaining Statistics
As a science communicator, I feel it would probably be best to bring everyone up to speed here. If you’re “fluent in science and statistics” feel free to skip ahead:
Quick Note 1: What is a Standard Deviation?
A standard deviation is a measure of how spread out the data is around the mean. In this case, a standard deviation of 9.7 hours tells us that most users' HtG01 values cluster closely around the mean of 25.8 hours, with fewer people falling much below or much above this range.
Statistically speaking, approximately:
- 68% of users fall within ±1 standard deviation (16.1 to 35.5 hours).
- 95% of users fall within ±2 standard deviations (6.4 to 45.2 hours).
This helps us understand that while most people’s HtG01 aligns closely with the average, there are outliers on either end of the spectrum.
Quick Note 2: Correlation vs. Explained Variance
Both correlation and explained variance describe the relationship between two variables, but they serve slightly different purposes:
- Correlation (here, 0.73) measures the strength and direction of the relationship between training volume and girth gains. It’s a straightforward way to see if more hours generally lead to more gains.
- Explained variance (here, 0.53) tells us how much of the variability in gains (HtG01) can be attributed to training volume. In simpler terms, it quantifies how much of the “story” about why people gain girth can be explained by their training hours.
Together, these metrics give us a fuller picture: training volume strongly predicts girth gains, but other factors (like technique or physiology) also play a role. Which brings us to the grey shaded area in the scatter plot.
Quick Note 3: Understanding the Grey Shaded Area
The grey shaded area on the scatter plot represents the 95% confidence interval for the predictions made by the model using training volume as the sole predictor of girth gains. In simpler terms, it shows the range within which the model expects most points to fall, given the relationship between training volume and girth gains.
Why Are Some Points Outside the Shaded Area?
While the grey area captures a lot of the data points, you’ll notice that several points fall outside of it. This happens because training volume explains only about half of the variability in girth gains (explained variance = 0.53). In other words:
- Training volume is the most significant predictor we have, but it’s not the only factor that influences girth gains.
- Individual differences (e.g., genetics, technique used, recovery, session frequency, etc) add variability, causing some points to deviate from the model’s predictions.
Framing This Another Way
To understand the variability in girth gains, let’s break it down into the factors that might contribute to someone’s progress. While our model primarily uses training volume to predict gains, we know that other factors—things we couldn’t measure—also play a big role. These include:
- Technique: How well someone performs their routine (e.g., using sufficient pumping pressure, good clamping technique, or advanced methods like PAC).
- Physiology: Individual differences, such as genetics, tissue response, or recovery ability.
We can think about gains using a simple equation for gain rate (how much gain someone achieves per unit of training volume):
Here’s what this means:
- c: This is a constant, representing the average gain rate for the group—essentially, the slope of the regression line (the dotted line in the scatterplot).
- Technique and Physiology: These represent individual factors that push a person’s results above or below the average (the dotted line).
- Error Term: This accounts for other unobserved factors or random noise that influences gains.
How This Relates to the Scatterplot
- If someone is average in both technique and physiology, their data point will likely fall on or very close to the dotted line. They’re getting predictable results for the amount of training volume they’ve invested.
- If someone’s technique is poor (e.g., insufficient pumping pressure, bad clamping form), or their physiology is less responsive (or perhaps that they overtrain - do more than they can recover from before the next session), their results will fall below the dotted line. They’re gaining less than the average person for the same training volume.
- Conversely, if someone uses more significant pressures, or advanced techniques (e.g.,RIP, PAC) or has a naturally responsive physiology, their results may fall above the dotted line, meaning they’re gaining more efficiently than the average.
In short, the dotted line represents the average expectation based on training volume alone, but individual technique and physiology can cause a person’s actual results to deviate significantly.
But Let’s Think a Little Deeper About Physiology.
Let’s return to the outliers - the fast responders and slow responders. Could it be that we are seeing the result not of factors like poor/good technique, misremembering/misrepresenting their volume, exaggerating their gains, or some other bias, but of a difference in phenotype? Namely; the “hard gainer” and “easy gainer” phenomena?
In a 2006 study reported in the Journal of Andrology by Shafir et al., “Histologic study of the tunica albuginea of the penis and mode of cavernous muscle insertion in it”, they found something extremely fascinating: “Twenty-eight cadaveric specimens (18 adults, 10 neonatal deaths) were studied morphologically and histologically after staining with hematoxylin and eosin and Verhoeff-van Gieson stains. The TA consisted in 20 specimens of 2 layers: inner circular and outer longitudinal, in 6 specimens of 3 layers: inner circular, longitudinal and outer circular, and in 2 of only one longitudinal layer. The CS TA was formed of one layer of longitudinal fibers.”
(It’s a little hard to see in this one that there are two layers unless you know what to look for. The longitudinal fibres are pointing "straight out of the screen" toward you so to speak, so you see them as round-ish blobs as you would see the cut end of a rope. The circumferential fibres on the inside are seen from the side as thin strands.)
Now, in a study of only 28 specimens you can’t really say much about what proportion you could expect to find if you were to scale up the study. Would the proportions remain 1:10:3? We don’t know, and I have not been able to find other studies which could elucidate the question. But what if the three men who had the slowest gain rate in our data are simply of the tri-layer phenotype who have two circumferential layers in their tunica? Because surely that would make girth gains harder, right?! And what if the exceptionally fast gains among the outliers on the other end of the distribution are of the mono-layer phenotype, who do not have a circumferential layer of fibres in their tunica?
This is a fully plausible hypothesis, and it feels a lot better to say “you lucky devil, you seem to have a mono-layer tunica” than to say “you’re either lying about your gains or misrepresenting how much time you spent”. It also feels better to say “you poor bastard, you probably have a tri-layer tunica” than to say “you’re not doing it right ffs, or you’re measuring with poor EQ, or exaggerating how much time you spent.”
But regardless of what hypothesis best explains the outliers, we feel good about not including them in the data crunching. We want to say something about what a majority of men can expect in terms of required workload to reach their first inch in girth; about 260 hours +/- 100 hours.
How does this number we have arrived at compare to what others have said about expected gain rate? Let’s take u/Hinkle_McKringlebry’s “realistic expectation from the first year of PE”, which we have already mentioned: half an inch in length and 0.25” in girth. Let’s take his recommended routine also, which includes 3x7 minutes of pumping once per day. If you do that for 6 days per week, that comes out to 109 hours per year, which should result in about 0.4” of girth gains if a user gains at the average rate we found in our study. But Hink is deliberately giving a conservative estimate because he wants people to have realistic expectations and not be too disappointed.
If instead we use someone who gains at a rate 1 standard deviation slower than average (36 hours per 0.1”), 109 hours would amount to 0.3” gains per year. Yup. If people set that expectation of 0.25” girth in the first year, and follow Hink’s recommended routine, chances are not too many people will be disappointed.
Actually, I had a chat with Hink today on Telegram, and I will quote one single paragraph of what he said:
“I think the ideal growth workload is somewhere between 30 to 45 minutes. If twice a day approach I think 20 to 25 minutes twice a day. Or approximately 20- 30 minutes if you're just doing one session”.
I agree completely with that recommendation. 2x20 minutes, sometimes with 10 more minutes of clamping added on top, and sometimes adding much lower intensity sessions of “Milking” for oxygenation and shape retention purposes, that’s my approach and for me it's helping me stay below par for the course, i.e. beat the average gain rate.
Other people say that it’s reasonable to expect about 0.5” in the first year, and if they recommend a workload which amounts to a total of 130+ hours of work, about 50% of users will be able to get there if our statistics are to be believed. If their recommended workload is a lot less than 130 hours of girthwork, we have doubts about that.
Whether the expectations you set should be optimistic or pessimistic (realistic) is a matter of perspective. We’re happy that our result seems to be very much in line with what people have been saying all along; girth takes time to gain. Now we have a more precise answer as to how long, and we also see that there is a lot of variation. It will take most people between 160 and 360 hours of girthwork to gain that elusive inch of girth. For some it will take more.
A Word of Warning: It’s tempting to read this and think; “Hah! This means if I do two hours of girthwork per day, I can probably get an inch of girth in six months. Now where is my clamp and my pump? Here we go!”
Most likely, that is not how it works at all. Yes, more is probably better. But only to a point! There is a biological limit to how fast the fibroblasts in your tunica can lay down more collagen and repair the fibres that are snipped by collagenase during and after your sessions. Nutrient delivery to the tunica is slow because it happens through diffusion. Constantly interrupting your fibroblasts with frequent sessions and not giving them time to produce collagen in peace might be counterproductive. To use a gym metaphor, although I generally think they should not be used too much where PE is concerned, training your biceps every day for a year will probably just result in injury and suboptimal growth, compared to hitting them two or maybe three times per week at most, with a few weeks off now and then for recovery. For each tissue type, there will be an ideal amount of work to stimulate growth. The goal should be to hit somewhere close to that peak growth stimulus - neither too far above or below.
Exactly where your own “recoverable volume” lies is probably determined by your cardiovascular health, the health of the endothelium inside your corpora cavernosa, how good your nocturnal erections are, whether you smoke and drink or have a healthy lifestyle, as well as a great many genetic factors. You can probably influence it to an extent by increasing blood flow - such as by tweaking the eNOS > NO > cGMP pathway by taking Citrulline and Arginine, NAC, Taurine, ALCAR, ALA, Omega-3, CoQ10, and adding a PGE5-inhibitor such as Cialis on top of that. Boosting your nocturnal erections and optimising endothelial health can only be beneficial. But supplements cost a lot, and the effect is probably small in comparison to other factors.
We could not detect any major difference between clamping and pumping in our data - the sample size is simply too small, and the error bars are therefore much too large. As I mentioned before, we also can’t say much about ”low pressure-long duration” vs ”high pressure-short duration” and similar questions about methods. For this we would need more data and better data.
My vision for the TSoPE subreddit, shared by the other guys on the Mod team, is that we can create more and better community data of this kind, to refine our understanding of gain rates and the relative benefits of different techniques. I have seen so many people come to PE desperately searching for answers to questions like; “why is there no consensus - should we clamp before or after pumping? Is clamping really more effective than pumping? Do bundles add anything of value? Is adding IR or vibration meaningful - exactly how much of a difference do they each make? Does it matter for my gains whether I get 4% expansion or 12% after a girth session?” The answer to all of these questions:
WE SIMPLY DON’T KNOW, BECAUSE ALL WE HAVE IS A BUNCH OF ANECDOTES - THERE’S NO SYSTEMATIC DATA!
(Sorry for shouting, but it is frustrating, is it not - that we just don’t really know?) Hopefully, over the next few years, we can collaborate and gather quality data which allow us to compare methods and arrive at better answers.
Again: Take the number “26 hours” with a pinch of salt. It’s ballpark. It’s approximate. The sample is small and inherently unreliable for the many reasons I have mentioned. But: It’s the best we have.
Finally, I want to thank every user who volunteered their data to this community effort, but most especially I want to thank Pierre for patiently collecting the data and analyzing it. It’s been a pleasure working with you Pierre!
/Karl - over and out!