Most baseball fans would say that Kyle Lohse made three mistakes in his game against the Atlanta Braves last Tuesday. The first mistake was the 2-0 sinker he threw Jason Heyward in the top of the fifth. Heyward hammered it for a two run home run and the lead. Lohse’s second mistake was the 3-2 slider that Freddie Freeman roped over the right field fence in the top of the sixth. Freeman’s laser beam extended the Braves lead to 3-1. But Lohse’s third mistake was different than the other two. It occurred earlier in the game and wasn’t a pitch. The mistake was his inability to get down a bunt with runners on first and second with no outs in the bottom of the second.
In place of a “productive out”, Lohse struck out after bunting a two-strike pitch foul. Carlos Gomez followed by grounding into a double play to end the inning – a situation that would almost never have happened if Lohse had bunted successfully. Instead, the inning was over. The rally killed. All because Lohse failed to make his out “productive”.
Ten years ago, Buster Olney championed a new stat deemed “POP” or “productive out percentage.” Developed by ESPN and Elias Sports Bureau, and introduced by Olney. Outs were deemed “productive” if:
- A baserunner advances with the first out of an inning.
- A pitcher sacrifices with one out.
- A baserunner is driven home with the second out of an inning.
Olney’s piece landed a month after Moneyball: The Art of Winning an Unfair Game hit the shelves. The abstract to Olney’s article, Smallball vs. Moneyball, perfectly summarized the two schools of thought on outs:
Many say it’s a baseball sin to waste your allotted 27, but teams like the Tigers say they’re the key to success.
Olney aimed to push back on the idea that giving away outs was bad. “Productive outs” and POP were intended to be the measuring stick that proved otherwise. So how does that debate look ten years later? Let me answer that question by asking another: how often do you hear POP mentioned or referred to today?
The sabermetric response to POP was skeptical and swift. Olney’s article didn’t present the hard data, only the results. So statheads, like Larry Mahnken, ran the numbers. Mahnken disliked POP as a stat. For him, POP’s parameters didn’t make much sense and, more importantly, a high POP didn’t appear to correlate with winning. Instead of creating a stat around an idea, sabermetricians suggested looking at the raw data to see if the premise behind POP was even statistically possible. Could an out ever be statistically beneficial to the offense?
Sabermetricians staked out the ground that giving away an out was never a good idea, while Olney, and gritty hitting coaches, claimed otherwise. Luckily, Tom Tango had already mined the hard data. Tango’s “Run Frequency Matrix”, compiled from figures between 1999-2002, calculated how often runs scored following each base/out situation until the end of the inning. For example, here are a team’s odds to score a run at the start of each inning, with no outs, and no one on base:
So 70.7% of the time, no runs are scored during an inning. One run is scored 15.4% of the time and so forth. Now, if that first batter draws a walk, considered a cardinal sin by pitching coaches, here’s how the odds change:
A lead-off walk suddenly gives the offense a 14.4% better chance of scoring that inning. Not only does the chance of scoring one run increase by 2.2% but the odds of having a big inning also increase dramatically. For example, the offense’s odds of scoring two runs almost doubles – from 7.4% to 13.2%. Meaning that the old mentality of lead-off walks leading to big innings is statistically justified.
So what does Tango’s Run Frequency Matrix say about Lohse’s bunt situation? Would a successful bunt have been statistically beneficial for the Brewers? Would the out have been “productive”? Or, like the sabermatricians argue, would giving away the out statistically lower the Brewers’ odds of scoring? Let’s see what the Run Frequency Matrix has to say:
Turns out both sides have a point. First, this proves that an out can be “productive”, or, in other words, statistically beneficial. If Lohse had successfully dropped down that bunt, the Brewers’ odds of scoring in the second inning of Tuesday’s game would have increased by 5.4%. The odds of scoring one run would have gone up 6.6% and the odds of scoring two runs 5.3%. But here’s where the sabermetrician’s side of the argument takes over. Though the bunt would have increased the overall odds of scoring, it also would have depressed the odds of the Brewers having a big inning. Giving away the out would have lowered the odds of scoring three runs 2.6%, four runs 1.7%, and five or more runs 2.1%.
So, Lohse failure to bunt definitely hurt the club, but how much? Here’s what the odds say:
Overall, the Brewers’ chances of scoring fell 21.5% because of the failed bunt. This precipitous drop plays into the sabermatrician’s hand. In their eyes, bunting cuts both ways. A good bunt can statistically increase a team’s chance of scoring a run or two while simultaneously decreasing the chance of a big inning. In addition, a failed bunt attempt can cripple on offense’s chance of scoring at all.
So the truth behind “productive outs” is more nuanced than old clubhouse coaches, POP, and sabermatricians would suggest. Case in point: bunting a runner from first to second with the first out of the inning is statistically not a “productive out.” It lowers a team’s overall odds of scoring by 3.1%. The chance to score multiple runs in the inning sinks across the board. Yet, moving that runner to second with one out does increase a team’s chance of scoring one run by 5.4% – the very definition of playing for one run.
In Olney’s original article on POP, the Angels were held in high regard as a team that successfully used “productive outs” to their advantage. Watching the Brewers’ style of play to start the season, it’s easy to see that Ron Roenicke came from the Angels’ smallball school of thought. Honestly, that makes me sweat a little bit, though not because I believe there’s no room for bunting, stealing, or “productive outs” in the Brewers’ game. There is a place and statistical justification for it, especially in the NL with the pitcher batting. Yet the Run Frequency Matrix shows that the odds behind smallball don’t always reinforce the clubhouse myths, and each “productive out” situation comes with a myriad of caveats and risks.
Roenicke’s offensive philosophy is exciting, but a bit too aggressive for my tastes. It is proven that “productive outs” have a place in the game but the odds aren’t as beneficial as most think. While Lohse could probably care less about his POP and Tango’s Run Frequency Matrix, as a veteran, I’m sure he would be the first to tell you that he hurt the club by not getting down the bunt. Sometimes the age-old perceptions and the statistical data match-up. And that one mistake lowered the Brewers’ chance of scoring by 26.9%. Not a statistical deathblow, but a small cut that, compounded with other minor mistakes, can drain the chances of winning right out of a game.