This Pitching Stat Deserves Your Attention Right Now

One simple metric that's nearly stabilized 10% of the way through the season

The last few decades have seen baseball analysis grow exponentially in its complexity. While batting average and pitcher wins dominated stats discourse for the first century+¹ of the sport, incremental breakthroughs eventually led us to where we are today—the Statcast Age. At first, on-base percentage struggled to catch eyes. Now, you’re behind as an organization if you’re not factoring xwOBAcon, vertical attack angle, vertical approach angle (those are different things!), and FRV into your evaluations.

All of those developments are great, and I love that so much data is readily available via Baseball Savant, Fangraphs, and the like. Baseball is truly in an analytic renaissance right now, with no sign of stopping.

But I have a soft spot for simplicity.

One of the reasons I still seek simplicity in baseball analysis is history. I can’t know exactly how fast Nolan Ryan’s fastball was, or what Rogers Hornsby’s xwOBA was. Instead I have their raw stats, recorded by humans rather than TrackMan. I can doctor those stats according to my needs, but I don’t have the luxury of those granular data with which the game is now flooded. So I am at odds with knowing that these advanced tools are more accurate at assessing performance, and knowing that most players in the game’s history will never be afforded that clearer lens.

Another reason is the elegance that comes with simple effectiveness. Pitcher win-loss record is simple but not elegant, because of how poorly it diagnoses and predicts performance. xwOBAcon is effective but not simple, since you need a combination of artificial tools and linear weights to get there.

But some stats are both simple and effective, that get you 95% of the way there with 95% less strain. Chief among these for pitchers is K-BB%.

New Methods to Reflect Old Values in Cy Young Voting

The Simple Effectiveness of K-BB%

All K-BB% entails is subtracting a pitcher’s strikeout rate (K%) by his walk rate (BB%)—two statistics that have been around since the 1800s. This therefore completely removes balls in play from the equation in favor of two of the most controllable plate appearance outcomes for a pitcher.

Think about it: Once a batter makes contact with a pitch, the ball can go anywhere! Batters have much more control over quality of contact than pitchers, since they are the ones making the contact.² Axing balls in play removes a lot of context, but sometimes that context can create more noise than signal. And the reason we know noise is inherent to balls in play is because of how long it takes for them to stabilize.

How “stable” a stat is depends on how many times it takes for it to reliably predict samples of the same size. For example, if I had ten equally talented clones of Aaron Judge, and had them collect 10 PAs each, their home run rates would likely vary widely even though they’re equally capable of hitting homers. In actuality, these clones would need 150-200 PAs to stabilize to a home run rate that would be useful for discerning how good they are at hitting them. This resource from Fangraphs is Fan-tastic for understanding and visualizing this phenomenon.

For pitchers, home run rate stabilizes around the 1300-PA mark. The last pitcher to face that many batters in a single season was Phil Niekro in the late 70s.

Meanwhile, strikeout rate stabilizes at ~70 batters faced, and walk rate ~170. Because of this, strikeout rate and walk rate need a lot less time to reveal inherent truths about a pitcher’s run prevention ability.

Tom Tango’s Fielding Independent Pitching (FIP)—on which fWAR is based—uses these “three true outcomes” (K, BB, and HR) as its backbone. As you might guess, simple K-BB% is even more predictive of run prevention than FIP in small samples since it doesn’t include HR, though both are wise to omit things like singles (670-PA) and extra-base hits (1450-PA).

K-BB% even duels with more advanced ERA estimators like xFIP, SIERA, and xERA as at least as good at predicting run prevention, and arguably a touch better. You have to include more sophisticated and granular data to inexact degrees if you want something better than K-BB%, but at that point, those are those few percentage points. You get 95% of the way there with just Ks and BBs.

K-BB% so far in 2026

Pretty much all qualified starters in MLB this season have reached 70 batters faced, meaning we can glean info from their K%. Admittedly, BB% hasn’t stabilized quite yet, with only one pitcher eclipsing 110 PA so far.³ Since K% does most of the heavy lifting anyway⁴, I’ll accept the risk.

Here are the top 10 qualified starters in K-BB% as of 4/15, according to Fangraphs:

1. Cam Schlittler - 35.8%

2. Shota Imanaga - 31.7%

3. Kevin Gausman - 29.5%

4. Jacob Misiorowski - 27.3%

5. Jesús Luzardo - 25.5%

6. Tyler Glasnow - 25.4%

7. Cristopher Sánchez - 25.3%

8. Emerson Hancock - 24.7%

9. Kris Bubic - 23.5%

10. Ryan Weathers - 23.3%

These aren’t necessarily the ten best pitchers in the league, but their peripherals suggest they will probably keep being pretty good this season.

Considering the yet unstable nature of BB%, here are the leaders in just K%:

1. Shota Imanaga - 37.8%

2. Jacob Misiorowski - 37.5%

3. Cam Schlittler - 37.0%

4. Dylan Cease - 36.0%

5. MacKenzie Gore - 35.3%

6. Kevin Gausman - 35.2%

7. Kris Bubic - 33.8%

8. Cristopher Sánchez - 32.6%

9. Gavin Williams - 32.6%

10. José Soriano - 32.0%

Shota Imanaga leads MLB in strikeout rate after four starts (37.8%)

Contrast those lists with the ERA list so far:

1. José Soriano - 0.33

2. Michael Wacha - 0.45

3. Shohei Ohtani - 0.50

4. Bryce Elder - 0.77

5. Chad Patrick - 0.95

6. Taj Bradley - 1.25

7. Jeffrey Springs - 1.46

8. Seth Lugo - 1.48

9. Dylan Cease - 1.74

10. Carmen Mlodzinski - 1.77

These pitchers have still been good of course. But will they maintain those ERAs? Absolutely not. Chad Patrick’s league-worst 2.7% K-BB% indicates some serious regression is in store for him, but you wouldn’t know that looking at his amazing ERA. Meanwhile, those guys in the first two lists are pretty much going to keep up those numbers. There will be some variation, but a whole lot less than ERA.

K-BB%-based WAR

We can also use K-BB% to crudely project what a pitcher’s full-season value might be in terms of a highly simplified Wins Above Replacement (kWAR).

The key step to do that is to convert the percentage to a quantity, for which we use an ERA scale (kwERA). Another Tango invention, the kwERA formula is: LgConstant - (12*((K-BB)/TBF))).⁵ This can then be converted to WAR via the easy heuristic of IP/2 - ER (kwER in our case), which is what Tango’s Cy Young Predictor uses and which gets pretty close to the real thing.

Here are the top 10 in projected full-season kWAR with current 2026 data:

1. Cam Schlittler - 7.6

2. Shota Imanaga - 6.5

3. Kevin Gausman - 5.9

4. Jacob Misiorowski - 5.1

5. Jesús Luzardo - 4.8

6. Emerson Hancock - 4.8

7. José Soriano - 4.7

8. Cristopher Sánchez - 4.7

9. Logan Gilbert - 4.3

10. Nolan McLean - 4.1

I doubt this list will end up being more accurate to full-season value than souped up projections like ZiPS. After all, the reigning Cy Young winners haven’t even been mentioned, and the maximum number of starts thus far is merely four. However, I would be more confident in it than current-season fWAR, which will be more accurate closer to the end of the season.

Once walks stabilize in a couple weeks (Schlittler is sure to reach ball four more than once this season), this analysis will be even more pertinent. And if you just want to keep it super simple in the spirit of the statistic, the percentage works perfectly fine. It’s certainly better for relievers—just ask Mason Miller.⁶

1. Normalized century with league average set to 100…

2. Pitchers have good control over the type of batted ball (ground ball, fly ball), but not over whether it finds a glove or not.

3. Can you guess who? Next week’s article will be about him…

4. From 2021-2025, K% explained about 41% of the variance in ERA, whereas BB% explained only around 4%. Together they explained 46%.

5. LgConstant = LgERA + (12*((LgK-LgBB)/LgTBF))). As of 4/15 the league average ERA is 4.12, there have been 4675 strikeouts, 2043 walks, and 20660 total batters faced. Crunching those numbers yields a LgConstant of 5.65, which is essentially replacement-level kwERA.

6. 70.4%.