Two-Strike Counts and Weak Contact

In an MLB.com article from earlier this month, Adam Berry investigates the secrets of the Rays’ pitching staff. First, read the article because it contains so many great pieces of information. I wish MLB.com did more pieces like this one.

And the guts of the article comes down to this quote:

The Rays don’t employ a one-size-fits-all philosophy, Snyder said, besides imploring pitchers to throw strikes and get to a two-strike count as quickly as possible. Beyond that, their approach is player-centered: They recognize what each pitcher does best, which they can typically tell by identifying what each does most consistently, and revolve everything around that.

Two-strike counts are important? In previous work, I’ve noticed pitchers who are behind in the count generate weaker contact along with those who have a diversified pitch mix. It’s time to dive into the effects of two strikes counts and determine if they can help pitchers limit hard contact.

I should have noticed and looked into the effects years ago since it was staring me in the face at Baseball-Reference. Here are the league-wide ISO values for hitters at various counts.

ISO for Different Pitch Counts
Season Two Strikes Pitcher Ahead Even Count Batter Ahead
2017 .105 .113 .180 .233
2018 .101 .106 .175 .217
2019 .113 .121 .196 .245
SOURCE: Baseball-Reference

Once hitters are facing two strikes, their power disappears as they defend the plate and try to just make contact.

I should just start diving into the numbers on how the theory is supported, but that’s not the case. All the pitchers who lead the two-strike rankings are high strikeout pitchers. (e.g. Jacob deGrom and Max Scherzer).

2019 2-Strike Leaders and Laggards
Rank Name 2-Strike% Ahead/Behind K% vISO BABIP ERA SIERA xFIP FIP
1 Chris Paddack 35% 1.88 27% .243 .237 3.33 3.83 4.05 3.95
2 Jacob deGrom 35% 1.43 32% .175 .282 2.43 3.29 3.11 2.67
3 Justin Verlander 35% 1.54 35% .301 .218 2.58 2.95 3.18 3.27
4 Max Scherzer 34% 1.70 35% .237 .321 2.92 2.93 2.88 2.45
5 Mike Clevinger 34% 1.27 34% .192 .306 2.71 3.31 3.09 2.49
6 Gerrit Cole 34% 1.46 40% .270 .275 2.50 2.62 2.48 2.64
7 James Paxton 33% 1.25 29% .264 .313 3.82 3.93 4.03 3.86
8 Jordan Lyles 33% 1.20 24% .285 .285 4.15 4.53 4.61 4.64
9 Madison Bumgarner 33% 1.59 24% .240 .289 3.90 4.15 4.31 3.90
10 Stephen Strasburg 33% 1.33 30% .204 .274 3.32 3.49 3.17 3.25
Average 34% 1.47 31% .241 .280 3.17 3.50 3.49 3.31
92 Trevor Williams 27% 1.04 18% .278 .303 5.38 5.08 5.25 5.12
93 Jake Arrieta 27% 0.95 19% .204 .316 4.64 4.82 4.46 4.89
94 Aaron Sanchez 27% 0.88 19% .226 .320 5.89 5.28 5.15 5.25
95 Brad Keller 27% 0.92 17% .165 .282 4.19 5.23 4.94 4.35
96 Mike Leake 27% 1.27 15% .259 .295 4.29 4.79 4.76 5.19
97 Andrew Cashner 26% 0.79 17% .202 .280 4.68 5.20 5.11 4.66
98 Jason Vargas 26% 0.77 19% .217 .276 4.51 5.25 5.44 4.76
99 Antonio Senzatela 25% 0.79 13% .222 .333 6.71 5.50 5.12 5.44
100 Brett Anderson 25% 1.07 12% .162 .278 3.89 5.17 4.79 4.57
101 Dakota Hudson 25% 0.79 18% .196 .274 3.35 5.08 4.55 4.93
Average 26% 0.93 17% .213 .296 4.75 5.14 4.96 4.92

The strikeout rate advantage overwhelms any weakly hit effects. Basically, strikeout pitchers generate weaker contact because they can get to two strikes.

This advantage can be seen in the inputs to SIERA.

Strikeouts are good…even better than FIP suggests. High strikeout pitchers generate weaker contact, which means they allow fewer hits (AKA have lower BABIPs) and have lower homerun rates. The same can be said of relievers, as they enter the game for a short period of time and pitch with more intensity.

I proceeded to spend a few hours dividing up the data to find any significant results including removing the strikeout. It was P-hacking at its finest but to no avail. There isn’t an obvious measurable stat adjustment to getting to two strikes prevents that isn’t already being measured.

That doesn’t mean there isn’t something to squeeze out, but for me, I’m going to continue using the Ahead/Behind ratio for two reasons. First, I was able to measure how much pitchers who are ahead limit hard contact.

Second, the Ahead/Behind ratio doesn’t correlate as much to strikeouts as the 2-strike%.

Correlation: r-squared
Ahead/behind to 2-strike%: .39
Ahead/behind to K%: .19
2-strike% to K%: .43

The batted ball effects of the Ahead/behind ratio are easier to isolate since it has a smaller influence on strikeouts.

The more I dive in, the coaching genius of “get to two strikes as quickly as possible” becomes apparent. Going back to the league-wide results with two strikes, any pitcher pitching staff allowing those numbers, no matter the reason (i.e. strikeouts or weakly hit balls), will thrive. With a two-strike goal, it’s the pitcher’s job to find a way to get to two strikes. Once at two strikes, the results will follow.

Maybe someone can massage the two-strike data to unearth a groundbreaking discovery. I’m likely not that person. While being ahead in the count matters, too many factors are in play with two strikes to make it a better predictor of weak contact as the Ahead/Behind ratio.





Jeff, one of the authors of the fantasy baseball guide,The Process, writes for RotoGraphs, The Hardball Times, Rotowire, Baseball America, and BaseballHQ. He has been nominated for two SABR Analytics Research Award for Contemporary Analysis and won it in 2013 in tandem with Bill Petti. He has won three FSWA Awards including on for his MASH series. In his first two seasons in Tout Wars, he's won the H2H league and mixed auction league. Follow him on Twitter @jeffwzimmerman.

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Lucas Mueller
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Lucas Mueller

Verducci did a feature on the Rays WAYYYY back in 2013 and they touched on pitchers getting to two strikes as quickly as possible. Likely has been an organizational philosophy for a while.

https://vault.si.com/vault/2013/04/01/the-rays-way