Matt Klaassen


Matt Klaassen lives in the Greater Toronto Area, where he spends most of his time reading and writing obituaries. For some reason, he is allowed to write for FanGraphs several times a week. If you just can't get enough of Matt, you can also follow him on Twitter.

Recent Posts

MLB: Tampa Bay Rays at Detroit Tigers

As I have noted in past installments of these ratings, this season has been one of the more static in terms of which players are on top and bottom of these ratings, at least the way I remember it. It may be that my memory is getting faulty, or maybe just selective. I dunno. But in this (likely) final rating of the season, there is still some interesting stuff to discuss beyond simply the ratings, leaders, and trailers.

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MLB: Boston Red Sox at Houston Astros

People love to talk about a young player having a “breakout season.” As a non-technical term, the notion of a breakout season is fine: a young player having his first big year. Sometimes, though, it is taken to mean something specific. A breakout in this case is taken to mean a season in which a player establishes a new level of performance such that previous performances need to be ignored (or at least weighted less heavily than usual).

The (contradictory) twin of the breakout season is the “outlier,” a season that is so much worse (or so much better in the case of bad player) that is should pretty much be ignored. Both of these concepts might have some merit, but I generally find them to be problematic and overused. Without getting into the details, they both usually end up being used as excuses for statistical cherry picking.

People also rarely talk about the opposite of a breakout; call it a “breakdown.” This is especially true right after a player has been celebrated for a breakout. As an example, take a look at 2012 Astros tiny second base sensation Jose Altuve. Specifically, check out his 2013 numbers. Wha’ happened?

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Cincinnati Reds v Pittsburgh Pirates

Unless you are a fan of the recently-struggling Cardinals (Baseball’s Best!), it is hard to imagine not enjoying the rise of the Pittsburgh Pirates. Sure, compared to the Pythagorean record or projected true talent, Pittsburgh seems to have been pretty lucky so far. First of all, almost any team that wins their division has some luck. Secondly, are we really going to complain about the Pirates having good luck for once?

In any case, I did not really see them as contenders of any sort this season, and I would guess I was not alone. The Pirates themselves thought they had a chance, given the moves they made to shore up their team over the last year, particularly the starting rotation.

The Pirates primarily have relied neither on big-time draft picks nor big-name free agent signing. Rather, they have put together a staff good enough to contend through shrewd trades and buy-low opportunities. Unless a team is going to get exceptionally lucky with their draft picks or has a huge payroll (not an option for Pittsburgh), this is what they have to do, thus, the Pirates’ rotation is worth examining.

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It is always worth reminding ourselves: sabermetric research has a long way to go. The (rather obvious) truth is that the objective accounting of everything in baseball is what Edmund Husserl called, in a different context, an “infinite task.” In other words, there is a goal that guides sabermetric research – one that can never be reached. Thus, the actual sabermetric project is never ending.

Fielding and even pitching value are just two of the many areas in which sabermetric research is well-known to have a long way to go. One area in which sabermetrics has done well over the years is run estimation. Run estimators do what their name suggests: estimate the number of runs that would result from a given combination of events. While we already know how many runs actually scored, there are situations for which one would want a different sort of estimate. For example, one might want to know how many runs an player would produce if one added his events to an “average” team in order to establish his neutral offensive value. Or one might want to know how many runs a projected lineup would produce. Things of that nature.

In any case, there are a number of types of run estimators. The most commonly used type is linear weights run estimators. There are various good reasons for this. While the principles behind the various linear weights offensive run estimators are basically the same, one difference can be the exact weights given or the way those weights are derived. It can be helpful to use the resources of a dynamic run estimator in order to get more accurate linear weights values appropriate to a particular run environment while still being able to apply them to individual players.

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mclouth bunt

This may be hard to believe, but I am sometimes wrong. Not just occasionally, but pretty often. It comes with the territory of being, uh, whatever I am. Really, though, anyone who reads baseball blogs shouldn’t be surprised by this. One need not descend to the banality of “can’t predict ball” sloganeering to understand the situation. The name of this column was inspired by Bill James‘ phrase “measuring the fog,” which he coined in the context of discussing the task of sabermetrics. That is, the job of sabermetrics (“the search for objective knowledge about baseball”) is not just to figure out what we know about baseball, but to delineate what we do not know: “the fog.”

So I am accustomed to being wrong, even if admitting it is not necessarily fun. I pay a lot of attention to projections (and make no apologies for doing so) while keeping in mind that they are more reliable than personal intuition as a whole, on an individual level they will still miss a fair bit. Those who produce respected projections understand this, and those who use them should, too. If a player does much better or worse than he is projected to do, whether by a respected projection or by my own projection or analysis, it is not big deal. Win some, lose some. Sometimes, I go out of my way to praise or mock a player, and he does pretty much the opposite. Sometimes it is predictable, sometimes it is not. But some of them just sting.

If I wrote a post for everything I was wrong about prior to the season, I would never post anything else. For today, I will just take three cases in which things went very differently than I thought they would, three players that I (somewhat publicly, in two cases) singled out prior to the season as likely to be very good or bad, and have gone the other way, making me look, well, just like any other fallible human being. Or an idiot, depending on hour perspective.

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Like last month’s rankings, this month’s updates of my crude catcher defense rankings once again are reassuring that these ratings are measuring something real, even if that is somewhat boring. But if we look more closely at the components, some interesting contrasts stand out. We will troll around the middle a bit, too, to see what interesting stuff might come up. Read on, catcher defense fans.

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Toronto Blue Jays v Kansas City Royals

It was a bizarre Thursday night at Kauffman Stadium. First, the Kansas City-Cleveland game was delayed for more than two-and-a-half hours by rain. Then, near midnight, during the sixth inning, the lights went out due to a preset computer program.

At some point in there, the Royals were up by a run when Cleveland’s Jason Kipnis hit a long fly ball to left. Alex Gordon went back trying to make the catch at the wall, but then awkwardly fell back, seemingly hitting the back of his head on the wall as the ball rolled forward. Kipnis came around for a three-run, inside-the-park home run to put his team up 3-1. The Royals actually battled back and ended up winning 6-5 on Eric Hosmer‘s solo home run, which was fun for Royals fans.

Gordon’s injury, however, was not. He did not get carted off, but from the looks of things, he hit his head pretty hard. The initial diagnosis was that Gordon suffered a mild concussion (to the extent that any concussion can be called “mild”) and a bruised hip. It is not clear how long Gordon will be out at this point, but it was definitely scary at the time. 3

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