As I stated in my earlier article, I would be posting data from my pitch-effectiveness measurement I introduced. Let’s start with the starting pitchers.

1. Top Ten Four-Seam Fastballs (Min 500):

The Stars: Chris Sale, Jacob deGrom, Max Scherzer, James Paxton

Young and Coming: Jose Berrios

Surprises: Rick Porcello, Chase Anderson, Jeff Samardzija

This group includes some bona-fide talent and some surprises. Porcello’s 1.90 Z-Score on the Sw+Whf% jumps out, considering his lack of stuff and general pitch to contact. Anderson is quietly putting together a solid season, with a 2.88 ERA in 122 innings of work. Samardzija’s incredible strikeout and walk peripherals have been well documented this year.

2. Top Ten Two-Seam Fastballs (Min 300):

The Stars: Sonny Gray, David Price, Chris Sale, Julio Teheran

Young and Coming: Jameson Taillon, Luis Castillo

Surprises: Jaime Garcia, Matt Garza

We see Sale again, which, considering what he has done this year, is not surprising. Garza has been generally terrible this year, so his inclusion in this list is unexpected. Castillo, a rookie for the Cincinnati Reds, has pieced together some quality starts out of the spotlight.

3. Top Five Cut Fastballs (Min 200):

(Only five because the small use of cutters)

The Stars: James Paxton, Corey Kluber

Young and Coming: Zach Godley

Surprises: Tyler Chatwood

We see Paxton again, who has established himself as a star this season. Godley has been great for the Arizona Diamondbacks, and Tyler Chatwood has been poor for the Colorado Rockies.

4. Top Five Sinker Fastball (Min 200):

(Only five because the small use of sinkers)

The Stars: Jake Arrieta, Jon Lester, Jose Quintana

Young and Coming: Trevor Williams

Surprises: None

An emerging starter for the Pittsburgh Pirates, an emerging ace for the Milwaukee Brewers, and…three Chicago Cubs. I gave the Cubs pitchers the benefit of the doubt and put them under “The Stars” category, but they may have pitched their way out of there this season.

5. Top Two Splitter Fastball (Min 200):

The splitter leaderboard included only nine starters, so this one is short. Kevin Gausman has rebounded from a horrendous start to be solid, and Ricky Nolasco has continued to provide what he always has: mediocrity.

6. Top Ten Curveball (Min 300):

The Stars: Corey Kluber, James Paxton, Carlos Carrasco

Young and Coming: Zach Godley

Surprises: James Shields, Alex Meyer, John Lester, Charlie Morton

We see Kluber again, and Godley again, and Paxton for a third time. No surprise considering the seasons they have put up. Shields’ days as a front-of-the-rotation starter are far behind him. Meyer has quietly put together some solid starts for the Los Angeles Angels as a complete unknown. Lester is a surprise here because this is his second leaderboard appearance, and he has not pitched well. Morton is mostly known for his injury problems, but he has developed some of the best “stuff” in the game in his first year in Houston.

7. Top Ten Slider (Min 300):

The Stars: Carlos Carrasco, Max Scherzer, Sonny Gray, Clayton Kershaw, Marcus Stroman, Zach Greinke

Young and Coming: Dylan Bundy, Mike Clevinger

Surprises: Patrick Corbin

Finally! The man we have been waiting to see, Kershaw, makes his first appearance. As does Scherzer. The star power of this group is by far the strongest. Bundy has been “Young and Coming” for decades it seems now, and no one knows if the flashes will become consistency ever. Still just 24 years old, though, so I will keep my hopes up. Clevinger has been a nice surprise for the Cleveland Indians, and Corbin has bounced back from a miserable 2016 to be solid for the Arizona Diamondbacks.

8. Top Ten Changeup (Min 300):

The Stars: Stephen Strasburg, Kyle Hendricks, Max Scherzer, Carlos Carrasco, Cole Hamels

Young and Coming: Luis Castillo, Eduardo Rodriguez

Surprises: Edinson Volquez

Scherzer again, which makes me feel better about the validity of this work. Carrasco for the third time in a row. His breaking and offspeed stuff are killer. Very few people outside of Cincinnati know Castillo, but this is the rookie’s second leaderboard appearance. Rodriguez has continued to flash this year, but injuries and inconsistency continue for the young Red Sock. Volquez is still embracing his mediocrity.

Starters Top Fifteen Overall:

Best Pitch: Chris Sale, Boston Red Sox, 4-Seam Fastball

Best Repertoire: Corey Kluber, Cleveland Indians

Biggest Surprise: Luis Castillo, Cincinnati Reds, Changeup

This list is almost all household names. In first and second, we have the AL Cy Young frontrunners. Jeff Sullivan recently wrote an article about Kluber’s curveball, and how it may be the best pitch in baseball. It isn’t number one here, but second place is not too shabby. His cutter also appears here, so his dominance is not hard to explain. Sonny Gray’s stuff is well known, and he shows up twice on this table, but his numbers are not spectacular this year. Lastly, watch out for Castillo. He’s a no-name rook, but he has been solid for the Reds, and the ranking of his changeup may be the evidence to support his success.

Next up is relievers.

We are yet to establish a truly effective method of measuring the effectiveness of a pitch and comparing pitches. One of the main problems with attempting to evaluate a pitch on it’s own is nearly impossible. If a pitcher has an exceptional fastball, that is going to elevate his slider. Another pitcher may have a slider with more “stuff,” but it will not rank as well without the other effective pitches in his repertoire.

I will attempt to create a method here that will allow us to measure effectiveness in an improved way, although there is no guarantee here that I will succeed.

Let’s start with the main things a pitch has to be effective. In theory, a perfect pitch would be thrown in the zone and generate whiffs, while allowing weak contact when it is put in play. Obviously, it is unreasonable to expect a pitcher to generate lots of whiffs inside the zone against major-league hitters, so throwing outside the zone is a necessity in baseball to get swings and misses.

So, I used two evaluators for pitch effectiveness. I formed my own measurement, Swing%+Whiff% (Sw+Whf%). Since many pitches are meant not to be thrown in the strike zone, I did not include Zone% or Strike%. The Sw+Whf% should evaluate the ability of a pitch to get swings, and swings and misses. That evaluator covers the “stuff” component of the pitch. For the second evaluator, I used xwOBA (expected OBA), which gives a “true” wOBA based off exit velocity and launch angles. This covers the contact management component of the pitch. These are not all weighted for the part they play in run scoring, so it is not perfect, but they should give us a solid idea of what a pitch could do.

Obviously, different pitches will have different average values for these evaluators. A breaking ball is going to create more whiffs than a fastball. Fastballs are easier to hit, and thus will have a higher xwOBA. These evaluators themselves can not be used to compare different types of pitches. This is where the Z-Scores come in to play.

A Z-Score measure how much something deviates from average. First, we take the standard deviation and mean of a data sample. Then, for the Sw+Whf%, we subtract the mean from the individual’s Sw+Whf%, and divide that number by the standard deviation. We have our Z-Score! If the Sw+Whf% is higher than average, the Z-Score will be above zero, which means it is better than average. If the Sw+Whf% is lower than average, the Z-Score will be below zero, indicating the pitch is worse than average. It is the same thing for xwOBA, except for xwOBA lower is better. So instead of subtracting the mean from the individual’s xwOBA, we subtract the individual’s xwOBA from the mean. Add the two Z-Scores together, and we have our total Z.

Example:

The average Sw+Whf% on four-seam fastballs, for pitchers with a minimum of 500 four-seamers thrown, is 63.78. The average xwOBA allowed on these is .347. Chris Sale owns an 85.89 Sw+Whf% and .238 xwOBA on his four-seam fastball. The Sw+Whf% STD is 7.14 and the xwOBA STD is .038.

Sw+Whf% Z-Score: (85.89-63.78)/(7.14) = 3.08

xwOBA Z-Score: (.347-.238)/(.038) = 2.86

Total Z-Score: 3.08 + 2.86 = 5.94

Sale’s 5.94 is an incredible score, with second-place Jacob deGrom sitting at 4.81, over 1 below Sale. After that, no one else even reaches 3.5. The Z-Score has no unit, so it can be slightly confusing. It is a measurement of how many standard deviations above or below average something is.

A few things to be careful of here. These numbers are not predictive. They are simply meant to measure the effectiveness of a pitch and allow us to compare different types of pitch in a more simplified way than run values. It is just a fun statistic to look at, not something used to project the future. We also have the same problem as run values. It is impossible to look at a pitch by itself, as a good fastball will elevate a good slider. I am attempting to determine something that will allow us to better differentiate a player’s specific pitches from each other, but for now, we have this. I will be posting all the specific pitch data and tables for each pitch for starters and relievers and doing some analysis in the next few days. This is just the introduction.

Some say the sample size of Luis Castillo’s season is 15 starts; I say it’s only eight.

July 25th was the first game in which BrooksBaseball.net registered a sinker from the 24-year-old. While some other sites show blips of the pitch peering out from behind the curtain – misread changeups? – you’ll read elsewhere that he learned the pitch right as the August sun crept up on the city of Cincinnati. After adopting the sinker, his following eight starts showed a clearer picture of the pitcher he’ll be in 2018.

The issue that resonated most with analysts during his debut at the end of June was his fastball’s tendency to stay straight. An old adage you’ll hear in baseball circles revolves around a straight fastball’s velocity mattering less, because if it’s a straight 98-99 mph pitch, theoretically, a major-league hitter will have a better chance of squaring it up.

Castillo never got that memo.

Unless the Yankees’ ace Luis Severino concentrates some adrenaline to kick up his average fastball velocity by season’s end, Castillo will claim the “velo crown” for starting pitchers – 97.8mph is his number (min. 200 pitches). While velocity doesn’t tell the whole story – I’m looking at you, 2016 Nathan Eovaldi, and your 97.8mph average four-seamer – for Castillo it’s a catchy interlude; a hook that gets your undivided attention. Even with the pitch and its “straight” tendencies, aggregating all 15 of his starts, the pitch maintained a whiff-per-swing rate inside the 85th percentile among all starters – 22.6% (min. 200 pitches).

It may seem dubious that the hardest fastball among starters in all of baseball could get better during any stretch of time, but Castillo wove into his repertoire a sinker that allowed his four-seamer to change its attack.

Above we’re looking at Castillo’s four-seam fastball location pre-sinker adoption (before July 24th) and post-sinker adoption (July 25th forward). The former being a tight concentration towards the outside part of the plate, while the latter is the much larger area of dark red, up in the zone.

This philosophy makes sense; take a straight fastball, stop throwing it for strikes down in the zone, and put it at the letters, making it nearly impossible for hitters to muster success. It worked. Castillo wasn’t able to execute this move sooner because he didn’t have another fastball to establish the zone with early in starts.

Before Castillo’s sinker, hitters were teeing off on Castillo’s fastball to the tune of a .658 slugging percentage. All the while, his slider and changeup – which we’ll touch upon shortly – were nearly unhittable with slugging percentages that couldn’t edge past the fabled “Mendoza Line.” After Castillo learned his sinker, that cringe-worthy slugging percentage on his four-seamer fell to a manageable .368. His sinker, meanwhile, was his early-count pitch, and he located it unbelievably well. Castillo’s worm-killer was second among starters with a 77% grounder-per-ball-in-play rate, trailing only Jordan Zimmermann, who can’t sniff the whiff rates of our Dominican-born phenom (almost as good as Carrasco’s sinker). As Eno Sarris mentions in a FanGraphs column, Castillo is creating a duo of skills that most pitchers envy: choppers and whiffs.

Those whiffs come from his slider and changeup, two pitches that stood out before the sinker, becoming much easier to “arrive at” in terms of sequencing with a two-percent cut in walks and improved ability to get ahead of hitters. His changeup – like with most righties – is a put-away pitch to left-handers; 33% usage rate overall that kicked up to 43% when Castillo had two strikes on a lefty. The sinker we’ve discussed at length also seems to correlate with an uptick in slider usage to left-handed hitters. It ticks up 8% when Castillo has two strikes on a hitter. My speculation is this has a bit to do with gaining confidence in the pitch through understanding eye-level adjustments that hitters have to make after realizing Castillo is now living up in the zone with his four-seamer (see the GIF above). Of course, this is merely speculation; it could easily be Castillo becoming more comfortable with the break of the pitch, eliminating fears that he doesn’t follow-through and leave it up in the zone – essentially a meatball.

Sitting just below Castillo’s changeup in terms of velocity is his preferred put-away pitch to right-handers, a compact slider that doesn’t jump out in terms of swinging strike rate, velocity, or even movement, but possesses an uncanny ability to avoid becoming line drives. A peculiar metric to stand out in, yes, but after understanding line drives go for hits three times more often than ground balls, limiting line drives becomes the best thing you can do if you’re offering a pitch devoid of Kershaw-territory whiff rates.

A 27% strikeout rate in just under 90 innings, with a ground-ball rate near 59%, is a combination very few pitchers possess; Castillo is one of them. With his sinker in play, those numbers became 25% and 61% respectively; just as dominant, yet more stable with the improved control I’d speculate the sinker brought about. I’ve heard a lot of Luis Severino and Michael Fulmer comparisons to Castillo because of the fastball-slider-changeup offerings, but one of the best young pitchers in baseball is becoming a unique beast with his sinker.

Castillo’s potential is the good kind of unbelievable, in contrast to another kind of unbelievable from this Cincinnati.com article titled, “Bryan Price: Luis Castillo is in the 2018 Rotation” which implies we needed to know one of the best young pitchers in all of baseball will be allowed to dominate as a starter come March 29th of 2018. March? Yes, March. Baseball and snow is quite possibly my second favorite pairing. Behind, of course, the whiffs and grounders Castillo generates.

A version of this post can be found on my site, BigThreeSports.com.

I’m on Twitter @LanceBrozdow

The Milwaukee Brewers have been a very entertaining team in 2017. Their early dominance of the NL Central over the struggling Cubs predictably came to an end after the Cubs decided to stop being bad and to start winning baseball games again. Now, thanks to some struggles, along with the surge of the Rockies and Diamondbacks, the Brewers are on the outside looking in during the playoff race. However, that doesn’t mean that the Brewers don’t have an interesting group of solid young pitching and an intriguing offensive core. Among all of these players, Zach Davies may be one of the most interesting. Davies was never a highly-touted prospect; he always lingered near the bottom of organizational top-10 lists during his time with the Baltimore Orioles and Milwaukee Brewers. In 2016, Baseball Prospectus had him ranked 8th in the Brewers system with a 45 FV, projecting him as a back-end starter with above-average command.

This year, Davies has sported a 4.38 xFIP and a 15.9 K%, which certainly profiles like a 4th starter’s stat line. However, Davies ranks in a tie for 27th (min. 100 IP) for WAR with 3.0 on the season up until September 16th, which definitely isn’t something to expect from a back-end starter. When you look at Davies’ numbers, you have to wonder how he has manged to succeed with such a low K rate and an inflated xFIP.  Even going back to last year, Davies had a higher K rate at 19.8%, along with a 3.94 xFIP in 163.1 innings, and posted a 2.7 WAR. These stat lines look fairly similar, except for the the fact that he reduced his K rate in 2017. What else changed that has allowed him to succeed with this decreased strikeout rate? One thing Davies has changed in 2017 is his HR rate, which decreased from 1.10 in 2016 to 0.87 in 2017. So, in essence, Davies traded strikeouts for home runs. How did he manage to do that?

Let’s revisit that scouting report from 2016, with an emphasis on the 55 FV for Davies’ command. In that same report, it’s said that he had battled control problems during his time in the Brewers system. Now, there has always been a little bit of confusion as to what exactly the difference is between command and control, but Baseball Prospectus did a great job of dissecting this issue. Basically, control is the ability to throw the ball in the strike zone, while command is the ability to throw the ball in precise locations in or out of the strike zone. Below is a very handy diagram from the aforementioned article.

In this same article, two new stats are introduced that help attempt to quantify command and control: CSAA (called strikes above average) and CSProb (called strike probability). While these stats were originally created to show how well a catcher frames pitches, they can also tell you a lot about a pitcher. CSAA attempts to quantify how many strikes a pitcher creates for his team solely on taken pitches, and quantifies command. For example, having a CSAA of 3.0% means that there is 3 percent better chance that pitcher’s pitches will be called a strike than your average pitcher. CSProb quantifies how likely a certain pitch is to be called a strike, and highlights control. If you have a 50% CSProb, then there is a 50% chance a pitch you throw will be called a strike. As it turns out, the 2016 scouting report on Davies was correct; he ranks at the top of the leaderboard in CSAA and is near the bottom in CSProb. In 2017, Davies ranks 6th among qualified pitchers in CSAA at 2.83%, and he has a CSProb of 43.8%.

Davies has always been good at working the corners with a low 90’s to high 80’s fastball that has a lot of heavy run/sink to it. Usually, pitchers like this have to ensure that they can nibble at the corners of the plate so that their “slow” fastballs don’t get completely crushed by the power hitters of the league (which are apparently Elvis Andrus and Didi Gregorius now). Davies does just that, as shown in his zone heat map from Brooks Baseball below. He stays low in the zone to the majority of batters and does a good job of working both sides of the plate.

A pitcher who lives on the corners like this usually tends to draw poor contact, and that’s exactly what Davies does, in whatever way you want to to quantify it. He is one of the better pitchers in a multitude of categories, as he ranks near the top in Baseball Savant’s barrels per plate appearance stat at 2.9, has one of the lowest average exit velocities at 85.0 mph, and only 28.3% of contact against him is classified as hard hit by FanGraphs. It seems like Davies has the ability to use his pitches to work the corners and manipulate contact in his favor, which explains how he started allowing fewer home runs in 2017.

You can see this in his numbers, like stated before, where he has worked to his strengths and traded strikeouts for weak contact. This is also supported by his ability to command the ball, and his 2.83% CSAA. Put both of these qualities together, and you get a pitcher who not only limits good contact but also excels in getting called strikes more often than the average pitcher. These qualities can also help show why Davies has an inflated xFIP. Davies’ contact rate has gone up 2.5% from 2016 to 2017, and since xFIP relies a lot on batted-ball events, it can help explain why his is fairly high despite his 3.0 WAR. Davies has shown that he has the ability to adapt to major-league hitting by identifying that he can be successful in limiting good contact while at the same time allowing more of it and striking fewer batters out. If he can keep it up, there’s no reason to believe he couldn’t be a mainstay in the Brewers rotation for a long time.

Earlier this season, Baseball Savant unveiled expected wOBA, which, around these parts of the Internet, has made some real waves. For those unfamiliar, expected wOBA, or xwOBA, predicts a batter’s wOBA from the launch angles and exit velocities of his in-play contact. Because certain speeds and angles are more conducive to hits — for instance, most consider an launch angle to be around 25 degrees — xwOBA is often interpreted as a rough measure of luck. In particular, the difference between a player’s expected and actual wOBA (referred to as xwOBA-wOBA) is often cited in discussions of just how “lucky” that player has been. If a hitter’s xwOBA is significantly higher than his actual wOBA, for example, one can deduce that he’s hit the ball far better than his actual results imply.

A few months ago, Craig Edwards wrote an excellent piece on the new statistic, and discussed the interaction between xwOBA-wOBA and player speed. He noted that most of the “luckiest” batters — those with negative xwOBA-wOBA figures — were generally some of the faster players in the league, and the least lucky batters were among the slowest. Intuitively, this makes sense, as faster players are more likely to beat out infield hits and take extra bases when given the opportunity.

Edwards also charted players’ xwOBA-wOBA against their BsR scores, producing a linear-looking graph (with an R-squared of 0.27) which confirmed at least a moderate link between the two statistics. He noted that because there was no “perfect metric” for player speed at the time, he chose to use BsR as a proxy. While BsR serves this purpose well enough, I do find it problematic that the statistic, by definition, includes runners “taking the extra base,” as this information is also reflected in the wOBA element of xwOBA-wOBA (i.e. when a batter stretches a would-be single into a double, his wOBA is that of a double, while his xwOBA remains at a single). I’d be more comfortable, therefore, comparing xwOBA-wOBA against a more “pure” form of player speed.

It’s fortunate, then, that in the time since Edwards’ piece, Baseball Savant has also released sprint speed, which captures a player’s feet traveled per second on a “maximum effort” play. Using a list of batters with at least 200 at-bats on the season, I’ve re-created the scatterplot used in Edwards’s article, replacing BsR with sprint speed:

As it turns out, the results are fairly similar — there is a link, albeit not an incredibly strong one, between a hitter’s speed and his xwOBA-wOBA. The trend is downward-sloping, meaning that faster batters are luckier, but there’s still a lot of scatter around the line of best fit. The highest point on the graph, belonging to Tigers slugger Miguel Cabrera, is particularly far from the trend line, as his 66-point xwOBA-wOBA is far above the expected difference of around zero.

I should also note that the above scatterplot, with an R-squared of 0.16, has a notably weaker correlation coefficient than did Edwards’s chart. The plot did get me wondering, however, how much stronger or weaker the correlation would be for different hit types. Common sense suggests that batter speed, as it relates to xwOBA-wOBA, plays a much more significant role on ground balls than on balls hit in the air. After all, a lazy fly ball to left field will be caught whether hit by Byron Buxton (tied for the fastest batter in the league) or Albert Pujols (the slowest), but Buxton will reach far more on a weak ground ball to the pitcher:

Again using the all-powerful Baseball Savant search tool, I gathered separate xwOBA-wOBA figures for fly balls, line drives, and grounders. Now, let’s see how the interaction between player speed and xwOBA-wOBA changes based on hit category:

There’s virtually no relationship at all for either fly balls or line drives — indeed, neither’s simple linear regression R-squared is significantly above zero — but ground balls are a different story. Not only is the smoothed line for grounders much steeper than for either of the other two hit types, but the R-squared was nearly 0.31. While this is by no means a high correlation coefficient, it does confirm a link between ground ball “luckiness” and player speed.

Because we now know that we should expect faster players to outperform their respective xwOBAs on ground balls (and vice versa), it may also be appropriate to adjust batters’ xwOBA-wOBA figures accordingly. Using the results of the simple linear regression for ground balls, I’ve calculated the difference between each major-league batter’s actual xwOBA-wOBA and his expected xwOBA-wOBA as per the regression. I’ve called the stat “Actual Less Expected xwOBA-wOBA” (It’s a mouthful, I know; let’s just agree to call it ALE xwOBA-wOBA), and while it’s a pretty rough measure, it provides us with a speed-neutral valuation of batters’ ground-ball “luckiness.” A high ALE xwOBA-wOBA indicates misfortune; Brandon Belt, for instance, has an actual xwOBA-wOBA 161 points higher than his sprint speed would suggest. Full lists of batters with the highest and lowest ALE xwOBA-wOBAs are as follows:

Finally, I multiplied each batter’s ALE xwOBA-wOBA figure by his ground-ball rate, as per FanGraphs (multiplied by 100 for aesthetic purposes). This should show us which batters have been the most and least lucky in the context of their own respective batted-ball profiles. As shown below, there are a lot of familiar names in these weighted ALE xwOBA-wOBA lists, but there are also a few differences:

As mentioned above, an R-squared of 0.31 isn’t big enough to draw any major conclusions. Even so, there’s value in controlling for player speed in any discussion of players outperforming or underperforming their expected wOBAs. By accounting for batters’ sprint speeds, we can get a purer look at which players have actually been the beneficiaries of good luck, and which batters’ negative xwOBA-wOBA on ground balls have resulted from their foot speed. Further, it helps to highlight players who actually have been unlucky; if a player has a ground-ball ALE xwOBA-wOBA close to zero, but a high overall xwOBA-wOBA, they’ve been hitting much higher-quality fly balls and line drives — neither of which are significantly impacted by player speed — than their results indicate. Miguel Cabrera, for instance, falls into that category; while his ground-ball ALE xwOBA-wOBA is relatively close to zero (indicating that he hasn’t benefited from any speed-neutral luck or unluck on grounders) his fly-ball xwOBA-wOBA is a whopping 0.166. So, even though Miggy isn’t one of the faster baserunners in the league, he’s still got a legitimate gripe against Lady Luck — and now, we can see which other batters do, too.

There’s no question that Statcast has revolutionized the way we think about hitting. Now in year three of the Statcast era, everyone from players to stat-heads to the average fan is talking about exit velocities and launch angles. But what can a player do to improve both their exit velocity and launch angle? It all comes down to the mechanics of the swing.

The next great revolution in baseball is leveraging data about swing mechanics to optimize exit velocities and launch angles. It’s a revolution that has already begun. Using technologies developed by companies like Zepp, Blast Motion, and Diamond Kinetics, players and coaches can now get detailed analyses of every swing during practice. Teams are already starting to integrate these swing analyses into their player-development programs. However, none of these sensors are currently being used during MLB games.

It’s only a matter of time before MLB starts tracking swing data during games, but until then we can use Statcast data and a little physics to reverse engineer the mechanics of the swing. A couple of weeks ago, Eno Sarris and Andrew Perpetua wrote some great articles about the importance of making contact out in front of the plate and how we can infer the contact point from Statcast data. Other than contact point, what are the other important characteristics of a swing? Well, let’s look at Eno’s favorite graphic, from the time Zepp analyzed his swing:

It all comes down to swing speed, attack angle, and timing! The time to impact is probably impossible to get from the Statcast data, so let’s focus on the two remaining metrics: swing speed and attack angle.

Swing speed

Statcast doesn’t measure swing speed directly, but nonetheless reports an estimated swing speed, computed using an algorithm with all the transparency of a black box. In fact, it’s so secretive that estimated swing speeds have all but disappeared from Baseball Savant in recent weeks. Just to find the data, I had to dig up a couple of the saved searches from Alex Chamberlain’s article from a few weeks ago on that topic. Here is the leaderboard of the fastest average estimated swing speeds as reported in that article:

Eno swings like Giancarlo Stanton!

Now, I don’t want to shatter anyone’s dreams of blasting a home run off of a Major League pitcher, but something is clearly off about the data. It turns out that not all reported bat speeds are equal. Physics tells us that as the bat rotates, the barrel (the end) of the bat moves the fastest and that the bat speed decreases in an approximately linear fashion as we move toward the hands. According to Patrick Cherveny, the lead biomechanist for Blast Motion, which is the official swing sensor of the MLB, measuring the barrel speed is essentially meaningless:

“We see some swing speeds where people claim that you get into the 90s. That would make sense if it’s at the end of the bat, but if you hit it at the end of the bat, it’s not going to travel as far because some of the energy is lost in the bat’s vibration. So that kind of a swing speed is essentially ‘false.’ Swing speed is dependent on where you’re measuring on the bat. In order to maximize quality of contact, the best hitters want to hit the ball in the “sweet spot” of the bat.”

Measuring the speed of the bat at the sweet spot, a two-inch-long area whose center is located six inches from the barrel of the bat, Blast Motion reports that MLB players swing the bat between 65 and 85 MPH. Zepp, on the other hand, reports the barrel speed, which accounts for its elevated values. Still, none of the swing-tracking devices on the market report swing speeds as low as those estimated by Statcast.

Let’s see if we can uncover more information about the black-box algorithm used by Statcast to estimate swing speeds. A quick linear regression between average estimated swing speed and average exit velocity for all batters with at least 100 batted ball events (BBE) in a season from 2015-2017 yields an R² of 0.99. Wow! Statcast estimates swing speeds almost entirely from exit-velocity data. No wonder the names at the top of the list are so obvious.

Exit velocity, however, isn’t the only velocity measured by Statcast. We also know the speed of the pitch as it is released from the pitcher’s hand. Thinking about the physics, the bat transfers energy and momentum to the oncoming ball at the point where the bat collides with the ball. Thus, any estimation of swing speed based on Statcast’s EV and pitch speed data represents the speed of the bat at the point where it makes contact with the ball. Since hitters want to hit the ball at the sweet spot, swing speeds estimated from Statcast data should fall in approximately the same range as those measured by Blast Motion.

Much of the research on the physics of bat-ball collisions has been conducted by Dr. Alan Nathan, so let’s start with one of his equations:

EV = e_Av_ball + (1 + e_A)v_bat

where EV is the exit velocity, v_ball is the velocity of the ball before it hits the bat, and v_bat is the velocity of the bat. Here e_A is a fudge factor called the collision efficiency, and depends on the COR of the ball, which was at the center of the juiced-ball controversy, the physical properties of the bat, and the point on the bat in which that bat strikes the ball. Thus, assuming all MLB players use a standard ball and bat, e_A can be viewed as a measure of quality of contact. Nathan found that at the sweet spot of a wood bat, e_A = 0.2. Using that value of e_A and the release speed and exit velocities from Statcast, we can estimate the bat speed for every ball in play. According to Nathan’s pitch-trajectory calculator, the average pitch slows down by 8.4% from the release point to when it crosses the plate, so we’ll also make that adjustment to the release speed reported by Statcast. Here’s the relationship between our physics-based model for swing speed and the estimated swing speed from Statcast/Baseball Savant:

Look at that! When you get a slope of 1 and an intercept of about 0, you know you’ve hit the nail on the head. This must be the equation that Statcast is using to estimate swing speed. After doing a little digging, it appears that Nathan gave them that exact formula, but assumed that the pitch slows down by 10% by the time it crosses the plate.

The problem with this algorithm is it assumes that the hitter always hits the ball at the sweet spot. Nathan’s paper actually shows that e_A varies linearly as a function of EV, from about -0.1 for the weakest hit balls to 0.21 for the best hit, depending on how far from the barrel the bat collides with the ball. To get a good estimate of swing speed, we’ll need to get a better estimate of e_A. Unfortunately, e_A must be computed independently for every hitter due to inherent differences in a hitter’s strength. For instance, when Giancarlo Stanton hits a ball with an EV of 100 MPH, he is making weaker contact than when Billy Hamilton hits a ball 100 MPH.

I calibrated e_A for each hitter with at least 100 BBE in a season by estimating that the average of the top 15 BBE by exit velocity corresponds to e_A =0.21 and the average of the bottom 15 BBE by exit velocity corresponds to e_A = -0.1 for each player. Since e_A and EV are related linearly, we can compute e_A from EV for each player. Finally, I will assume that every player uses a standard 34 in., 32 oz. bat. Since Nathan’s study used a 34 in., 31 oz. bat, I subtracted 0.42 MPH from the estimated swing speeds, because every extra ounce reduces that bat speed by about 0.42 MPH. Here’s a look at our new average estimated swing speeds:

We see that swing speed still correlates strongly with exit velocity, but with a much more reasonable R² value of 0.81. Much of the remaining variance is due to the quality of contact, as estimated by e_A. The colors here show the soft-hit rates from FanGraphs. We can see not only that slower swing speeds result in more soft contact, but also that the regression line strongly divides hitters based on their soft-contact rates. Hitters above the line tend to make better contact and hit the ball more efficiently than those below the line, given their swing speeds.

Knowing the value of e_A also gives us an estimate of where the ball hit the bat in relation to the barrel. Nathan found that e_A ~ d², where d is the distance from the barrel. Since a quadratic function has no inverse, we’re forced to infer d from our computed values of e_A by assuming a linear relationship between the two variables. Once we know where the ball struck the bat, we can also estimate the barrel speed and hand speed, assuming that those speeds are proportional to distance from the axis of rotation.

I have no idea how accurate these estimates are, but they look pretty good! The swing speeds at the point of contact line up nicely with those from Blast Motion (65-85 MPH range and league average of 70 MPH), as do the barrel speeds (Zepp claims 75-95 MPH) and hand speeds (Blast Motion says 23-29 MPH). There’s a lot more uncertainty in the barrel and hand speeds than at the point of contact, because they require additional assumptions about bat size, axis of rotation, and distance from barrel of the point of contact. Even with all of those assumptions, the accuracy probably isn’t much worse than those of the swing-tracking devices on the market today, which claim an uncertainty of about 3-7 MPH for individual swings.

Here are the fastest and slowest average swing speeds in a season during the Statcast era:

At the top of the list we see some well-known sluggers and … Rickie Weeks? Who knew he had such elite bat speed? Unfortunately for him, his average e_A in 2016 was the lowest of any player in the Statcast era, indicating that he was making a ton of weak contact. Weeks is the quintessential over-swinger, whose impressive bat speed is often nullified by a lack of bat control. That’s completely unsurprising for a player’s whose 2016 highlight reel features at least one hack that would make even Charlie Brown blush:

I was also going to include a table of all of the fastest individual swings, until it turned into an exercise in how many times I can write Giancarlo Stanton’s name. He has 18 of the top 19 swings by barrel speed, which tops out at 108 MPH.

Attack Angle

Unlike swing speed, Statcast doesn’t give us an estimate of attack angle. Instead, we’ll again turn to some research done by Dr. Alan Nathan, this time from his 2017 Saberseminar presentation. To better understand the geometry of the bat-ball collision, let’s look at a diagram from his presentation:

The attack angle, or swing plane, is the angle that the bat is moving at when it hits the ball. Drawing a line between the centers of the bat and ball at the time of impact defines a second angle, called the centerline angle. When a hitter swings the bat such that the attack angle lines up with the centerline angle, he generates his maximum exit velocity and launches the ball at an angle equal to that of the attack angle.

Armed with this information, we can compute the attack angle by looking at the launch angles when a hitter produces his highest exit velocities. Nathan does this by plotting EV against LA for each hitter (below is his figure for Khris Davis’s BBE, whose attack angle is about 20°). He then divides the data, presumably binning the data by launch angle and then pulling out the top few BBE by exit velocity in each bin (red points). Once the data has been divided, a parabola can be fit to the red points, such that the attack angle corresponds to the peak of the parabola.

I found that the computed attack angle is fairly sensitive to the number of bins and number of data points in each bin, so this method is far from perfect. Ultimately, I chose the number of bins based on each player’s standard deviation in launch angle (~3° bins), and selected the top 20% of data points by exit velocity. I then computed a second version of attack angle by averaging the launch angles of the top 15 BBE by exit velocity (just as I did when computing swing speeds). Finally, I averaged the values from the two different methods to get a final value for the attack angle.

This method of computing the attack angle gives us what I’ll call the “preferred” attack angle. Batters change their attack angles slightly based on pitch location, but the preferred attack angle represents the plane of a hitter’s natural swing when he gets a good pitch to hit (à la batting practice).

A lot of digital ink has been spilled over the last few years trying to make sense of how to evaluate hitters using launch angles. While a ton of progress has been made, we still have a long way to go. Who knew launch angles could be so complicated? Here, we see a relatively weak correlation between attack angle and launch angle, because launch angle is also strongly dependent a hitter’s aim, timing, and bat speed. While we don’t have any direct measurements of aim or timing, we can see from the color scale that players with flatter swings (lower attack angles) have more margin for error when it comes to timing, and therefore tend to have higher contact rates than players with uppercut swings (larger attack angles).

The fly-ball revolution is even more evident when looking at league-wide attack angles instead of launch angles. There was a lot of buzz before this season about players reworking their swings to increase their launch angle. Not all of them were successful though, as the average launch angle only increased by 0.3°, despite a nearly 2° jump in attack angle.

Here are the highest and lowest preferred attack angles in a season during the Statcast era:

It’s good confirmation to see Ryan Schimpf’s name on this list, though it’s interesting that his attack angle isn’t the extreme outlier that his GB/FB ratio and LA are. An analysis of attack angle may also finally give us an answer to why Brian Doziers’s home runs have gone missing this season. His 2017 batting line is almost identical to that of 2016, except his ISO (and HRs) have plummeted. The biggest difference is his attack angle has skyrocketed from 20° to 29°. We know that the optimal LA for hitting home runs is about 24°, so he’s probably getting too much loft on his fly balls this year. All of these guys at the top of the list would probably benefit by flattening out their swings a bit. Interestingly, Joey Gallo, everyone’s other favorite extreme fly-ball hitter, has an attack angle right at 24° this year. He has built the perfect swing for his batted-ball profile, which explains why he is among the league leaders in HR/FB ratio.

This turned out to be an extremely lengthy primer on swing mechanics, but there plenty of questions that can be tackled with estimates of swing metrics. For instance, can we use swing speed and attack angle to predict future exit velocities and launch angles? How much do hitters reduce their swing speeds on two-strike counts? How do attack angles change with pitch location? But, alas, those questions will have to be answered at a later time.

A complete list of swing speeds and attack angles for players with at least 100 BBE is available here.

If it weren’t for Adam Engel, Carlos Carrasco would have shut out the Chicago White Sox on Wednesday night. It’s hard to believe something stood out to me more than the preceding sentence’s qualifier, but baseball possesses the quality of unpredictability, and I will never complain.

Carrasco is an artist, mixing five pitches with such care that I often find myself gravitating towards his starts despite my lack of association with the Indians’ fan base. On Wednesday, what I noticed more than Engel ruining a shutout was Carrasco veering away from a pitch vital to his repertoire — his changeup. Due to the graces of BrooksBaseball.net, I can confirm the lack of changeup usage was unusual for Carrasco; five instances of the pitch were his second-fewest in any start this season. Wednesday was the longest outing of Carrasco’s season and matched his season-high “game score” of 89 (50 is average) from a battle back on April 22nd against — you guessed it — the White Sox.

Carrasco’s beauty stems from his ability to execute flawlessly with a game plan contrary to what you would expect. His season averages reflect the bigger picture, yet on a given night he can meander in unprecedented directions. My wonder surrounding the lack of his usual third pitch brought me to another contrarian aspect of Carrasco’s game: this changeup possessed the best ground-ball per ball-in-play ratio of any pitch in baseball (min. 200 pitches). 82% of the time when contact is made between the lines, the batted-ball result of this changeup is a ground ball. With 6% of batted balls falling into the “fly-ball” category, Carrasco’s pitch is one of the hardest in baseball to hit in the air.

Does this make it the best changeup in baseball? That depends on what qualities you believe make a changeup great. Per FanGraphs, Clayton Kershaw — shocker — holds the highest pitch value for a changeup at 6.9 runs per 100 pitches (0.0 is an average offering), with Carrasco just behind him. If you subscribe to limiting line drives as the better indicator of changeup success, the honor would go to Stephen Strasburg, who coincidentally gets the most whiffs per swing on his changeup at 51%. I’m not arguing that Carrasco has the best changeup in baseball; I’m highlighting how absurdly hard it is to do anything but hit Carrasco’s changeup on the ground. That in itself deserves as much attention as one that generates excessive swings, or is throw by the left hand of a legend — I tip my hat to you, Mr. Kershaw.

If you read my most recent recent column on the Orioles’ Dylan Bundy — whom I already consider to be “great” (yeah, pretty bold) — you can tell I’ve become intrigued by the Baseball Prospectus rabbit hole that is pitch-tunneling. In as simple terms as you can get, the “tunnel point” is where the hitter has to decide whether or not to swing, with movement more than 2.6 inches between the tunnel point and home plate considered above-average. The concept is fresh, with only bits of hard evidence for suggesting how to correctly apply the statistic, but one of its beliefs makes intuitive sense. In a vacuum, if your pitch moves more than average beyond the tunnel point, it becomes harder to hit. My thinking with Carrasco’s changeup is simple: it must have a lot of downward, “late” break to force hitters into topping the ball at such a high rate.

Ah, if only baseball was that easy.

Carrasco’s pitch sequence of fastball-changeup is his fourth-most commonly used pitch pair; it doesn’t stand out in terms of post-tunnel break among his other pitch pairings, nor when you compare that break back to the league average for a typical fastball-changeup sequence. What it does stand out on is something called “flight-time differential.” Carrasco’s .0223 is the third-lowest in baseball among pitchers who have thrown a fastball-changeup sequence more than 50 times. This stat is another way to show velocity differences between pitches. The short flight-time differential holds up when we observe Carrasco’s 5.7 mph difference between his average fastball and changeup velocities (fourth-smallest, qualified pitchers).

Good news: this all jives with Harry Pavlidis’ research. Harder changeups with smaller velocity differentials between that pitcher’s fastball means more ground balls, while a larger velocity gap between the two pitches means more whiffs. While ground-ball inducers tend to throw their change earlier in counts, whiff inducers favor the pitch as a put-away offering. While that sentence isn’t all-encompassing, Carrasco’s deviation from conformity continues.

As with most right-handed pitchers, Carrasco tends to throw his changeup to left-handed bats substantially more than right-handed bats, favoring the benefits of arm-side movement a changeup generally possesses due to the pronation of a pitcher’s hand. But unlike conventional thought that suggests the pitch’s ground-ball rate is such that early-count looks are likely, Carrasco throws the pitch more as he gets deeper into counts.

Just as Carrasco plays second fiddle to Corey Kluber in the Indians rotation, his changeup plays second fiddle to his slider, both allowing for rampant underappreciation. The pitch is so good this year that Carrasco has muddied the stigma that high-velocity, or low-velocity differential, changeups should remain early-count offerings. Once again, Carrasco is veering from the path of predictability.

Even after four seasons where progressive improvement hasn’t ceased, due to injuries we still haven’t seen a 200-inning campaign from the righty. 2018 will be his age-31 season, and as father time comes knocking, it’s unfortunate that we may be observing the tail end of Carrasco’s peak performance. With the Indians firmly intertwined with the phrase “playoff bound,” Carrasco will get his first reps on an October mound. If history provides any indication of the future, we know Carrasco will both stand out from him predecessors and succeed.

In regards to an obscure September outing and the lack of changeup usage, digging deeper might unearth logical reasoning, but with Carrasco, I think mystery adds to the legend of an under-the-radar arm.

I can be found on Twitter – @LanceBrozdow – tweeting about the greatest of all games.

A version of this post can be found on my website, BigThreeSports.

When calling balls and strikes, consistency matters. As long as an umpire always calls borderline pitches the same way within a game, players seem to accept variations from the rule book strike zone. While there have been many excellent analyses of umpire accuracy, these studies tend to focus on conformity to a fixed zone, rather than on the dependability of those calls.

Disgruntled fans can turn to Brooks Baseball’s strike zone plots when they feel an umpire has had a bad game against their team. For example, the following zone map seems egregiously bad:

The calls seem very capricious, especially on the outside (right) of the zone. Balls (in green) are found in the same locations as strikes (in red), and some called strikes landed much further outside than pitches that were called balls.

On the other hand, the zone map below appears fairly consistent:

One might quibble with a couple of the outside calls, but the called strikes, for the most part, are contained within a ring of balls. Notice also that pitches in the lower-inside corner were consistently called balls. While this umpire didn’t establish a perfectly rectangular zone, he did establish a consistent zone; neither pitcher got those calls on the inside corner, and hitters on both teams generally knew what to expect.

In this post, I will propose a metric for assessing the inconsistency of an umpire’s strike zone. This metric does not assess how well the umpire conformed to the rule-book zone or the consensus MLB zone. Rather, it uses some tools from computational geometry to compare the overall shape formed by called strikes with the shape formed by the called balls.

Data from MLB Advanced Media describes each pitch as an ordered pair (p_x, p_z), representing the left/right and up/down positions of the ball as it crosses the front of the plate. This pitch-tracking data includes measurements of each batter’s stance, which can be used to normalize the up/down positions to account for batters of different heights. If we draw a scatterplot of these adjusted positions corresponding to called strikes during a given game, the outline of the points represents what we define as the umpire’s established strike zone.

More precisely, the established strike zone is what mathematicians call the “convex hull” of these points. If you draw the points on a sheet of paper, the convex hull is what would remain if you trimmed the paper as much as possible, without removing any points, using only straight cuts that go all the way across the sheet.

A similar construction describes the alpha hull of a set of points: replace the paper cutter with a hole punch that can only punch out circular holes of a given radius. Punch out as much of the paper as possible, without removing any of the points, and what remains is the alpha hull. Unlike the convex hull, the alpha hull can have empty region in its interior. We can therefore define an umpire’s established ball zone as the alpha hull of points corresponding to called balls.

A consistently-called game should have the property that the established ball zone lies entirely outside of the established strike zone. Any called strikes that fall within the established ball zone (and any balls inside the established strike zone) are inconsistent calls. Since it is reasonable to expect that a consistent umpire will establish different zones depending on the handedness of the batter, we calculate established zones separately for left- and right-handed batters, and then count the number of inconsistent calls from each side of the plate.

Over the course of a game, an umpire’s inconsistency index is the ratio of inconsistent calls to the total number of calls made. For example, the plots below show the established strike and ball zones for the game between the Reds and the Giants on May 12, 2017. Of the 239 calls made that day by the home-plate umpire, 14 balls fell within the established strike zone, while 5 called strikes landed in the established ball zone, resulting in an inconsistency index of (14+5)/239 ≈ 0.0795.

How do MLB umpires fare under this metric? Quite well, actually. Using data for the 2017 season (through September 10), the average inconsistency index for all games called was 0.0396. Moreover, of the 2112 games analyzed, there were 183 games where the home-plate umpire scored an inconsistency index of 0.0, meaning that the established strike zone fell completely within the established ball zone. The 15 most consistent umpires, based on their average inconsistency index over all games called in 2017, are given in the table below.

While the strike zones of these umpires may not robotically correspond to the rectangles we see on MLB broadcasts, the zones they do establish are remarkably consistent.

Graphs and computations in this article were produced in R, using the PitchRx and alphahull packages. Source code for producing these examples is available on GitHub.

*Apologies for the bad writing, as this is my first-ever community post on FanGraphs.*

At the time of this writing, it’s been seven days since rosters expanded in the major leagues. Still, the International League (AAA) home-run leader has yet to appear in a major-league game season. Since the 2000 season, there have been three International League home-run champions that had not appeared in a major-league game that same season. Bryce Brentz, leading that league with 31 home runs and winner of the Triple-A Home Run Derby, is about to be fourth.

The Red Sox of the old days had the reputation of being offensive powerhouses by working long at-bats and possessing big power in the middle of the lineup. This year, it’s been quite the opposite. Red Sox pitching has been absolutely amazing this season; the pitching WAR is tied for second place (with the Dodgers) while also having the fourth-best ERA in the majors at 3.76 ERA. Compared to how great the Red Sox pitching is, the hitting is bad. REALLY BAD. Their pitching and hitting are night and day. It’s well documented that the Red Sox aren’t hitting for power this year, sitting dead last in the AL with only 146 home runs. Perhaps teams don’t need to hit home runs to be productive? The advanced metrics say otherwise. Out of all qualified players, the Red Sox batter with the highest wRC+ is Dustin Pedroia who has a 106 wRC+, and 2016 MVP runner-up Mookie Betts is running a 101 wRC+. All in all, the Red Sox offense has been below average this season. The emergence of Rafael Devers and the spark that Eduardo Nunez has provided to the Red Sox have both softened the blow, but there still appears to be a glaring weakness.

In the absence of David Ortiz, Hanley Ramirez was supposed to step up and become a middle-of-the-order power threat. What’s inexcusable is his performance versus lefties this year. As someone who’s destroyed lefties his entire career, he’s suddenly slashing .194/.312/.419 against lefties in 2017. His career OPS/wRC+ vs. lefties is .902 OPS and 138 wRC+ respectively.

HanRam has been having one of his worst seasons hitting-wise versus lefties. Despite insisting that he will improve against lefties, Red Sox fans have yet to see the results come out.

Another factor is Chris Young’s performance. Chris Young was brought to Boston to club lefties. He’s always been able to hit them, and his splits against lefties prove just that.

Discarding 2014 and this current season, that chart is a thing of beauty. Chris Young’s performance this year has been concerning; he hasn’t had an RBI since August 6! Chris Young was a player who was specifically brought onto this roster for the specific purpose of facing tough lefties. He is having his worst season hitting lefties yet. As of right now, he is batting only .184 against lefties this season with one home run, four extra-base hits, and four RBI.

The Red Sox need Bryce Brentz. Brentz certainly has the prospect pedigree, being drafted by Red Sox in the first round of the 2010 Major League Baseball Draft out of South-Doyle High School. Once rated as the No. 5 prospect in the Red Sox system, he stood out with his plus raw power. FanGraphs’ Kiley McDaniel had this to say about him a few years back:

“Brentz has easy plus raw power from the right side and is a solid athlete, but it doesn’t translate to defense, where his fringy arm limits him to left field. There’s some holes, lots of swing and miss and trouble with spin from right-handed pitchers, but also 20-25 homer power with a floor of a solid platoon bat.”

The key word here is “solid platoon bat,” something he’s finally evolved into this year. This year, down in Triple-A, when facing left-handed hitters, Brentz was hitting .279 with nine home runs, 25 RBI, and 17 walks. His OPS against lefties is 391 points higher than Chris Young’s OPS in the majors this season (.957 OPS). Rhys Hoskins, who took the majors by storm, was the only player ahead of him in the IL in terms of wRC+.

Brentz had worked with PawSox hitting coach Rich Gedman this past offseason, which has suddenly changed him into someone who destroys left-handed pitchers and is at least passable against righties. By introducing a toe-tapping procedure to Bryce Brentz, Gedman has turned him into a major home-run threat. I think it’s time to believe that after 6+ seasons in the minor leagues, Bryce Brentz finally has things figured out.

The basis behind why Dave Dombrowski won’t call up Bryce Brentz is, to say the least, questionable.

Dave Dombrowski said IL HR champ Bryce Brentz willl not get callup from Pawtucket. Brentz hit 31 HR but no 40-man roster spot available.

— Nick Cafardo (@nickcafardo) September 4, 2017

No 40-man roster spot available? C’mon. Off the top of my head, I could name off a few minor leaguers who don’t deserve this spot over Brentz. Most notably, the walk machine himself, Henry Owens. Owens was sent down to Double-A to work on mechanics, but instead, he’s walking 8.68 batters per 9. Ben Taylor, who made the Opening Day roster for the Red Sox, has had considerable minor-league success, but the results haven’t translated to the majors. He’ll most likely end up as a career middle reliever or minor-league journeyman. Sure, these players have their uses, but they don’t deserve their spots as much as Brentz does. After his hard work in the offseason, his performance needs to warrant him a 40-man spot. Additionally, after Chris Young becomes a free agent next year, Brentz can serve as the fourth outfield for the Red Sox in 2018. If the Red Sox don’t add Brentz to the 40-man by the offseason, he’ll become a free agent. It’s almost guaranteed that a team such as the Athletics or the Reds would be willing to give him a chance.

There’s another problem. At the moment, the Red Sox really lack good pinch-hitters. When your best hitters off the bench are Brock Holt, Sandy Leon, Rajai Davis, etc, the outcome looks really bleak. Brentz is a minor-league veteran who is a power threat off the bench, something the Sox currently lack. His career hasn’t progressed much (until now at least) since he shot himself in the leg during the spring training of 2013. If fact, if you go to some online forums, his spring-training incident has created tons of puns that have to with guns; the former top prospect had become a joke. Similar to the rest of the “comeback” stories (such as Rich Hill, Eric Thames, etc) that fans have loved to watch in recent seasons, the story of Bryce Brentz should warm the hearts of fans.

Something else stands out. During the Red Sox’s recent 19-inning game, this tweet was sent out. While it may have been mostly a joke, it really exemplifies the lack of power the Red Sox have.

Brentz would have already homered this inning

— Red Sox Stats (@redsoxstats) September 6, 2017

This really speaks about the Red Sox offense. Bryce Brentz is the spark plug that they need.

As seen by the Nationals calling up Victor Robles just the other day (considered late), the Red Sox certainly still have time to call up Bryce Brentz. If any Red Sox personnel is reading this, the rest of Red Sox Nation and I have this to say to you: “Hey, It’s worth giving Brentz a shot.” He’s deserved it.

When the Houston Astros sent two of their top pitching prospects, Vince Velasquez and Mark Appel, to the Philadelphia Phillies for Ken Giles in December 2015, they were expecting an excellent flame-throwing reliever, and possibly their closer of the future. In his first two years in the league, Giles amassed a 1.56 ERA in 115.2 innings. His work as a setup man/closer went largely unappreciated due to the losing nature of the Phillies, but Giles pitched like one of the best relievers in the game.

But his first year in Houston did not go as planned. Giles couldn’t maintain a hold of the closer job, as he blew five of his 20 save opportunities and finished with a 4.11 ERA. His 2.86 FIP proved he might have suffered from bad luck, and he still displayed incredible stuff (nearly 14 K/9), but he did not execute as expected or needed for the Astros.

2017 has been a different story for Giles. His 29 saves rank ninth in the league, and he has blown only three opportunities this season. His 2.30 ERA is legitimate, supported by a 2.14 FIP. Giles has been one of the best closers in baseball, but his name is hardly mentioned among the top guys in the league. And he’s been especially locked in of late.

Last night (September 5th at the time of writing this), Giles struck out the side in a 10-pitch inning to earn the save against the Seattle Mariners. The only ball he threw came when the batter barely checked his swing. Here he is hitting triple digits on the outside corner to get Ben Gamel looking and close out the game:

It was the second night in a row that he struck out the side for 1-2-3 ninth.

Since June 7th, Giles has a minuscule 0.86 ERA and .147 average against. FIP will rarely support a mark that low, but his 1.57 mark in that category is still exceptional. He’s striking out more batters and walking fewer, accumulating a K-BB% of 31.3%.

Giles has given up one run since July 16th, in 20.1 innings of work. His FIP is under 1, at an absurd 0.82, and he’s sporting a ridiculous 45.1% K-BB% in that time. He has also show the ability to be stretched out of late, as he has gone 1.2 or more innings in three of his last ten appearances. What has made him so effective this season?

It all starts with the slider for Giles, which ranks third in run value among relievers at 12.9 runs. Run values aren’t the best metric, but they definitely give you an idea of the effectiveness of a pitch. Just look at it:

The pitch starts at the “TEXAS” on Rougned Odor’s jersey and finishes below his knees. There is about nothing a hitter can do with that.

Look at a heat map, by pitcher viewpoint, of Giles slider’s location. He is burying the majority of his sliders along the bottom of the zone. Now look at a heat map of the average against the pitch, by zone. Where the majority of the pitches are going, hitters aren’t doing much with. At all. Per Brooks Baseball, hitters have only put the ball in fair territory on 25% of their swings at the pitch. They rarely put the ball in play, and they don’t do much with it when they do.

But this is actually not new for Giles. He was third last year in slider run value among relievers, at 12.6 runs. Where Giles has greatly improved his effectiveness is with his fastball. It was worth a run value of -13.3 in 2016, but it’s currently sitting at 3.4 this year. It has not been incredible, but paired with the slider, it doesn’t need to be.

Here is a comparison of his fastball in 2016 vs. 2017:

The batted-ball numbers are down across the board. He’s throwing it in the strike zone more often as well, which would cause you to expect he is pitching more to contact with the pitch. However, the Contact% has steeply declined, and the swinging-strike rate is way up. Obviously, with a 137 wRC+ allowed this year, the pitch is still not great. But when you have a slider running a -14 wRC+, it does not need to be.

Here is a heat map comparison of the two pitches: 2016 vs. 2017

The spray is much tighter in 2017, and he is throwing across the middle of the zone a whole lot less. Improved command of a pitch will obviously lead to more success. But another element might be involved in the improvement of his fastball. Giles has added nearly four inches of horizontal movement this year, from -1.7 to -5.6.

A 2016 fastball:

And the fastball from last night again:

The run to the right on the pitch is clear. Movement of any kind will always help to keep a hitter off balance, and while we can’t be sure, it looks like this may be what has given life to Giles’ fastball. His confidence with the pitch has grown, as his usage of it exploded from roughly 50% to 68.4% in August. And it appears this improved fastball may be keying his emergence as one the best closers in the game.

Giles lost some respect and notoriety with his poor 2016. But with the year he has put together so far, especially the way he’s pitching of late, he has earned all of that, and then some, back. He’s locking down the back of Houston’s bullpen. The Cleveland Indians displayed the importance of relievers in the playoffs last season, so don’t be too surprised if Giles is at the forefront of a charge to the World Series for the best team in the American League.