When we last left our discussion of Edge% we were looking at the differences in the rate of called strikes based on the count. Generally speaking, umpires were less likely to call strikes on the Edge in pitcher-friendly counts and more likely to give those calls in hitter-friendly counts.

While we learned a bit from that analysis, it was really just the tip of the iceberg. There are a number of additional ways to cut the data, and that is the focus of this article. Count is just one dimension when we are thinking about what might influence the likelihood of close called strikes. There are a number of additional dimensions we can layer onto count, and that’s precisely what I show in the (admittedly large) table below.

Read the rest of this entry »

Chris Davis, with 37 home runs so far this season, has been generating a lot of buzz lately — both on the field and more recently with some comments he made during the All-Star break. When he was asked about the all-time home run record, Davis said:

“In my opinion, 61 is the record, and I think most fans agree with me on that.”

I have no idea if most fans agree with him, but it probably shouldn’t be  surprising that a guy within spitting distance of a 61 home run season would view that as the mark to beat — rather than 73 home runs, which is essentially out of range. So, just for fun, let’s figure out what Davis’ chances are of reaching Roger Maris.

At Tom Tango’s website, there was a discussion that tried to put a number on Davis’ chances of reaching that mark. Tango performed a “quick back-of-envelope calculation” to do so, but today, I’ll be providing you with an interactive tool that might make it easy for you to perform a more sophisticated calculation for situations like this (and many other types of situations).

Read the rest of this entry »

If this were any other stat, it wouldn’t be worth a post. If this were instead “All-Star Break Home-Run Update,” it’d be a waste of your time, because you could simply just look up the stat on the FanGraphs leaderboards. They’re right up there! But, at the moment, FanGraphs doesn’t house and update any pitch-framing statistics, and while that could change in time, that’s the way things are today, meaning this post could have some substance. Most people can’t look this stuff up on their own, so I’m here to provide for you while crossing something off my weekly quota. Everybody wins.

FanGraphs is pretty selective for the intellectually curious. And, of course, baseball fans, and intellectually curious baseball fans have generally been interested in pitch-framing research. It’s just another thing that players can be good or bad at, so fans want to know where their catchers rank. Right now, we have a little break in regular-season action, so it seemed like a good time to post the latest numbers, through the middle of July. It was either this or a .gif post about Yasiel Puig and no there weren’t any other options. I’ll give a quick explanation, before the data.

Read the rest of this entry »

The last time I wrote about Edge% it was in the context of the Tampa Bay Rays using it to get their pitchers into more favorable counts on 1-1. But now I want to take that topic and drill a little deeper to understand how often edge pitches are taken for called strikes.

Overall, pitches taken on the edge are called strikes 69% of the time. But that aggregate measure hides some pretty substantial differences. Going further on that idea, I wanted to see how the count impacts the likelihood of a pitch on the edge being called a strike.

Here are the results:

Read the rest of this entry »

Well friends, we are now approaching that time of year where a significant drop in a pitcher’s velocity passes the 50% threshold in terms of signaling that they will finish the year down at least one full mph.

The table above breaks out the percent of pitchers who experience at least a 1 mph drop in their four-seam fastball velocity in a month relative to that same month a year ago and who also went on to finish the season down a full 1 mph. It also shows the relative risk — meaning, the increased likelihood that a pitcher will experience a true velocity loss at season’s end when compared to those pitchers that didn’t lose 1 mph in that month.

For example, pitchers that lost velocity in May finished the season down a full 1 mph 47% of the time, compared to just 6% that didn’t lose 1 mph in May — an increased likelihood of 7.8.

Read the rest of this entry »

In his Major League debut for the Mets, 23-year-old Zack Wheeler struck out seven hitters in his six innings of work. Of those seven strikeouts, six came on fastballs — and of those six, four came on whiffs induced by fastballs.

This got me wondering, what pitchers this year have generated the largest percentage of their strikeouts off of their fastball? And how many generated those strike outs on swings and misses on fastballs*?

Read the rest of this entry »

Jeff Sullivan’s recent enjoyable trot through San Diego Padres statistics and history led to a number of commentors thinking about San Diego’s park factors. The Padres changed the outfield dimensions of Petco Park in the off-season, and since park factors are backwards looking and rely on multiple years of data, changing dimensions can throw a bit of a monkey wrench into the calculations. So, it’s possible that our park factors are now somewhat behind the times, and we need to keep this in mind when looking at the park adjusted numbers (such as wRC+, ERA-/FIP-/xFIP-, WAR, etc…) for San Diego players, both hitters and pitchers.

It’s not quite so simple as noting that the changing dimensions have made the old park factors useless, however. Moving in the fences helps home runs, yes. This is undeniable. But it also can decrease triples and doubles, as well as effect the more odd elements of park factors, such as walk-rates, strikeout rates and pop-up rates.

It’s too early in the season to construct terribly useful park factors for the new dimensions, but we can do some harmless back-of-the-napkin mathematics to at least determine if the recent numbers suggest at least the early signs of serious run environment changes.
Read the rest of this entry »

Last year I reintroduced VOL, a custom metric that attempts to measure the relative volatility of a hitter’s day to day performance. It is far from a perfect metric, but at the moment it’s what we have.

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 »