When 2 Worlds Collide: How the NFL Is Learning from Analytics

Anyone who knows me knows my extreme passion for football—especially when it relates to the NFL and my beloved Chicago Bears (BEAR DOWN!). So when I saw a recent article about how the Bears’ new GM Ryan Poles was taking a fresh approach to building the draft board using data, it felt like two worlds colliding!

As the Director of Account Management at Watershed, I have the pleasure of working with some of the most forward-thinking companies and individuals within the L&D space. My day-to-day life is providing world-class client experiences and building genuine relationships with organizations that put data at the center of their learning.

So when the opportunity arose to intersect these passions, I got EXCITED! This blog post explores how two very different worlds are leveraging the power of data analytics to make informed decisions.

A new GM, a new approach to the draft for the Bears

The Bears and I go way back. As a third-generation Chicago Bears season ticket holder living in Nashville, Tennessee (location of the 2019 NFL Draft), I was extremely lucky and excited to represent my team in the inner circle of the draft event while also announcing a 7th round selection. Unfortunately, my selection did not remain on the team long and has since moved around the NFL.

Fast forward to 2022, and the Chicago Bears Organization has changed a lot—most notably, Ryan Poles has been taking a forensic look at the scouting department processes.

With the recent conclusion of the 2022 NFL Draft and the upcoming 2022 NFL season quickly approaching, I was intrigued by the Bears' changes. You may be saying to yourself: Craig, you are crazy. How does NFL player data relate to anything useful within L&D data, tools, and processes?

The Answer: Using data to make data-based decisions relates to any areas in life, work, L&D, or in this case drafting the next NFL hopeful.

Using survey tools to analyze player performance

In the world of learning, observation checklists are often used to record and assess physical activities, such as observing hand hygiene practices in medical institutions. You can centrally collect this data into your Learning Record Store and view the results in your dashboard.

You also can view this data at an aggregated level to look at broader trends or drill down to assess individual performance. Tools such as xapiapps let you record learner interactions, often via set “checklist” criteria, while recording handwritten notes simultaneously.

The Chicago Bears’ scouting department recently equipped their scouting department with survey tools that work on a similar basis. This approach allows anonymized data to be centrally aggregated and independently assessed.

By enabling real-time anonymous data (in the same way that L&D uses xAPI), the Bears' scouting department could remove the challenges and pitfalls of "groupthink," with everyone involved providing real-time feedback without knowing the response of their peers.

“The idea was that by polling the scouts and delving deeper into why everyone was, or wasn’t, on the same page, the whole team could be sure they had their draft board constructed properly,” said Ryan Poles. “Then we also did that going horizontal, so not only in positional stacks, but you did that across the board as well. That’s where it gets tricky. Do you take this position and this position if all things are even?”.

“Gut feel” rules, but data can validate your intuition

Okay, so when you look at what the data is telling the Bears, you may not expect to see any surprises. A scout probably knows who looks best on the training field, so you may ask, "What difference does it make?".

But think about most sports commentary. Data is used to tell the game's story more than you may realize. For example, "The Bears had 75% offensive possession in the first half. Justin Fields had 86% pass accuracy."

So when the data is observed around top players, there may be few surprises, but it helps validate opinions. But looking at data extremes, such as the top- and bottom-ranked players, has incredible value.

In learning, we call this "outlier data." Organizations use it to assess many different things. For example, it can set benchmarks for "good" or "bad" individual performance—helping you set goals and get a realistic feel for the levels you should strive for.

But it also can tell you more about your learning content than you may think. For instance:

  • Why did everyone engage with this resource/video/etc. more than our other content?
  • If everyone passes an assessment question - is it too easy?
  • We've even seen everyone fail a particular assessment question, only for the team (whom we shall not name!) to investigate and realize the topic wasn't covered in the course!
This image shows outlier content Caterpillar identified content from their video library.

Back to the Bears, and it turns out the big impact when making player assessments was when the prospects were close to being evenly matched. It led to more in-depth discussions and additional tape review and helped shape not only individual position rankings but also to step back and view the strengths of the overall team shape.

Again, in L&D, we see data used this way—aggregated views by organizational hierarchy (region, department, team) give leaders insights on general trends. Individual data supports the learner and line manager to progress in their personal journeys.

New season, new insights. Tune back for the second half.

So far, so exciting! But as we all know, getting the data is just the start of any analytics journey. So I'll be keeping an eye on how the Bears perform. I'm also excited to learn more about the insights they uncover as the season progresses.

Keep an eye out for my next blog post, where I'll explore the tech the NFL uses for analytics and take a deeper dive into the stories the data is telling. Be sure to sign up for our Insights blog at the top left of this page to have the latest posts delivered straight to your inbox.


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