(Photo by Gavin Baker/Icon Sportswire)
In today’s game, speed is king. We look for the swiftest runners (on-base and defensively), quickest catcher pop-time, hardest exit velocity, and fastest pitch speed. SAs of April 11th, there have been 31 pitches recorded at 100MPH+ within the first 15 games of the Major League Baseball season. Veteran Aroldis Chapman of the New York Yankees along with new kid on the block Jordan Hicks of the St. Louis Cardinals own the lions share of those pitches. Both account for the 12 fastest, with Chapman primarily a four-seam fastball pitcher and Hicks using the two-seam.
Without dropping much of a bombshell, both four and two-seam fastballs dominate the velocity leaders with a couple of cutters and sinkers thrown in.
Four-seam fastballs are the most prominent in the league and account for the fastest pitches in baseball. Two-seam fastballs have the advantage of more movement, but slower speeds; these pitches sometimes get pegged as ‘sinkers’.
So, in a baseball world gripped by velocity, how has the FS fastball been working for power pitchers in 2018? I’ll take a look back at the previous few seasons worth of data, then talk about how effective the current hardest throwers are fairing using basic Statcast data.
Placing the at-bat threshold at 100, I looked into the three-year rolling average for the top-25 pitchers in terms of weighted on-base average when a pitcher throws at least 98 MPH and results in an event (strikeout, ball in play, etc). I set the starting point at 98 MPH as it’s on the high end of the fastball spectrum with an ideal concentration of pitchers.
I gif’d the wOBA leader Pedro Baez of the Los Angeles Dodgers as he demonstrates the effectiveness of his four-seam fastball; 98.2MPH, 2364 spin rate, and a -2.4″ horizontal movement.
Below are the current four-seam fastball average velocity leaders (minimum 15 batted ball events/pitches). Note that Felipe Rivero of the Pittsburg Pirates is now known as Felipe Vazquez.
We can see minor velocity variance; a standard deviation of 0.66 MPH. But, the events are what differentiates the top 10. We have Yankees’ starter, Luis Severino, well ahead of the pack with Miami Marlins reliever Tayron Guerrero and Anaheim Angels’ pitching version of Shohei Ohtani following well behind.
Obviously, we can’t punish relievers for pitching less but you also have to take into consideration that starters (generally) appear no more than once a week (though they still throw a lot more pitches on average). Regardless, with all the high-90’s velocity, the ability to differentiate is razor thin.
FS fastballs are known for their two most common events; the whiff and the fly ball. Since there is much more subjectivity to actual contact, we’re going to stay with the desired outcome of missing bats as our focus.
I’m going to take a quick assessment of the relationship between velocity and whiffs; does the harder a pitcher throws correlate with higher swings and misses?
Above is a collection of 370 pitchers who’ve thrown at least 2500 four-seam fastballs since 2015 (Chapman is on the upper far-right of the scatterplot). We see that 51% of whiffs are the result of pitch velocity. Not the strongest, but certainly a fair relationship.
Lets now take a look at the four-seam spin rates. Do we have a relationship between spin and whiffs? It wouldn’t appear so.
Again, these figures are from 2015 to the present and include 309 pitchers who have thrown at least 2,500 four-seamers in that time frame; a much worse relationship. Houston Astros starter Justin Verlander, Washington Nationals starter Max Scherzer, and Los Angeles Dodgers reliever Kenley Jansen are the three front-runners in spin rate yet have a large separation in whiffs.
Clearly, and perhaps unsurprisingly so, four-seam fastball velocity has a larger impact than spin rate when it comes to missing bats. What about how both metrics relate to each other directly, this time using batting average?
Guerrero and Atlanta Braves reliever Arodys Vizcaino are the least and most successful, respectively. A visual assessment would find that the lowest range of batting averages stay confined, or rather reserved, to 95/96 MPH regardless of spin rate. While its true that the majority of (power) pitchers fall in that speed level, if you increase velocity it seems that success rate varies slightly more.
So then do we have any indication of more/less success in terms of movement when it comes to missing bats?
As you can see, to either side of the plate (around 0.0″), there is almost no correlation between horizontal movement and whiffs. This is a collection of 395 pitchers who’ve thrown at least one FS fastball in 2018.
So the only relationship we can make using FS data is that of velocity and whiffs. So I’ll use the top ten pitchers, rank them by average velocity, and come up with an assessment for the two most effective; one for each throwing arm.
For a little context, average movement (in inches) and velocity on a four-seam fastball is as follows. Keep in mind that vertical movement isn’t pure; its only a measure of how much less break it has vs. a pitch with no backspin.
- Left-handed pitchers have an average horizontal movement of 5.5 with a 9.4 average vertical movement. League average velocity is 91.2 MPH.
- Righties have an average horizontal movement of -4.2 and an average vertical movement of 8.9; average speed is 92.4 MPH.
This is a plot using movement data from the previous chart for visual purposes; for reference, 8-inch vertical movement is league average. The bottom right corner is Anaheim Angels reliever Jose Alverez, who appears to have the most general activity on his four-seamer.
Here’s a visual example of what type of movement a batter sees on Alvarez’s four-seam fastball; the smallest circles represent release point, followed by recognition point, then commit point (purple).
Going back to the scatter chart, top right-ish is Houston Astros reliever Ken Giles who has much less break than any on the list while the far left point represents teammate and starting pitcher Charlie Morton.
Using all the collected information for this short time-frame presents us with Shohei Ohtani for righties and Aroldis Chapman representing the southpaws as the pitchers using their four-seam fastballs most effectively.
Chapman uses his the most, gets an incredible spin rate, while missing bats at the second-highest rate and has one of the lowest BAA. The movement on his four-seamer is fair but Chapman’s velocity alone causes issues for hitters. Shouldn’t be much of a surprise here despite the fact that Chapman isn’t as effective as he once was.
Ohtani isn’t using his four-seam as frequently as others in the group; he’s got a full arsenal to work with, so we can’t fault him for that. But, he’s generating a lot of whiffs and has the lowest BAA of the hardest throwers. His movement, like Chapman’s, isn’t as active as the rest of the pitchers and you could argue that his low batting average and high whiffs are results of a greater variety of pitch options.
It looks like you’re using raw whiff total rather than whiff rate in the first two scatter plots. That would explain why Jansen has 3x fewer whiffs compared to Scherzer and Verlander, who are starters that throw about 3x more innings.
Also, using wOBA on individual pitch types is very misleading. Pitchers who use their 4-seamer to get ahead in the count and primarily non-fastballs with 2-strike counts will always have a higher wOBA on their 4-seamer than guys who use their 4-seamer with 2 strikes, since it’s impossible to get a K with less than 2 strikes. SwStr% or wOBAcon are more useful for that purpose.
Thanks for the feedback.
You’re not going to get any kind of reliable correlation if you’re comparing average velo/spin with total whiffs, though. Signal to noise ratio is minuscule. Even if every pitcher had the exact same whiff rate on their 4S fastball, the way you analyzed the data would show a very low R-squared. It would be much more informative to look at whiff rate as the dependent variable in both cases.
Your comment was edited, so my reply looks out of place now. If I knew how to edit mine, I’d do so.
Don’t worry about that. We knew what was discussed. I just felt it was best not to get into a debate because the fundamentals you state are correct. I used the data the way I did because there really wasn’t that much of a variance between rate and total; a bad relationship is a bad one. I don’t even consider 51% of whiffs are the result of average pitch speed a good example. I now see I took the wrong angle but the final result would have been the same.
Get ’em next time.
There’s a huge difference between whiff rate and total whiffs, though. 51% of whiffs being explained by velocity would be a huge result if it was valid. We haven’t even taken into account hitter quality, pitch location, etc. Tons of other variables. 51% is way too high.
I ran the StatCast data using Whiff Rate on 4-seam fastballs (minimum 1000 thrown). R-squared for Velo vs Whiff% was 0.25, and for Spin vs Whiff% was 0.23. So it’s about the same for each of them. Since they’re very highly correlated, using both together only increases R-squared to 0.35 meaning there’s a lot more to whiffs than just velocity and spin rate. I suspect location has a lot to do with it (high in the zone gets a lot more whiffs, low in the zone gets hardly any), as does pitch sequencing (if all you throw is 4-seamers it’s easier to time it), pitcher’s arsenal (whether or not the pitcher has a similar pitch in the same tunnel that behaves differently in movement or velo), etc. So 25% for Spin and Velo is pretty significant.
Cool! Thanks for the info.