Although I was disappointed with last year’s Annual, I have been very much looking forward to reading this year’s, as I have enjoyed it tremendously in the past, and have even contributed an article or two, I think. To be fair, I am only interested in the hard-core analytical articles, which comprise a small part of the anthology. The book is split into 5 parts, according to the TOC: The “2013 season,” which consists of reviews/views of each of the six divisions plus one chapter about the post-season. Two, general Commentary. Three, History, four, Analysis, and finally, a glossary of statistical terms, and short bios on the various illustrious authors (including Bill James and Rob Neyer).
As I said, the only chapters which interest me are the ones in the Analysis section, and those are the ones that I am going to review, starting with Jeff Zimmerman’s, “Shifty Business, or the War Against Hitters.” It is mostly about the shifts employed by infielders against presumably extreme pull (and mostly slow) hitters. The chapter is pretty good with lots of interesting data mostly provided by Inside Edge, a company much like BIS and STATS, which provides various data to teams, web sites, and researchers (for a fee). It also raised several questions in my mind, some of which I wish Jeff had answered or at least brought up himself. There were also some things that he wrote which were confusing – at least in my 50+ year-old mind.
He starts out, after a brief intro, with a chart (BTW, if you have the Kindle version, unless you make the font size tiny, some of the charts get cut off) that shows the number, BABIP, and XBH% of plays where a ball was put into play with a shift (and various kinds of shifts), no shift, no doubles defense (OF deep and corners guarding lines), infield in, and corners in (expecting a bunt). This is the first time I have seen any data with a no-doubles defense, infield in, and with the corners up anticipating a bunt. The numbers are interesting. With a no-doubles defense, the BABIP is quite high and the XBH% seems low, but unfortunately Jeff does not give us a baseline for XBH% other than the values for the other situations, shift, no shift, etc., although I guess that pretty much includes all situations. I have not done any calculations, but the BABIP for a no-doubles defense is so high and the reduction in doubles and triples is so small, that it does not look like a great strategy off the top of my head. Obviously it depends on when it is being employed.
The infield-in data is also interesting. As expected, the BABIP is really elevated. Unfortunately, I don’t know if Jeff includes ROE and fielder’s choices (with no outs) in that metric. What is the standard? With the infield in, there are lots of ROE and lots of throws home where no out is recorded (a fielder’s choice). I would like to know if these are included in the BABIP.
For the corners playing up expecting a bunt, the numbers include all BIP, mostly bunts I assume. It would have been nice had he given us the BABIP when the ball is not bunted (and bunted). An important consideration for whether to bunt or not is how much not bunting increases the batter’s results when he swings away.
I would also have liked to see wOBA or some metric like that for all situations – not just BABIP and XBH%. It is possible, in fact likely, that walk and K rates vary in different situations. For example, perhaps walk rates increase when batters are facing a shift because they are not as eager to put the ball in play or the pitchers are trying to “pitch into the shift” and are consequently more wild. Or perhaps batters hit more HR because they are trying to elevate the ball as opposed to hitting a ground ball or line drive. It would also be nice to look at GDP rates with the shift. Some people, including Bill James, have suggested that the DP is harder to turn with the fielders out of position. Without looking at all these things, it is hard to say that the shift “works” or doesn’t work just by looking at BABIP (and even harder to say to what extent it works).
Jeff goes on to list the players against whom the shift is most often employed. He gives us the shift and no shift BABIP and XBH%. Collectively, their BABIP fell 37 points with the shift and it looks like their XBH% fell a lot too (although for some reason, Jeff does not give us that collective number, I don’t think). He writes:
…their BABIP [for these 20 players] collectively fell 37 points…when hitting with the shift on. In other words, the shift worked.
I am not crazy about that conclusion – “the shift worked.” First of all, as I said, we need to know a lot more than BABIP to conclude that “the shift worked.” And even if it did “work” we really want to know by how much in terms of wOBA or run expectancy. Nowhere is there an attempt by Jeff to do that. 37 points seems like a lot, but overall it could be only a small advantage. I’m not saying that it is small – only that without more data and analysis we don’t know.
Also, when and why are these “no-shifts” occurring? Jeff is comparing shift BIP data to no-shift BIP data and he is assuming that everything else is the same. That is probably a poor assumption. Why are these no-shifts occurring? Probably first and foremost because there are runners on base. With runners on base, everything is different. It might also be with a completely different pool of pitchers and fielders. Maybe teams are mostly shifting when they have good fielders? I have no idea. I am just throwing out reasons why it may not be an apples-to-apples comparison when comparing “shift” results to “no-shift” results.
It is also likely that the pool of batters is different with a shift and no shift even though he only looked at the batters who had the most shifts against them. In fact. a better method would have been a “delta” method, whereby he would use a weighted average of the differences between shift and no-shift for each individual player.
He then lists the speed score and GB and line drive pull percentages for the top ten most shifted players. The average Bill James speed score was 3.2 (I assume that is slow, but again, I don’t see where he tells us the average MLB score), GB pull % was 80% and LD pull % was 62%. The average MLB GB and LD pull %, Jeff tells us, is 72% and 50%, respectively. Interestingly several players on that list were at or below the MLB averages in GB pull %. I have no idea why they are so heavily shifted on.
Jeff talks a little bit about some individual players. For example, he mentions Chris Davis:
“Over the first four months of the season, he hit into an average of 29 shifts per month, and was able to maintain a .304 BA and a .359 BABIP. Over the last two months of the season, teams shifted more often against him…41 times per month. Consequently, his BA was .250 and his BABIP was .293.
The shift was killing him. Without a shift employed, Davis hit for a .425 BABIP…over the course of the 2013 season. When the shift was set, his BABIP dropped to .302…
This reminds me a little of the story that Daniel Kahneman, 2002 Nobel Prize Laureate in Economics, tells about teaching military flight instructors that praise works better than punishment. One of the instructors said:
“On many occasions I have praised flight cadets for clean execution of some aerobatic maneuver, and in general when they try it again, they do worse. On the other hand, I have often screamed at cadets for bad execution, and in general they do better the next time.”
Of course the reason for that was “regression towards the mean.” No matter what you say to someone who has done poorer than expected, they will tend to do better next time, and vice versa for someone who has just done better than expected.
If Chris Davis hits .304 the first four months of the season with a BABIP of .359, and his career numbers are around .260 and .330, then no matter what you do against him (wear your underwear backwards, for example), his next two months are likely going to show a reduction in both of these numbers! That does not necessarily imply a cause and effect relationship.
He makes the same mistake with several other players that he discusses. I fact, I have always had the feeling that at least part of the “observed” success for the shift was simply regression towards the mean. Imagine this scenario – I’m not saying that this is exactly what happens or happened, but to some extent I think it may be true. You are a month into the season and for X number of players, say they are all pull hitters, they are just killing you with hits to the pull side. Their collective BA and BABIP is .380 and .415. You decide enough is enough and you decide to shift against them. What do you think is going to happen and what do you think everyone is going to conclude about the effectiveness of the shift, especially when they compare the “shift” to “no-shift” numbers?
Again, I think that the shift gives the defense a substantial advantage. I am just not 100% sure about that and I am definitely not sure about how much of an advantage it is and whether it is correctly employed against every player.
Jeff also shows us the number of times that each team employs the shift. Obviously not every team faces the same pool of batters, but the differences are startling. For example, the Orioles shifted 470 times and the Nationals 41! The question that pops into my mind is, “If the shift is so obviously advantageous (37 points of BABIP) why aren’t all teams using it extensively?” It is not like it is a secret anymore.
Finally, Jeff discusses bunting to beat the shift. That is obviously an interesting topic. Jeff shows that not many batters opt to do that but when they do, they reach base 58% of the time. Unfortunately, out of around 6,000 shifts where the ball was put into play, players only bunted 48 times! That is an amazingly low number. Jeff (likely correctly) hypothesizes that players should be bunting more often (a lot more often?). That is probably true, but I don’t think we can say how often and by whom? Maybe most of the players who did not bunt are terrible bunters and all they would be doing is bunting back to the pitcher or fouling the ball off or missing. And BTW, telling us that a bunt results in reaching base 58% of the time is not quite the whole story. We also need to know how many bunt attempts resulted in a strike. Imagine that if a player attempted to bunt 10 times, fouled it off or missed it 9 times and reached base once. That is probably not a good result even though it looks like he bunted with a 1.000 average!
It is also curious to me that 7 players bunted into a shift almost 4 times each, and reached base 16 times (a .615 BA). They are obviously decent or good bunters. Why are they not bunting every time until the shift is gone against them? They are smart enough to occasionally bunt into a shift, but not smart enough to always do it? Something doesn’t seem right.
Anyway, despite my many criticisms, it was an interesting chapter and well-done by Jeff. I am looking forward to reading the rest of the articles in the Analysis section and if I have time, I will review one or more of them.