Like half my social-media feed, I woke up to the awful news of the suicide of Anthony Bourdain, whose work I have loved going all the way back to the 1999 New Yorker piece that became Kitchen Confidential, his first book. The chef-turned-writer-turned-television-journalist had a remarkable gift for illuminating any corner of the world he wandered via A Cook’s Tour, No Reservations, The Layover, and Parts Unknown, bringing a rare and genuine empathy, compassion and gusto along with him. His career-changing discovery of his writing voice was among the many that inspired me as I embarked upon my own change from graphic design to writing about baseball.

Read the rest of this entry »

A few days ago, I looked at the possibility of major league hitters no longer showing any hitting improvement, on average, once they debut in the majors. I believe both the banning of PEDs and teams being able to evaluate MLB ready talent are the keys to this change.

Read the rest of this entry »

I wish I could remember the date. One of my favorite pastimes is looking at the box scores of games I attended that were meaningful for some reason. I was there when Johan Santana struck out 17. I saw Carlos Gomez score from second base to win Game 163 of the 2009 Twins season. But, for the life of me, I can’t remember the date of this game. It was at Target Field — I know that. I was with my wife and two family friends, Abbey and Andrew. We were in the upper deck overlooking left field. Right next to me, a man — a Twins fan, I discerned from his hat — was watching with a companion from England.

From what I could overhear, this companion had never seen a baseball game before, and the other man was trying to explain the basic goings on of the on-field action. He was teaching her how baseball was played, ostensibly. And he was doing a fine job, I remember. He would slowly and assuredly explain how the runners moved, the idea of balls and strikes, tagging up, foul balls, etc. Basically, everything a newcomer to the game would need to know. I don’t even remember who the Twins were playing — the Royals? This is bothering me. But sometime later in the game, just as the English spectator was starting to recite what happened back to her friend in a way that signified that she was beginning to understand, it happened. Just when the traveled fan must have felt pretty good about his lesson, he was shouldered with the unenviable task of explaining just what the hell a balk was. That poor so-and-so.

Read the rest of this entry »

Before the season, I put up a three-part series (1, 2, and 3) that explained how linearly-weighted stats like wOBA, while useful for comparing players to each other, don’t necessarily reflect each player’s true contribution to their team’s run scoring.  You see, the weights used to calculate wOBA are based on league averages.  So, for a team with league average breakdowns in walk rate, singles rate, home run rate, etc., wOBA (and its offspring, wRC+) ought to work very well in figuring out how valuable a player is (or would be) to an offense.  However, when it comes to particularly bad or good offenses, or to those with unusual breakdowns, wOBA will lose some of its efficacy.

Why?  There are synergistic effects in offenses to consider.  First of all, if a team gets on base a lot, there will be more team plate appearances to go around, which of course gives its batters more chances to contribute.  Second of all, if the team gets on base a lot, a batter’s hits are generally worth more, because they’ll tend to drive in more runs.  And, of course, once the batter gets on base in such a team, it will be likelier that there will be a hit (or series of hits) to drive him in.  The reverse of all three points is true in a team that rarely gets on base.

But it goes even beyond that.  Let’s say Team A gets on base 40% of the time, and Team B gets on only 20%, but their balances of the ways they get on base are equal (e.g. each hits 7x as many singles as they do HRs) .  A home run is going to be worth something like 14% more to Team A, due to more runners being on base.  However, to Team B, a home run is worth over ten times as much as a walk, whereas to Team A, it’s worth only about 5 times as much.  That’s because Team A has a much better chance of sustaining a rally that will eventually drive in that walked batter.  Team B will be much more reliant on home runs for scoring runs.

Read the rest of this entry »

I got my start at FanGraphs by writing Community Research articles. As you may have noticed, community authors have been very busy this season, cranking out a lot of interesting articles. One that caught my eye the other day was triple_r’s piece on the importance of strikeouts for hitters. The piece correctly pointed out, as other studies have, that there’s basically no correlation between a hitter’s strikeout rate and his overall offensive production. Strikeouts don’t matter; case closed, right? Well, not exactly.

Let me present a hypothetical situation. Say there’s a group of players who go to an “anti-aging” clinic in Florida and pick up some anabolic steroids. Let’s say these hypothetical players are named Bryan Raun, Ralex Odriguez, Tiguel Mejada, Phonny Jeralta, Celson Nruz, and Barry Bon… nevermind. Yet, after using the steroids, it appears that the group of them, on average, has not improved. The steroids didn’t improve their performance, right? But, wait — let’s also say that while visiting Florida, some of them contracted syphilis, which spread to their brains, causing delusions and severely impacting their judgment, strike-zone and otherwise. The players whose brains aren’t syphilis-addled have actually improved quite a bit, but their gains are completely offset by the losses suffered by those whose central nervous systems are raging with syphilis. So, the fact that the steroids actually do improve performance has been completely obscured by another factor that is somewhat — but not necessarily — associated with the steroids.

Read the rest of this entry »

In last episode’s thrilling cliffhanger, I left you with a formula that I brashly proclaimed “does a great job of explaining the trends” in strikeout rates for meetings between specific groups of batters and pitchers.  Coming up with a formula to explain what was going on wasn’t pure nerdiness — making formulas to predict these results is the point of this research project.  You see, the goal of my FanGraphs masters is to come up with a system by which we can look at a batter and a pitcher, and tell you, our loyal followers, some educated guesses of the chances of pretty much every conceivable outcome that could result from these two facing off against each other.  Getting a sense of the expected strikeout rate is merely the first step in what will likely be a long process of continuous improvement.

The idea of this matchup system is to not only give you estimates that are more free from the whims of randomness than “Batter A is 8-for-20 with 5 Ks and 1 HR in his career against Pitcher B,” but also to provide some evidence-based projections for matchups that have never even happened.  How do we propose this can be done?  By looking at the overall trends and seeing how players fit within them.  Can it really be done?  It definitely looks that way to me.  Today’s installment will be about attempting to convince you of that.

Read the rest of this entry »

“Riddle me this,” wrote editor Dave Cameron to me some time ago, “what happens when an unstoppable force meets an immovable object?”  OK, that’s not exactly how it went down.  What he actually did was to present me with the challenge of research, with the goal being to develop a model that would forecast the expected odds of an outcome of each match-up between a specific batter and a specific pitcher. Rather than talking about how players have done in small samples, can we use our understanding of player skillsets to develop an expected outcome matrix for each at-bat?

For example, such a tool might tell you that Adam Dunn has a 40% chance of striking out against Stephen Strasburg, a 10% chance of drawing a walk, a 5% chance of hitting a ground ball, etc… Forget I said those particular numbers — I completely made them up in my head just now.  You may be thinking “well, why should I care about that?  Rather than just being inundated with match-up data that is little more than randomness, such a tool might give you some idea of how much of a gain in expected strikeout rate a team would get by switching relief pitchers with a man on third base and less than two out. Or what the probability of getting a ground ball is in a double play situation, which might influence the decision of whether or not to bunt. Knowing the odds of potential outcomes could be quite beneficial in understanding the risks and rewards of various in-game decisions.

This project has been — and will continue to be — a major undertaking, as you can imagine.  This isn’t the kind of thing that can just be thrown together, but I really think the results could be great. Today, I’ll be sharing with you the findings of my research into perhaps the most important aspect of these matchups — K%, or strikeouts per plate appearance.  This will introduce the sort of process that will be involved in figuring out all of the other elements of the matchup tool. Read the rest of this entry »

“Stabilization” plate appearance levels of different statistics have been popular around these parts in recent years, thanks to the great work of “Pizza Cutter,” a.k.a. Russell Carleton.  Each stat is given a PA cutoff, each of which is supposed to be a guideline for the minimum number PAs a player needs before you can start to take their results in that stat seriously.  Today I’ll be looking at the issue of stabilization from a few different angles.  At the heart of the issue are mathy concepts like separating out a player’s “true skill level” from variation due to randomness.  I’ll do my best to keep the math as easily digestible as I can.

Read the rest of this entry »

I’m always looking for new angles to unlock the mysteries of BABIP, so I was intrigued by Jeff Sullivan’s exploration of pull rates against pitchers.  So I grabbed the data from baseball-reference.com, and set to work subjecting it to my usual rigmarole of correlations and multiple regressions.  You know how they say if your only tool is a hammer, everything looks like a nail to you?  Well, plug your ears — there’s about to be a lot of wild, uncontrolled pounding going on in here…

I’ll cut right to the chase — did I find anything interesting relating to pitchers’ overall effectiveness when it comes to their Pull%, Middle%, and Opposite%, as I’m calling them?  Well, I found one decent connection that will seem obvious and stupid after you think about it, and a slight but kind of interesting connection.  I’ll provide you with some correlation tables that have left few stones unturned.  But, mainly, the research might help to set some things straight about how important this stuff actually is for pitchers.

Read the rest of this entry »

Some of you may recall that before being promoted from a FanGraphs Community Research writer to an actual FanGraphs writer, my primary focus was on the relationship between batted ball types (infield fly balls, in particular) and BABIP for pitchers.  At the time, I’d been leaving park factors out of the equation in a [vain] attempt to keep things simple, but now I want to give them a bit of attention.

Read the rest of this entry »