Data Driven Factors that Lead to Recruiting Success
Author: Marshall King
David Kramer
The Effects of the Pandemic on Athletics
As Notre Dame students who worked in athletics during COVID-19, David Kramer and Jacob Messineo were worried.
Jacob Messineo
In fall 2021, Kramer had worked unpaid for several months as a baseball team manager. Messineo was helping with recruiting analytics for the football team whose successful 2020 season had been impacted by the pandemic.
They knew that at Notre Dame and other major universities, football funds nearly all the other sports. They’d seen other schools cut some of those programs.
“Going into our senior year, I was worried we were going to cut some programs as well,” said Kramer.
How the Project Began
As part of the Sports Analytics Club, Messineo and others watched film of football games, logged statistics and did other analytics. From talking with graduate assistant coaches and seeing recruits visiting campus, he had a sense for Notre Dame’s recruiting model. “With ND, we usually have a pretty solid recruiting class and that’s something the coaching staff emphasizes,” he said.
The two were curious about the factors that lead to recruiting success and began gathering data for a capstone project in their Sports Analytics class as undergraduates in the Mendoza School of Business. For their model, they compiled 970 seasons from 2013 to 2020 from various Division I schools and studied 150 variables from each season, including every committed recruit for each year and a recruit rating score with a two-year lag, in alignment with when most top high school football recruits commit to their respective colleges.
Even with that amount of data, they faced some of the same challenges that are inherent in those who try to create successful football teams. “The biggest surprise for us was trying to figure out the best way to capture the relationship between recruiting classes and success on the field,” said Kramer.
They learned that data on high school recruits, particularly those who weren’t highly touted, lacked quality. There just isn’t a lot of data being collected on small Midwestern high schools, they said.
The Data Driven Discoveries
Kramer and Messineo incorporated a “SportsCenter Metric” to look at whether media coverage and social media hype contributed in any way to recruiting success, but the relationship proved far more complex than touchdowns outside of the red zone, special teams touchdowns, and flashy defensive plays. “It’s easy to say that winning teams do well on social media and by and large get better recruits, but on the other end of the spectrum why are lesser known schools struggling?” said Kramer.
Their project preceded student athletes being allowed to cash in on Name Image Likeness contracts and included a lot of data that preceded the current more lenient transfer portal. What they learned is that the two largest factors in garnering top recruits was participation in a Power 5 conference (roughly 70 schools), and wins and losses. They saw step changes from eight to nine wins in a season and from nine to 10, but the biggest advantages for a school came with 11 to 12 wins in a season and a New Year’s bowl game. “The ‘dynasty’ factor in college football proved to be huge, and non-dynasty teams proved to struggle,” Kramer said.
Where are Kramer and Messenio Now?
Since graduating in 2022, Kramer is working with a startup in Chicago focused on Artificial Intelligence and Messineo is doing data science consulting in New York City. They remain huge sports fans and continue to wonder about the impact of their tweets or videos of the Notre Dame football team going viral. “We like to be informed fans. But we, like other fans, wonder how our actions affect what’s happening on the field,” said Kramer.
They see value in using data to answer questions related to sports.
They also know that sports can’t fully be quantified. That’s part of what they love about it.
Learn more about how Mendoza’s Undergraduate and Masters Business Analytics programs can prepare you for success.
