Continuing the discussion about organizational issues and challenges, a fundamental issue is understanding and balancing the relative strengths and weaknesses of human decision makers and mathematical models. This is an important discussion because before diving into specific questions related to predicting player performance it’s worthwhile to first think about how modeling and statistics should fit into an overall structure for decision making. The short answer is that analytics should serve as a complement to human insight.
The “value” of analytics in sports has been the topic of debate. A high profile example of this occurred between Charles Barkley and Daryl Morey. Barkley has gone on record questioning the value of analytics.
“Analytics don’t work at all. It’s just some crap that people who were really smart made up to try to get in the game because they had no talent. Because they had no talent to be able to play, so smart guys wanted to fit in, so they made up a term called analytics. Analytics don’t work.”
The quote reflects an extreme perspective and it is legitimate to question whether Charles Barkley has the background to assess the value of analytics (or maybe he does, who knows?). But, I do think that Barkley’s opinion does have significant merit.
In much of the popular press surrounding books like Moneyball or The Extra 2% analytics often seem like a magic bullet. The reality is that statistical models are better viewed as decision support aids. Note that I am talking about the press rather than then books.
The fundamental issue is that models and statistics are incomplete. They don’t tell the whole story. A lot of analytics revolves around summarizing performance into statistics and then predicting how performance will evolve. Defining a player based on a single number is efficient but it can only capture a slice of the person’s strengths and weaknesses. Predicting how human performance will evolve over time is a tenuous proposition.
What statistics and models are good at is quantifying objective relationships in the data. For example, if we were interested in building a model of how quarterback performance translates from college to professional football we could estimate the mathematical relationship between touchdown passes at the college level and touchdown passes at the pro level. A regression model would give us the numerical patterns in the data but such a model would likely have little predictive power since many other factors come in to play.
The question is whether the insights generated from analytics or the incremental forecasting power actually translate into something meaningful. They can. But the effects may be subtle and they may play out over years. And remember we are not even considering the financial side of things. If the best predictive models improve player evaluations by a couple of percent maybe it translates to your catcher having a 5% higher on base percentage or your quarterback having a passer rating that is 1 or 2 points higher. These things matter. But are they dwarfed by being able to throw 10 or 20 million more into signing a key player?
If the key to winning a championship is having a couple of superstars. Then maybe analytics don’t matter much. What matters is being able to manage the salary cap and attract the talent. But maybe the goal is to make the playoffs in a resource or salary cap constrained environment. Then maybe spending efficiently and generating a couple of extra is the objective. In this case analytics can be a difference maker.