As I was watching the Rangers game last night I couldn’t help but be overwhelmed with the thought that baseball is a funny game. Despite being around for well over 100 years, there is still so much we don’t know about the game. So many different things happen over the course of a baseball game that we can’t keep it all straight.
To those who know me (or follow me on twitter) it is pretty obvious I am a numbers guy. I like having tangible evidence to help keep my perception in line with reality. The numbers help me understand the game at a deeper level. Fortunately for me, no sport produces the amount of data that baseball does. I know the game is more than numbers, but the numbers are important. Almost every MLB team has a well-staffed analytical department for that very reason. The numbers DO matter.
Another reason baseball is so unique from other sports is that there isn’t a clock in baseball. There are no 15-minute quarters, or 24-second shot clocks in baseball. Each team has 27 outs to score more runs than the other team. An out is one of the most precious commodities in baseball, and thus should be protected fiercely.
I said all of that so that I could say this: I really hate sacrifice bunts. I am aware that bunting is part of the game. I am aware that people have been doing it for over 100 years. I am aware that most of the “old school” baseball types like the idea of a player “sacrificing” himself for the good of the team. Trust me, I get it.
The problem I have with sacrifice bunting is that it is proven to not be the most effective way to score runs. It isn’t a wild mathematical theory that may or may-not be true. There is a large body of evidence based on decades of data that show sacrifice bunting is almost always a bad idea.
Below is a simple chart known as the “Run Expectancy Matrix.” This chart was compiled based on data from the 1993-2010 seasons. You may ask why this matrix doesn’t encompass a broader range of historical data? The reasoning behind that is context. Baseball is a very cyclical sport in that there are clear-cut offensively dominated periods, and pitching dominated periods. By starting the data in 1993 we are looking at what amounts to a very offensive friendly era. These changes are due to a myriad of factors like mound height, strike zone, size of the stadium, and much more.
For example a team with 0 outs and a runner on 2nd is expected to score 1.170 runs that inning. But if that same team is batting with 1 out and a runner on 2nd they are expected to score only .721 runs in that inning.
Like I said, this matrix is compiled based on what has actually happened in baseball over this time period. This isn’t what someone thinks will happen; this is real, tangible data. This simple chart pretty much sums up my feelings about the sacrifice bunt.
Let’s look at the scenario that came up in the game last night. In the bottom of the 5th inning, Ian Kinsler led off with a double. So the Rangers had a runner on 2nd base with 0 outs. Their run expectancy for the inning was 1.170 meaning Texas was expected to score at least 1 run in the inning.
Elvis Andrus, the Rangers #2 hitter stepped to the plate. Lineup optimization is an argument for another time, but a guy hitting in the #2 slot in any lineup should be capable of driving in runs. (In his book, “The Book–Playing The Percentages In Baseball” Tom Tango actually advocates the #2 slot should be the best hitter in the entire lineup)
Instead of swinging away, Elvis Andrus laid down a sacrifice bunt. It can be debated who actually called for the bunt, but that is beside the point. Elvis “sacrificed” himself so Ian Kinsler could move 1 base closer to home. Sounds pretty solid huh?
Instead of just saying “that is the way baseball is played,” let’s look at the handy Run Expectancy Matrix and see how the run expectancy changed. With a runner on 3rd base and now 1 out, the run expectancy is .989. So the sacrifice bunt actually DECREASED the likelihood that Texas would score a by .181. This is not a large number by any stretch, but it is a 15% drop in run expectancy. In the case of last night’s game, this decrease became relevant as Josh Hamilton and Adrian Beltre both flew out to center field to end the inning, stranding Kinsler at 3rd base in an inning that on average should have netted at least 1 run.
This is also the case when a runner is sacrificed from 1st base to 2nd to avoid the double play. With a runner on 1st and 0 outs a team is expected to score .941 runs while a team with a runner on 2nd and 1 out is expected to score only .721 runs. These differences are even larger (23% decreased run expectancy), and again, this is real.
Now there are two sides to every coin. Some times late in games it is ideal to play for only 1 run. There is an entirely different run expectancy matrix that deals with the probability of scoring only 1 run. In these extreme scenarios, sacrifice bunting CAN be a good idea.
The batter at the plate also matters when a team is thinking about sacrifice bunting. If it is a very weak hitter, sometimes the team is better off choosing to have that hitter bunt as opposed to swinging away. That is an extreme case though. It would require a VERY poor hitter to make sacrifice bunting a good idea in the scenarios I discussed.
None of this is groundbreaking data. It isn’t like I whipped out my calculator and waded through decades of data after the 5th inning last night so I could write this piece. This doesn’t even scratch the surface on all the data out there regarding sacrifice bunts. There are tons of books and articles out there that a person can read to better understand these concepts. To see this and other run expectancy matrixes you can click here.
People who don’t like advanced analytics are quick to discount anything involving numbers claiming, “The game is more than math.” I totally agree with that sentiment. The game is more than math. But in this instance, the math is pretty cut-and-dry. By sacrificing outs to move runners, teams are hurting their odds of scoring runs in most instances. You wouldn’t knowingly take worse odds to win the same prize in Vegas because your gut told you to, would you?I know not everyone will agree with me on this. That is part of the beauty of baseball. This is just one aspect of the game that is cause for much debate, and I love that.
Thanks to the enlightening conversation in the comments section, it became relevant to include the run expectancy chart for scoring a single run at any point in an inning. This chart is included below:
Another common situation is the bunt with nobody out and a man on 1B. If the bunt is successful, the odds of scoring just one run move from 44.1% to 41.8%, a decrease of 2.3%.
Not factored into this table is the odds that a bunt attempt is even successful. As was seen Alberto Gonzalez’s attempt in Thursday’s game against the Mariners, it is not always an automatic that the sacrifice bunt will be placed successfully. This is one of several variables that must be weighed in these situations, including the quality of the hitter, and the game situation. Sometimes all these variables can stack up to make the sacrifice bunt worth the 3.7% increase in the probability of scoring one run, and sometimes they don’t.