Santos’ Swift Slider
The slider is baseball’s hardest pitch to hit — at least when it comes from a guy like Sergio Santos.
This season, the 28-year-old White Sox reliever has used his slide piece to rack up nearly 13 strikeouts per nine innings. And consider this: For every 100 sliders Santos throws, he racks up 34 swings and misses — the best in the major leagues for any pitch thrown at least 250 times. That’s also more than double the average rate for sliders. Even more incredible, out of every 100 swings against his sliders, batters miss 63 times — also the highest rate in baseball. That’s simply absurd.
And pretty nasty.
Consider the following:
This graph indicates whiff rate by vertical pitch location, with the blue line representing Santos’ slider and the red line representing the average right-handed slider. The gray bands indicate confidence, and the horizontal bars are approximations of the vertical borders of the strike zone.
It’s not that Santos’ slider is unique in terms of where it gets whiffs. As you can see in the graph, both Santos’ slider and the average slider have their peak whiff rates in the same location — about half a foot below the bottom edge of the strike zone. Santos’ peak is just much, much higher — about 65% compared with 25%.
Perhaps more impressive is his ability to get batters to chase his slider below the zone:
For the average right-handed slider, we find peak swing rates in the lower-middle portion of the zone, which is unsurprising. But with Santos’ slider, batters swing most often when the pitch is at or below the bottom of the strike zone — an area conducive to whiffs. From this, we can infer that deception is a principal reason for his slider’s success.
Santos is very adept at getting batters to chase sliders below the strike zone, where the chance of making contact is extremely low. But while having good velocity and movement can make a slider hard to hit, a pitcher first needs to get the batter to swing at the pitch. Being ahead in the count can force the batter to swing at sliders to protect the zone, but having the pitch look like a fastball is essential. We know that Santos’ slider is disguised well because of the location of swings against the pitch.
When batters swing at Santos’ sliders below the zone — which they do quite often — they don’t make contact. The contact rate against his sliders below 1.5 feet above the ground — an approximation of the bottom of the called strike zone — approaches 10%. For context, the average contact rate is 32% for sliders from right-handed pitchers in the same zone. This underscores the importance of getting batters to chase pitches below the zone, and by extension, deception. While we know that quantifiable attributes like velocity and movement matter, it’s important to keep in mind that qualitative attributes like deception play an important role too, even if we don’t exactly know how important they are.
References and Resources
- PITCHf/x data from MLBAM via Darrel Zimmerman’s pbp2 database and scripts by Mike Fast/Joseph Adler/Darrel Zimmerman
Any chance you can show the data from Al Albuquerque’s slider?
greg,
The whiff rate (whiff/pitch) of Albuquerque’s slider is also excellent, at 25 percent.
Does Santos’ slider move downwards more than usual? If so, hitters might think the pitch is headed to a similar location to other sliders, but it ends up lower.
This. How do you call it deception without looking at vertical movement? The pitch may start in the same location as other right handers’ sliders but end much lower. This reminds me of how Lidge was able to strike out so many hitters in years past – his slider had significant downward movement, at least to my untrained eye. This to me would induce many swing and misses below the zone.
DD,
Movement matters, but actually does not have a very strong relationship with the effectiveness of breaking balls. The correlation between something like pitch type linear weights and vertical or horizontal movement is not very strong.
Santos does have good movement on his slider. However, there are many breaking balls with more movement that are much less effective.
Josh, the relationship (or lack thereof) between movement and effectiveness also extends to fastballs, does it not?
Sean,
You are right, the relationship between movement and effectiveness of fastballs is also less than expected. I have found that with four-seams specifically, vertical displacement due to spin matters (significant relationship), but horizontal displacement does not.
Sky,
His slider does move downwards more than the average slider. But we should also keep in mind that the batter needs to decide to swing before most of this movement occurs, hence the emphasis on deception. If the batters could recognize slider, I doubt they would swing as often as they do against the pitch.
Great stuff Josh!
Really interesting. I enjoyed reading this.
I saw in the comments that movement does not strongly correlate with effectiveness. I’d be curious to see a graph of 1 pitcher’s movement vs. whiff rate — here, see exactly what the relationship (or lack of relationship) is between the movement on Santos’ slider and the likelihood he gets a swinging strike on it.
Aaron,
If I do a quick logistic regression of whiff rate (dependent variable) to horizontal movement + vertical movement (independent variables) using the right handed sliders from the post (n=10000), I find that both types of movement are statistically significant, but that the effect sizes are really small. If I input Santos’ movement, then I get a predicted whiff rate (whiff / pitch) of about 15%, which is higher than average, but not nearly as high as what his whiff rate actually is.
Of course this was pretty quick and dirty, but it does confirm the lack of importance of movement.
Oops, I meant to say “whiffs” not “whiff rate.”
Great post, and a great follow-up.
That’s quite useful.
Could you post the equation your (quick and dirty) regression gets you?
marc,
Without splitting up the data for batter handedness (so including lefties and righties), here are the results:
Family: binomial
Link function: logit
Formula:
whiff ~ pfx_z + pfx_x
Parametric coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.633871 0.039651 -41.207 < 2e-16 ***
pfx_z -0.027619 0.008566 -3.224 0.00126 **
pfx_x -0.045266 0.010214 -4.432 9.34e-06 ***
—
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ‘ 1
R-sq.(adj) = 0.00235 Deviance explained = 0.297%
UBRE score = -0.17328 Scale est. = 1 n = 10000
If I look at sliders thrown to just right handed batters, pfx_z no longer has significance (p = .12) but pfx_x does. For left handed batters, both types of movement are very significant.
I have also not accounted for the count in which these sliders were thrown, and MLBAM classification issues play a role here as well.
i know this is slightly off topic, but on the related topic of dominant relievers…
is anyone else amazed by jonny venters getting a 72% GB rate? not to mention striking out 10/9 IP to go along with it.
If it’s high, let it fly
Cool piece, but I can think of another slider-slingin’ Sergio who has an absurd whiff rate: Mr. Romo of the Giants.
I would be interested in a post about other converted position players that have been successful, and the amount of time the conversion took. I am still in awe in regard to the fact that a few years a go he was slugging.
I didn’t think Santos was that difficult to hit.
You are a gentleman and a scholar, sir.
Well Mr Santos and his great slider has now has 2 losses and 2 blown saves to go along with 6 earned runs in a span of 4.2 innings in 5 appearances.
This is pretty good, Josh, but next time please post about women’s rights or racism or something, instead. Thanks