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The Royals traded away all of their best breaking balls

The Royals pitching staff had a pretty poor go in 2018, thanks much in part to trading away some of their best pitches.

World Series - Boston Red Sox v Los Angeles Dodgers - Game Four Photo by Sean M. Haffey/Getty Images

If you follow me on Twitter (@duvy_013), you may have seen at some point in time that I like to try to rank individual pitches.

There is also an entire Twitter account dedicated to pitch quality, you can find them @qopbaseball.

If you hop on over to FanGraphs, you can find their section for pitch values where they determine the value of each pitcher’s individual pitches.

The point in all this is to say that an attempt to create a reliable valuation for individual major league pitches has sort of taken the baseball world by storm. As a fan that is infatuated with the art of pitching, I have (obviously) dove into the trend and am trying to create what I believe to be the most efficient form of pitch valuation.

For personal (and perhaps professional reasons), I am going to keep the majority of my pitch valuation formula private. Here is the gist of how I calculate the data and compare it among all eligible MLB pitchers:

  • Pitchers must have recorded at least 250 total pitches and thrown a minimum of 100 sliders/curveballs in the 2018 season
  • Z-scores were then given to each pitcher for nine statistical categories based on the results of their peers
  • I then give a weight to each z-score because getting swings-and-misses is statistically more beneficial than inducing ground balls, for example
  • Calculate z-scores to determine which pitcher’s slider or curveball was the best in MLB

Here’s kind of what the top 10 table looks like when it’s all said and done:


player_id player_name pitch_percent x y z a b c d e SCORE
player_id player_name pitch_percent x y z a b c d e SCORE
518397 Scott Alexander 10.8 -0.830837925 -0.844654705 -0.815312478 -1.186138391 -0.906699272 -0.155401489 0.123687237 2.797076463 7.349004981
607192 Tyler Glasnow 8.9 -0.830837925 -0.771526033 -0.659395572 0.251780106 -1.756269306 0.968373105 0.136827769 2.280791758 7.152241362
621381 Matt Strahm 15.3 -0.830837925 -0.405882676 -0.203638462 -1.569583323 -0.49726793 0.395539952 0.360216813 1.960980344 6.223947425
595014 Blake Treinen 22.3 -0.603306422 -0.463615838 -0.463499972 -0.099711082 -1.244480129 0.632700175 0.636167986 1.595924292 5.739405895
621242 Edwin Diaz 37.3 -0.21825311 -0.298114107 -0.379544715 -0.579017248 -1.30589483 -0.020402593 0.623027454 1.762185468 5.145634339
519151 Ryan Pressly 26.7 -0.375774919 -0.059483705 -0.235621417 0.57131755 -1.694854605 0.630875865 0.715011178 1.92340605 5.063710189
608638 JT Chargois 49.5 -0.638311269 -0.275020843 -0.263606502 -0.866600947 -1.694854605 -0.038645687 0.149968301 1.203547917 5.053264696
547973 Aroldis Chapman 25.4 -0.282428662 -0.394336043 -0.623414748 -0.227526059 -0.384674311 0.151082491 0.241952025 2.58396882 4.889383159
527055 Arodys Vizcaino 30.2 -0.334935932 -0.390487166 -0.447508494 -1.122230902 -0.139015506 0.149258182 0.123687237 2.048209986 4.755333405
465657 Joakim Soria 11.6 -0.638311269 -0.024843808 -0.003744992 -3.454854242 0.260180052 0.660064816 -0.769868941 0.755117773 4.506887906

*For those that may ask about pitch percent factoring in, the entire set of data actually suggests that there is correlation between throwing the pitch more and pitch effectiveness*

The first thing that I want to address is that I never compare one pitcher’s slider to another pitcher’s curveball, or vice versa. The reason for this is that certain factors of a curveball impact results differently than certain factors of a slider. You can play around with the “Search” function over at Baseball Savant and find several differences in the outcomes for sliders and curveballs that move and spin similarly.

The second thing I want to address is that I put eligibility rules on here for a reason. If Drew Butera were to throw a curveball in a relief appearance when the Royals were getting whooped, I don’t want that data in my formula. I only want pitchers who threw a fair amount of breaking pitches to be involved in the research and the only good way to do that is force a sample size.

So any way, now that I’ve explained myself just enough to make you believe that I’m not making stuff up, let’s get to the Royals part of this.

Here is a small grouping of my complete slider rankings from 2018:

1.) Scott Alexander
3.) Matt Strahm
10.) Joakim Soria
52.) Kelvin Herrera
91.) Jakob Junis
133.) Brad Keller

You that read right. And you read that right as well, Scott Alexander, Matt Strahm, and Joakim Soria threw the first, third, and tenth best sliders in all of Major League Baseball, respectively, in 2018. The Royals traded them for a return of...well...nothing. At least the team’s best slider in 2018 (Herrera) returned a decent prospect return.

The Royals didn’t trade much away in terms of good curveballs, but they didn’t have anyone that ranked in the top 100 curveballs either, as Jorge Lopez lead the charge for KC at #114.

One reason I’m pointing this out is to ask, “Could the Royals have been half-way decent in 2018 if they hadn’t traded away their four best relievers?”

The easy way to do this is to look at the WAR of the aforementioned relievers and the WAR of their replacements in KC’s bullpen. According to Baseball Reference, Justin Grimm cost the Royals 1.4 wins, Blaine Boyer cost them 1.8 wins, Brandon Maurer cost them 1.2 wins, Burch Smith another 1.2, and Jason Hammel 1.6. That’s a grand total of 7.2 wins. Scott Alexander was worth 0.3, Matt Strahm was worth 1.4, Joakim Soria was worth 0.8, and Kelvin Herrera was worth 0.2 in Washington. That’s an additional 2.7 wins, giving the Royals a grand total of 10 extra wins in 2018, making their record 68-94.

Alright, so it probably didn’t matter much in 2018. Let’s be generous and say the Royals actually win 70 games with the reliever swap, so what? What’s the real question here? What’s the underlying issue?

Have the Royals struggled identifying and properly valuing their talent recently?

Even if you don’t buy into my pitch data, you can head on over to FanGraphs pitch value page and see Scott Alexander’s slider comes in third place on their per/100 pitch basis (minimum 50 IP). Although I hesitate to recommend their pitch value data, you can at least get a sense that I’m not pulling this out of my rear, but you can also get a sense of why I’m trying to develop my own, as Strahm is no where to be found in the top 30.

I certainly don’t claim to be some kind of expert on the matter of Major League Baseball. There’s a reason you’re reading this article: no one is paying me to keep it a secret. It’s not a perfect system (yet) but I believe there’s a ton of truth to it, and a ton of reliability that I just personally have not seen in other places.

Using pitch data (and maybe some better scouting) could have potentially helped prevent the disastrous Padres trade. It also could’ve potentially helped the Royals find proper valuations for Scott Alexander and Joakim Soria before the 2018 season, as they would’ve been two of the Royals best pitchers, and were traded for what appears to be very little in return. Perhaps it also helped the Royals find Heath Fillmyer, though I have no idea what went into his scouting before the Royals acquired him.

In any case, I got done running on 2018 sliders and thought it was something that Royals fans may find interesting (if not upsetting). In the future I hope to formulate a more reliable data set with long(er)-term implications that could be used to project minor league pitchers as well, though MiLB data is still not always publicly available.

So there ya go. I’d love to hear your thoughts in the comments.