My first love was not baseball, my first love was science. Not the class in school or the act of looking at bacteria under a microscope, but what science really is. At its core, science is the curiosity to discover the way things work, to push the boundaries of knowledge, and to discover the way the world really is. Given the magnitude of those challenges, the gains made by an individual are usually small and the project can never be completed.
In the same way that sabermetrics can not encapsulate the entirety of baseball, science can not encapsulate the entirety of nature. If you're waiting for a perfected WAR to give you an exact and complete measurement of a baseball player, you will be disappointed. If you're waiting for a physicist to give you an exact description of where matter and gravity come from, you will be disappointed. Which is fine, because that's not really the point. The point is the discoveries that come along the way, discoveries that come of asking the big questions and searching for the truth.
Which is all well and good if you're at the cutting edge of scientific research, but that's not me. That's probably not you either. It can be just as important for the rest of us to apply the same philosophies. To ask what you know and how you know it. To doubt and test what you think that you know. To change your mind when presented with new evidence. In short, to understand the difference between belief and truth and to know which triumphs over the other.
Because I like to read, I stumbled across something called the planning fallacy (Wikipedia link) that I found to be both interesting and suggestive. From the Wikipedia:
Another study asked students to estimate when they would complete their personal academic projects. Specifically, the researchers asked for estimated times by which the students thought it was 50%, 75%, and 99% probable their personal projects would be done.
13% of subjects finished their project by the time they had assigned a 50% probability level; 19% finished by the time assigned a 75% probability level; 45% finished by the time of their 99% probability level.
To summarize, people have a systematic bias towards greatly underestimating the amount of time required to complete tasks. There's a good chance that you have experienced this in your own life as I have in mine. Here's a common example that you can probably relate to.
With a specific time deadline, people tend to work backwards through the tasks that they need to accomplish. In college, I often had classes at 8:30 AM, so I'd need to leave my apartment at 8:10 to complete the twenty minute walk to campus. So I'd need to start eating breakfast at 7:50 to have enough time to eat, so if I was up at 7:30 that should be plenty of time to get dressed, get ready, and everything else that I had to do.
And when I left my apartment with my breakfast in hand at 8:20 I would think that I had to be a completely undisciplined moron for this to be happening again.
The problem is almost always oversimplification. The world is a complicated place full of random chaos and your brain works by grouping clusters of that chaos into an easier to understand model. In my model of the morning, there were only a few things going on. Walking to campus, eating breakfast, and getting ready were all treated as constant and as a totality of what needed to be accomplished that morning.
There were no terms in my equation for running out of bagles, a quick conversation with my roommate, not being able to find my pants, or an interesting looking article on Fangraphs that I'd just read the beginning of. My model was incomplete, but nobody wants to block aimless time wasting into their schedule.
The problem is almost always oversimplification. If you want to know how long it will take to get ready in the morning or how long it will take to drive home during your lunch break, don't make a list of tasks and guess, time yourself when you do it. Measure the act itself, not your mind's recreation of a simplified version.
As my language promised you at least a vague tie in to baseball -- this post has very little to do with baseball and not this post has nothing to do with baseball -- I will do my best to follow through. If we accept some of my assertions to be close to the mark, I would stretch the simplified model concept to roster construction.
It's easy to make a mental checklist and say that a World Series caliber team needs as many as possible of the following:
- At least one ace starting pitcher.
- Rotation depth.
- A strong back end of the bullpen.
- Speed on the basepaths.
- Batters who can hit for power and average.
- Athletic and reliable fielders.
- Timely hitting.
- Clubhouse cohesion.
It's decidedly counterintuitive to say that most of that is probably bullshit. The universe is complicated and there's no grocery list or ten step plan to make it do what you want. All of those things sound good and probably are good, but which one is the most important? What mixture is optimal?
If it's knowledge and not belief that you want, then you have to test the world and listen to the answer that it gives. The true best method for optimizing the Yankees' roster probably won't be what you thought originally and it definitely won't be as easy as trading A-Rod or signing a free agent.
Because in April, nobody had a model that had a term for Michael Pineda missing the whole season or the Red Sox having a firesale or going down to the wire with the Orioles.