Much like the 2017 season Kevin Pillar has had an excellent start to the 2018 season and looks like he is ready to break out offensively and show his true potential at the plate. In 2017, it looked like Pillar would achieve this but from mid-May onwards he dropped to his career norms. Kevin Pillar is one of the best defenders in baseball and is a gold glove candidate year after year. The purpose of this article is to project the offensive numbers for Pillar and show he will have a career year offensively. The cells with red text in the diagram below will be inputs that will be described throughout the article. The cells that do not have red text contain the following formulas for the projection.

Cell E2 (at-bats): =F2-N2-Q2-R2

Cell G2 (hits): =AK2*AA2+K2


Cell H2 (singles): =G2-I2-J2-K2


Cell I2 (doubles): =IF(AB2>0,E2/AB2,0)


Cell J2 (triples): =IF(AC2>0,E2/AC2,0)


Cell K2 (home runs): =AO2*AN2*(AA2+K2)


Cell L2 (runs scored): =AP2*(G2+N2+Q2-K2)+K2


Cell M2 (runs batted in): =AQ2*(AA2-R2)+(1.565*K2)+R2
1.565 represents an approximate average of the number of runs batted in per home run.

Cell N2 (walks): =AH2*F2

Cell O2 (intentional walks): =AI2*F2


Cell P2 (strikeouts): =AJ2*F2


Cell Q2 (hit by pitches): =IF(AD2>0,F2/AD2,0)


Cell R2 (sacrifice flies): =IF(AE2>0,F2/AE2,0)


Cell S2 (stolen bases): =AF2*(H2+I2+N2+Q2)*AG2


Cell T2 (caught stealings): =AF2*(H2+I2+N2+Q2)*(1-AG2)

Cell U2 (batting average): =IF(G2>0,G2/E2,0)


Cell V2 (on-base percentage): =(G2+N2+Q2)/(E2+N2+Q2+R2)

Cell W2 (slugging percentage): =(H2+(2*I2)+(3*J2)+(4*K2))/E2

Cell X2 (on-base plus slugging percentage): =V2+W2


Cell Y2 (isolated slugging percentage): =W2-U2  

Cell Z2 (weighted on-base average): =(0.687*(N2-O2)+0.718*Q2+0.881*H2+1.256*I2+1.594*J2+2.065*K2)/(E2+N2-O2+R2+Q2)

Cell AA2 (balls in play): =E2-P2-K2+R2

Cell AM2 (line drive percentage): =1-AL2-AN2

For the remainder of the article, statistics for Kevin Pillar will be discussed that will contribute input to the spreadsheet in order to fill out the formulas as presented above. Plate appearances (PA) which is cell F2 in the spreadsheet will be discussed first. The number of PA’s Pillar had in the 2015, 2016 and 2017 seasons were 628, 584 and 632 respectively. In 2016 Pillar spent time on the DL with a thumb sprain, so I will take the average of the 2015 and 2017 plate appearances when he played almost every day. Pillar’s predicted plate appearances for 2018 will be 630.

The above chart shows the at bats per double rate (AB/2B which is the # of At Bats/# of doubles) and similarly at bats per triple rate (AB/3B) over the past 3 seasons. Pillar has increased the amount of doubles in each season since 2015, and this season Pillar leads MLB with 18 doubles and I predict he will stay near the top of the league in doubles which will project to a career high at bats per double rate of 13. Discounting last season where Pillar only had one triple and an AB/3B rate of 587, I will take the average between the 2015 and 2017 numbers to get a rate of 283.5.

Next, I will discuss plate appearances per Hit by Pitch (PA/HBP) and Plate Appearances per Sacrifice Fly (PA/SF). Since it is unpredictable how much a batter will be hit by a pitch I will use the three year average of 109.4. For PA/SF I will also take the three year average and arrive at 177 for 2018.

Next, Stolen Base attempts per times on base (SBA/TOB) and Stolen Base percentage SB% will be discussed. Time on base (TOB) is calculated by adding total hits, walks, and hit by pitch of a player. Stolen base percentage is the number of stolen bases divided by the number of attempts. The data for the 2015-2017 seasons are shown in the table below. To arrive at SBA/TOB for 2018 take the 3-year average 2015-17 and arrive at SBA/TOB= 0.124. For SB% just consider 2016 and 2017 where the numbers where very similar and take the average and arrive at a SB% of 70% for 2018.

Next walk (BB), strikeout (K) and Intentional walk (IBB) percentage along with Batting Average on Balls in Play (BABIP) will be discussed. These values for the last 3 seasons are tabulated below.

As can be seen above, Pillar’s BB% increased from the 2016 season in 2017. So far in 2018 Pillar has a BB% of 5.6%. Pillar is showing more patience at the plate and I predict a career high in BB% of 5.5%. This increased patience at the plate will result in a lower strikeout rate of K%=18.5%. For the IBB% I will just take the three-year average to arrive at 0.053%. Pillar’s BABIP is way above his career high to start 2018 at 0.355. Pillar leads the league with 20 doubles and already has more RBI (17) then he did all of April and May 2017 with only 13 RBI.  While his BABIP will likely drop closer to his career average, Pillar will post a career high BABIP of 0.335.

We will now discuss Pillar’s Line Drive Percentage (LD%), Ground Ball percentage (GB%), his Fly Ball percentage (FB%) and Home runs per Fly Ball rates (HR/FB) over the past three seasons to help predict these values for the upcoming 2018 season.

LD% for 2018 mirrors the three-year average because the values are relatively similar over those 3 seasons and it works out to be 20.9%. Similarly, the GB% for 2018 is calculated from the 3-year average as 43.4%. FB%= 100-LD%-GB% which is equal to 35.7%. The three-year average HR/FB rate for Pillar is 6.7, which is the value I will use for his 2018 projection.

Finally, Kevin’s Runs per Times on Base (R/TOB) and Runs Batted In per Balls In Play (RBI/BIP) stats will be examined. R/TOB= (R-HR)/(H+BB+HBP-HR) and RBI/BIP= (RBI-(HR*1.565)-SF)/(AB-HR-SO). Tabulated below is Pillar’s hits, runs, home runs, walks, hit by pitch and sacrifice flies, strikeouts, at bats and runners batted in stats over the past three seasons.

Using the data above for 2015 R/TOB = 0.348. For 2015 RBI/BIP = 0.066. This data along with the data for the 2016 and 2017 seasons are tabulated above. The three-year average for R/TOB for Pillar is 0.327 so I will use this three year average of Kevin’s R/TOB for 2018. Pillar, in 2017, had a down year in RBI effecting his RBI/BIP so I will take the average RBI/BIP for the 2015 and 2016 seasons to arrive at a predicted RBI/BIP of 0.076.

Below is the final spreadsheet projecting the offensive production of Kevin Pillar for the 2018 season.

To conclude Kevin Pillar has shown signs of breaking out offensively in the last few seasons only to drop off to his career averages in batting stats. As proven in this article Kevin will break out offensively in 2018 and become a complete player adding above average offensive production to add to his gold glove caliber defense.

References

https://www.baseball-reference.com/players/p/pillake01.shtml

https://www.fangraphs.com/statss.aspx?playerid=12434&position=OF

https://www.baseball-reference.com/players/split.fcgi?id=pillake01&year=2017&t=b

Chris Archer is the classic two-pitch pitcher. He’s got a high velocity, albeit slightly flat, fastball, as well as one of the game’s dirtiest sliders. His fastball has never missed many bats, and it’s always had a wRC+ of over 120 in every year save for 2013. His fastball is nothing like that of the “spin king” Luke Bard, as it holds a pretty pedestrian 2192 RPM, less than the average MLB fastball spin rate of 2264 RPM. However, his high velocity fastball, ranking 21st this year for starters averaging about 95 mph, contrasts well with his wipeout slider.

As you can see here in this sequence to strikeout Josh Donaldson, the two pitches complement each other well. It is hard for any hitter to adjust from a 97 mph fastball to a knee buckling slider.

His slider, save for this year, has been devastating. Nobody really likes to hit his slider, and that is why he is one of the most prolific slider throwers in the game. He’ll make lefties and righties alike to look silly.

For example, Yangervis Solarte and Zack Cozart have both fallen victim this season.

https://twitter.com/PitchingNinja/status/993181847315238914

https://twitter.com/PitchingNinja/status/99743282699065344

The bread and butter for Archer is fastball away and up away and sliders below the zone with two strikes. Despite a 95 wRC+ on his slider this year, compared to his career mark of 65, the swinging strike rate on his slider is still high and there’s no reason the pitch can’t return to form.  There’s not much that’s changed with this pitch. The average spin rate is about the same as the last year, sorry @Trevor Bauer.

Archer’s past two years have been somewhat rough.  In 2016, Chris Archer went 9-19. Only he and James Shields had done that in the last 14 years.  I know I’m not really supposed to talk about wins and losses, but James Shields -1.1 WAR season is not great company.  In 2017, Chris Archer posted a strong 4.6 WAR and fell victim to a .325 BABIP that is almost 30 points higher than his career.  He’s still looking to repeat the 5.2 WAR 2015 with a 3.23 ERA and a 2.90 FIP.

Archer has also always liked to throw the slider more with two strikes.  Over his career, he has thrown 2719 sliders with two strikes, compared to 3734 sliders before two strikes.  Archer has always relied heavily on the two strike slider to both lefties and righties.

This year, Archer has faced 134 lefties and struck out 23, just under 18%.  For his career, he strikes out lefties at a 25.3% rate. Strikeout rate stabilizes after about 70 batters faced, so clearly something is up with Chris archer when he faces lefties. Here was Chris last year against lefties, his best year striking them out.

And here is Archer this year.  Can you tell the difference?

No lefties are missing whiffing in the zone, or above it anymore.  While Archer doesn’t have the typical rise or spin rate of a high fastballer, he needs his second best pitch to generate some whiffs.  Archer has thrown 217 fastballs this year to lefties, 14 swinging strikes. No swinging strikes with 2 strikes. Archer has yet to strikeout a lefty with his fastball.  Now this in not a huge difference, as lefties struck out on his fastball just 14 times last year, with a 7.5% whiff rate on it.

The issue is not the fastball, but that lefties just aren’t missing his slider.  They know the slider is coming; it’s Archer’s put away pitch. And when he puts it right below the zone, it’s practically unhittable.  That’s what Archer wants to do, throw below the zone and back foot sliders to lefties. This year and last, Archer induced 147 swinging strikes to lefties on sliders below the zone, on 330 sliders, a whiff rate of 44%.  But when he throws the slider in the zone to lefties, it has a whiff rate of 10%, compared to 18% last year.

The reasons for Archer’s troubles against lefties may be explained in a great piece, http://theprocessreport.net/archers-struggles/, which  Jason Collette wrote for the Process Report.  

Archer has moved from the first base side of the rubber, last season on the left, to the third base side of the rubber, this year on the right.  How has he done with this so far?

His fastball has been failing against righties and lefties and his slider results are divergent.  

Pitchers don’t move where they stand for no reason.  Clearly, it makes it harder for righties to see the slider coming out of his hand, and they have a far worse angle on the pitch.  But, a slider is also a hard pitch to throw to a batter of opposite handedness, especially when you have to throw it for a strike.  When Archer’s slider is coming at an angle where lefties can read the spin even easier, they will miss a little bit less. For example, here is a slider in the zone to a lefty, and J.P. Crawford doesn’t miss.  

https://baseballsavant.mlb.com/videos?video_id=1938512583

Archer’s move from one side of the rubber isn’t easy.  He’s facing batters in a way that he’s likely never faced them before.  And this has resulted in some dramatic success against righties. But if he wants to throw fastballs or sliders in the zone to lefties, they just aren’t going to miss.  At this point in his career, Archer is hardly going to try to develop a great slider. It’s just not in his DNA. So, we know the results of Archer’s change, and he has to ask himself, what is the cost.

Writing about Marcell Ozuna has become a sort of an annual early-summer tradition. Back in 2016 on May 20th, Craig Edwards wondered if Marcell Ozuna, then 25 years old, was breaking out. That hot streak didn’t last, as Ozuna saw his 132 wRC+ pre-Edwards article drop to 97 post-Edwards article.

Just over a year later in June of 2017, Craig revisited another Ozuna hot streak. That one lasted the rest of the season, and qualified as a breakout in most minds.

Now, nearly a year after that second article, some things have changed. Ozuna is no longer a Marlin. He’s a Cardinal! He’s the Cardinals cleanup hitter. And, through May 20th, Ozuna has been the St. Louis Cardinals worst hitter.

What?

Among the twelve Cardinals with at least 25 plate appearances so far, Ozuna’s 63 wRC+ ranks twelfth. Last. Worse than Matt Carpenter, whose recent hot streak lifted his mark from 60 to 89. Worse than Dexter Fowler. Worse than Francisco Pena. Last. Dead last.

Compounding the issue, or at least the prevalence of the issue, is that every single one of Ozuna’s plate appearances have come from the cleanup spot in the batting order. Individually, he’s been the second-worst cleanup hitter with at least 75 plate appearances in that spot. He’s dragged the Cardinals as a team down to the third-worst cleanup hitter production.

Of course, we know players go through slumps. Early season slumps tend to stick in our heads more because they’re more noticeable. Even still, this has been Ozuna’s third-worst slump of his career. He’s only had a powerless stretch this long once before. And yet, it doesn’t appear that the Cardinals have an alternative for his lineup spot.  Ozuna is, or was supposed to be, the Big Bat.

On the surface, it seems like there’s hope for a bounce back. While I haven’t been as adamant as I was for Carpenter, I’ve noticed plenty of bad luck plaguing the St. Louis slugger.

According to Baseball Savant, his average exit velocity is at a personal Statcast-era best of 92.6 mph, up nearly 2 mph over last year and in the top 30 across the MLB. His average launch angle is up a half degree from last year and in line with his career norms. And with the tenth worst gap between his expected wOBA and actual wOBA so far, we can attribute much of his slump to luck.

Luck doesn’t explain all of the difference between this year and last, though. Even if Ozuna’s actual wOBA was in line with his expected, he’d own a .339 wOBA and a wRC+ similar to that of Harrison Bader. Don’t get me wrong, a wRC+ around 115 is good – it’s 15% better than league average – but it’s not what we wanted from the Cardinals biggest offseason acquisition and it’s not what we expected from a guy who just least season broke out to mash 37 home runs while hitting .312 en route to a career best 142 wRC+.

Two paragraphs ago, I cited Ozuna’s average exit velocity and average launch angle. In my opinion, that’s the most common fallacy in the widespread use of Statcast data. Neither of those metrics are best thought of as averages. Ozuna’s average exit velocity is up, but what if he’s hitting every ball 92 mph or one ball 112 mph and another 72 mph? What if he’s hitting them into the ground where that exit velocity will do less damage?

To answer that, I compared Ozuna’s average exit velocity on line drives and fly balls to his exit velocity on grounders over the last three years. To tease out some luck, good or bad, impacting his production, I used expected wOBA instead of actual wOBA.

In 2016, Marcell Ozuna hit his line drives and fly balls only 2 mph harder than his grounders. In 2017, he sacrificed exit velocity on his ground balls to increase his exit velocity on line drives and fly balls. So far in 2018, he’s back to a near-even exit velocity between his grounders and line drives or flies.

Sometimes a gain in air ball exit velocity at the expense of ground ball exit velocity is indicative of an uppercut. Sometimes it might indicate a player has lowered their hands. Maybe they’re selecting better pitches to drive. Usually, it means a player’s swing is directed more ‘upward’ than it used to be.

On the other hand, when a player gains ground ball velocity as the expense of air ball velocity, the opposite is likely true. A swing that’s flatter or on a more downward plane will generate harder hit ground balls at the expense of harder hit line drives and fly balls.

Going off the above, it looks like Ozuna had a downward swing in 2016. It looks like he had a more upward swing in 2017. Now, it looks like Ozuna is back to a downward swing.

I went to the video room to see if I could identify any difference between Ozuna’s swing this year and last. While I couldn’t find anything noticeably different in the swing, there are significant differences in his set up and trigger. I’m going to show you two pitches. He didn’t swing at either. There are still big differences.

First, a take from 2018:

Next, one from 2017:

And here’s a still side-by-side of his stance, below.

Don’t make anything of the bat placement. He moves it around, and I didn’t take care to match that up. But look at his feet. His back foot is a little closer to the plate and his front foot is a little more towards third base. I don’t know how much of a difference that makes, and whether it’s good or bad. But it’s different.

Then there’s this:

Now that’s something. Ozuna has a toe tap. He had a toe tap last year, and he had one the year before that. Last year, after tapping his toe, his stance was slightly closed. This year, it’s dead straight. Last year, he had a bend in both legs during his tap. This year, his front leg is straight. Last year, his butt looks a little lower than this it does this year – he used to attack the ball from a slightly lower position. All together, in 2017,  Ozuna looked more like a tightly coiled spring. He was in a more powerful and explosive position.

Next, compare his 2016 toe tap (far right), when his air ball / ground ball exit velocity splits were nearly identical to what we’ve seen so far in 2018.

It’s not identical, but it’s really close. In 2016, Ozuna used a stiffer front leg. It looks like 2018 Ozuna, following his monster 2017 season, has gone back to his 2016, pre-breakout toe tap.

The obvious follow-up question: why? Why change what wasn’t broken? It hasn’t helped his plate discipline. It hasn’t improved his contact quality. It’s made him, essentially, the same hitter he was back in 2016. Was it a coach’s decision? Ozuna’s? An accident? In any case, despite breaking out in 2017, Marcell Ozuna isn’t the same hitter he was a year ago.

The St. Louis Cardinals, riddled thus far with injuries, are desperate for someone to step-up offensively.  They might start by taking a closer look at his toe tap.

So far this year, more than 170 starters have thrown at least 10 innings. Of those starters, Jacob deGrom has been the fifth best in all of Major League Baseball. In the prior three seasons he was 12th overall, then 28th, then 12th again. He’s already been worth more than two wins…in less than a third of a season. Last year, he was worth 4.4. John Edwards noted just how berserker his start has been:

Nine wins, y’all. DeGrom is on pace to be worth nine wins. The last pitcher to be that good was Randy Johnson in 2004. Being that deGrom is “only” the 5th best pitcher so far this season, that means four others — Max Scherzer, Justin Verlander, Gerrit Cole, and Luis Severino — have been even better, and that they’re on pace to break that nine WAR barrier, too. Given that less than a third of the season has passed, maybe none of them will, or maybe we’re in for a heck of a season from the mound despite a ball that favors hitters.

DeGrom might be of particular interest, though, because he’s showing us a completely different look this year than in the past. Just see for yourself.

Those heat maps are all from the catcher’s perspective. DeGrom is combining his crazy high talent level with a whole new level of conviction. The result? Video game-like command that’s yielded a career-high 12.1 strikeouts per nine and a typical 2.45 walks per nine.

DeGrom is just baffling hitters. His four-seam fastball is generating whiffs at more than twice the average rate of the whole league. It’s always been above average but it’s off the charts this year. What’s interesting is it’s got less run right now, per Brooks, meaning it’s straighter. That isn’t fascinating on its own, but his changeup is straighter, too. Basically, the two pitches look more like each other for deGrom in 2018 than they ever have, but they’re working different parts of the zone. That means they’re creating a wrinkle for hitters that they’ll continue to have a difficult time ironing out moving forward.

All of his offerings have created pretty much league average swing-and-miss or better. There are two outliers: the slider and the sinker. Like the fastball and changeup, the slider appears tighter in its movement to the plate, with less drop but slightly more side-to-side break. I can’t discern if it’s playing up because of that, or because of his other stuff, or if he’s due for some regression on whiffs there. It’s something to keep an eye on, though.

Meanwhile, the curve is plowing away at the low, glove side corner. And the sinker isn’t a pitch anyone uses for whiffs very often, but deGrom’s has been about 80% worse than average this season. Instead of throwing it more arm side, though, he’s using the other side of the plate so it zings back to the edge of the zone to steal called strikes.

Let’s take a breath and recap. DeGrom’s generating a crazy amount of whiffs with his fastball up in the zone. He can mess with hitters’ eye level with his changeup low in the zone. The sinker can steal strikes on the edge. And then the curve and slider are breaking toward that same spot with pinpoint authority. Is this even fair?

Hitters will certainly say no, but that’s kind of the point. Bless their hearts, though; they’re trying. DeGrom’s improved command has coaxed them into 8% less hard contact against him so far this year compared to last year. That’s nice by itself, certainly. But it’s fueled almost the entirety of deGrom’s 8.6% increase in soft contact generated. He now leads the league by that measure at 29.9%. Hitters are hitting less against him, and when they do manage to put the bat on the ball, they’re making life easy for defenders.

The last pitcher to show this kind of jump — from really good to amazing — was Corey Kluber from 2013 to 2014. In 2013 he was worth 2.8 wins in 147.1 innings. A year later he was worth 7.4 wins in 235.2 innings. He generated more soft contact, too, but only half as much as deGrom has added this season, and it didn’t come directly from his hard contact allowed. He struck out about two more batters per nine than the year before. His stuff was in the zone but he didn’t quite command it like deGrom has.

There isn’t much precedent for what Jacob deGrom is doing this season. Time will tell if he maintains his new dominance, but for now he’s pacing nearly the entire league. He’s leveling up. 

—

League average whiff rates and WAR from FanGraphs. Heat maps and deGrom whiffs per pitch type from Baseball Savant. Gif made with Giphy.

When I was playing in the Arizona Fall League in 2012, I led the league in line-outs. At least it seemed like it. It was the fall before I was Rule-5 eligible and I was hoping to show the Padres I could hit high level pitching. Unfortunately, a .726 OPS in the desert wasn’t going to have them breaking down my door with a team-friendly extension in hand.

If only there were x-stats! XwOBA is the shiny new eight-figure toy that we hitters can play with after an 0-15 slump. “But I was hitting the ball hard. See, look!” Back in the pre-Statcast dark-ages, a lineout might have had some anecdotal benefit buried in the bottom of a report. Now we have the data.

There’s been a lot written about xwOBA this week. Craig Edwards, Tom Tango and Jonathan Judge have all weighed in. I was especially interested in the ways they addressed it’s predictive capabilities.

Judge’s study compared season xwOBA for pitchers with the following season. Tango explored the correlations of small sample sizes of xwOBA to a larger sample.

I looked at this through the lens of a player. When a guy is getting lots of hits but they are bloopers and seeing-eye grounders (remember when ground balls went through the infield?) it’s soft hot streak. Likewise, a guy might be hitting the loudest .220 in the history of the PCL.

If you’re hitting the ball hard, they’ll start falling. Right? I wanted to test this theory by measuring xwOBA’s predictive capability month-to-month.

Methodology

(All data from BaseballSavant)

I started by getting data for each month of the regular season in the Statcast Era (2015-) for players with 50 PA in that month. I then did a series of inner joins in R to get what I’ll call “double-months.” A double month is when a player has 50 PA in two consecutive months. So Aaron Judge in April-May 2017 is one player-double-month. 

The column labels in the Double Month data frame were: “wOBA,” “xwOBA,” and “Next month wOBA.” I ended up with 3,173 data points. Running these correlations gives us an idea of how your month might predict your next month.

I also wanted to see whether you’d be better off using your entire previous season to predict the next month. For this I got full-season data (min 200 PA) for 2015 and 2016 and did another series of inner joins to get a data frame representing the previous full-season metrics and the current month metric. These columns would look like this:

“Previous season wOBA,” “Previous Season xwOBA,” “Current season month wOBA.”

I got 2311 of these data points.

For good measure, I also created a data frame for double-seasons. If you had 200 PA in two consecutive seasons, congratulations: you just got a double-season. There ended up being 532 of them.

Finally, I ran all the correlations.

Results

Double-Months

wOBA to Next Month wOBA: r=0.203

xwOBA to Next Month wOBA: r=0.274

Previous season to current month:

wOBA to wOBA: r=0.238

xwOBA to wOBA: r=0.25

Double-Seasons

wOBA to wOBA: 0.403

xwOBA to wOBA: 0.451

The differences are small, but they are consistent. xwOBA appears to be a better short term predictor than wOBA. What interested me the most was that while wOBA predicts your next month better if used in large sample size, the opposite is true for xwOBA. If you want to use xwOBA, you’re (slightly) better off using the most recent data.

Let’s talk about this in baseball terms. Baseball is so complex that a couple broken bat bloopers here and there can give you a really good month. Maybe you’re getting shifted but the pitcher doesn’t execute his spot and misses away and you shoot the wide open side of the infield a couple times. Maybe you made the mistake of hitting the ball hard in the middle of the field against the Cubs. Stats like wOBA practically scream regression to the mean.

But there’s no hiding from Statcast. If you’re hitting the ball hard it probably means you’re seeing the ball well and are consistently on time. Plate appearances aren’t independent events; we feel things in the cage one day that might get us locked in for a week. Or the other way around.

Ozzie Albies is one of this season’s breakout stars, however the one thing that stands out to me about the Atlanta Braves second baseman, is that he’s tied for the home run lead in the Majors with 13. This is pretty impressive considering this is his first full season in the and since he was never projected to be a power hitter in the Minors. He is also a stolen base threat and is decent defensively. Is he becoming a contender to Jose Altuve for the title of best second baseman in the game or is this unsustainable?

Let’s start by looking at the basics: Albies is hitting .277/.312/.588 with a .376 wOBA. One look at his batting line and we can clearly see that he’s not an elite contact hitter, who walks at a below average level. This is proven further by his 4.2 BB%. Interestingly his below average walk rate isn’t due to a high strikeout rate, as he strikes out at a decent 18.4% of the time. In other words he’s generally putting balls in play. His .275 BABIP implies that he’s not getting lucky either, while his unsustainable .311 ISO combined with his 34.5% Hardhit% indicates that his power is not really as good as it seems. A look at his HR/FB% makes it even more obvious: 21.0% is more than double his highest previous rates of 8.2% (from last season) and 7.6% (his highest rate in the Minors).

Albies swings at pitches outside the strike zone at a 35.8% rate, and surprisingly connects 76.1% of the time. Albies hits pitches outside the strike zone more often than other hitters. Think about that for a moment. He swings a lot at pitches inside the zone too (80.0%), but connects at a surprisingly below average 84.8% rate. What’s going on here? He also swings at an above average rate as seen through his 54.9% Swing%. If it wasn’t obvious before, he prefers to swing rather than take a pitch. I can’t imagine how that won’t affect him negatively in the future, once pitchers start challenging him more at the plate. According to: this analysis by Jeff Zimmermann  ,

Albies has improved his launch angle from 15 to 17.3 degrees. Combined with the fact that he also hits more fly balls (43.1 FB%) than ground balls (36.1 GB%), and there‘s at least some merit to him improving his power this season. However, everything else appears to be the same according to him.

So what conclusion does all of this information bring us to? Albies has improved his power but not nearly as much as his current production indicates. Despite his improved launch angle, he still doesn’t hit the ball particularly hard and seems to have too many of his fly balls end up becoming home runs. His plate discipline is below average and he swings at too many pitches that he shouldn’t. This is something that should and most certainly will be taken advantage of by pitchers in the near future. What happens when they start challenging him more at the plate? Will he keep connecting so well with pitches outside the strike zone?  In short, I just don’t think that he’s going to keep up his current pace. I fully expect more of his fly balls to be caught and for his batting average to thus drop to the .260- .270 range. My guess is that he finishes with 20-23 home runs and a batting line in the vicinity of .265/.300/.440. Albies‘s biggest concern going forward should be his plate discipline. If he becomes more patient and starts taking more balls, he can truly become an elite second baseman. Until then he‘s just a good player riding performing better than his talent level indicates.

Why I Care About Spin (and You Should, Too)

After last week’s deep dive on Gerrit Cole’s release point change and resulting spin increase, I decided it was time to brush off the old physics textbooks and try to identify a causal link between the two. Before I get into the results, I’ll warn you that the second part of this article where I talk about which mechanical changes correspond to the trends we see in the data is almost entirely guesswork. I’m in way over my head on this stuff and you should consider most of it wild speculation in the hopes of provoking the interest of people who can write “biomechanics” without a spell-checker. But as my dad (who happens to be a mathematician himself) has said, “sometimes asking the right questions is more important than finding the answer yourself (Forman, 2018)”.

I think it’s important to explain to readers why I decided to revisit the question of release point and spin. Up to this point, baseball Research and Development departments and private labs like Driveline have learned an incredible amount about the effects of spin on a baseball; however, how to increase one’s own spin rate remains to be understood.

The significance of this research should not come as a surprise to anyone who has been paying attention to baseball since the public dissemination of Trackman data. As noted in last week’s piece, Trevor Bauer has spent five years of his life trying to naturally boost his spin rate and I’m guessing he’s not the only pitcher going down that rabbit hole. If this link between release point and spin truly exists and is widely generalizable, breakout pitchers could be identified long before their true talent level is shown in their ERA and WHIP. Observers could test the sustainability of a pitcher’s success just by looking at changes in their release point. As this summer’s historically slow free-agent market has demonstrated, teams are starting to turn inward to their player development systems for a cheap, alternate talent pool. If this research is confirmed, teams could unlock the true spin potential of their own players, consequently spiking the talent level of the entire field (which fans of the game like myself love to see).

More than anything, this research question makes me excited about the future of baseball. I see a baseball future in which pitchers intentionally vary their fastball spin rate to high and low extremes to get maximum separation on their four-seam lift and sinker drop. One where hitters take batting practice off of virtual reality AI replications of pitchers with realistic spin patterns and pitch physics so their first time facing the pitcher feels like the third time through the order. Harnessing spin rate is not just another tool to which the rest of the league will soon respond. It is an entire framework for understanding the game we all love that changes the nature of the competition itself. Now, how do we get there?

Gerrit Cole’s Adjustment

First, data was scraped from Baseball Savant on every pitch Gerrit Cole has thrown in the 2018 and 2017 season. Because we want to examine within-pitch spin variation, a subset was created containing only four-seam fastballs. A simple linear regression was run using all available release point coordinates and release velocity. We use the variables “release_pos_x,” “release_pos_y,” and “release_pos_z” as regressors. X-axis release point is measured from the center of the rubber from the perspective of the catcher, so right-handed pitchers will have negative values. Z-axis release point measures the height of the release point using the bottom of the rubber as a baseline. Y-axis release point tracks the extension of the pitcher. All measurements are in feet.

First, the estimates suggest that there is a positive relationship between an increase in y-axis release point and the spin rate of that pitch. The plot below demonstrates this. Velocity is listed on the x-axis because it is such an important predictor of spin rate. To see the effect of y-axis release point, pick any given velocity value and look at the difference in spin between a point with a relatively small y-value and a large one. The results are pretty jarring:

The color of the points represents how many standard-deviations away from Cole’s mean spin-rate that pitch was. Because spin-rate varies so much from pitcher-to-pitcher, we should look to see how changes in release point affect within-pitcher spin variation.

This same observation between y-axis release (extension) and spin has been documented previously in Nagami et al., as follows:

“The angle at which the fingertips reached forward over the ball during the top-spin phase was highly correlated with ball spin rate. In other words, ball spin rate was greater for the pitchers whose palm was facing more downward at the initiation of the back-spin phase.”

Because the angle between the palm and ground increases as release position along the y-axis increases, we can confirm our intuition: the longer you hold onto the ball, the more spin it has. Can this be used to help transform pitchers with mediocre fastball spin to elite rotation anchors as has been seen with Gerrit Cole this year? To answer that, we need to have a more sophisticated understanding of the biomechanical process of spinning the baseball.

Again, Nagami et al. has an answer,

“The greater the ball speed, the more downward it must travel. To accomplish this, pitchers with a faster speed would need to hold the ball longer, which means that the palm would have to face more downward at the initiation of the back-spin phase. This would result in a longer period for acceleration to produce spin, and thus produce a higher ball spin rate.”

This suggests that because higher velocity pitches have to be thrown at a steeper angle downward [because downward acceleration due to gravity has less time to act on the pitch], the pitcher then holds the ball longer as it is traveling down the y-axis and thus has more time to impart spin on the ball. Work is force times distance. If we want to transfer more energy into an object, we can either increase the magnitude of the force or apply it across a larger distance vector. We already knew that higher velocity pitches have higher spin. The results of our regression, however, suggest that even after controlling for velocity, release position along the y-axis (that is, releasing the ball further in front of the rubber) has a statistically significant effect on the spin rate. This means that for two pitchers with equivalent velocity, a one-foot increase in y-axis release increases the spin rate of that pitch by half a standard deviation. While no pitcher can actually extend his release point by an entire foot, small adjustments in spin can have career-altering results. In combination with a velocity increase and z-axis release point increase, it seems Gerrit Cole has found his optimal release point for maximizing spin. If this isn’t his peak, the MLB better look out.

Next, there is the problem of accuracy. Can an individual pitcher adjust his y-axis release position to improve the spin rate of his fast ball to a significant extent while still throwing strikes? The answer seems to be yes. As the spin rate of a pitch increases with fixed action of rotation, the deflection force increases orthogonal to the velocity vector of the ball. It speeds the air above the ball, which decreases the air pressure relative to the air below it. The air below it travels upward, pushing the ball along with it and generating “lift”. This is referred to as the Magnus effect. Not only does this means pitchers can spin the ball more without sacrificing strikes, but the Magnus effect alone makes pitchers more effective for two reasons. First, because hitters cannot optically track the ball in the last few milliseconds of the pitch, their brain oftentimes has to linearly extrapolate the trajectory and guess where the ball will end up at the point of contact. This means a small amount of lift can create the perception of a “rising fastball” in the batter’s mind. Second, vertical ball movement decreases the area of pitch-plane and bat-plane intersection. More simply put, the ball is harder to hit with upward movement.

Why is a Higher Release Point Better?

Second, and perhaps more surprisingly, a higher z-axis release point was significantly correlated with spin rate. Last week I forgot to mention that clicking on these plots takes you to my official “plotly” page where the graphs are all cool and interactive, so try it out if you’re interested.

I tried to find a convincing explanation for why the estimate for the z-axis was positive without any luck. A few potential explanations come to mind. First, the higher you hold the ball, the more gravitational potential energy it has. Conservation of energy and the fact that the ball is thrown downward suggests that extra potential energy could be transferred to rotational kinetic energy, which is directly proportional to angular velocity. One of the problems with this theory is that, in general, the gravitational potential energy is not large enough to have a significant impact on spin compared to the overwhelming kinetic energy the pitcher is transferring to the ball.

The second (and more likely) potential explanation I came up with is that when pitchers throw with a three-quarters delivery, they decrease the component of force that they exert orthogonal to the moment arm on the ball. This is the only force that matters for torque (and the resulting rotational acceleration). When managers say the pitcher throws “through” the ball instead of “around” it, this is what they’re talking about. The rest gets transferred as translational kinetic energy, which is applied to the center of mass and contributes to what we call “velocity”. However, theoretically the math does not change along with the arm angle. The only thing that would change is the spin axis, which means the Magnus effect would have less of an upward component and would push the ball sideways. Because Trackman calculates spin regardless of the axis, this should not affect our estimate. The change would have to be a mechanical quirk that could be picked up on a high-speed camera.

We have to keep in mind, however, that not all spins are equal. For example, throwing over-the-top has the same transverse spin rate but adds gyro-spin. Gyro spin is the spin of a projectile which is rotating around a spin axis that is parallel with the direction of the velocity vector (as shown in the picture below). This is sometimes referred to by those within the industry as “not useful” spin, due to the fact that it does not trigger the Magnus effect. This change would again have to be due to another mechanical quirk at the release point that are beyond my abilities to track as a college undergrad who has no biomechanical experience and a Khan-academy video’s worth of knowledge. Answering the question of why z-axis release height is correlated with spin rate really should be left to a dedicated biomechanical researcher who has access to a lab.

Is this true for everyone?

Our next task is to test whether or not this trend is generalizable. This is a little easier said than done. In order for release point to be a useful regressor, it has to be variable so that we can test the effects of a change. The problem is that release point consistency is also a skill that Major League teams prioritize both for command and tunneling (making two distinct pitch-types seem alike until the very last second). Ideally, we’d have release point data distributed as a Gaussian, but for now we will have to make do with release point varied as a conscious effort by the pitcher. That causes another problem: if our regressors covary with a variable that correlates with spin rate and that variable is erroneously left out of the regression, it will create an endogeneity problem. This is especially prevalent with release point data that is roughly constant until a conscious correction is made, meaning the release point varies with time (along with potentially velocity, a different pitch-mix, stride length, workout regimen, etc.). This means a study of multiple pitchers will have time-variant error. We are using a fixed effects model, meaning that we time de-mean both the regressors and variables of interest (as shown below). Data on every four-seam fastball thrown by this year’s starting pitchers over the last two years was collected and spin was regressed on the release point. For those following along at home, we used the absolute value of the X-axis release position so we get the measure of sideways extension for both left-handed pitchers and right-handed pitchers.

I’m going to give you a taste of one of the applications of this research. We can calculate predicted change in spin rate by using the regression coefficients above. If we weigh changes in release point, multiply them by the standard deviation in spin, and add them together, we should be able to get an idea of which pitchers making mechanical changes and (more importantly) how important those changes are in terms of spin rate. Below is a list of pitchers who rank the highest in “weighted release point change” based on recorded changes in release point from 2017 to 2018.

First thing’s first, while this isn’t the most important thing in the world, it is comforting to see Gerrit Cole’s name near the top of the list in the metric we created with his spin change in mind. Full disclosure, I was only going to show the first ten pitchers but realized he was sitting at 11th. Still pretty good. Second, there are a lot of interesting names accompanying him. Mike Foltynewicz has made drastic strides this year in limiting hard contact, which has been reflected in his ERA and WHIP. I like Daniel Mengden quite a bit this year. He has had flashes of brilliance including his most recent outing where he limited the Red Sox to 1 earned run over 6 innings. Also, it is interesting how a lot of the guys listed here are known as extreme low-spin pitchers (Dallas Keuchel is a great example). This can also have a tactical advantage by exploiting the flip-side of the Magnus effect. The lower your transverse spin, the more drop you have relative to the rest of the league. For them it might be disadvantageous to be on this list. As a result, it might be worth examining the rate of return a pitcher gets from arm angle changes at different ends of the spin spectrum. We note that some pitchers our model predicts would increase spin rate actually experience a decline in spin rate which demonstrates the complexities of the biomechanical process of spinning a baseball. It should be kept in mind that our model is relatively simple, that our model should be used as a general guideline for understanding mechanical changes and not the last word on spin rate, and that release point should not be studied independent of other factors. For example, more complex models might start by examining the interaction effects of release point changes and velocity to determine diminishing or increasing marginal returns to mechanical tweaks as velocity increases.

Where do we go from here?

As mentioned earlier, the study of spin rate and the relationship between spin and release point has wide applications for internal baseball research and development departments along with casual observers wondering if a short-term spike in spin rate is sustainable. While I realize I’m getting into the habit of ending articles by saying smarter people should take a look and see if this is a real thing, the next step is figuring out exactly why we are seeing these trends in the data. Then, we will finally have a strong basis for answering the question of which factors contribute to a pitcher changing his own spin rate.

Between catching pitches for one of the worst pitching clubs in Baseball (The Royals have the worst team ERA in baseball), and being made a fool by Adeiny Hechavarria at the plate (5/14/18), Salvador Perez is having an embarrassing year. Yet below the obvious misfortune, a slow insidious killer lies. Salvador Perez seems to have forgotten about the strike zone.

In 2016 Salvador Perez won a Silver Slugger award. How can a relatively recent award winning catcher have forgotten about the strike zone? Well, the thing is, the strike zone and Ol’ Salvador have been in a tenuous relationship for a long time now. From 2016 to 2018, nobody in the MLB has swung at more outside pitches than Perez. Over the past 4 years, Perez has swung at 42.5%, 44.2%, 47.9% and 49.1% of pitches outside the strike zone (O-Swing%), respectively. All these percentages place him near the top of the leaderboards for each of these years. His contact rate on outside pitches during that time (O-Contact%) is 73.6%, 65.8%, 70.4%, and 63.1%, respectively. The nature of Perez’s efficacy on swinging for outside pitches is worth a deeper dive.

Does Perez benefit from his lack of plate discipline? In order to simplify the the study, I am going to only be looking at Salvador Perez in 2018 so far. Whether the lack of discipline worked for him in the distant past is not the focus, instead I am going to look at the efficacy of this kind of batting for Perez moving forward, using 2018 data to support my prediction. Perez’s season started April 24th due to a MCL tear. As of the end of play on 5/18, Perez has seen 333 pitches this year. Perez has swung at 56.4% of those pitches, meaning that he has swung at roughly 187 of all of the pitches he has seen this season. Of this 187 pitches swung at, Salvador Perez has swung at approximately 46 pitches outside the strike zone this season. One look at Perez’s Swing% heat map shows that he seems to believe that the strike zone is larger than it actually is.

Perez swings at a markedly higher percentage of pitches outside the strike zone than his contemporaries. Jorge Alfaro, and Wilson Ramos are the only two Catchers so far in 2018 that have swung at outside pitches at anything near the rate of Perez’s O-Swing of 49.1%, with the other catchers at a rate of 44.1% and 43.2% respectively, (Min PA 100). Perez has been a far better contributor to his team this season when he has shown more plate discipline. He has had a far inferior wOBA on days in which he has an O-Swing above 50%. His average wOBA on 50% O-Swing days is an abysmal .237, which is .067 less than league average for catchers and is .078 less than the overall league average. In comparison, on days in which Perez has an O-Swing% below 50, his wOBA is .440, a vast improvement, and a wOBA that puts him .04 above Mike Trout. If an outlier game against Detroit on May 5th in the below 50% dataset in where he had a wOBA of .000 is removed, his below 50% O-Swing wOBA would become .484, a number that would put him not far off the wOBA of Mookie Betts (.495). All this is to say that Perez is a very valuable hitter on the days in which he shows better, more league average (29.9% O-Swing) plate discipline.

What of the pitches that Perez swings on outside the strike zone, and actually makes contact? Perez boasts a 63.1 O-Contact%, which is the best contact percentage of Catchers (100 PA minimum) with above an 40% O-Swing. Are these contacts worth anything, or are they just mostly foul balls and popups? Perez has made contact with 22 pitches outside the strike zone. (There is a discrepancy of approximately 6 pitches here between the data supplied to FanGraphs, and the data supplied to BaseballSavant. I have decided that this slight difference does not compromise the integrity of the article, as my conclusions are the same. As such, some of the pitch numbers may be slightly off due to the slight difference between the O-Swing and O-contact% of FanGraphs and the statistical equivalent Chase and Chase Contact% of BaseballSavant, however the use of BaseballSavant was necessary for the exact pitch breakdowns.) Of these 22 pitches Perez has fouled off 13 of them, and has hit the other 8 chased pitches. Of these 8, he hit into an out in 7 of them, with the remaining contact being a single. So while Perez’s contact numbers while chasing are impressive, they amount to naught. Even with this high contact percentage the previous conclusion still stands, Perez is a bad hitter when he is in a chasing mood, and a very good one when he works the strike zone.

Is there something special about the 46 pitches that Perez chased outside the strike zone? (The data of both sites confirm that Perez has swung at 46 pitches outside the strike zone, so there is no problem here.) Is the number mostly made up of pitches that are right on the edge of the zone? The answer to both these questions is no. Perez has been lit up for a total of 19 swinging strikes to just the outside bottom-right of the Strike Zone alone. Meaning that of the 46 chased pitches so far this season, a staggering 41% of them have been swinging strikes to the outside bottom-right. The final tally of Perez’s adventures outside the strike zone sit at a pitiful, but not wholly unexpected, 24 Swinging Strikes, 14 Fouls, 7 hit into outs, and 1, lone, sad, pathetic, inconsequential, single.

In conclusion, Salvador Perez desperately needs to work on his plate discipline if he wants to continue to be a Major League catcher worth anything close to the $7.5M and $10M the Royals are paying him this year and the next. If Perez cannot reverse the negative course that his batting discipline has been on the last couple of years, his O-Swing% having jumped 4.9% in the past two years alone, he will begin to become an non-factor at the plate. Perez’s WAR has been in a steady decline ever since his O-Swing% began the leap to its current heights. If Salvador Perez cannot find more discipline at the plate, the former Silver Slugger will no longer be worth having on a Major League Team.

‘

(Data courtesy of Fangraphs and Baseballsavant)

It’s been a tough couple years for Michael Brantley. In 2016, he played in just 11 games. In 2017, he played in more than eight times as many…and still topped out at just 90 games. He registered a mere 418 plate appearances in that span because of injuries and was only worth 1.5 wins.

These injuries were the kind that start small, like inflammation or a sprain so often do, and cost a player a few games. Then news comes out about them being more serious than expected or about how the player has experienced a setback. And when those types of injuries start to pile up and happen in back-to-back years, it’s easy to wonder when, exactly, that player will be themselves again. Or if they ever will.

So far in 2018, though, Michael Brantley is showing us he’s back to being his vintage self.

Alone, the numbers this season are impressive. But compared to 2014, they’re downright eerie. It’s as if he’s looking into a mirror and seeing the 2014 version of himself looking back. He was worth 6.5 wins that year. The biggest difference is that he’s traded in steals for more power — he had 23 stolen bags in ’14 and is on pace for about 5 this year — but that matches the direction of today’s game, anyway.

Everything else paints a special picture. The league’s average strikeout rate has hovered around 16.5% for the last five years. Its average isolated slugging is around .150, and the average weighted on-base average is about .325. Brantley has been 50% better than average at not whiffing, at least 20% better at driving the ball, and 60% better at creating offense. Those kinds of results put him in rarefied air.

If we look at the single season leaderboards, we can see just how rare. Here’s a list of qualified players since 2014, which was when Brantley was last healthy for a full season, who have struck out in less than 10% of their at-bats and had an ISO of .170 or better:

• Michael Brantley, 2014
• Victor Martinez, 2014
• Michael Brantley, 2015
• David Murphy, 2016

There were 537 qualifiers over that time period. It happened four times. Brantley did it twice. No one managed to do it in 2013 or 2017. While we’ll have to wait to see if they can keep it up, the only three players to do it so far in 2018 are Brantley, teammate Jose Ramirez, and Nick Markakis(?!).

In many ways, ISO and strikeout rate in tandem can inform us a great deal about who’s being productive and how. Brantley’s skill at deciding when to swing is truly unique.

But what really makes his start to the 2018 season special is that he’s 31. With evidence building over the last several years that players peak earlier than we ever thought, it was fair to wonder if the time he lost to injury meant we were all robbed of some of his best years. Aging curves consider as large a pool of players as possible, though, so getting to witness players who force exceptions is always a blast. His 15 game rolling wOBA and K% averages tell us we’re having a pretty good time.

The bigger the gap between the red and blue lines, the better. We can see what he was like at his peak in 2014 and his valleys over the last couple years. As the space between the two lines grows in 2018, so does the one where we get to appreciate what he’s doing. We don’t know when the next injury will come or when Father Time will show up. We should enjoy this Michael Brantley while we can.

—

Data from FanGraphs.

Hot take: the Astros are good at baseball. This is thanks in no small part to Gerrit Cole’s early success on the mound. After the news broke that the Astros signed the righty, several analysts wondered why they would give up three national top-100 prospects (Musgrove, Moran, and Feliz) for two years of control over what some called a “soft” upgrade at starting pitcher. We found out in a big way. After 8 starts, he leads all qualified starters in FIP (1.56), WAR (2.8), K-BB% (35.6%), and is second only to Max Scherzer in Z-Contact% (75.3%). He has been, to date, (subject to some debate) the most valuable starting pitcher of 2018.

He is not the only bright spot of the Astros’ 2018. 35-year-old Justin Verlander looks to be returning to his 2011 AL Cy Young self. While it is early, he seems to be a promising candidate for this year’s award as long as his teammate Gerrit Cole doesn’t steal it from him. Charlie Morton, once journeyman, has found his home and filed his very own claim to being one of the very best pitchers in the league. I haven’t even mentioned Dallas Keuchel, proud owner of his very own 2015 Cy Young trophy, who is a top-35 pitcher on STATS’ command leaderboard and is finding his way back to his pinpoint control that helped his team win their first franchise World Series in 2017. Oh… also Lance McCullers is filthy. It’s looking like blue skies ahead for the reigning world champs and the beginning of the season only confirmed the rosy outlook. Then, a simple tweet:

If only there was just a really quick way to increase spin rate. Like what if you could trade for a player knowing that you could bump his spin rate a couple hundred rpm overnight…imagine the steals you could get on the trade market! If only that existed…

— Trevor Bauer (トレバー・バウアー) (@BauerOutage) May 1, 2018

Ever since Trevor Bauer pointed out the potential source of the increased spin rate of Gerrit Cole, there has been a cloud surrounding the validity of the Astros’ recent pitching success. It’s easy to understand the frustration for someone who claims to have spent a solid five years of his life trying to naturally improve his spin rate. The advantage of a high spin rate has been documented extensively in the literature by people a lot smarter than me, so I’m not going to go into it (plus my last encounter with the subject ended with a B in high school physics). All you need to know is the faster the ball spins, the more it moves in the last few moments before it reaches the hitter, which makes it harder to hit which, as you might guess, a pitcher generally likes.

In the Statcast era, teams are clamoring for every inch of an advantage and detecting small changes in fastball spin-rate is everything. If the Astros really are using some sticky substance (or just training new acquisitions in the art of spinning baseballs), we should be able to detect it in some way. Here are the average spin rates before and after the transition to Houston of some of their finest pitchers:

Just looking at this table, however, can be misleading. Average spin-rates can look a lot different depending on where you split the data. We would never know, for instance, if Gerrit Cole’s spin rate spiked to 2300 the start before moving to the Astros, which got lost in the pure volume of data suggesting a lower overall spin-rate before the move. It is important to understand exactly where this significant change in spin rate occurs.

The key behind detecting significant changes in data is this:

,

where X is the observed prior data. η here is what’s called a changepoint. Change point detection uses likelihood-based estimation to find the number of different population means (or variances) in a time series. That’s just a mathy way of saying it looks at how likely the data fundamentally changes as some point (or points). Before we can say the Astros are cheating, we should look at if the change in spin rate is really that significant to begin with and determine where that change actually occurs. We’re going to be using Bayesian changepoint detection. The advantages of Bayesian detection as opposed to Binary segmentation are twofold. First, the probability of having a change point is directly proportional to the prior probability of observing the data. This helps prevent overreaction to new information and makes the overall estimation process much more robust, which is especially important in this case. It is tempting to see a big number next to the post-Astros spin-rate chart and jump to conclusions, but it is important to appropriately weight the prior probability of that spike occurring. Second, detecting the changepoint requires a much smaller window of data. This is important in this case as well. If we are correct that the change happens in a 1-game window, i.e. it happens as a result of a game-to-game transition to a different team, predicting changepoints among small data-windows is especially important. Specifically, our algorithm computes the probability of having a particular changepoint configuration as follows:

,

where π(η) is the prior probability of that configuration and f(X|η) is the likelihood of the observed data given the change point configuration. There’s some other math behind the detection algorithm, but for now we’ll just take a look at the plots. First up: Charlie Morton.

There’s a pretty clear changepoint here. The posterior probability spikes at timepoint 23. That game date is 9/30/2015, a late-September game against the Cardinals, notably while Morton was pitching for the Pittsburgh Pirates. The Astros weren’t even his next team (in November, he was traded to the Phillies). Several analysts weighing in on the spin question have noted that spin rate is positively related to velocity. In an oft-quoted interview with Matt Gelb of the Philly.com:

“For some reason, I just went out there and tried to throw the ball hard one game. I wound up throwing it harder.”

Below is the change-point detection plot for Morton’s velocity. It looks like there was clearly a change during the spin-rate spike.

Regardless of the cause, our likelihood-based examination suggests it would be naïve to attribute Morton’s spike to an organizational conspiracy to increase fastball spin with a foreign substance.

Second, Gerrit Cole:

There is a clear spike at time 86, which is the time of his trade. Something has changed. However, look at the spike in spin rate at time 44. This could provide a hint to a given organization that a player is capable of a spike in spin rate given a change in mechanics. There is a 20% probability that that game contained a change point, which would be higher except Cole’s spin significantly declined right after that start. If spin rate is associated with a specific mechanical quirk, not only could that help us acquit the Astros, but also identify potential steals on the free agent or trade market that have yet to harness the maximum potential of their spin rate.

Some have hinted that high fastballs increase spin-rate by a significant enough margin to where a change in location could be responsible for Cole’s dramatic spike. Below is the graph of Cole’s spin rate broken down by location.

Cole’s spin rate increases by about 100 RPM when he pitches high and inside. That’s a significant jump, but not enough to explain a 300 RPM spike.

In a recent interview with MLB radio, manager A.J. Hinch mentioned that two things could potentially be behind the change in spin rate (without divulging any organizational secrets). First, he said that sinkers have a drastically lower spin rate than four-seam fastballs, and their pitching staff has prioritized the four-seamer.

Below are his pitch-usage charts before and after his transition to the Astros thanks to Brooks baseball:

First thing first, Hinch is right about pitch usage. Sinker rate is way, way down. But, as you can see below, it cannot account for within-pitch spin variation, as his individual sinker and 4-seam spin are both spiking this year.

The second factor that Hinch hinted at was “getting behind the baseball”. We can examine the relationship between release point and spin rate and see if this can really explain such a significant jump. Below is a 3D plot of spin rate by release position.

This seems like it could actually explain a good bit of the change. There are two changes associated with the spike in spin rate. First, he’s releasing the ball much further along the Y-axis than before. Second, it looks like he is releasing the ball higher, but closer to his body than the pitches at the very top left of the distribution. Almost every pitch he has thrown in the neighborhood of (-2.2, 54.4, 5.6) has been at around 2300 RPM. Further research should be done on the physical mechanics that generate that spin and if this could really be a causal relationship.

After a quick look at Cole’s mechanics, it does look like there is a conscious change in release point. Below is a screenshot from Cole’s start against the nationals on 9/29/2017. This looks like one of those pitches further along the x-axis than our sweet-spot that we found on the release point chart. See how his elbow is almost parallel to his shoulder.

Remember that one-game blip in spin rate that showed up on his changepoint plot? Below is a screenshot of a 97-mph fastball from that start. Notice how his elbow is higher than his shoulder. This could be the change that Hinch was talking about when he said “getting behind the ball” could be behind the increase in spin. When watching the video of the previous pitch, it looks almost as if he’s throwing around the ball instead of throwing through it.

Apologies for the blurry screenshot (it was one of two fastballs with media on baseball savant). Lastly, here’s a screenshot from this year. His release point is much, much more vertical than the previous two.

Overall, we should not jump to conclusions on the Gerrit Cole spin question. Just to be perfectly clear, I personally have no idea how mechanical changes actually affect spin rate. I haven’t done the experiments myself and certainly have not spent as much time as Trevor Bauer trying different grips and substances in a controlled setting. However, this article suggests that there is an association, whether it be correlative or causative, in Gerrit Cole’s release point that has come with an increase in spin on both two-seam and four-seam fastballs. If further research can confirm this association, the results could be of incredible use to teams looking for value either in their player development system or trade market.