MY take on the session: I was grateful that there was at least some interest in thinking about how to teach sports analytics. The field is extremely new in academia, and currently has no real “core:” It’s a multidisciplinary field; sports, statistics, operations research, business, and computer science are all involved.
This made me excited to learn that our course at the HS level is indeed cutting-edge. In many ways, the pioneers at the HS level have the opportunity to influence the way these courses are taught.
One of the biggest impediments to moving the field forward is the fact that the most interesting (visual tracking) data is owned by teams and businesses. This means that any research from such places can’t be replicated or verified currently. An incentive for more open access is not yet available. More sharing between universities, analytics businesses and sports teams is needed.
I’m also hopeful that Jeremy Abramson from USC because a potential contact: I’d love to share resources, ideas, and experiences in designing our courses.
Here’s a rough account of party of the conversation:
Moderator : Mike Magazine, University of Cincinnati
MM: I’ve believed in doing this session for a long time. We need it, and thus something we should teach. I’ve taught courses on sports analytics and bracketology. What motivated you, and why is it important?
JA: #1: I love sports, and nobody else was doing this. Mine is an undergrad class to get them interested early in the field.
JO: I was washed as a larger initiative to engage our students. a 1 hour class, no prerequisites. Ultimate goal: to turn the students on to the discipline.
NR: Lots of demand from students, and it’s such a great field to teach modeling.
EK: Like Nils, I am an operations researcher, and kids don’t love financial analysis or marketing or policy: How can we get more people in modeling? Sports was a perfect hook. 3 years now, 50 MBA students.
MM: What do you want to achieve? What are the takeaways for students who won’t go into sports analytics?
EK: Many work for sports leagues. Dependent on the technical training of the students. Techniques in the research track are not accessible with less training. But the focus is on the questions: to teach them something about the game they wouldn’t understand otherwise. Example: individual metrics are common, but sometimes poorly used by management: The course is a nice laboratory to think about the value of this.
JA: First, that it’s real, not just “geeking out.” Real impact on the field and the organization’s bottom line. A technical background certainly helps a lot. The value of a technical background is very helpful in the sports industry.
JO: Basic familiar examples: Belicheck’s going for it on 4th down. How do you separate the BS from accurate claims / accurate statements? Condiitonal probability: basic statistical concepts. The sports vehicle allows them to think about hard problems. Sports helps kids bang their heads against such a hard problem more.
MM: Yes, Belicheck was blasted for his 4th down choices, but the decision was right. Plus the right decisions don’t mean the best outcomes every time. To distinguish that is a valuable takeaway.
NR: We have a very international group. Students learn how important it is to ask the right question, collect data, and communicate your results to stakeholders and audience. Also, how important it is to base conclusions not on outcomes. So many people try to explain the causalities of luck. It provides a framework to disentangle luck from signal.
EK: Students do projects in the class: they can run with concepts and applications in places I don’t understand. One of my favorites: a group evaluated players in cricket using the techniques. They found that the salaries and compared it to “run valued added” state. They found that the ratio of pay/runs created that all the Indian National were highly over-valued. The non-nationals were highly under-valued.