2013 Situational Hitting

Digging around in the recesses of Baseball-Reference yields intriguing information, and by clicking Leagues>2013 MLB>PH/HR/Situ Hitting tab, you'll see data that is truly eye-opening. I'll concentrate on individual data and will look at only those players with at least 100 PA through July 11th.

I won't discuss in great detail every aspect of this table but will explain what the columns are, allowing you to investigate it in greater depth at your leisure. The first column of interest is Ptn%, the percentage of plate appearances in which the hitter bats opposite of the pitcher--obviously, left-handed hitters will have a greater advantage than right-handed (by the way, all column headings have thorough explanations if you place your mouse over the column heading). The next two columns break down hits by type, infield or bunt, and the next two sections measure measure pinch hitting effectiveness and home runs. I'll mention that so far this year there have been two inside-the-park home runs by Jason Kipnis and Angel Pagan.

The next columns measure sacrifice bunting, and for my purposes, I'll leave out the six NL pitchers in the top 20 (Bronson Arroyo, Mike Minor, Lance Lynn, Barry Zito, Juan Nicasio and Homer Bailey). This chart shows the effectiveness of non-pitcher bunters:

In 13 sacrifice attempts, Elvis Andrus has been successful 9 times, around 69% of the time. The MLB average for all players is around 72%--I've measured this in the past (read this post if you wish) and will probably do it again to see if bunting effectiveness has decreased over time. I'll admit based on prior research that number seems low by a healthy margin. I guess I know what I'm doing this weekend.

In addition, who bunts anymore? That's right, the NL, except for Andrus...and Alcides Escobar...and Marwin Gonzalez...and Brendan Ryan...and Munenori Kawasaki...and Manny Machado (I'll admit this surprises me)...and J.B. Shuck...and Ichiro Suzuki. Keep in mind these are sacrifice situations only--I've stated over and over that bunting for a hit is excellent strategy, since teams are batting .393 when bunting for a hit so far this year. In an era where runs are decreasing, the sacrifice has jumped from the NL and used in the AL--so far this year, there have been 712 sacrifice attempts in the NL and 359 in the AL. What I really need to research is whether sacrifice bunts actually work--in other words, does the sacrifice lead to a run, and was it absolutely necessary? Let me give two scenarios (I do the same with stolen bases):

1. A runner on 1st is sacrificed over to 2nd and subsequently driven in for a run, and no more runs are scored in the inning. The sacrifice was ESSENTIAL, although an argument can be made that the runner could have been advanced in another manner, but I'll leave that aside for now.

2. Same situation, runner on 1st sacrificed over but the next hitter hits a home run--that hitter would have been driven in anyway--totally retrospective analysis admittedly, but that's how strategy is evaluated. Was the out worth it--in this case no, because the runner would have scored anyway. In addition, I also argue that if subsequent runs are scored in an inning that a sacrifice wasn't necessary.

It'll be fun as I dig into this more, because we are in the midst of a tactical change from station-to-station baseball to more aggressiveness on the base paths in all regards, be it base stealing, sacrifices or advancing an extra base--the more difficult runs are to score, the more chances teams will take on the base paths.

The next set of data is fascinating and often misinterpreted and deals with grounding into double plays (GIDP). Common baseball metrics often just show the number of GIDP, leaving out the very important factor of HOW OFTEN hitters batted with runners on base. This chart shows the "leaders" in GIDP:

Matt Holliday leads the majors with 22 GIDP, putting him on a pace for roughly 38 or so. This could be a problem in that the Major League record for a season is 36 by Jim Rice in 1984. Holliday is a good candidate to hit into double plays, not being fleet of foot himself and batting behind Carlos Beltran, who's not as fast as he used to be. Holliday's ground ball to fly ball ratio is higher than his career norms and also higher than Major League averages. All of this has really hurt the Cardinals this year.

Holliday is clearly having a rough year in this respect in that of these players, he's also the leader in percent of opportunities in which he actually did GIDP. Right behind him is Albert  Pujols, who is noticeably running slower this season, yet GIDP a fully 30% less than Holliday. This is a concept I'll return to in a moment, but when looking at GIDP, it's just as important to see how often the opportunity arose. Look at those players grouped at 13--you have a range from Jose Altuve at 25% of his opportunities to Dustin Pedroia at 13%, quite a spread.

I'll skip the next section, Productive Outs, but not because it isn't interesting. I've been fascinated with the next category, Baserunners, for as long as I've known about this data, which has been around 3 years or so. This data is as important, no, even MORE SO than RBI, because this tells the complete story--as much as RBI are considered to be some kind of measure of hitting effectiveness, it's as much a function of opportunity as anything else (see my post on this if interested). This table doesn't tell all, but it sure helps give greater illumination to how truly effective hitters are with runners on base. I'll break this into three parts, beginning with the first section, the percentage of base runners that are driven in:

I'll use teammates Prince Fielder and Miguel Cabrera as examples--Fielder leads the majors in base runners on base when he's at bat with 314, with Cabrera third with 290, 24 fewer opportunities than Fielder. This table lists the base runners driven in. In this case, Fielder drove in 18% of the base runners, which is a very good number (the MLB average is around 14%), but Cabrera drove in 65, 10 MORE than Fielder in 24 fewer opportunities. That 22% of base runners scored is a VERY GOOD number.

It's important to know base and out situations when viewing this information, and it isn't shown, but the next sets of columns give some indications. The first shows what happens when there's a runner on 3rd with less than two outs:

Be certain that you understand what you're seeing here--this is ANY situation where there's a runner on 3rd, be it 1-3, -23, bases full or just a solitary runner on 3rd. This measures how often that runner on 3rd scored and does not take into account the other base runners. We begin to run into the issue of small sample size, but think about this situation--it can be argued that there is no excuse for the batter not driving in a runner on 3rd, be it with a hit, sacrifice fly or productive out. How often these hitters are successful is at the right, and I think many would be surprised to learn that hitters are successful 50% of the time. Of course, this overlooks the very real phenomenon of PITCHING in tight situations, since the pitcher is throwing cutters inside on the wrists while the batter is looking for a pitch to drive. Talk about your perfect balance of pitcher vs. hitter--about half the time the hitter succeeds, about half he doesn't. It's very difficult to look at these numbers without pooling them over multiple seasons to get enough opportunities to make some inferences. This is primarily why I compile play-by-play data so that I can look for these instances and see what differences there are given the outs and precisely how the bases are occupied. Even so, this data is very illustrative and shows that Miguel Cabrera, while really good, isn't infallible--he's only slightly above the MLB average in this admittedly small sample size.

This last table is less useful to me, and shows how often a runner on 2nd is advanced when there are no outs:

Even greater sample size issues, but take it for what it's worth, a measure of moving base runners along in whatever manner possible. The MLB average is 55%, and in both this and the previous table, the NUMBER of opportunities are just as important--when you aggregate the data by teams, the percentage takes on less value than how many opportunities they had.









I've compiled data going back to 1945 for all of these categories and will dedicate separate posts to each of them over the next week. I've already seen some amazing long-term trends that I've suspected but now can empirically see, and there's some interesting career performances over this time span. I also compiled some base running data that is equally illuminating. None of this data is intended to be taken by itself but instead to help flesh out individual success and see how it compares both to other players in a given year and over time to see how the game changes. I hope you enjoy it as much I enjoy researching it.