Analytics has any number of practical uses, but the best and original use is to verify feelings that we may have when we watch the games. Emotional responses are often rife with bias. We often remember what we want to remember and forget what we want to forget so that we can forward the narrative we have built up in our minds. As you can see by the title, we are looking at Astros efficiency. So, what exactly is efficiency?

To explain it simply, it is the percentage of runners that end up crossing home plate. However, one of the things we have neglected when looking at efficiency is the pitching end of it. The best numbers are numbers that serve as a mirror image of each other. If it is good for a high percentage of runners to score then it is also good if we prevent a high percentage of runners from scoring.

We call this segment the lab because the numbers are what they are, but the key comes in how we interpret them. So, we end up running a bit of an experiment where we set up a hypothesis and test it. In this case, we would postulate that good teams plate a higher percentage of their runners and prevent a higher percentage of the other team’s runners. For our purposes, base runners can be interpreted as hits + walks + hit by pitches. Obviously, we have situations where runners also reach on errors and fielder choices, but it is usually best to keep this as simple as possible.

So, if our hypothesis is correct then the teams with the highest run differentials would also have the positive gaps in efficiency. So, we will set up our table with runs scored and runs allowed, offensive efficiency and defensive efficiency, and run and efficiency differential. We know that teams with higher run differentials are the better teams. Will they also have higher positive differentials in efficiency? Let’s find out.

Offensive vs. Defensive Efficiency

RunsRuns AllowedOff EffDef EffRun +/-Eff +/-2017896700.415.367+196+.0482018797534.395.321+263+.0742019920640.409.378+280+.0312020279275.403.383+4+.0202021863658.405.356+205+.0492022737518.382.316+219+.0662023827698.401.363+129+.0382024740649.377.352+91+.0252025686665.358.365+21-.007

If you felt like the Astros were leaving a small village on base last season then your perceptions were backed up by the numbers. Their .358 efficiency rating was the worst in the World Series era. This becomes particularly acute when you look at the aggregate numbers over the time period. The pitching side saw worse years than last year, but the net result had the first negative differential in the time period.

Our hypothesis was that the best teams would have the best efficiency differential. There wasn’t a perfect correlation here because there never are in human endeavors. However, the club had four seasons with a +200 or better run differential. Three out of four of those seasons also were amongst the four best efficiency differentials. The notable exception was 2017 which saw the fourth best efficiency differential, but the fifth best run differential. That’s still pretty damn close.

The null hypothesis would suggest that efficiency is more or less dumb luck. It would be the intellectual equivalent of Lou Brown (from “Major League”) uttering, “I know he hasn’t done much against this guy, but I gotta hunch he’s due.” Serendipity is certainly a thing, but it also is not an action plan. The more intelligent fan out there would call that regression to the mean. In essence, as an analyst I have always considered intangibles to be something we haven’t figured out how to measure yet.

We used to consider clutch hitting an intangible. We used to consider pitch framing an intangible. Over time we figured out how to measure it. In short, the more you can measure the less uncertainty there is. When you can minimize the unknown you can also minimize risk. As the correlation above shows, you can’t completely eliminate it, but you can minimize it.

What do these numbers mean?

I start by looking at the hitting and pitching numbers independently. Do we see any patterns? The offensive numbers have more patterns than the pitching numbers. They simply fell off the side of the cliff the last two seasons. This is probably the main reason why the hitting coaches were jettisoned. The hope is that new voices can make them more efficient.

On the pitching side, the numbers were more scattershot. 2018 and 2022 stick out like a sore thumb in a positive sense. 2018 was the single best pitching season for any American League team since the DH was instituted in 1973 up to that point. 2022 was obviously both even better and the World Series season with the best bullpen in franchise history. The other seasons saw them give up 640 or more runs and all of them had efficiency ratings between a .352 and .383 in efficiency.

Given those parameters, the 2025 staff exists at around the midpoint. The staff loses Framber Valdez, but hopes to be better with better health and more depth. I hate to keep beating this drum, but the key to the season will come at the plate. The aggregate in the time period for offensive efficiency was .394. That seems like a tall order to get to, but if the team can get back to a ,375 efficiency then that would have been 718 runs scored with the same number of base runners. That is an extra 32 runs on the season. Most sabermetricians look at ten runs as being the win mark, So, that’s an extra three wins. The Astros win the AL West with three wins. That’s especially true if one of them comes against the Mariners. Obviously, it’s a lot easier said than done. Ultimately, we are simply explaining something mathematically that we see with our own eyes and feel emotionally. Will the Astros get back on the right side of the efficiency battle? What do you think?