The Conversation Most Players Recognize
If you play pickleball, you’ve probably heard some version of this conversation before.
Someone says, “We need more courts.”
Someone else responds, “No, we just need to rotate faster,” or “Shorten the games.”
It usually turns into a debate about process versus capacity.
I recently got into pickleball myself and play at a very busy set of courts with a strong and thriving community known as the Encanto Pickleballers. It’s a group made up of players of all ages and abilities, working closely with the City of Phoenix to make pickleball more accessible and to help meet the growing demand for courts. It didn’t take long to start hearing these same conversations happening in real time.
Recently, I had a chance to explore that question more formally. After a conversation with the group that was considering adding a fourth court and discussing ideas like shorter games, I built a small discrete event simulation to evaluate what would actually happen under different scenarios.
The goal wasn’t to create a perfect model. It was to create something realistic enough to support a decision.
Starting From a Full System
The model doesn’t start with empty courts or light traffic. It starts at what most players would recognize as a typical busy period, like a Saturday morning. All courts are already in use, all paddle spots are filled, and everyone else is waiting to get on.
In other words, the system is already full when the analysis begins.
Modeling Real Game Variability
To represent game length, I used a triangular distribution. For standard scoring (games played to 11 points), having played and watched many games already, I assumed games could be as short as 8 minutes, most commonly around 13 minutes, and occasionally stretch out to about 22 minutes. For shorter 9-point games, I used a slightly tighter range of 7 to 18 minutes, with most games around 11 minutes.
These values reflect typical recreational play across all skill levels. Not tournament conditions, just what you would expect to see at a busy public park.
Results were averaged across 100 simulation replications to account for variability in game length.
What Happens With Three Courts
With three courts under those conditions, the results are not surprising, but they are useful to quantify. Average wait times come out to a little over 20 minutes, with the longest waits pushing into the mid-30 minute range. That lines up well with what players experience. Most waits feel manageable, but every so often, you end up waiting much longer than expected.
The Appeal (and Limitation) of Shorter Games
One option that often comes up is shortening games. When I tested a 9-point format, the model showed a noticeable improvement. Average waits dropped to around 17 minutes, and overall capacity increased by roughly 16 percent.
From a purely operational standpoint, that works.
But there’s a social reality that matters just as much as the math. In this case, that option had already been discussed within the group and was overwhelmingly unpopular. So while shorter games improve the system, they may not be a viable solution.
What Adding a Fourth Court Changes
That leaves the other option: adding capacity.
When I introduced a fourth court into the model, still using standard scoring, the change was much more significant. Average wait times dropped to around 15 to 16 minutes, and the longest waits came down as well. The system didn’t just get slightly better. It felt noticeably different.
When Process Improvements Hit a Limit
What’s happening here is something that shows up in a lot of operational systems. Early on, you can improve performance by tweaking the process. You can adjust rules, change behavior, or tighten up how things are managed. But once demand reaches a certain level, those improvements start to produce smaller and smaller gains.
Eventually, you run into a hard limit. At that point, the issue isn’t how the system is being run. It’s that the system is simply too small for the demand placed on it.
That’s when adding capacity starts to outperform process improvements.
A Simple Way to Quantify the Impact
A simple way to think about the impact is this: a fourth court creates roughly 17 additional player opportunities per hour during peak periods. Over the course of a typical four-hour busy window, that translates to around 65 to 70 additional players getting on the court.
That’s not just a marginal improvement. It’s a meaningful increase in access.
One important assumption in this analysis is that the number of players stays fixed. In reality, that may not hold. If wait times decrease, more players may show up. That’s a common pattern in many systems. Improvements can attract additional demand, which can offset some of the gains.
Even so, under current conditions, adding a fourth court provides a substantial improvement in both average wait times and the overall player experience.
A Familiar Pattern Across Systems
What I find interesting about this isn’t just the pickleball application. It’s how familiar the pattern is.
The same dynamics show up in hospitals, clinics, airports, and call centers. You have variability, shared resources, and queues forming under peak demand. The question is almost always the same: do we fix the process, or do we add capacity?
Simulation helps answer that question in a way that intuition alone usually can’t. It allows you to test scenarios, understand tradeoffs, and make decisions with a clearer picture of how the system actually behaves.
Beyond the Numbers
But there’s another side to this that doesn’t show up in the numbers.
Talking with players and seeing feedback from the Encanto Pickleballers community, it’s clear that this isn’t just about wait times. For many people, pickleball has become an important part of their daily lives, helping them stay active, improve their health, and build meaningful social connections.
Some players have shared stories of losing weight, or finding a much-needed outlet for stress. Others talk about the friendships they’ve built and the sense of community that keeps them coming back.
That context matters.
Because when you think about adding capacity, it’s not just about reducing a queue.
It’s about increasing access to something that is clearly providing real value to people.
Final Thought
Sometimes the answer is better process design, and sometimes, the system just needs to be bigger.
If you’re working through similar questions around wait times, capacity, or system flow, I’m always interested in exploring these types of problems.
This analysis was conducted using ExtendSim Discrete Event Simulation.
If interested in learning more, you can reach me at astrozuggs@gmail.com
