Starling Marte and Dealing with Personal Bias

I was wrong about Pittsburgh Pirates outfielder Starling Marte. Last December, I wrote up Marte, and determined he “may be in for a precipitous fall next season.” Boy, was I wrong on that. Marte had the finest season of his young career, solidifying himself as one of the better young outfielders in the game. Looking over that article now, I realize that while the research seemed to fit, I let my personal biases color my evaluation of Marte. So, consider this an apology if you avoided Marte on my recommendation. Let’s try to avoid this mistake again.

It wasn’t hard to look at Marte’s 2013 numbers and see regression. Approach was a big part of that, and one of the places where my bias came into play. Marte isn’t a strong walker, and he strikes out in nearly 25 percent of his plate appearances. Let’s address the former issue first. Anyone who reads RotoGraphs is going to value on-base percentage, and, thus, might be more inclined to like players who walk more often. Luck plays a role any time a hitter puts the bat on the ball, but a guy who walks a fair amount can at least provide value in that way even if his luck is terrible. That’s how I look at it, at least. So, when I saw Marte’s low walk rate, I assumed much of his value was tied into his batting average (more on this later).

The strikeouts are easier to explain. Guys who strike out as much as Marte typically don’t post strong averages. Taking a look at the players who posted similar strikeout rates confirms this, for the most part. There are exceptions, like Paul Goldschmidt and Matt Kemp. I would also be remiss to note that Mike Trout struck out more often than that trio, and still posted a strong average. The exceptions appear to be some of the best players in the game, and there was, and still is, reason to doubt Marte is in that category.

The other thing all three of the elite players have in common with Marte is the ability to post uncommonly high BABIPs. For the elite players, we just assume they can keep it up because they are awesome. They also have larger samples, to be fair, so assuming this is easier. It was more difficult with Marte, who had a .333 BABIP in under 200 plate appearances his first season, and then saw that shoot up to .363 during his sophomore season. At the same time, Marte showed the type of skills needed to post a high BABIP. He put the ball on the ground quite a bit, and was able to use his speed to beat out balls other players wouldn’t. I noted this in my original article, but, again didn’t want to believe it. A .363 BABIP should bring skepticism, and I didn’t think he would do that again. Marte responded by posting a .373 BABIP in 2014. Again, that’s not a number I would expect to repeat, but I do think something in the .340 to .350 range might be the norm for Marte moving forward. Posting a high BABIP consistently is going to help Marte post strong averages, even when he’s striking out so much.

Now, what’s the point of all this? My failures with Marte led my to figure out a particular bias in my methods. Marte doesn’t have the type of approach I like to look for in a player. I would prefer a player who walks more and strikes out less, and, really, who wouldn’t. But that doesn’t mean I can write off or discount guys like Marte because they don’t fit my prototype. Instead of trying to prove Marte was a candidate for regression, I should have been looking at it saying “why was he so successful despite doing this?” I believe that would have led to a better, and more informed, conclusion to my initial article.

I think this is fairly common among anyone who researches fantasy players each season. We all have a perceived idea of what we like in a player, and that can shade our opinion. Instead of ignoring, or writing off those who don’t necessarily fit our profile, we should push ourselves to ask “why” instead of running with our biases. In Marte’s case, there was plenty of reason to think regression was in the works, but as I looked into more, I found that wasn’t necessarily the case. And yet, I ignored that because I just assumed luck had to be at play.

Most of you who read this site have a pretty good understanding of what to look for in a successful ballplayer, and I suspect most of you can identifying flukes by simply looking at one or two stats. This approach probably works most of the time, but, every once in a while, it leads to a really talented player slipping through the cracks. Don’t allow your biases to become your Starling Marte.