Tennis, Outliers, and the Power of AI

The US Open is in full swing. While the spotlight is on big serves, long rallies, and tie-breaks, there’s another layer to every match: the data. Every serve speed, rally length, and unforced error gets tracked. Coaches and analysts lean on AI to cut through the noise, spot patterns, and find the moments that really matter.

Not every data point is equal, though. Sometimes, one number jumps off the page. An outlier that changes how you look at the match.

Let’s take a look at an example.

Understanding the Overall Distribution

A probability plot is one way to test whether data follows a normal pattern. In this case, we’re looking at unforced error counts across 30 simulated matches to see how consistently a tennis player performs:

Most matches cluster neatly around the curve, but one result, the 60-error match, clearly sits far from the rest. With a p-value of 0.044, the test suggests the dataset may not perfectly follow a normal distribution, largely because of that extreme performance. In tennis terms, the player is generally steady, but one match went so far off script that it pulled the whole dataset out of balance.

Zooming In on the Outlier

The outlier test makes this point even clearer:

The outlier test in Minitab Solution Center indicates that the match with 60 unforced errors wasn’t only notable, but it was a statistically significant outlier: the kind of result that raw numbers alone can’t fully explain.

For many, though, raw p-values and test statistics aren’t intuitive. That’s where Minitab’s AI-generated overviews add value. Instead of leaving the results buried in statistical jargon, Minitab automatically explains findings in plain language so anyone can grasp the significance.

In this case, the AI overview clearly flagged the 60-error match as a statistically significant outlier and explained the test outcome in direct, accessible terms:

This ensures that analysts, coaches, or business stakeholders don’t miss the importance of the anomaly. Everyone begins with a shared understanding of the result before moving on to investigate the reasons behind it.

See how Minitab uses AI in Minitab Solution Center.

Digging Deeper with a Fishbone Diagram

Once the significance of the outlier is clear, the natural next step is to explore why it occurred. That’s where structured brainstorming tools help.

We began by building a Fishbone (Cause and Effect) Diagram, located in the Brainstorm utility in Minitab Solution Center, to map out potential factors contributing to the high number of errors:

This initial version highlighted common issues: fatigue, late swing timing, poor grip adjustments, racket tension, and environmental factors like wind and heat.

To broaden the analysis, we then used Minitab’s AI Quick Generate feature. With one click, the AI expanded the brainstorm by suggesting additional possibilities — from grip material degradation and shoe traction problems to court surface conditions, performance anxiety, and inadequate nutrition. The result was a richer, more comprehensive diagram:

From Outlier to Insight

The same principle that applies in tennis carries over to business. Outliers in data are rarely just noise; they often point to deeper issues. A spike in defects might signal a supplier problem. A sudden drop in customer satisfaction could reflect a service delay. A dip in production yield might trace back to a single machine on the line.

Success comes from more than just spotting the anomaly. It’s about clearly communicating its significance, investigating the causes, and adjusting strategy with confidence. That’s where Minitab adds value. From rules-based guidance that has supported analysts for decades to today’s AI-powered overviews, brainstorming tools, and forecasting features, Minitab ensures that outlier detection leads to action, not confusion.

Whether on the court or in the boardroom, the lesson is the same: data tells the story, but AI helps you understand what it means and how to act on it.