The Football Coach and the Data

The great Roman emperor Marcus Aurelius once said: “The secret to all victory lies in the organization of the non-obvious.”

New Las Vegas Raiders Defensive Coordinator Patrick Graham recently echoed these sentiments in urging NFL teams hiring new coaches to “dig a little deeper” in their quests to find their dream candidate.

“There’s always some hidden meaning behind the numbers,” Graham said. “What are people saying about him? The players, that’s one thing. But what are opposing offensive coaches saying about him?”

Too often when interviewing candidates for various roles, we as leaders rely on antiquated methods — looking at resumes and speaking to references who often fail to tell the entire story. What Graham is urging all of us to do instead is to essentially think and behave more like data scientists and to sharpen our critical-thinking skills to see what others may not.

Not all win totals are created equal, not all productivity is the same. There is nuance to numbers — and we as leaders have an obligation to read between the lines and try to put information in context.

Here are three ways we can better think like data scientists without actually having any mathematical backgrounds:

1. Be aware of our assumptions. Our assumptions and prejudices greatly influence what we see and what we don’t. We assume a win-loss record indicates our skills, but do we actually know the circumstances around these numbers?

2. Apply context. This is a critical component of evaluating candidates. Graham acknowledges that 10 wins in two years is simply not good enough — and doesn’t run or hide from the ultimate measuring stick. But he wants those who evaluate his talents to understand the context around the results. To find the non-obvious, we need to recognize that there may be external factors that help explain the numbers. Not all data should be approached the same way each time.

3. Focus on generating insights that produce good ideas. This is critical. We must understand that information, like a resume, is not always insight. Observations are not insight. A phone call with a reference is not insight. A real insight is a discovery that causes us to re-examine what we think we already know, which is a major theme of Graham’s plea.

We live in the information age. We have mounds and mounds of data at our disposal, but if we only focus on what lies immediately in front of us, we rarely find what really matters.

We have a duty to behave more like data scientists and to refrain from beginning with the end in mind.