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St. Louis Cardinals: Exactly How Much Has Bad Baserunning Cost Them?

The Cardinals’ have struggled to run the bases for the better part of two years now. So far, the only tangible effect has been Chris Maloney’s “reassignment”. Nevertheless, Matheny has continued to preach aggresiveness, to this dismay of those actually watching the games.

I intend to show the effect the Cardinals’ outs on the bases have had on their ability to score runs. A run-expectancy matrix can help. A run-expectancy matrix shows you the number of runs, on average, a team can expect to score from a given on-base state to the end of the inning. For example, with the bases loaded and no outs, a team can expect to score about 2.2 runs by the end of the inning. On the other hand, with nobody on and two outs, the offensive team’s run expectancy is about 0.098 runs. Here’s the basic run-expectancy matrix:

To estimate the number of runs the Cardinals have left on the bases, I charted every out on the bases thus far in 2017 (53). In each of those 53 instances, I charted the actual outcome and the outcome had the mistake not been made. Then, I subtracted the run-expectancy of the actual outcome from the mistake-free one.

In total, the Cardinal’s actual run expectancy is about 22 runs lower than it would be without base running mistakes. If you add those 22 runs to the Pythagorean record formula, the Cardinals should be 38-37, or 1.5 games behind the Brewers.

Not all outs on the bases are created equal, though.

All those formulas are useful, but they make a few key assumptions. First, they assume average speed on the bases. Second, they assume an average hitter at the plate. The creators of run-expectancy arrived at the above numbers by studying the results of MLB games over a five year period. That’s thousands of innings and at bats for the numbers to even out. But, when you look at just 53 instances, it’s possible for there to be some small sample size error. So let’s look at a couple of specific plays from this season.

April 18

With the Cardinals leading the Pirates 1-0 in the 5th, Greg Garcia came to bat with Jose Martinez on first. With nobody out the run-expectancy was 0.8.

Garcia lined a double into center. Martinez rounded third and scored easily, but Garcia was thrown out trying for third. Now, it’s possible a throw from the outfield was cut off by the first baseman and redirected to third to nab Garcia. However,  quick review of the video shows that not to be the case.

With one run in, the Cardinals could have expected about 1.1 more runs had Garcia stayed put at second. Instead, with nobody on and one out, their run expectancy dropped to .59. There’s about 1/2 of one of those 22 runs.

Luckily the Cardinals hung on for a one-run win.

May 13

Leading the Cubs 3-1, Magneuris Sierra was on first with one out and the pitcher, Carlos Martinez, at bat. Sierra tried to steal second (Lester was on the mound) but was thrown out for the second out.

Run expectancy says the Cardinals went from scoring about .5 a run on average to .2. But the pitcher was hitting. Assuming Carlos would have bunted him over, the run expectancy would have risen to .319. Lower than it was, but higher than if Carlos would have, say, struck out.

This is an example of a time where run-expectancy breaks down. In the National League, pitchers hitting has a tendency to ruin even the best laid plans. And because most formulas make the basic assumptions mentioned above, it’s hard to criticize Sierra’s mistake.

May 18

I bet you’re surprised I got this far without mentioning Matt Carpenter.

Well, on May 18 Carpenter committed one of the stupidest, irresponsible, boneheaded, bordering-gross-criminal-negligence baserunning mistakes I’ve ever seen.

Carlos had pitched an utter gem, and the game was 0-0 in the 9th. Carpenter lashed a sure-double into left. It appeared the Cardinals were well on their way to a win, as their run expectancy rose from about half a run to 1.1.

Then Carpenter rounded second, and headed for third.

He was nailed at third easily. The Cardinals run expectancy dropped all the way down to 0.25. They didn’t score in the inning, and went on to lose the game.

As you can see, run expectancy isn’t the perfect tool for evaluating base running. Sometimes calculated risks have to be taken based on the speed of the runner or quality of the hitter, two things run-expectancy ignores.

Taking the extra base is always a calculated risk. By ignoring the times the Cardinals have been successful, I was setting them up for failure in this scenario. But when you are among the league leaders in outs on the bases, the particulars of those outs require some serious consideration.

The conclusion is this: the Cardinals reckless baserunning has cost them as many as three wins thus far this season.

Thanks for reading! Be sure to follow me @colingarner22.

Colin Garner
Colin is a catcher at Drury University who's a big fan of pitch calling, bullpenning, and Game of Thrones. Gets very frustrated with nonsense from people around him while attending games.
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