Sabermetrics & Decision-Making

William Lamar Beane III is a former American professional baseball player, mostly known for his baseball front-office career as a General Manager for The Athletics and application of statistical analysis known as sabermetrics, a different way of evaluating players. 

In 2003, author Michael Lewis released his book Moneyball: The Art of Winning an Unfair Game, which explores the way Beane, together with Paul DePodesta, used sabermetrics to obtain undervalued players, and by that assemble a winning team at a fraction of the cost of hyped players. 

The main idea of applying sabermetrics was to find value in players where other teams thought differently, due to them not using sabermetrics. The purpose was simply to analyze each player in more depth, and find a scientific way of comparing the skills between players in order to extract value from the metrics. 

It’s all about evaluating skills and putting a price on them. Thirty years ago, stockbrokers used to buy stock strictly by feel. Let’s put it this way: Anyone in the game with a 401(k) has a choice. They can choose a fund manager who manages their retirement by gut instinct, or one who chooses by research and analysis. I know which way I’d choose.”  

William Lamar Beane III

Under Beane’s leadership, the Athletics became one of the most cost-effective teams in baseball, and in 2002 they became the first team to win 20 consecutive games. The introduction of sabermetrics have transformed the way many teams and players think about how they are valuing the game, and many have followed Beane’s strategy and are now using similar methods for estimating baseball value. It can absolutely be argued that Beane’s introduction of sabermetrics to baseball has changed the way we look at baseball, what is valued, and how to value the game as a whole. 

I have previously written some pieces on the applications of uncertainty and how leveraging uncertainty can generate value for anyone that chooses to act on it. I would now like to argue that uncertainty management is making its way into the process of high value decision-making in the same way as sabermetrics conquered the baseball field. 

The common denominator between sabermetrics and uncertainty is that they can both reveal underlying value that otherwise would go lost. And when I say value, I mean the value of getting a deeper understanding of the decisions you are about to make and use that knowledge to add value to the purpose of the decision. The classic expression ”looks can be deceiving” fits perfectly here, as a full understanding of something rarely stops at just looking at the surface. As in the case with baseball, where the mere perception of baseball value has shifted from gut feeling to a more statistical approach, seeing value in metric performances on the pitch. 

Uncertainty on the other hand will not provide you with an absolute number, but rather you will receive an understanding of the likelihood of meeting a specific goal. In order to understand the full value of uncertainty, you need to understand the spread of uncertainty. A common mistake when trying to leverage uncertainty is to assign a single probability to a specific goal. The problem here is that you are labeling a specific value with a probability. But if you for example label a goal with a probability of occurring with 30%, what do the remaining 70% represent? Does it mean that you will be above or below your goal, and how likely is it to go either way? When the stakes are high, knowing these outcomes may very well be a make-or-brake-it situation.

Thus, in order to really generate value from uncertainty, you need to map out the whole spectra of possibilities. Probability consists of 100%, so let’s use every single % to our advantage and gain a full understanding of the decision we are about to make.

For you to fully understand the merits of analyzing the full spectra of possible outcomes instead of just the probability of success, let’s look at the graphs below*:

The graph illustrates the expected time of completion for three separate projects. For simplicity, all three projects have a 40% probability of meeting their expected completion time. Hence, if time is something that is of high value when choosing which project to pursue, Project 3 (P3) is the clear choice.  However, what happens when we evaluate the remaining probability?

As we can see in the second graph above, Project 3 still holds the probability for earliest project completion, but it also demonstrates a probability for completion after Project 2, or even the majority of the probability of occurrence for Project 1 for that matter. As such, while at first sight appearing to be the obvious choice, Project 3 does in fact hold the widest spread of possible outcomes, while for example for Project 2 there is less spread to the potential outcomes. 

By examining the remaining probabilities and mapping out the whole spectra of your 100%, we can now see the full picture and actually understand the meaning of the probabilities that we don’t assign to our goal, providing us with more information to base our decision on. By and large, comparing between set probabilities to desired targets/goals is not the best of practices, as it leaves great room for error or misinterpretations of the information you are valuing your decision on. Instead, you need to understand the full spectra in order to evaluate and compare decision on equal terms!

In the beginning I mentioned sabermetrics as an example because it shifted the perception of player value from gut feeling to metric measurements. In order to make our decision-making more effective going forward, more industries and leadership should learn from the obvious merit of incorporating a new way of thinking regarding their decision-making practices, as shown by Beane.

What would be your 20 consecutive games won?

*Original graph design by Mikael Palmblad.

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