- Sports Analytics in Organizations
This fall the plan is to do something a little different with the blog. Rather than data driven analyses of sports marketing topics, I want to spend some time talking about using analytics to support player and in-game decision making. The “Moneyball” side of the sports analytics space.
The focus will mainly be at the level of the organization rather than at the level of specific research questions. In other words, we will talk about providing effective analytics support within an organization, rather than presenting a series of analyses. My hope is that this evolves to being something of a web based course on using analytics to drive decisions in sports.
I’ve spent a lot of time over the past few decades working on analytics projects (across multiple industries) and I’ve developed opinions about what firms do right and where mistakes are made. Over the last few years, I’ve thought a lot about how analytics can be used by sports organizations. Specifically, about how lessons from other industries can be applied, and instances where sports are just different.
The history of statistical analysis in sports goes way back, but obviously exploded with the publication of Moneyball. A huge number of sports fans would love to be a General Manager but few people have the athletic ability to gain entry as a former player. Using statistics to find ways to win is (maybe?) a more accessible route.
But this route is not without its complications. Using stats and data to win games is an intriguing and challenging intellectual task. What data should be collected? How should the data be analyzed? How should the analysis be included in the decision making structure? These are all challenging questions that go beyond what a fan with some data can accomplish.
What I’m going to do in this series is talk about how to approach analytics from both a conceptual level and an operational level. Conceptually, I will cover how humans make decisions in organizations. At the operational level, we will discuss what types of analyses should be pursued.
What I won’t do in this series is talk about specific models. At least not very much. I may drop in a couple of analyses. This limitation is done with purpose. It’s my feeling that the sports analytics space is overly littered with too many isolated projects and analyses. The goal here is to provide a structure for building an analytics function and some general guidance on how to approach several broad classes of analyses.
What will this series include? Some of the content will be based on whatever becomes top of mind or based on the response I get from readers. But some things will definitely appear. There will be material about how analytics can best compliment a human decision maker. I will also talk about how lessons from other industries can be helpful in the sports context. There are more similarities than differences between sports and “standard” businesses. But there are some important differences.
We will also talk about models and statistical analysis. But this will be done in broad terms. What I mean is that we will discuss classes of analysis rather than specific studies. For example, we will discuss player selection analyses but the emphasis will be on how to approach the problem rather than the creation of a particular forecasting model. There are a variety of ways to analyze players. We can use simple models like linear regression or more complex models that yield probabilities. We can also forgo the stats and use raw data to look for player comparisons. We will discuss the implementation challenges and benefits of each approach.
This series is a work in progress. I have a number of entries planned but I’m very open to questions. Shoot me an email and I’ll be happy to respond in future entries or privately (time permitting).
Next: Understanding the Organization