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.

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.

                            HOW MUCH DOES APRIL MATTER ?

April is the first hard evidence of what may be in store for the new season. But like any single month, April usually has some extreme results. Typically 40% of all teams start conspicuously well (.600 +)  or poorly (.400 minus) where by season’s end only 12% of all teams are at those outer edges of win percentage.  With 85% of the season to go, plenty of time remains for fates to change.  Or do they really alter that much post-April ?   This study focuses on that question and is based on the April records of all teams from 2000-2017  compared to their post-April results and chances for making the playoffs.  Two issues are addressed.  First, how closely have teams’ remaining matched their Aprils and second, what effect April has had on teams’ playoff chances.

                 PREDICTICE VALUE OF APRIL FOR REMAINDER OF SEASON (ROS) RESULTS

April records of all teams from 2000-2017, were divided into six win % categories: Excellent (.650+ win % ), Good (.550 – .650), Slightly Positive  (.500 – .549), Slightly Negative (.450-.499),  Weak (.350 -.449) and Poor (below .350).   Teams in each April win category were compared to their post-April and full season win/loss percentages. The percentage of teams in each April category who played at playoff level (.580), contention level (.540+) or near-contention (.500) after April were also measured as well as the percentage of teams in each April win category who made the playoffs.  Following are the results:

Each level of April performance has had better remaining performance and playoff chances than the level immediately below it. Contrasts between the top 1/3 and bottom 1/3 April teams are stark.  Two thirds of teams who’ve started well (.550 +) play  .500 + ball afterward and roughly half make the playoffs.  Only one third of teams who have been .449 or worse in April play .500+ ROS and just 9 % of these early strugglers have made the playoffs. Nearly 4 of every 5 of all playoff teams were .500+ or better in April and less than 1 in 10 playoff teams started .425 or worse. April has done a good job of quickly identifying contenders and non-contenders.

Much of that comes from April strongly relating to prior season results and current season records having solid resemblance to prior season records. So April simply confirms that many (if not most) teams are headed for the same general fate as last year.  While these are generally reliable maxims, they’re hardly infallible. Many teams April wrongly suggest things will stay the same.   Likewise April often may indicate a change is coming in the current season and it doesn’t materialize.   That will be explored next – just how often does April “fool” ?

HOW MANY TEAMS ARE APRIL “FOOLERS” ?

Roughly 3 in 8 teams had more than .100 pt change in their April v. rest of season (ROS) win percentage. For some teams at the April extremes, even huge April v. ROS differentials don’t change their basic seasonal fate. The 2003 Yankees started the year at .769 win %  but played “only” .596 ball the rest of the way and won over 100 games.  Conversely a .260 point jump in win % for the 2000 Tigers over the last 5 months still didn’t push them over .500 on the season. Still for a great number of teams the last 5 months can wash out a great deal of the good or bad April does. Hot starters fall apart and teams buried in April can rise from the ashes.

To show both the amount and type of “foolery” April provides a logical starting point is prior year records. Fully 70 % of teams with 90+ wins the prior year have .500 + Aprils v. 37% for teams with 69 minus wins. Since many teams tend to have Aprils which are “characteristic” of their prior season , measuring the true amount of “April deception” can be done in two ways: 1 – How many Aprils “characteristic” of last year give true v. false signals of another similar season ?  2 – How often do  “uncharacteristic” Aprils end up being true v. false signals of a better or worse year ?

 1^st – How reliable are “characteristic” Aprils.  The following chart illustrates this

Prior Yr Wins     April Win Category       # Teams         % win  90+   % win 81+    % Playoffs

90 +                       .520 +                            83                       64 %               84 %             62 %                                   80-89                     .520 +                           71                        48 %              76 %             49 %                                         70- 79                    .499 minus                  73                           5 %              22                  7%                                              Below 70              .499 minus                   65                          3 %                6 %              6 %

As can be seen, prior year good teams with good Aprils have had even better odds than their already favorable odds of making the playoffs.   On the other side, prior year sub-.500 who have sub-.500 Aprils have seen their already thin odds get even slimmer.  Only 9 of 138 such teams have overcome the odds of a bad start.   The percentage of teams finishing above .500 is also remarkably different.   80 % of good teams with good Aprils end up plus .500 on the year, where a mere 14 % of bad teams with bad Aprils do. Conclusion: “characteristic” Aprils are highly reliable indicators of either continued contention or non-contention.    

“Highly reliable” does not mean “perfect”. The 90+ game winner/good April formula didn’t work for  defending world champions 2004 Marlins, 2013 Giants nor defending NL West champion 2008 D-Backs or 2005 Dodgers.  Nor did bad team/bad April deter the 2015 Rangers, 2011 D-Backs, and two Rockies teams (2009, 2007) plus the 2007 Cubs from rising up and making the playoffs.  But these are exceptions.

Applying these principles of “characteristic” Aprils to 2018 would bode well for the Red Sox, Yankees, Astros, Cubs and Diamondbacks. It would not for the Tigers, Rangers, Marlins, Padres, White Sox, Orioles, Royals and Reds.  Only 1 in 15 sub-.500 prior year teams with sub-.500 Aprils have made the playoffs so such odds would indicate  none of the above 8 will either.  Of course none of these clubs were expected to contend but neither were the Braves, Phillies, Pirates, and Mets who’ve had good Aprils.  Which brings up “uncharacteristic” Aprils.

^2nd – How often are “uncharacteristic” Aprils false or true signals of a change in team fortunes ?

Logically when Aprils deceive, teams often return to their “true selves” (good or bad) in the last 5 months. The data largely supports that logic.  But it also supports the notion that some uncharacteristic Aprils correctly signal changes in a team’s fortunes. This time the data is parsed more finely to show where the false and true indicators of change can appear.

Prior Yr

90 + wins  + .401 – .499 April      26 % win 90+  – 77 % win 81+    29% in playoffs

90 + wins  .399 minus April        14 % win 90+  – 43 % win 81+     21 % in playoffs

70-79 wins  .600 plus April         39 % win 90 + – 72 % win 81 +    44 % in playoffs

70 -79 wins .500-.599 April        22 % win 90 +  – 51 % win 81 +    25 % in playoffs

Below 70 win  .600 + April         18 %  win 90+   – 64 % win 81 +   18 % in playoffs

Below 70 win  .500-.599 April     7 % win 90 +   – 22 % win 81 +   14 % in playoffs

More extreme “uncharacteristic” Aprils have greater accuracy in signaling real change. Of the 90 + winners who started below .400 in April, less than half finished above .500 and only 22 % made the playoffs. By contrast, 90 + prior year winners who had .520+ Aprils made the playoffs 62 % of the time.  So very bad Aprils from previously good teams are strong early warning signs of trouble ahead.

What about extremely strong Aprils from previously bad teams ? Of the 29 teams who had prior losing records but .600 + plus Aprils, 20 finished above .500 and 10 made the playoffs.  This includes some notable turnarounds:  2000 Cardinals, 2000 White Sox, 2006 Tigers, 2012 Orioles, 2013 Red Sox, 2015 Astros, 2015 Cubs, 2017 Rockies, 2017 D-Backs.    Of course, excitement over great Aprils by prior bad teams should be tempered by the fact that 65 % of such teams still have missed the playoffs. This may somewhat damp expectations raised by the start of the 2018 Mets.  Although the 2015-2016 Mets were playoff teams so it’s quite possible their strong April 2018 could be a legitimate sign of revival.  Teams who gained 15 or more wins over the prior year showed an average of 120 points jump in April v. prior year    win %.  The Mets have jumped 220 points.

Mildly uncharacteristic Aprils have a higher incidence of sending wrong signals. Only 22 % of the bad (“below 70”) teams with .500-.599 Aprils finished over .500. Only 23% of the 90+ winners who started .400-.499 became bad sub-.500 teams thereafter. Former 70-79 winners with good but not great Aprils (.500-.599) have boosted their playoff odds but at 20% those odds are still below average. Seven teams fit this description in  2018 – Mariners, Pirates, Phillies, A’s, Braves, Blue Jays, Giants.  Historical odds are that 1 will make the playoffs, 2 would be very optimistic.

However, one type of mildly uncharacteristic April is very noteworthy. Previously good teams who show up with modestly bad Aprils (.400-.499) have seen a big drop in playoff odds (29% for bumpy .400-.499 starts v. 62% for “characteristic” good Aprils).  That is not good news for 2018 Dodgers, Nationals or Twins who all were expected to be playoff teams again.  Which brings up the next issue – how does April influence playoff chances ?

                  WHAT IMPACT DOES APRIL HAVE ON MAKING THE PLAYOFFS  ?

As one old adage goes “you can’t win the pennant in April but you can lose it”. While there is a definite truth in this, April has the fewest games of any month, and still leaves ample time to recover. The 2001 Oakland A’s had a miserable 8 -17 April but went on to win 102 games.  That said, they were the only sub-.400 April team to win 100 + games and only one of 8 teams who started that poorly and still made the playoffs.   Another 10 such bad start teams played the rest of the season at contender levels (.540+ ) yet failed to make the playoffs and their poor Aprils were instrumental in that.

While it is clearly possible to recover from a bad start, bad Aprils leave a diminished margin of error. Two-thirds of all playoff teams start the year solidly (.530+ ), the vast majority (78%) are at least .500 + and fully 95 % of all playoff teams have avoided disastrous (sub. 400) Aprils.   Teams who’ve started miserably haven’t been able to count on fellow playoff contenders being in the same underwater boats.  They have to play serious catch-up with rivals whose yachts have begun to float away.  An “average” playoff team has a .564 April win percentage.  So a club with a 9 -15 April usually has to catch teams who’ve gone 14-10 or better.  If the 15-9 April teams plays at “only” .537 ROS and gets 88 wins, it takes a .580 ROS from the poor April starter to overcome that.  Playing at .580 + level ROS has been done by only 15% of all teams, which equates to being one of the top three teams in one’s league after May 1^st.

Those 8 playoff teams who started sub-.400 averaged a .604 ROS win percentage.  Five of the eight were .580 + ROS and the lowest ROS win percentage was .572 which translates to 93 wins over a full season.    In addition, there were four teams with a .580+ ROS that missed the playoffs since their average Aprils were 9 – 14.    Despite Herculean May 1^st-on efforts and being better ROS than their key divisional or wild card rivals those four teams (2004 Giants, 2005 Indians, 2011 Red Sox, 2012 Angels) lost out on the playoffs because of their inferior April records to rivals who had solid or even stellar starts.

Losing ground to playoff competitors due to a bad April and rivals’ typically good starts makes the task tougher. Even slightly below .500 starts mean a club usually needs to play at .560+ ROS to catch up as most playoff bound teams are already above .550 + in April. The other side is that very strong Aprils can provide a cushion to play at less than .550 ROS (the average playoff team is .581 ROS). There are even occasions where teams with less than a .520 ROS have made it in due to strong Aprils:            2016 Mets, 2015 Astros, 2014 A’s, 2006 Cardinals, and 2000 Yankees.  Ironically there are two World Series winners in that group (Cardinals and Yankees).   The Cardinals were particularly unusual as they were the only team to make the playoffs with a sub-.500 ROS record.

The lone .600 + ROS who missed the playoffs (2005 Indians) provide a classic example. They were the second best team in the AL after April, outplaying their division rival and subsequent World Series winner White Sox   84-55  to  82-56  ROS.   However, with the 17-7 April of the Sox, and Indians’ poor 9 -14 start, Sox gained a 7.5 game cushion.  The double whammy was that the Tribe’s poor April also cost them the wild card to Boston.   Had the Indians played even 12-13 instead of 9-14 they’d have ousted Boston.   Of course Cleveland’s 13-16 July didn’t help either, nor did going 1 -5 the last week of the season (including a 3 game sweep by the Sox) after the Indians had whittled the lead down to 1.5 games on Sept. 24.  But the April cushion built by the Sox allowed them to withstand an incredible Aug/Sept run by Cleveland and the Tribe was forced to play unbelievably well to stay in the hunt.   This all happened before the second wild card was introduced in 2012, and if it had applied back then Cleveland would have made it as that second wild card.   So has this second wild card now made it easier for April stumblers to recoup ?

HOW THE SECOND WILD CARD HAS CHANGED PLAYOFF ODDS

Adding a second wild card team has changed the odds in some meaningful ways as follows:                         Prior to 2012, teams had a 94 % chance of making it with 92+ wins , but only 43% with 87-91 wins did.  After 2012, teams had a 100 % chance of making it with 92+ wins, and an 83 % chance with 87 -91 wins.

Clearly 87 – 91 wins stands nearly twice the chances of making the playoffs than before when 92 wins was the benchmark to lock in a spot.  Prior to 2012 good Aprils helped but a team needed to be stoking the engine every month to reach that 92 plateau.  Only when teams hit the magic 580 ROS (94 win pace full season) did they have a near lock (96%) getting in.  Now that lock is at 560 ROS. This is a critical difference. That can make it easier for a strong April team to ease into the playoffs with a lower ROS or it can make it easier for a team that stumbles in April to recoup. In fact for those teams who started less than .450 and then made the playoffs prior to 2012, the average ROS was .604.  For the three teams that have done that since 2012, the average ROS is .574.  But that only 3 teams have made it with subpar Aprils says the new wild card system has not been a boon to bad April starters.

It’s a lot easier to have a .600+ April than a .600 + ROS.  111 teams have had such Aprils, but only 42 have done so ROS.  The new wild cards have actually made it easier for the good April teams to coast in with lower ROS records and there is no shortage of good April teams.  Since 2012, each year an average of 15 teams start the year with .520 + Aprils to compete for 10 playoff spots.  Of those teams, 56% (or 8 per year) make it in.    That leaves only 2 spots on average per year for sub-.500 April teams to compete for.

Following is a breakdown of the number of playoff teams after 2012 who’ve played at given ROS levels categorized by their April win levels

As can be seen, 49 of the 60 playoff teams were .500 + in April. However, 91 % of the under .500 April teams who made the playoffs were forced to play .560 + ROS to get in.   Whereas only 64% of teams who started .550 or better were able to play under .560 ROS and still make it. So the second wild has so far given the fast start April teams a better chance to ease coast into playoffs at a lesser ROS pace rather than make it easier for the slow starters catch up.   But the same math could be applied to any month, good or bad, so to paraphrase the classic Passover question:  why is April so different from all other months ?

PSYCHOLOGICAL EFFECTS OF GOOD OR BAD APRILS

Despite the fact that April is only 15% of the overall season, when early-mid season personnel decisions are being made, April results can still have considerable impact.   April’s record can influence decisions such as: the patience a team has for a younger player with early season struggles or whether the team tries for a mid- May trade to replace an injured starter and/or considers promoting a top notch AAA player despite his arb clock issues.  When trading season starts in June, 35% – 40% of the team’s record at that point has been baked in by April’s wins and losses.  Even by the late July deadline, April still comprises 25% of the season.  If early results have affected fan attitudes and attendance, ownership may be either more or less willing to commit dollars to bigger name players at the deadline.

These factors may give April importance beyond its mathematical impact on the standings.   That teams tend to mirror their April win-loss %’s as the season progresses may be in part that April can create a sort of self-fulfilling prophesy.   The 2014 Cleveland Indians were 75-60 after April 30^th, the Oakland A’s were 70-63 over the same time. Yet Oakland got the wild card by 3 games over Cleveland due to a 18-9 April v. the Indians 10-17.   Early season results were still impacting July decisions as the A’s were buyers and the Indians sellers.   To be sure, the A’s record worsened post-July and the Indians got better.  But without Jon Lester (2.35  ERA with A’s) and Jeff Samardzija (3.14) who knows how much worse it might have been for the A’s .  While ridding themselves of Justin Masterson may have helped the Indians and trading Cabrera didn’t hurt, how much better would they have been had they gotten an OFer and starting pitcher in July instead of being sellers ?

Conversely, the 2016 White Sox benefited from a 17 -8 April despite a May tailspin which left them with a 29 -27 record on June 4^th and only 2 games behind in the AL Central. They then traded a very talented younger prospect named Fernando Tatis Jr.  for James Shields.  Needless to say this is a trade that has not worked well short or long term.  Despite the fact that the Sox had 3 straight losing seasons prior to 2016,  management seemed to believe that April/early May represented the success they felt the team was capable of as opposed to the more recent reality of losing.  Successful Aprils can sometimes keep wishful thinking alive for too long.

CONCLUSIONS

Clearly April has proven to be a good proxy for the team’s chances going forward that season. But April records have to be viewed in context of all evidence.  How much of a factor were injuries, over or under performances, or new offseason acquisitions?  Thoughts of dumping contracts and rebuilding, while too premature for May 1^st, are still logical for poorer prior year clubs who are off to bad starts.

For clubs in the playoff hunt, April can have real impact since 3-5 more or fewer wins can make/break. Since 2013 teams with 87-88 wins have made the playoffs in 10 of 11 cases where only 2 of 14 teams with 85-86 wins have. Clearly these margins apply to other months’ results too, but as noted before, a very good or bad April can affect team decisions in June and July.   Having some breathing room afforded by a 16-10 April instead of the catch-up pressure of a 10-16 start can play into the psychology as well.

Winning the division winner is far preferable to having to win a one-game playoff as a wild card and April provides a good checkup on division rivals. Both Boston and NY look like they will fight it out all year although Toronto can’t be ignored. In the Central, the Indians’ are in a strong position with their chief rival, the Twins, are 4.5 games back already and the rest of the Central bad teams who’ve started with bad Aprils.   Houston’s good start helps especially since all of their closest chasers (Mariners, Angels, A’s) were sub-.500 teams last year, but the Angels improved through offseason acquisitions.  In the NL East, even though the Nationals are 5 games back they’re chasing teams who were all sub-.500 last year (Mets, Braves, Phillies).  Where the Dodgers, who are 8 games back, are pursuing a D-Back team that won 93 last year.  Turner’s absence hurt in April but so did the lack of offense from the rest of the team which may continue particularly since Seager is lost for the year.    Plus the bullpen woes cannot be overlooked. So April’s 12-16 record cannot be easily dismissed as an aberration.  Nor can the historical evidence of diminished playoff odds (20-25% range) of good teams who’ve had the Dodgers’ kind of April.   We shall all see soon enough.

Following up on a recent Jay Jaffe post, I am examining the question of whether there is a World Series hangover. Unlike that post (which was great, but answered a slightly different question than I am interested in), I compared the full season performance of World Series winners and losers relative to their true talent level. I looked at all teams that went to the World Series in 2012-2016. I only went back to 2012 because that is far back as I could find projections in my less than thorough internet search. Finally, I omitted 2017 because they have played too few games this year for my purpose. As a proxy for true talent level I used projected wins from Clay Davenport.

Why did I look at actual versus projected win totals? I did not find changes in absolute win totals informative in terms of the question I was asking. Teams change year-by-year. Changes in absolute win total could simply reflect talent level changes. By using projected wins as a baseline I hoped to control for, at least somewhat, changes in talent level. Using projected wins as a baseline also allowed me to examine whether any changes in performance across years was due to over/under performing in the World Series year versus over/under performing in the year after the World Series.

Let us get to it. Below you will find the projected win total (pWins), the actual win total, and the average projected win total (average pWin) for the World Series teams in the year they went to the World Series (WSyear) and the year afterwards (WSyear+1). Busy figure, bear with me.

My main point here is that the average projected win total is similar for the World Series year and the year after (a 1.3 win increase). The second point is to show the raw data as good practice. Next, I cared about how the teams performed compared to their projections in each year. That information can be found in the figure above, but better yet, here is a figure showing actual win total minus projected win total for the year the teams went to the World Series.

This is interesting. Teams that went to the World Series outperformed their projections by 8.2 games on average. With the exception of the 2012 Tigers all teams outperformed their projections (note that the 2017 Astros and Dodgers outperformed their projected win totals by six and seven games, respectively). The probability of 9/10 teams outperforming their projected win total is 0.010. Teams that go to the World Series outperform their talent level. What about in the year after the World Series? Below is the same figure as above with the year after the World Series added.

Alright then. In their post World Series season teams have, on average, performed right at their true talent level (-0.8 wins). What have we learned? Obviously the sample is small and the data for the year after the World Series trip is quite noisy. That said, within this sample, teams were projected to win a similar number of games in their World Series year as the year after. They substantially outperformed their projections in the year they went to the World Series. They then came back to earth in the year after their World Series trip.

Keeping in mind my question was regarding a year-by-year change in a team’s performance relative to their true talent level, I conclude that there is a World Series hangover of a sort. Yet, its nature is quite different than one might think. Rather than teams underperforming after going to the World Series it appears that they over-performed in the year they went to the World Series. In other words, any World Series hangover may result from our powerful friend regression.

Fortunately Statcast now has a function that allows to sort for batted ball direction. This opens the chance for some new studies. Until now we just had launch angle (LA) and exit velocity (EV), however, that is not quite perfect because we already new that it is easier to pull fly balls for power. This was known intuitively for a long time https://www.fangraphs.com/fantasy/getting-to-know-fly-ball-pull-percentage-fb-pull/ but was hard to quantify until now.

One of the effects is certainly that parks are bigger in center field than they are down either line. However I also looked at EV and average distance of balls pulled, hit to center and oppo at angles of 20-35 degrees which are typical HR angles. For this article I only looked at right handed hitters, -45 to -15 was defined as pull, -15 to 15 as center center and 15 to 45 degrees as oppo.

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You can see that pulled balls yield a 343 ft distance and 92.4 EV. To center it is slightly lower (91.4/338) but to opposite field it drops dramatically to 290/86.2. From a physics standpoint that makes sense because the contact on inside pitches is supposed to be further out front so that the swing is slightly longer and thus has more time to accelerate to contact which probably means more bat-speed at impact.

wOBA supports this, while liners are relatively stable in production, the wOBA of pulled fly balls is dramatically higher. On grounders this trend is reversed and oppo grounders are better than pulled grounders.

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I also looked at the top and bottom 20 of the league in pull and oppo LA:

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You can see that pull LA has a pronounced positive effect while oppo LA even has a slightly negative effect. It might make sense to try to lift more on pulled balls and slightly try to suppress LA (“get on top”) on oppo hit balls. Not sure if this is possible with the same swing though, I think usually the guys having a high FB pull rate also have high grounder pull rates because that is the natural tendency of the swing.

So it seems to be pretty simple: pull the ball in the air and be productive.

However it isn’t quite as simple. Already before Statcast it was known that pulled balls are hit on the ground at a much higher frequency https://www.fangraphs.com/blogs/the-pros-and-cons-of-pulling-the-baseball-2/.

Launch angle supports that, pulled balls last year had an average LA of 5.6 degrees vs 13.1 for balls up the middle and 20 degrees oppo.

This makes sense and actually is something that isn’t easily combatted with the modern swing. The modern swing goes slightly up and pulled balls are hit out front. You can lift a ball like this but if you are a little too far out front the bat has risen above the plane of the pitch which means you hit the top of the ball and roll over hitting a hard topspin grounder, often into the shift.

This is especially pronounced on low pitches.

There are some hitters who have developed a tool to combat that rolling over with the uppercut swing as I have shown in this article https://www.fangraphs.com/community/finding-keys-to-elevate-the-ball-more/ by using a steeper bat angle but it is not easy to do as the league still tends to have much lower launch angles on low and especially away pitches https://www.fangraphs.com/community/effect-of-pitch-selection-on-launch-angle-and-exit-velocity/.

I broke this down a little more looking at batted ball directions and pitch locations inside the zone

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You can see that low pitches that are pulled are especially hard to lift, most extreme is that on low and away pitches but even the down and in pitch only yields a modest 6 degree LA.

I also looked at pulled balls above 10 degrees on low pitches. The leaders in that stat were in this order Stanton, Machado, Salvador Perez, Hunter Renfroe, Nelson Cruz and Mookie Betts. Those were some pretty good hitters last year, so maybe that is a skill that deserves further examination.

We all have seen Bryce Harper pull outside pitches for a homer and it does happen but generally trying to pull anything away is not a good receipt. If it works it usually is on pitches up (still yields a positive 7 degree LA to pull up and away pitches).

An adjustment that might make sense is trying to hit up and away and middle away balls to center rather than the other way. That way you could bring down the average EV of those pitches from a too-high-upper-20s average EV to a better low 20s EV, which yields a better BABIP on those pitches which tend to yield lower EVs. I elaborated in this article why mid-20s LAs are ideal but actually average LAs should be lower (between like 12 and 18 or so)
https://www.fangraphs.com/community/why-launch-angle-can-only-be-optimized-not-maximized/.

Overall an LA optimizing strategy using batted ball direction could look like this.

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So pulling the ball is good but only if you have the skill to put it in the air. Selecting the right pitches to do it certainly helps. On pitches that are low and away it still makes sense to follow the old advice to hit it were it is pitched. And for pitchers it might make sense to work the outside corner more, however that is also a fine line since you need to prevent the old Jose Bautista strategy of creeping closer to the plate and turning the outside pitch into a middle pitch. For this you need to pitch inside some to keep the hitters honest.

This post was co-written by John Edwards. If you’re not already familiar with his work, you can (and should!) follow him here. 

Launch angle and exit velocity became a big deal when MLB released them through Statcast at the start of the 2016 season. They instantly told an old story in a new way. It wasn’t surprising to see Nelson Cruz, Giancarlo Stanton, or Miguel Cabrera at the top of the leaderboards. We knew they knocked the snot out of the ball. But now we knew that they knocked the snot out of the ball in excess of 110 mph or better, and at 34 degrees or better.

Two years later and the terms are nearly ubiquitous, even speckled through broadcasts. But they’re often provided without context as colorful notes in single instances. Do we really care how fast the ball went out in the moments we’re watching, or at what angle, as long as it went out? It doesn’t tell us how the dinger or double or snagged liner happened, just that it did; and we just saw it with our own eyes.

So, what about that how? What’s contributing to a player generating that record exit velo or optimal launch angle?

Swing speed.

Swing speed could help inform us of how well a player is tuned into their timing at the plate and where they’re making contact, both of which tell more of our old story in an exciting new way than launch angle or exit velo alone. But the problem with swing speed is we don’t have that data. It’s simply unavailable: while Baseball Savant used to feature bat speed it no longer does.

Fortunately, enough data exists that we have approximations to work with. David Marshall reverse-engineered the formula Baseball Savant used in calculating swing speed – and now we can play around with those numbers!

Let’s look at who the best hitters were by bat speed last season, with a minimum of 100 batted ball events.

Since the formula for predicted bat speed is essentially average exit velocity (AEV) accounting for pitch speed, and AEV is the majority factor in the equation, the leaders in bat speed are also among the leaders in AEV. But there are still some differences, and they’re some very important differences! The speed at which a pitch comes in affects how fast it goes out, so players facing pitchers who throw harder might register lower average exit velocities than a player with comparable bat speed facing pitchers who throw slower.

But bat speed isn’t consistent from plate appearance to plate appearance. Sometimes you check your swing, other times you let loose. But there’s an important trade-off: many of the hitters with superb bat speed strike out frequently, and hitters with low bat speed (Mallex Smith, 50.0 MPH or Billy Hamilton, 51.0 MPH) make a lot of contact without striking out. Low bat speeds allow for more contact and fewer whiffs, but high bat speeds allow for better contact at the expense of greater whiffs. As a result, bat speed is loosely correlated to contact% (R^2 of 0.09), and better correlated to contact% than exit velocity (R^2 of 0.08).

MLB hitters are aware of this loose correlation. Since 2015, they’ve swung .4 MPH slower than average with two strikes, collectively opting for more contact and foul balls so they can stay alive longer in at-bats. But the guys who are the best at managing this are among the best in the game at generating offense, and they don’t all necessarily slow down their swings at the same rate. However, each had a wOBA with two strikes roughly 15% better than overall league average in 2017.

The leaders here show us multiple paths to success with two strikes in regard to players slowing down or speeding up their bats. Beyond that, we get a few bands of players worth noting. Anthony Rendon and Francisco Lindor were really in a class of their own last year when it came to generating offense. They’re the only two players who were about 80% better than the average player overall, and lost less than 60 percentage points of wOBA with two strikes. Rendon only swung .2 MPH slower in those instances while Lindor swung .9 MPH slower.

That’s not to say they were the best, though. Joey Votto (2.1 MPH slower), Austin Barnes (1.0), Mike Trout (.4), Bryce Harper (.4 faster), and Rhys Hoskins (.5 faster) generated the most offense with two strikes. Collectively, they were so much better than most of their peers that they were able to absorb a bigger drop in effectiveness with two strikes in the count and still pose a considerable threat.

Whether swinging slower or faster than average with two strikes, the way these players optimized their swing speed with two strikes informs us of their approach more than their launch angles or exit velos alone. But what about the guys at the other end of the spectrum?

Giancarlo Stanton and JD Martinez had the largest differences in offense created with two strikes of anyone in the league. You can see all the data here. Per John’s own Statcast database, they each had dips in wOBA of more than 160 points when their backs were up against the wall, implying that the way they sold out for power when they were down to their final strike really didn’t work in their favor. They both swung slower than the .4 MPH average drop in those instances, and a peak at their heat maps suggests they were way more willing to hack at offerings out of the zone, too.

A lot of their peers actually acted in a similar manner, too. It turns out that 40 of the 50 players who saw the biggest drop in wOBA with two strikes slowed down their bat in those counts. They’re even more diverse of a group of players than the ones who saw the least drop. There might not be another offensive context where you’ll see Carlos Correa ranked with Lonnie Chisenhall, or Jose Altuve with Michael A. Taylor, or Josh Donaldson with Patrick Kivlehan.

Examining players in this light provides a unique perspective to some of the game’s most critical moments. Despite the variance in the quality between these players, the 2017 approximations suggest that they didn’t exhibit much of a two strike approach at all. Slowing down your bat but expanding your strike zone to chase pitches that are inherently less hittable seems like a recipe for Ks. 

If swing speed can tell us who’s optimizing their approach at the plate — or who isn’t — can it also help us predict a outbreak? We compiled hitters with at least 100 batted ball events in 2016 and 2017 (using batted ball events since our predicted bat speed equation uses exit velocity), and saw which hitters saw the most improvement from 2016 to 2017. Unsurprisingly, swinging harder resulted in much better production at the plate.

Conversely, most hitters who declined in bat speed declined in production (except for Delino DeShields, curiously enough).

But having a slow bat speed isn’t necessarily a bad thing, nor should all players strive to increase their bat speed. We discussed previously how bat speed and contact% are inversely related — not swinging out of your shoes every at-bats means that you have better time to react to pitches and make contact.

For hitters like DeShields, Suzuki, and Gordon, they want as much contact as possible – their maximum bat-speed isn’t comparable to guys like Gallo and Judge, so there isn’t really a way to sell out for power here. Judge and Gallo can get away with striking out so much because the few balls that they put in play frequently go yard, but if someone like Gordon adopted that approach, the increase in power wouldn’t compensate for the increased strikeout rate.

Instead, Deshields, Suzuki, and Gordon produce by making as much contact as possible and relying on their speed to beat out hits on their weak contact. By relying on their speed and balls-in-play for production, it’s beneficial for these hitters to not swing out of their shoes.

Using bat speed to predict breakouts is similar to looking at exit velocity changes to predict breakouts, but has its trade-offs: it’s better in that it accounts for differences in pitch velocities faced, but it’s worse in that our bat speed predictions are only approximations.

They’re still something, though, and they give us more of a predictive look at what goes into making a great hitter than hearing about their launch angle or exit velo in isolated instances.

                                      HOW MUCH DOES APRIL MATTER ?

April provides the first hard evidence of what may be in store for the new season. But like any other single month, it usually has some conspicuously extreme results.  Typically 40% of all teams start notably well (.600 +)  or poorly (.400 minus) where by season’s end only 12% of all teams are at those outer edges of win percentage.  With 85% of the season to go, plenty of time remains for fates to change.  Or do they really change that much ?        This study focuses on the April records of all teams from 2000-2017 compared to their post-April results and odds for making the playoffs.  Two issues are addressed.  First, how closely teams’ remaining five months have corresponded to their Aprils and second, what effect April has had on teams’ playoff chances.

              PREDICTICE VALUE OF APRIL FOR REMAINDER OF SEASON (ROS) RESULTS

April records of all teams from 2000-2017, were divided into six win % categories: Excellent (.650+ win % ), Good (.550 – .650), Slightly Positive  (.500 – .549), Slightly Negative (.450-.499),  Weak (.350 -.449) and Poor (below .350).   Teams in each April win category were compared to their post-April and full season win/loss percentages. The percentage of teams in each April category who played at playoff level (.580), contention level (.540+) or near-contention (.500) after April were also measured as well as the percentage of teams in each April win category who made the playoffs.  Following are the results:

Each level of April performance has had better last 5 months performance and playoff chances than the level immediately below it. Contrasts between the top 1/3 and bottom 1/3 April teams are quite significant.  Two thirds of teams who’ve started well (.550 +) remain near contention by playing  .500 + ball afterward and roughly half make the playoffs.  Only one third of teams with .449 or worse Aprils play .500+ thereafter and just 9 % of these early strugglers have made the playoffs. Nearly 4 of every 5 of all playoff teams were .500+ or better in April and less than 1 in 10 playoff teams started .425 or worse. April has done a good job of quickly identifying contenders and non-contenders.

Much of that is due to April records strongly relating to prior season results. Current full season records also have had solid resemblance to prior full season records. So April simply confirms that many (if not most) teams are headed for the same general fate as last year. While these are generally reliable maxims, they’re hardly infallible. Many teams do change fortunes and sometimes April wrongly suggest things will stay the same.   Likewise April often may indicate a change is coming in the current season and it doesn’t materialize.   That will be explored next – just how often does April “fool” ?

HOW MANY TEAMS ARE APRIL “FOOLERS” ?

Roughly 3 in 8 teams had more than .100 pt change in their April v. rest of season (ROS) win percentage.          For teams who start extremely well or poorly, even huge April v. ROS differentials haven’t change their season’s destinies. 2003 Yankees started the year at .769 win %  but played “only” .596 ball the rest of the way and won over 100 games.  Conversely a .260 point jump in win % for the 2000 Tigers over the last 5 months still didn’t push them over .500 on the season. But for a significant number of teams the last 5 months can wash out much of the good or bad April does. Hot starters fall apart and teams buried in April can rise from the ashes.

For determining both the amount and type of April deception the logical starting point is prior year records. Fully 70 % of teams with 90+ wins the prior year have .500 + Aprils v. 37% for teams with 69 minus wins. Since so many teams tend to have Aprils which are “characteristic” of their prior season , measuring the true amount of “April deception” can be done from two angles: 1 – How many Aprils “characteristic” of last year give true v. false signals of another similar season ?  2 – How often do “uncharacteristic” Aprils end up being true v. false signals of a better or worse year ?

 1^st – How reliable are “characteristic” Aprils.  The following chart illustrates this

As can be seen, prior year good teams (90+ wins) with good Aprils have had twice the chances of making the playoffs as an average team (30%). On the other side, prior year sub-.500 teams with sub-.500 Aprils have had very little chance of making postseason. Only 9 of 138 such teams have overcome their bad starts.   The percentage of teams finishing above .500 is also remarkably different.   80 % of good teams with good Aprils end up plus .500 on the year, where a mere 14 % of bad teams with bad Aprils do. “Characteristic” Aprils are highly reliable indicators of either continued contention or non-contention.    

“Highly reliable” does not mean “perfect”. The 90+ game winner/good April formula didn’t work for  defending world champions 2004 Marlins, 2013 Giants nor defending NL West champion 2008 D-Backs or 2005 Dodgers.  Nor did bad team/bad April deter the 2015 Rangers, 2011 D-Backs, and two Rockies teams (2009, 2007) plus the 2007 Cubs from rising up and making the playoffs.  But these are exceptions.

Applying these principles of “characteristic” Aprils to 2018 would bode well for the Red Sox, Yankees, Astros, Cubs and Diamondbacks. It would not for the Tigers, Rangers, Marlins, Padres, White Sox, Orioles, Royals and Reds.  With only 1 in 15 former sub-.500 /sub-.500 April teams having made the playoffs historical odds would indicate that none of the above eight teams will either.  Of course these clubs weren’t expected to contend but neither were the Braves, Phillies, Pirates, and Mets who were also bad teams from last year.  The difference is that latter four teams have had good Aprils.  So what do their “uncharacteristic” Aprils mean ?

 How often do “uncharacteristic” Aprils send true or false signals of change ?

After a surprising start in April many teams revert to their “true selves” (good or bad) in the last 5 months. But some percentage of uncharacteristic Aprils often correctly signal changes in a team’s fortunes. This time the data is parsed more finely to show where false and true indicators of change may appear.

“Uncharacteristic” April at the extreme ends tend to be fairly accurate in signaling real change, particularly changes for the worse.  Very bad sub-.400 Aprils by former 81+ winners have shown that most are in trouble that season.    Nearly 60 % fail to achieve even a .500 season and only 15 % make the playoffs.  Some 90+ winners (21%)  with below .400 Aprils get off the mat and still make playoffs.  But that contrasts sharply with their prior year 90+ winning brethren who start April at .520 + and have made the playoffs 62% of the time.

What about extremely strong Aprils from previously bad teams ? Prior 70-79 winners who play .600 + in April have done quite well as 44 % have made the playoffs.  One caution, however, is small sample size as only 14 teams fall into this category. For really bad prior year teams (70 wins and below)  who start .600+ most achieved .500 seasons and 1 in 5 made the playoffs. This includes some notable turnarounds:  2000 Cardinals, 2000 White Sox, 2006 Tigers, 2012 Orioles, 2013 Red Sox, 2015 Astros, 2015 Cubs, 2017 Rockies, 2017 D-Backs.    Of course, excitement over great Aprils by prior bad teams should be tempered by the fact that most still have missed the playoffs. This may apply to expectations raised by the start of the 2018 Mets.  Although the 2015-2016 Mets were playoff teams so it’s quite possible their strong April 2018 could be a legitimate sign of revival.  Teams who gained 15 or more wins over the prior year showed an average of 120 points jump in April v. prior year win %.  The Mets have jumped 220 points.

Mildly uncharacteristic Aprils send wrong signals of change more often than not. Which is logical as a 65 game winner who goes 14-13 in April has shown less transformative evidence than one who’s 18-9. Nor has a former 100 game winner with a 12-13 April shown the same reason for concern as a 7 -18 start might.   Only 22 % of “below 70” teams with .500-.599 Aprils finished over .500. Former 70-79 winners with decent .500-.599 Aprils have upped their playoff odds but only to a subpar 20%. Seven teams fit this description in  2018 – Mariners, Pirates, Phillies, A’s, Braves, Blue Jays, Giants.  History says 1 will make it, 2 would be very optimistic.

However, one type of mildly uncharacteristic April is noteworthy. While previous 90+ winners with modestly bad .400-.499 Aprils still have a high recovery rate (77 % end up over .500) they’ve had a big drop in playoff odds. Only 29% make it after such starts.  That is not good news for 2018 Dodgers, Nationals or Twins whose goal is to make the playoffs.  Which brings up the next issue – how does April influence playoff chances ?

   WHAT EFFECT DOES APRIL HAVE ON MAKING THE PLAYOFFS  ?

One old adage is “you can’t win the pennant in April but you can lose it”. While there is definite truth in this, April has the fewest games of any month, and still leaves ample time to recover. The 2001 Oakland A’s had a miserable 8 -17 April but went on to win 102 games.  That said, they were the only sub-.400 April team to win 100 + games and only one of 8 teams who started that poorly and still made the playoffs.   Another 10 such bad start teams played the rest of the season at contender levels (.540+ ) yet failed to make the playoffs and their poor Aprils were instrumental in that.

While it is clearly possible to recover from a weak start, bad Aprils leave a diminished margin of error. Two-thirds of all playoff teams start the year solidly (.530+ ), the vast majority (78%) are at least .500 + and fully 95 % of all playoff teams have avoided disastrous (sub. 400) Aprils.   Teams who’ve stumbled early can’t count on fellow playoff contenders being in the same underwater boats.  They have to play serious catch-up with rivals whose yachts have begun to float away.  An “average” playoff team has a .564 April win percentage.  So a club with a 9 -15 April is trying to catch teams who’ve gone 14-10 or better.  If the 15-9 April teams plays at “only” .537 ROS and gets 88 wins, it takes a .580 ROS from the poor April starter to overcome that.  Playing at .580 + level ROS has been done by only 15% of all teams, which equates to being one of the top three teams in one’s league after May 1^st.

Those 8 playoff teams who started sub-.400 averaged a .604 ROS win percentage.  Five of the eight were .580 + ROS and the lowest ROS win percentage was .572 which translates to 93 wins over a full season.    In addition, there were four teams with a .580+ ROS that missed the playoffs.   Despite being better ROS than their key divisional or wild card rivals those four teams (2004 Giants, 2005 Indians, 2011 Red Sox, 2012 Angels) lost out on the playoffs because of their inferior April records.

The other side is that very strong Aprils can provide a cushion to play at less than .550 ROS. There are even occasions where teams with less than a .520 ROS have made it in due to strong Aprils: 2016 Mets, 2015 Astros, 2014 A’s, 2006 Cardinals, and 2000 Yankees.  Ironically there are two World Series winners in that group (Cardinals and Yankees).   The Cardinals were particularly unusual as they were the only team to make the playoffs with a sub-.500 ROS record.

The lone .600 + ROS who missed the playoffs (2005 Indians) provide a classic example. They were the second best team in the AL after April, outplaying their division rival and subsequent World Series winner White Sox   84-55  to  82-56  ROS.   However, with the 17-7 April of the Sox, and Indians’ poor 9 -14 start, Sox gained a 7.5 game cushion.  The double whammy was that the Tribe’s poor April also cost them the wild card to Boston.   Had the Indians played even 12-13 instead of 9-14 they’d have ousted Boston.   Of course Cleveland’s 13-16 July didn’t help either, nor did going 1 -5 the last week of the season (including a 3 game sweep by the Sox) after the Indians had whittled the lead down to 1.5 games on Sept. 24.  But the April cushion built by the Sox allowed them to withstand an incredible Aug/Sept run by Cleveland and the Tribe was forced to play unbelievably well to stay in the hunt.   This all happened before the second wild card was introduced in 2012, and if it had applied back then Cleveland would have made it as that second wild card.   So has this second wild card now made it easier for April stumblers to recoup ?

HOW THE SECOND WILD CARD HAS CHANGED PLAYOFF ODDS

Adding a second wild card team has changed the odds in some meaningful ways as the following illustrates.

Clearly 87 – 91 wins has had twice the chances of making the playoffs than before. Before 2012 teams needed to be stoking the engine every month to get to 92 wins. However with the bar now lowered to 88 wins since 2012, this allows more margin for error. That margin can go two ways. It can benefit teams who stumble in April and need a lesser ROS to get in.  In fact for teams who started less than .450 and then made the playoffs prior to 2012, the average ROS was .604.  For the three teams that have done that since 2012, the average ROS is .574.   But the post-2011 lower win threshold can also help teams who come out strong in April who don’t have to tear it up in the last 5 months to get in.   So which has it helped most ?  Following is a breakdown of the percentage of playoff teams after 2012 who’ve played at given ROS levels comparing those who had .600 + Aprils v. below .600 Aprils.

Those who started .600 or better had a much lower burden to meet ROS.   Only 26% of the 600+ April teams who made playoffs achieved .580 ROS where over half of the below .600 Aprils had to meet that burden.  While having a .600+ April is no assurance of making the playoffs, it virtually is if teams play at least decently thereafter.   Here is what has happened to the 34 teams with 600+ Aprils from 2012-2017.

As can be seen, 600+ April and 520+ ROS has been a successful formula 20 of the 22 times. The only two to miss were 2013 Rangers (91 wins) and 2012 Bucs (90 wins), who also happened to be the only 2 of 42 teams who’ve won 90+ since 2012 and missed the postseason. Playing .520 is hardly a torrid pace as it equates to 84 wins over a full season.   Playing at .500- .520 ROS isn’t playoff caliber yet 3 teams still made it, all helped by .650+ Aprils (2016 Mets, 2015 Astros, 2014 A’s).   The 7 teams who collapsed to sub-.500 after hot Aprils clearly didn’t deserve to get in.

One final cut of the data can establish who has been helped more by adding 2 more wild cards – the April surgers or the April stragglers. .565 ball equates to 92 wins and .520 ball equates to 84 wins . Teams who play at .565 levels ROS should make the playoffs.  Teams between .520-.564 should contend.

This defines the task confronting a good team with a sub-.500 April. Every year on average there are 4 such teams who rebound with .500+ ROS at a typical year looks like. However, they have to find a way to better several of the 11 teams who’ve started .500 + in April and are still +.500 and there are only 10 playoff spots.   Their only remedy is to play .565 + ball ROS and for one team (2012 Angels) even that wasn’t enough due to a terrible 8 -15 April.     But the .565+ April starter has it easier as a .520-.564 ROS gives him a 2/3 shot of still making it where only one team of 11 who started below .500 (2016 Giants) was able to eek into playoffs with less than .565 ROS.    The April .565 starter still has a shot with a tepid .500 – .520 ROS, but those are all teams as noted before that were red-hot in April (.650+).

Due to the abundance of teams starting well or decently (in April 2018, 13 teams were .565+ and 19 were .500+) the poor starters simply have to be a lot better ROS than their rivals to shove their way through the crowd.   The second wild card has benefitted the good April starters moreso than the good teams with bad Aprils. Of course the same math could be applied to any month, good or bad, so to paraphrase the classic Passover question:  why is April so different from all other months ?

PSYCHOLOGICAL EFFECTS OF GOOD OR BAD APRILS

Despite the fact that April is only 15% of the overall season, when early-mid season personnel decisions are being made, April results can still have considerable impact.   April’s record can influence decisions such as: the patience a team has for a younger player with early season struggles or whether the team tries for a mid- May trade to replace an injured starter and/or considers promoting a top notch AAA player despite his arb clock issues.  When trading season starts in June, 35% – 40% of the team’s record at that point has been baked in by April’s wins and losses.  Even by the late July deadline, April still comprises 25% of the season.  If early results have affected fan attitudes and attendance, ownership may be either more or less willing to commit dollars to bigger name players at the deadline.  These factors may give April importance beyond its mathematical impact on the standings.   That teams tend to mirror their April win-loss %’s as the season progresses may be in part that April can create a sort of self-fulfilling prophesy.

The 2014 Cleveland Indians were 75-60 after April 30^th, the Oakland A’s were 70-63 over the same time. Yet Oakland got the wild card by 3 games over Cleveland due to a 18-9 April v. the Indians 10-17.   Early season results were still impacting July decisions as the A’s were buyers and the Indians sellers.   To be sure, the A’s record worsened post-July and the Indians got better.  But without Jon Lester (2.35  ERA with A’s) and Jeff Samardzija (3.14) who knows how much worse it might have been for the A’s .  While ridding themselves of Justin Masterson may have helped the Indians and trading Cabrera didn’t hurt, how much better would they have been had they gotten an outfielder and starting pitcher in July instead of being sellers ?

Conversely, the 2016 White Sox benefited from a 17 -8 April despite a May tailspin which left them with a 29 -27 record on June 4^th and only 2 games behind in the AL Central. They then traded a very talented younger prospect named Fernando Tatis Jr.  for James Shields.  Needless to say this is a trade that has not worked well short or long term.  Despite the fact that the Sox had 3 straight losing seasons prior to 2016,  management seemed to believe that April/early May represented the success they felt the team was capable of as opposed to the more recent reality of losing.  Successful Aprils can sometimes keep wishful thinking alive for too long.

CONCLUSIONS

Clearly April has proven to be a good proxy for the team’s chances going forward that season. But April records, also have to be viewed in context of all the evidence.  How much of a factor were injuries, over or under performances, or new offseason acquisitions?  Thoughts of dumping contracts and rebuilding, while too premature for May 1^st, are still quite logical particularly for poorer prior year clubs who are off to bad starts.

For clubs in the playoff hunt, April can have real impact since 3-5 wins one way or another can be make/break. Since 2013 teams with 87-88 wins have made the playoffs in 10 of 11 cases where only 2 of 14 teams with 85-86 wins have. Clearly these margins apply to other months’ results too, but as noted before, a very good or bad April can affect team decisions in June and July.   Having some breathing room afforded by a 16-10 April instead of the catch-up pressure of a 10-16 start can play into the psychology as well.

Winning the division winner is far preferable to having to win a one-game playoff as a wild card and April provides a good checkup on division rivals. Both Boston and NY look like they will fight it out all year although Toronto can’t be ignored. In the Central, the Indians’ are in a strong position with their chief rival, the Twins, are 4.5 games back already and the rest of the Central bad teams who’ve started with bad Aprils.   Houston’s good start helps especially since all of their closest chasers (Mariners, Angels, A’s) were sub-.500 teams last year, but the Angels improved through offseason acquisitions.  In the NL East, even though the Nationals are 5 games back they’re chasing teams who were all sub-.500 last year (Mets, Braves, Phillies).  Where the Dodgers, who are 8 games back, are pursuing a D-Back team that won 93 last year.  Turner’s absence hurt in April but so did the lack of offense from the rest of the team which may continue particularly since Seager is lost for the year.    Plus the bullpen woes cannot be overlooked. So April’s 12-16 record cannot be easily dismissed as an aberration.  Nor can the historical evidence of diminished playoff odds (20-25% range) of good teams who’ve had the Dodgers’ kind of April.   We shall all see soon enough.

All stats and tables from Baseball Reference. All batted ball data from Baseball Savant.

After Gerrit Cole’s magnificent start on Friday, and indeed, his string of magnificent starts to open the season, it follows that we would hear a lot about him and his resurgence (some might say his breakout). Lost in the buzz around Cole is the start-of-season performance of another former teammate of his, Francisco Liriano. Liriano has not been otherworldly, but has so far looked more like he did in 2013 than in 2017. That is to say, he’s looked quite good!*

*Before you start playing the Small Sample Size Song, I know this is a small sample size, but I wanted to write about it, so stow your tunes at the doorstep.

Here’s a summary of his 6 starts so far, including Friday’s 7 innings of 1-run ball:

April 2

Pretty good for a guy that ran a 5.66 ERA last season! As you can see, Liriano pitched quite well in his first start, allowing only 6 baserunners over 6.2 innings, making for a WHIP just under 1. He also allowed only 4 line drives, and pitched the Tigers to a 6-1 victory over Kansas City. A pleasant surprise to open the season for the more pessimistic among us (such as me).

April 9

Another surprising start for our friend Francisco. This time, he only went 6 innings, and allowed 2 earned runs instead of 1, but I’d take that from my projected #5 any day. His WHIP here is exactly 1, and he allowed just 2 line drives this time around. More good results from the 34-year old.

April 17

Liriano didn’t look as good this time around, but he was never supposed to be an ace in Detroit. We presumably signed him for depth, and while the deal seemed a head-scratcher at the time, Al Avila is looking pretty smart now that Daniel Norris has gone down. This start, Liriano went 5 innings and allowed 8 baserunners along with 2 runs. He had his highest strikeout total of the season, but aside from fly balls allowed, everything else was worse. The thing is, this still isn’t a bad start. It is not, by definition, a Quality Start™, but it’s still a relatively okay one, and it’s certainly still above what we expected from Liriano.

April 22

Another passable outing from Francisco. 2 of his 3 earned runs came on the next pitcher allowing inherited runners to score, but that doesn’t change the box score, or the fact that he allowed 6 baserunners in 5.1 innings. His line drives were down, but if you’ve been paying attention, you’ll notice that he’s allowed a home run in every start except his first. I’d hesitate to call allowing a home run three starts in a row a definite trend, but it’s certainly starting to look like one. Hopefully Franky can turn it around soon.

April 28

This is more like it! This qualifies as a quality start. Again, 2 of the 3 earned runs were from inherited runners scoring, and again, that doesn’t change the box score, but it’s always nicer to see a 6 in the IP column than a 5. What’s more, Frenchy did not allow a home run for the first time in almost a month. The worries begin again when you take a look at the number of baserunners allowed (8), and are slightly heightened when you see that he only struck out one batter, but on the plus side, his 2 walks are the lowest he’s allowed since his first start with the Tigers.

May 4

Finally, we come to yesterday’s start, which by every metric is the best. Liriano pitched 7 strong innings, allowing 1 earned run on 5 baserunners. What’s more, not only did he not allow a home run — he didn’t allow a single line drive! With only 2 walks again, Francisco seemed to recapture the magic he’d shown in a majority of his 2018 starts.

This article was supposed to be uplifting for Tigers fans. I went into this thinking I’d be able to write nice things about Francisco Liriano and demonstrate that while he’s no Gerrit Cole, he’s still much better than people are giving him credit for.

To my great despair, this does not seem to be the case. Liriano sports a lovely 2.97 ERA and a very respectable WHIP of 1.073. These, plus his H/9 and BB/9, are down a striking amount from 2016, and all but his BB/9 are his best since 2006(!). Unfortunately, his peripherals tell a different story. Liriano’s FIP is 4.13, which is mostly attributable to his paltry 6.4 K/9, itself a point of concern — it’s his lowest ever, by 1.1. But the batted ball data paints an even more dismal picture for Francisco’s future.

Since 2016, Liriano’s hard hit percentage has remained pretty stable, from 33 to 33.7 to 32.7 this year. That puts him in the company of such luminaries as Jeff Samardzija, Brandon Morrow, and Sean Newcomb. That’s a little harsh; this year, he’s closer to guys like Justin Verlander, Clayton Kershaw, and Masahiro Tanaka. But it’s early in the season, and I’d still say Liriano comps closer with JC Ramirez than Corey Kluber.

The more worrying data lie in Liriano’s expected outcomes. Statcast measures expected wOBA (xwOBA) based on batted ball profiles and compares it to actual wOBA. Since Statcast began tracking batted balls in 2015, Liriano’s wOBA and xwOBA have remained within 15 points of each other. This season, there is a 109 point difference. A sobering number, to say the least. Couple that with the fact that his xSLG is a frightening .513, it seems our ostensibly-resurgent pitcher has just been exceedingly lucky. I haven’t watched a Liriano start yet this year, but I have listened to a few, and I distinctly recall hearing quite a few exclamations of astonishment from Dan Dickerson directed toward our middle infield.

What originally started as a post meant to proclaim the newfound prowess of a dubious offseason acquisition ended up as a bleak prediction for his future. But we must remember, in our unexpected despair, that this is baseball, and hope spring eternal for the simple reason that we really have no idea what could happen. Nobody could have predicted Andruw Jones’ death spiral, or Rick Ankiel’s conversion from pitcher to outfielder.

And so I remain foolishly optimistic that Liriano’s success is for real. If he starts to pitch poorly, I will probably appeal to small sample size until at least July, while ignoring the massive amount of cognitive dissonance required to hold that position and still write this article. Luckily for me, I don’t care. I am a Tigers fan first, and I am duty-bound to have faith in our players until their last breath, or at least their last breath in a Detroit uniform.

My realistic prediction is that Liriano will pitch to a 4.5-5 ERA for the rest of the season, and I won’t be disappointed. My homer prediction is that he continues to showcase his recaptured abilities and pitches to a 3-3.5 ERA. I will be pleasantly surprised if that happens. If it doesn’t, well, this team was supposed to suck anyway.