When general managers gather in New York for the NFL draft this week, they’ll be awash in statistics, scouting reports, interview data, and video clips as they look for a way to digest it all and make the best draft selections for their respective teams.
How should the selection be made? They can use a rule of thumb, like filling the biggest hole in their roster, or picking the best player available. But how is best measured? University of Iowa professor Jeff Ohlmann says several teams are arming their staffs with information based on analytics in efforts to gain any edge that they can.
One of Ohlmann’s research focuses is how a sports team can optimize its draft selections, and this work has led to the development of a smartphone app to help fantasy football and baseball team owners pick their teams. Ohlmann says that a sports draft is a great example of what is called a “sequential decision-making problem with uncertainty,” an area of research that attracts interest of both academics and practitioners. For example, logistics companies need to design routes to supply goods without knowing exactly how much product will be required by the retailer or precisely how long it may take to get there.
In football, the uncertainty lies in not knowing how well a player may perform in the NFL, or even if that player will be available when a team is “on the clock” because some other team may pick him first. Using probability distributions to model the uncertainty, Ohlmann and other researchers have developed models that use mathematical techniques to maximize the expected value of the players which a team drafts. At its heart, the model tries to help general managers overcome the fundamental handicap of not knowing what players will be available to draft in future rounds.
In Ohlmann’s approach, a team projects opposing teams’ selections to forecast which players will be available to the team in future rounds. He says that while there will surely be errors in guessing which players other teams will select, the errors often cancel each other out to create a sufficiently accurate forecast of the draft as a whole. The result, he says, is a model that produces a draft strategy that typically dominates alternative drafting rules-of-thumb.
“Rules of thumb have flaws in them and you can do better if you try to predict what will happen in the future,” he says. “A draft strategy based on analytics isn’t guaranteed to dominate, but more often than not, it will be the best strategy.”
He acknowledges data analysis is not a crystal ball and will not be 100 percent accurate in identifying which players are going to be successful. “But it may help avoid a bad decision in cases when the front office personnel are 'fooled by their eyes’ and let emotions affect their decisions.”
Ohlmann, who teaches in the department of Management Sciences in the Tippie College of Business, has used his sports research to develop and teach a first-year seminar, Sports Analytics, to introduce analytical tools to students using a topic that many students are naturally interested in.
“My goal is to show students how to formulate sports-related questions and then use data and math to try to answer them rather than just qualitatively debating them,” he says.