Making the Grade: Data Analytics and the NFL Draft

How are NFL teams using data analytics in the NFL draft process?

By definition, data analytics is the process of analyzing and interpreting large sets of data to extract valuable insights and information.

This involves using statistical and computational methods to identify patterns, trends and correlations within the data, to make informed decisions or predictions. The value of data analytics and perception of data-driven business intelligence is only growing and used in countless industries, including finance, healthcare, marketing, science and sports.

The increased emphasis and importance of data analytics in the sports world can be felt from the collegiate levels to the pros – often influencing everything from play calling to evaluating talent, and personnel are increasingly buying into the idea of data analytics being a key competitive advantage.

And what better day to focus on the impact of data analytics in sports than one of the biggest professional sports days of the year, the NFL Draft.

The NFL Draft is a crucial event for every team as it provides an opportunity draft the next franchise superstar, trade for immediate needs and find the diamond in the rough in the later rounds – and more importantly for some franchises, avoid wasting high draft picks on “busts”.

In this article, we will explore the impact data analytics has on the NFL draft, specifically on evaluating prospects, draft day strategy and trade analysis.

Data Analytics – Evaluating NFL Draft Prospects

NFL teams have several scout teams responsible for finding the next big pro star, and in addition are using data analytics team in the process. The data analytics team use data to evaluate prospects based on performance data from college games, combine metrics, and other factors, which provides a valuable perspective in addition to human scouting teams.

Machine learning algorithms are used to identify patterns and correlations in player data to predict how players will perform at the professional level. Rick Spielman, ex-GM of the Minnesota Vikings, recently gave an interview on the Pro Football Focus (PFF) NFL Podcast where he discussed the importance of his data analytics team.  Spielman’s example referenced data surrounding a left tackle, saying

“His arm measurement was less than 33 inches long and he ran a 5.25 or slower 40. Well, (our) analytics (department) said there have been seven guys with those measurements that came in and were drafted in the first three rounds and none of them ended up being starters or being significant players in the league. That would throw a red flag up for me and for us as we went through the decision process.”

The ability to reference these analytics is one thing, but the storage and maintenance of the relevant data is just as crucial. Spielman noted that his analytics staff maintained a database of all players scouted in the last 15 years and used that data to compare to each prospect for the incoming draft class.

Data Analytics – Developing NFL Draft Strategies

Data analytics also plays a crucial role in draft strategy to balance team needs with the best available talent. This has led to a shift in drafting based on the player’s value rather than solely on the positional need.  

As a result, teams are drafting differently and have become more willing to trade down in the draft to acquire more picks, increase their draft capital, improve the chance later round impact players or stock up for the following year.

For example, in 2018, the Baltimore Ravens’ decision to select Lamar Jackson was based in part by using data analytics. The Ravens used a machine learning algorithm to analyze the performance data of college quarterbacks and identify the specific traits that were most predictive of success in the NFL.

Based on the analysis, the Ravens identified Jackson as a highly promising prospect and ultimately traded up with the Philadelphia Eagles in the first round to select him.

Given that three of the four quarterbacks taken before Lamar were questionnable picks that cost significantly higher draft capital (Baker Mayfield at 1, Sam Darnold at 3 and Josh Rosen at 10) it’s easy to see why teams are buying in on the value of data analytics.

Data Analytics – Assessing NFL Draft Trades

Finally, data analytics are utilized heavily in the NFL draft to evaluate potential trade scenarios.

Teams use data on historical trade patterns, player performance data, or other factors to identify potential trade scenarios that are most likely to improve their trade position.

According to a study by PFF, trading down in the first round of the NFL draft has become a more frequent occurrence in recent years, proving that teams are making more data-driven strategic decisions during the draft.

Just this year, the Chicago Bears traded down in a blockbuster trade that sent the first overall pick to the Carolina Panthers in exchange for star wide receive DJ Moore and multiple draft picks which included two first round picks and a second-round pick.

In this case, analysts seemed to agree this was a move that benefited both parties based on their current rosters and future needs – one has to wonder if both teams relied heavily on data analytics when working out the compensation for each side.

Overall, the rise of data analytics will only continue to grow as an integral part of the NFL draft process.  The use of machine learning algorithms, historical data, and other analytical tools allows teams to identify patterns and correlations in player data, leading to a more data-driven approach to drafting. As data analytics technologies continue to advance, we can expect them to play an even bigger role in shaping the future of the NFL draft and other professional sports.

If you have any questions about how data analytics can help your organization, please contact a member of the ADAPT team at [email protected] or visit www.schneiderdowns.com/adapt for more information.

About Schneider Downs Technology and Data Services

Schneider Downs Technology Advisors are committed to providing value-added technology services and solutions to our clients. We strive to create an ongoing mutually beneficial relationship with our clients by focusing on client satisfaction, delivery of quality service and the continuous education and training of our staff. In today's demanding world market, one of the most challenging tasks facing a company is utilizing and processing information effectively and efficiently.

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