Can We See Double Plays Coming?

In a playoff atmosphere, everything gets magnified a bit. Situations are always more tense, every little thing seems to carry more weight. It seems that, if you ask enough people, almost every play could be labeled as a “game-changer” by someone. That’s because there are only so many plays to go around. Outs are finite. In the playoffs, they seem astronomically finite. So when a home run is hit, or a great defensive play is made, it seems to matter more. An approving eyebrow raise in the regular season turns into a full-on shout in the postseason. A minimal eye roll on a random Wednesday in June morphs into an audible “UGH” sound in October.

Nothing can produce an audible reaction faster than a double play. The double play seems to be the only situation where both sides react in an equal and opposite way. Home runs are one-sided. Run-scoring singles are one-sided. Double plays raise sounds out of both sides. Someone is quite happy and someone is quite upset. This is because the twin killing is seen as such a momentum changer — it’s two outs for the price of one. The idea of momentum is something some in the statistical community scoff at a bit. They scoff because they scoff at things they can’t measure, at least accurately. But I’ll come right out and say it, I believe in momentum. I believe that when I’m in a good mood, I do better work. I pay more attention, I get things done quicker, and the overall product is of higher quality. I don’t know how to measure this on a baseball field, but I believe it exists there, too. To think that everything on a baseball field is static and has no affect on anything else in the entire game seems shortsighted. Double plays take wind out of sails, so to speak. They bother people.

The current Tigers roster can do a lot of things well. They can certainly pitch. They can hit. They have at least one person who can field. Something they don’t do very well is run the bases. From 2012 to 2013 — the current Prince Fielder era — the Tigers have ranked dead last in BsR, and it’s not even close. They are a team of basecloggers, if you will. They make up for their lumbering ways by ranking second overall in wRC+ over that span. So the Tigers are slow, but they are good at hitting. And now they are one game away from getting eliminated. Why? Their pitching has been quite good, but the bats aren’t there. A hobbled Miguel Cabrera certainly doesn’t help. Some of their hitting woes may be explained by the fact that they have hit into 12 double plays so far in the playoffs. That’s a lot. Too many, if I may say so. They hit three in their last game, and it cost them dearly, as they all ranked within the top four worst outcomes of the game.


The only team with more double plays this postseason is the recently-departed Dodgers, who also rank poorly when it comes to base running. So the question is: does a reputation of poor base running and slow players lead to a high rate of double plays in the postseason? Could we have seen this coming?

To start, I looked at the teams with the highest GDP rates in the postseason (again, this is in the expansion era).

Year Team GDPRate(Po)
2005 SDN 5.83%
2001 HOU 4.85%
2000 CHA 4.42%
2007 CHN 4.39%
2007 BOS 4.06%
2007 NYA 3.92%
2002 NYA 3.80%
1995 CIN 3.64%
1998 HOU 3.55%
2013 LAN 3.54%
2006 OAK 3.47%
2013 DET 3.31%
1997 BAL 3.24%
2000 OAK 3.13%
2008 LAN 3.12%
2005 ATL 3.09%
1995 COL 3.09%
1997 NYA 3.08%
2007 ARI 3.01%
1999 TEX 2.97%
2010 TEX 2.84%
2007 PHI 2.83%
2010 MIN 2.78%
2011 DET 3.20%

Here we see both the Tigers and the Dodgers as having some of the highest rates in recent history. If the teams in this list all suffer from similar base running ineptitude, we should be able to see a correlation between a high GDP rate in the postseason and a poor BsR in the respective regular season.

Year Team GDPRate(Po) BsR(Rg)
2005 SDN 5.83% -2.6
2001 HOU 4.85% -8.8
2000 CHA 4.42% 5.3
2007 CHN 4.39% -7.3
2007 BOS 4.06% -3.8
2007 NYA 3.92% 10
2002 NYA 3.80% 0.3
1995 CIN 3.64% 7
1998 HOU 3.55% 8.7
2013 LAN 3.54% -11.5
2006 OAK 3.47% -11.3
2013 DET 3.31% -19.4
1997 BAL 3.24% 1.9
2000 OAK 3.13% 1.9
2008 LAN 3.12% 3.2
2005 ATL 3.09% 2.1
1995 COL 3.09% -2.1
1997 NYA 3.08% -4.3
2007 ARI 3.01% 9.2
1999 TEX 2.97% -2.1
2010 TEX 2.84% 8.2
2007 PHI 2.83% 16
2010 MIN 2.78% -4.5
2011 DET 3.20% -10.5

That doesn’t really flush out, it seems. Here’s a graph with the same data.


OK, so if base running value, or lack thereof, doesn’t correlate, perhaps we can predict playoff rates by just simply looking at the regular season rates. I haven’t seen any studies done of GDP rate normalization, but perhaps the sample doesn’t need to be that big for things to match up.


To be totally honest, I thought this one would be a lot more close. But it seems that the playoffs just aren’t long enough for these rates to level out.


This was my last attempt to find something of substance. I took the rate at which the team surpassed the league rate to see if that matched up at all. It’s the closest of the bunch, but still way far from being a reliable comparison.

There is no one quick litmus test for double play rates in the postseason, it seems. It’s most certainly a mixture of player speed and ground ball rates and a good amount of randomness thrown in. There is still work to be done in this, more metrics to test, but I’m not sure what could be gleaned from it. Would managers construct their lineups to avoid plays that happen a little over 3% of the time, at most? Probably not. The Tigers may hit into more double plays tonight, and it may be because they’re slow, or it could be for all sorts of reasons. At this point, it doesn’t really matter, I guess. If there is such a thing as momentum, any more double plays — with elimination looming — will certainly help kill it for the Tigers.

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David G. Temple is the Managing Editor of TechGraphs and a contributor to FanGraphs, NotGraphs and The Hardball Times. He hosts the award-eligible podcast Stealing Home. Dayn Perry once called him a "Bible Made of Lasers." Follow him on Twitter @davidgtemple.

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GIDP’s are really a factor of luck, right? In a small sample size such as the postseason, there is probably a lot of luck involved. I would imagine that singles+walks and GB% would be the two biggest factors that can be easily measured (speed is harder to measure). There must be a way to figure out what an expected GIDP rate would be, but I see no point in applying that to the playoffs because a) not much will change for the regular season and b) it’s a really small sample size.


Unless you link it to pitcher, I can’t see the worth of even looking. It might be interesting to look at FIP and ERA differences and GDP rates.

Jonah Pemstein

I don’t know how much of a link there would be, because groundball/single pitchers have good FIPs in addition to good ERAs (if they get a lot of DPs)