How mathematicians are trying to make NFL schedules fairer

ESPN had a nice article about applied mathematicians at the University of Buffalo who are working with the NFL to create fairer schedules. A few quotes:

“This is a field I’ve worked in for 46 years, including 43 as a professor,” Karwan said by phone last week. “I’ve worked on very difficult problems that take more than 12 hours on the supercomputer to solve. And this is by far the hardest any of us have ever seen.”

And:

In developing the schedule, NFL assigns “penalty points” to outcomes such as three-game road trips, games between teams with disparate rest, and road trips following a Monday night road game. In their final proof of concept in 2017 before receiving the grant, Karwan and Steever took the 2016 schedule and lowered the penalty total by 20 percent…

The first step is based in both math and reality. Before creating the schedule, the NFL identifies a small number of games — usually between 40 and 50 — to lock in. The league refers to this as “seeding.” It helps accommodate expectations from television partners for key games in certain time slots, as well as about 200 annual requests from owners who prefer their stadiums not be used in a given week because of concerts, baseball games, marathons and other potential complications…

At that point, the NFL asks its computers to run schedule simulations until it finds one that has an acceptable penalty total. Usually that means juggling the 40 to 50 pre-seeded games. Karwan and Steever believe the key to improving the schedule is to better choose those pre-seeded games, allowing the computer to see stronger schedules that would otherwise be blocked by the initial choices through a process known as integer programming.

Not surprisingly, this research was publicized by the MIT Sloan Sports Analytics Conference, an annual conference dedicated to the integration (insert rim shot) of mathematics and sports.

Veteran teacher shows how achievement gaps in STEM classes can be eliminated

This press release from UC Santa Cruz definitely gave me food for thought about new things to try in my own classes. A few short snippets:

[Professor Tracy Larrabee] uses a three-pronged approach to support underrepresented students in her class.

“The first is that we have had a very diverse teaching staff,” she said. “We have one professor, four TAs and four MSI tutors, and during this time it just happened that of those people, half were female, we always had at least one African American, one Latinx, and one non-gender conforming tutor so that everyone could feel a connection to someone on the teaching staff.”

“Another technique I use is to emphasize failure as the appropriate path to learning,” she said. “Engineering is hard; it’s good to fail the first time you attempt a problem. People who fail at a problem the first time tend to retain things better than those who luck into the right answer.”

Her final tactic is to explicitly discuss stereotype threat. This is the risk that someone (i.e., from an underrepresented minority) might take routine negative experiences as confirmation that they are fundamentally unsuited for something like higher education.

“One of my African American MSI tutors—who are extremely high achieving students selected to provide supplemental tutoring to others—told me it was like having a light bulb go off for him,” Larrabee said. ”Until I discussed the issue in class, he felt like he didn’t belong in this major, but after we talked about stereotypes, he realized it wasn’t that he was unsuited for the material. It was hard for everyone!”

My Favorite One-Liners: Part 117

I absolutely love this joke. The integral looks diabolical but can be computed mentally.

For what it’s worth, while it was able to produce an answer to as many decimal places as needed, even Wolfram Alpha was unable to exactly compute this integral. Feel free to click the link if you’d like the (highly suggestive) answer.

Source: https://www.facebook.com/CTYJohnsHopkins/photos/a.323810509981/10151131549924982/?type=3&theater