Watts. So Hard to Understand?

Time to get back on the writing saddle again. It took a bit of time to catch up on sleep and then shift my focus back to everyday life after returning from Zwift Community Live in Mallorca.

But there’s been one thing on my mind since that trip: watts – and why they seem so hard for people to understand. What some have referred to as an “obsession” of mine really started with my first post of the year, Team OWL’s ZRL Lessons: Why w/kg Falls Short in Women’s Racing. That series wasn’t about being fixated on weight; it was about a Zwift race categorization system that isn’t equitable based purely on physics.

I followed up a piece on why the CAT upgrade system favors heavier riders. Not one to point out a problem without exploring possible solutions I proposed: ZRL Categories: Watts the Solution.

A discussion I had at Zwift Community Live made it clear that, despite my best efforts, I hadn’t really nailed the explanation of watts.

Watts. So hard to understand?

On Zwift and in real‑world cycling, watts describe how much power a rider is putting out. That power is the primary driver of how fast they travel from point A to point B on most flat or rolling race routes. When you compare riders at the same absolute watts, Zwift’s physics make their finish times much more similar than you’d expect if you only looked at watts per kilogram (w/kg).

If a 50kg rider and a 90kg rider both ride at 150w, their times on a typical flat or rolling Zwift course will be very close, with only small differences coming from how the game handles weight in its speed calculations. Once aerodynamics and other factors are held constant, changing weight alone does not dramatically change time at a fixed power.

Watts per kilogram (w/kg), on the other hand, mixes power with body weight and is better thought of as a rough indicator of a rider’s fitness relative to their size, not a direct predictor of speed on flat or rolling terrain. A higher w/kg generally helps you go uphill faster, but on the majority of Zwift courses, raw watts tend to dominate.

IRL women’s pro peloton example

In the women’s pro peloton, a tall, more muscular time trialist like Marlen Reusser (around 70kg, 180cm) can naturally sustain higher absolute watts than a smaller climber like Niamh Fisher‑Black (around 55kg, 160cm), simply because she has more muscle mass available to produce force. That extra muscle lets her push a bigger gear at the same cadence, which is exactly what you need in mostly flat individual time trials where aerodynamics and raw power dominate.

On a flat TT course, the rider who can hold the highest steady watts with a good aero position will usually be faster, and a larger TT specialist like Reusser is built for that scenario.

Hypothetical Example

If Marlen Reusser averages 350w in an individual time trial at a body mass of 70kg, her w/kg is:
$350 \div 70 = 5.0\ \text{W/kg}$

For Niamh Fisher-Black at 55kg to match that same 350w average, her w/kg would need to be:
$350 \div 55 \approx 6.36\ \text{W/kg}$

So in this example:

• Marlen: 350w at 70kg → 5.0 W/kg.
• Niamh: 350w at 55 kg → ≈6.4 W/kg.

That means the smaller rider would need to ride at well over 1w/kg higher than the larger rider just to produce the same absolute power, which is a huge physiological gap – even at the pro level.

“Say Watt?” – the Zwift conversation

My first realization that some people were missing the point became clear at Zwift Community Live – and, somewhat surprisingly, it was in a conversation with a Zwift employee. We’d had quite a bit of interaction virtually over the years, but this was the first time meeting in real life. After dinner, he approached me and introduced himself. Within minutes, he shifted the conversation to how he had run tests to prove what I had written in ZRL Categories: Watts the Solution was incorrect – and that in reality, the lighter rider would win the race. To be transparent, he doesn’t work in a technical role at Zwift—he simply has access to running simulations.

He went on to explain his test procedure:

• 5 riders weighing: 50kg, 60kg, 70kg, 80kg, 90kg
• each rider individually raced Tempus Fugit in Watopia (flat!)
• each rider raced at 150w (constant across riders)

His result: the lighter rider won.

If I ran the same test on Best Bike Split can you guess what the results would show?
Exactly the same.

Weight	Watts	Results
50kg	150w	00:32:16
60kg	150w	00:32:33
70kg	150w	00:32:51
80kg	150w	00:33:10
90kg	150w	00:33:31

So was he right? Did I get it all wrong?

Or do his results (and Best Bike Split’s) actually confirm my point: raw watts are a more equitable option for determining race categories in ZRL than the current w/kg model.

The ground rules: variables

In science, a variable is any factor that can change and be measured in an experiment. Scientists carefully decide which variables to change, which to measure, and which to keep the same so they can draw clear conclusions. The independent variable is what the researcher deliberately changes, the dependent variable is what is measured as a result, and controlled variables are all the other factors kept constant to make sure only the independent variable affects the outcome.

In our case, we’re applying these ideas to compare cycling race performance across riders of different weights. In the experiment the Zwift employee conducted, power is held constant in watts (150w) while rider weight changes. In the experiment we’re about to conduct, power is held constant in watts per kilogram (3w/kg) while, again, rider weight changes. The course and other conditions are kept the same so that differences in performance can be confidently linked to the rider’s weight and how power is expressed.

In both experiments:

• Independent variable: rider weight
• Dependent variable: race time
• Controlled variables: course, equipment, environment, and either fixed watts or fixed w/kg

Same riders, new control variable

Let’s see what happens if these riders were racing in ZRL. Since ZRL categorization is based on w/kg, we need to set a fixed w/kg instead of fixed watts as in our earlier example. For the sake of simplicity, we’ll use the 50kg rider from the previous test: at 150w they were riding at 3w/kg. That 3w/kg will replace the fixed 150w in the earlier example.

Weight	w/kg	Results
50kg	3 w/kg	00:32:16
60kg	3 w/kg	00:30:28
70kg	3 w/kg	00:29:01
80kg	3 w/kg	00:27:54
90kg	3 w/kg	00:27:05*

While I’m a paid subscriber of Best Bike Split, their free version maxes out at 2.9w/kg* for the 90kg rider. Because I want to be sure anyone can easily replicate this data on their own, I went with that data.

The results are clear: when categorization is based on w/kg, the more you weigh, the more likely you are to win the race on the majority of Zwift routes. It’s pure physics: higher body mass at the same w/kg means higher absolute watts, and on flat and rolling terrain, absolute watts drive speed.

The weight “No Fly Zone”

The weight “no‑fly zone” is a perfect example of how socially sensitive a variable can be, even though it’s just a number. In science, weight is treated as a neutral, measurable factor that can be controlled or changed to study its effect on performance, health, or behavior. But in everyday life, when the subject of weight comes up, people often become very defensive because it is tied to appearance, self‑worth, and judgment, not just to data. This contrast shows that while weight is a simple variable in an experiment, it carries a lot of emotional baggage in our culture.

For the Zwift employee, the question his experiment answered was focused on weight: “Who wins at the same watts – the light rider or the heavy rider?” My focus in writing ZRL Categories: Watts the Solution was different: I was looking at race categorization, and how to group riders so that races are fair, competitive, and fun.

My whole purpose in that article was to present one alternative to the current category system. There are many more possible approaches I haven’t touched on yet.

W/kg vs watts for race categorization

Now, let’s get to the heart of it: What’s a better tool for race categorization on Zwift – w/kg or watts?

I’m a huge fan of pro cycling. I watch most of the races – both women’s and men’s pelotons – across road, cyclocross, and track, from the classics to the Grand Tours. In the past few years, though, I’ve enjoyed the women’s races far more than the men’s. Watching men’s cycling can feel like watching a movie where you already know exactly how it ends – and how fun is that? In women’s racing, there are still clear favorites, but on any given day you know there’s a real chance of an unexpected victory. That element of surprise is what makes the race so exciting.

That’s not a digression. My point is that what makes racing on Zwift fun is racing in a category where, on any given day, you could be the one who delivers that unexpected victory.

What the numbers actually say

Based on the ZRL w/kg category system, our 3w/kg test produced finish times ranging from 00:27:05 to 00:32:16. That’s a 5 minute and 11 second gap between riders in the same “category.”

Weight	w/kg	Results
50kg	3 w/kg	00:32:16
60kg	3 w/kg	00:30:28
70kg	3 w/kg	00:29:01
80kg	3 w/kg	00:27:54
90kg	3 w/kg	00:27:05*

In contrast, under the watts‑based system I proposed, finish times at 150w ranged from 00:32:16 to 00:33:31. That’s a 1 minute and 15 second gap – still a difference, but far tighter.

Weight	Watts	Results
50kg	150w	00:32:16
60kg	150w	00:32:33
70kg	150w	00:32:51
80kg	150w	00:33:10
90kg	150w	00:33:31

Which group is better matched? Which system is more conducive to that unexpected victory that keeps people coming back to race?

Conclusion: fairer categories, better racing

At its core, this isn’t a debate about whose physiology is “better” or whether weight should matter; it’s about how we choose to group riders so that Zwift racing is fair, competitive, and genuinely fun.

On flat and rolling routes where absolute power dominates, w/kg‑based categories quietly tilt the field toward heavier riders by rewarding the higher raw watts that come with higher body mass at the same w/kg.

A watts‑based system, while not perfect, pulls riders’ finishing times much closer together and gives more people in a given category a realistic chance to contest the win.

If the goal of ZRL and Zwift racing is to create hard, honest races where unexpected victories are always on the table, then it’s time to rethink how we use watts and w/kg – and to recognize that sometimes the simplest number, raw watts, might be the fairest place to start.

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