Debunking a Statistical Myth
November 8, 2005 11 Comments
Over the last few weeks, I’ve read a lot about the supposed Moneyball backlash the White Sox World Series victory supposedly foreshadows. When you couple that with the Dodgers’ inane knee-jerk firing of GM Paul DePodesta, baseball traditionalists are crying triumph.
Bob Cook’s recent piece over at Flak Magazine is one of those pieces. While Williams’ piece is fraught with jabs at the sabermetrics society, he concludes with a keen observation:
The concept at the core of “Moneyball” is not statistical acronyms such as VORP and OPS, but finding players whom the marketplace undervalues. Williams did that with the White Sox by signing players who weren’t over the hill, but had worn out their welcomes elsewhere and therefore would be available for bargain prices.
There is, I would say, kernels of a very accurate representation of the White Sox World Series victory. The lessons of Moneyball focus more on finding hidden value in players through statistical analysis when working under a constrained budget. I don’t think Williams did that by signing players who were not welcome elsewhere. The only one of the White Sox unwelcome anywhere else was Carl Everett. Rather, Williams took a chance on a bunch of players and got great pitching. Since baseball is so dollars-oriented, there’s bound to be a little bit of Moneyball in every General Manager not named Brian Cashman.
But my main complaint with Cook’s article is this gem:
Williams’ White Sox won the World Series this year with a strategy that included use of the sacrifice bunt and the stolen base, two plays that statheads will tell you, with convincing evidence, result in fewer runs than if a team just let its players swing away and wait for the next batter to move them over.
This is a charge I see repeated over and over again by people who are afraid of the statistically-minded community, and it’s simply not true.
Statistics do not say that the use of the sacrifice bunt results in fewer runs than swinging away every time. Rather, it’s necessary to explore when it’s a good idea to use the sacrifice bunt and when it is not. There is indeed a place in baseball for the sacrifice bunt if it’s used properly. Let’s take a look at some numbers culled from the 2005 Expected Run Matrix.
This season, a team that had a runner on first and no out could be expected to score 0.8968 runs. In a runner-on-first, no-out situation, what happens to your expected run output if you sacrifice? Well, it declines. A team with a runner on second and one out could be expected to score 0.6911 runs. That’s a 22 percent drop in scoring. In that case, it doesn’t make sense to sacrifice (unless your pitching is up) because it doesn’t help your chances of scoring a run.
But what happens if you have a runner on second and no on out? The whole picture changes. A runner on second and no one out results in 1.1385 runs on average. A runner on third and one out results in .9795 runs for a drop of 14 percent. While bunting here doesn’t necessarily help your chances of scoring a run, the effect is not that important.
Teams will score a run with a runner on third and one out 98 times out of 100 (unless it’s your favorite team and than they never succeed there). Teams succeed in this situation nearly all the time as you can see that it’s much more likely that a run will score than that it will not. (EDIT: My original statement wasn’t a correct interpretation of the run expectancy matrix. 0.9795 runs score with a runner on third and one out. That’s not the same as saying that the run scores 98 times out of 100.)
With runners on second and first and no out, bunting is a statistically irrelevant move. In that situation, a team goes from scoring 1.4693 runs to 1.4144 runs. The percentage drop is negligible.
So stats-minded analysts will tell you that, while bunting never really adds to your run-scoring chances, there are times when it’s acceptable to bunt. You never want to see your team surrendering outs in a meaningless fashion. So if a sacrifice bunt is in order, it better not be to move a player over from first to second.
What about stolen bases? Again, stolen bases have to be used judiciously. Take, for example, a runner on first and no out. That situation, if you recall, results in 0.8968 runs. If he’s caught stealing, the run expectancy value drops to .2796, a 70 percent decrease. If he’s successful, the run expectancy value goes up to 1.1385, a 27 percent increase. In pure numerical terms, the loss is about 2.5 times the gain.
Much math, discusses previously at Baseball Prospectus and ESPN.com during its Golden Era, shows that you need 3 successful steals for every caught stealing attempt.
Again, stats-minded folks don’t believe the stolen base should be discarded. Rather, it should be used intelligently. This season, Scott Podsednik, credited with leading the White Sox revamped offense, was caught stealing 23 times while successfully swiping 59 bases. He actually cost the White Sox runs by running so frequently.
So all of this analysis shows a few things, in my opinion. First, people who look down upon statistical analysis tend to do so because of the seeming complexities of the numbers. Math is hard, and it can be tedious to wade through all of these numbers. Second, statistically analysis is born out of the game and not the other way around. By studying the game, stats analysts can better discern what strategies work and what don’t work. Without the numbers from games already played, though, stats can’t predict anything. The numbers can help us understand what to do in the future, but we can only do that by learning from the past.