The Incredibles can be considered a masterpiece of modern cinema. Brad Bird’s 2004 Pixar film follows a family of superheroes in a world that’s openly hostile to their kind. As such, the family is forced to camouflage itself as a normal one. Mr. Incredible has an unfulfilling job. Mrs. Incredible thanklessly raises three kids. At the outset of the movie, we, the viewers, know the positive potential for superheroes from our prior knowledge of popular culture. For some reason, the movie tells us that the existence of superheroes is a bad thing, so we’re left with a major dissonance. Yandy Diaz’s situation in Cleveland was a lot like the beginning of The Incredibles.

In an age of celluloid superheroes, Diaz looks like a real-life version. The “most jacked player in baseball” according to stack.com (the mere existence of this website makes me uncomfortable) has put up minor league numbers of someone with super vision, not super strength. Indeed, his career-low in on-base percentage at any minor league stop with at least 20 plate appearances is .399. In 2018, he split time between Triple-A Columbus and Cleveland, where his OBPs were .409 and .375, respectively.

Unfortunately, like the existence of superheroes, Diaz’s power remains in our collective imaginations. Despite his mammoth yolkedness, his professional career high in home runs in any full season is nine. Even Delino Deshields hit 12 in the minors once, and he is a small, fast player, and he plays like it. You can usually tell what kind of player someone is by their body type, but not so with Diaz.

As you may guess, there have been zero ground-ball home runs in the Statcast Era. Indeed, cold hard data and common sense align on this topic. This is the great barrier separating our reality from SuperYandy, his radioactive spider so to speak. Diaz’s professional ground-ball rates have ranged from the low-50s to the mid-60s. To put that in context, the following chart shows the ten qualified MLB players with the highest GB%, along with Yandy’s 2018 season:

The most valuable player here is Lorenzo Cain, and by a wide margin. As you can see, Cain posted one of the highest BABIPs and walk rates of the bunch, and he finished 24% above average offensively. Combine that with his stellar baserunning and center-field defense, and Cain was one of the most valuable players in the majors in 2018. However, the chart makes it clear that hitting grounders is generally not great for hitters’ production. Without a strong BABIP and BB%, it’s nearly impossible to be above average while hitting that many grounders.

Luckily for Diaz, he seems to be skilled at achieving high marks in both of those categories. We’ve already talked about his SuperVision, and the Steamer projection system expects Diaz to put up a .368 OBP while walking over 12% of the time in 2019. Meanwhile, Yandy’s BABIP is directly tied to his potential to become baseball’s Mr. Incredible.

BABIP has always been an attention-grabbing stat. When it first jumped into the analytical scene, it was dismissed as randomness. The thought was that hitters can’t control where the ball goes, or the quality and positioning of defenders, so we should expect batting average on balls in play to fluctuate with luck, and to an extent, it does. But more recent thought suggests that players do have some control over their BABIP. Cain is much faster than Trey Mancini, so even though they have identical ground-ball rates, we can expect Cain to beat out more of those grounders. Indeed, Cain had 27 infield hits in 2018, while Mancini finished with only 11.

So what other controllable factors can lead to higher BABIP? For one, batters can influence how hard they hit the ball, and they can influence how high they hit the ball; indeed, we find that each of these factors affects batting average on balls in play. From 2015-17, balls struck at 100 mph led to base hits 49.8% of the time, and that percentage only increases with harder hit balls. (It should be noted that that includes home runs, which are not included in BABIP). In addition, each type of batted ball is associated with BABIP performance, as outlined in the following chart:

Diaz has always specialized in that ‘non-flies’ category: during his limited MLB career, just 20.8% of his batted balls have been fly-balls, which is the sixth-lowest during that time-frame. However, not all non-flies are created equally; we’ve already discussed the association between hitting the ball hard and reaching base successfully. Among the 480 players with 50 or more batted balls in 2018, Diaz finished 24th in average exit velocity, just behind the AL and NL MVPs, Mookie Betts and Christian Yelich.

This is the kind of hitter Diaz is as of this winter. He’s a non-fly-ball hitter who consistently makes great contact with an above-average eye and low strikeout numbers. In the field, Diaz had a strong reputation in the minors. Baseball America even named him the strongest defensive third baseman in the Carolina League in 2014. Additionally, throughout his minor league career, he played every position except shortstop, catcher, and pitcher.

Put all of this together and you have yourself a high-floor, multi-positional major league baseball player, which makes his time with the Indians franchise seem peculiar. Diaz began his American baseball career in 2014, and he cruised through the minor leagues as a consistently great hitter. By just 2015, he got his first taste of Triple-A ball, and 25 games into 2016, he was permanently at that level. And then… the Indians never really gave him a shot. For Columbus in 2016, as a 24-year-old, he hit .325/.399/.461, good for a 149 wRC+. No call-up. In 2017, he was even better: .350/.454/.460, 163 wRC+. Finally, the Indians called him up after over 800 extremely successful Triple-A plate appearances, and he fared okay in his cup of coffee. In 2018, he spent most of his time in Triple-A, again, despite his 132 wRC+ in his 120 MLB plate appearances.

For some reason, despite his overwhelming success as a professional baseball player, Cleveland barely gave him a chance to succeed. He’s potentially Mr. Incredible, an extremely strong player with great potential being held back. For one, it seems like Cleveland, and Terry Francona, viewed Diaz’s defense as “a work in progress,” despite countless public reports to the contrary. Maybe that’s the case, but even if we assume Diaz’s fielding isn’t actually as good as those reports make it out to be, I still find it hard to believe that Diaz would not have been a better first baseman than Yonder Alonso in 2018. We must consider that it is possible that the front office knows something about him that would make him less appealing.

To Tampa Bay, Diaz is not just a high-floor Ben Zobrist type, but he also has tantalizing upside. There’s no way of knowing this for sure, but I am willing to guess that the strongest three players in the major leagues are, in some order, Yandy Diaz, Aaron Judge, and Giancarlo Stanton. We don’t know if Cleveland tried to convert Diaz into someone with Judgian power, as John Sterling would say, but we can assume the Indians at least thought about it. Maybe they tried to change his swing and he was resistant. Who knows? There’s little doubt that this is the Rays’ plan though. They’re hoping that Diaz can change his swing a la J.D. Martinez and become SuperYandy, a slugger without the strikeouts. Even if that doesn’t work out, they still end up with a valuable player, and if it does, Diaz could end up as one of the most valuable players in baseball. It will take time before we know if he was one of the best players traded this offseason, but rest assured, he’s already one of the most interesting.

Something appears to have happened to Madison Bumgarner. Specifically, his four-seam fastball has gone missing. Depending on which data source you use, it figuratively and literally disappeared. Regardless of data source used, Bumgarner’s fastball isn’t performing.

Two leading data sources disagree on what has happened to Bumgarner’s fastball. Because of this, I chose to look at both sources independently: Pitch Info (through Brooks Baseball) and Statcast (through Baseball Savant). This analysis spans four seasons, 2015 through 2018, encompassing Bumgarner’s two best and two worst complete seasons.

According to Pitch Info, Bumgarner threw four-seamers in 2018 at a career-low frequency — 34.5% of the time in 2018, down from 48.2% in 2016 and 49.6% in 2015. It has been losing effectiveness since its peak in 2014. Using Pitch Info’s runs above average metric, we see Bumgarner’s four-seamer peaked in quality at 1.25 runs above average per 100 pitches in 2014 and has dropped each year since then: 0.97 in 2015, 0.39 in 2016, -0.35 in 2017, and -1.14 in 2018, a career low.

As seen in the Pitch Info Whiff Percentage charts above, Bumgarner’s four-seam fastball had its lowest whiff rate of our period of study in 2018 (seen on the left), likely leading to it’s ineffectiveness. Similarly, Bumgarner’s four-seam is measured to have had more vertical sink, independent of gravity, than it had throughout this period (seen on the right). Depending on the pitch, more movement generally increases whiff rates. A four-seam fastball moving more like a two-seamer, however, would lose swing-throughs: sinkers (two-seamers) generate more contact in the form of ground balls.

Bumgarner produced his highest ground-ball rate with his fastball since 2013 while also generating the fewest whiffs with his fastball of his career. Couple the results with the change (increased vertical movement), and it appears his fastball began to behave like a two-seam fastball.

This seems to be clear already. According to Statcast, Bumgarner threw his four-seam fastball only once in 2018, as compared to 38.6% of the time in 2016 and 41.1% of the time in 2015. He replaced them mainly with two-seam fastballs, but also with some curveballs and changeups.

When comparing Statcast to Pitch Info, I wondered if Statcast could have been misclassifying four-seam fastballs as two-seamers. Through looking at the above plots, however, it’s clear a cluster of pitches was missing in 2018. The above graphs are of every pitch Bumgarner threw, by horizontal (x-axis) and vertical (y-axis) movement, colored by Statcast pitch classifications. Even when ignoring pitch type labels, a pitch type is seen to be missing. Specifically, Bumgarner’s high-rising, fairly straight pitch was no longer thrown. On a side note, notice how inconsistent 2017’s movements were: likely because Bumgarner had to recover form a major shoulder injury and struggled.

With Statcast data, we can evaluate what happened with greater depth than through other methods. Below is a table of statistical changes in both Bumgarner’s two-seam and four-seam fastballs.

Velocity is measured in miles per hour, spin in revolutions per minute, extension is feet from the rubber, and horizontal and vertical movements are in inches from release point. Ignore 2017, as it was a very inconsistent year (as seen with the movement chart above). Both two-seam and four-seam fastballs in 2015 and 2016 had significant vertical rise due to spin. In 2018, however, Bumgarner couldn’t or wasn’t spinning his fastballs as much, resulting in less rise and more downward movement. This could be why Statcast is misclassifying his fastballs.

Why has Bumgarner lost spin on his fastballs? The data suggests two reasons why, both of which could be correlated. He’s lost velocity, and release speed correlates with spin rate. Similarly, Bumgarner has less extension on his fastballs than in 2016. His 2018 extension is similar to his 2015 extension, but because he’s lost velocity, the loss of extension could be penalizing. This loss of extension could explain the loss of spin if it’s related to grip or release.

Extension loss to home plate reduces the perceived velocity the batter anticipates, making it easier for the batter to time the pitch. Both loss of velocity and extension would, when combined, greatly benefit the batter at the expense of Bumgarner’s fastball.

What could have caused the loss of velocity and extension? Bumgarner is 29 years old, so there is a chance he’s entered his decline. The likely culprit, however, is injury: Bumgarner fell of a dirt bike in April 2017, injuring his left shoulder, and he broke his left hand on a line-drive comebacker in spring training in 2018, requiring surgery. Being left-handed, both injuries could have significantly affected his 2018.

One year away from free agency, Bumgarner likely hopes he can recover lost velocity and spin on his fastball. Whether it was an organizational change, a declining skill set, or driven by injury, his 2018 fastball difference was one to forget. His shoulder should be better healed, one year further removed from his accident, and hopefully his throwing hand does the same.

This and other postings like it can be found on my personal blog, First Pitch Swinging.

Every year, nearly every team in baseball receives significant contributions from unexpected sources. The 2018 campaign was no exception, with a number of players putting up productive seasons while primarily coming off the bench. For the purposes of this article, we’ll define a bench player as any player with (roughly) 100-plus plate appearances over the course of the season that did not appear as a team’s primary starter at any one position. Additionally, I sorted out players that were traded (or acquired) midseason (Manny Machado for example) or called up to start full time but did not record enough time to count as the team’s primary starter at their position (per Baseball Reference). Players with 500-plus PA were excluded from consideration on account of extensive playing time (apologies to Chris Taylor, Matt Kemp, Jurickson Profar, and Ben Zobrist), and one player was selected at each position, along with honorable mentions. Without further ado, let’s look at the best of the bench in 2018:

Catcher: Elias Diaz, Pirates

Honorable Mentions: Tyler Flowers (ATL), Luke Maile (TOR)

Diaz certainly served as a key bright spot during an up-and-down season for the Pirates, filling in admirably for the oft-injured Francisco Cervelli. The young Venezuelan had somewhat of a breakout campaign in 2018, posting a 114 wRC+ while contributing positive defensive value behind the plate. Overall, Diaz put up a solid 2.0 WAR in 277 PA on the season. After a weak offensive showing in 2017, Diaz appears to have made notable strides in plate discipline this season, improving his walk rate from 5.5% to 7.6% while dropping his strikeout rate from 19.0% to 14.4%. This decline in strikeout rate represents one of the 10 largest such drops in the league between 2017 and 2018 among players with at least 200 PA in each season. Combined with Cervelli’s excellent output, Diaz helped guide the Bucs to a league-leading 5.3 WAR from catchers:

While Flowers and Maile each put up solid seasons in their own right, Diaz ranked among the top 10 bench players in all of baseball by WAR this season and established himself as one of the better backup catchers in the game. Read the rest of this entry »

At some point in the coming days or weeks or months, Bryce Harper, with an assist from Scott Boras, will make a choice as to which team he will sign a contract to play baseball with in 2019, and possibly beyond. A lot of factors will go into what his choice of team will be. It is almost certain that one of those factors will not be the city and state (i.e., local) income taxes he would be paying were he to choose this team or that.

Maybe I have that wrong. Maybe the local income tax burden will be high on a list of factors Harper considers. Maybe he’ll meticulously pore over a table showing each team’s possible effect on his overall tax bill. More likely, though, beyond the idea that a team he’s considering plays in a no-income-tax state or a super-high-income-tax state, I can’t imagine him or Boras actually stressing out over it.

That said, it is worth noting that the difference in local income taxes to be paid by Harper next year would vary widely depending on which team he chooses. And when I say “widely”, I’m talking a difference of millions. Mo’ money, mo’ taxes. Nice problem to have, amirite?

What follows is my attempt to quantify just what this difference might be, by team. Before diving in, two points.

The first point is to dispel any lingering notion that, if Bryce Harper were to choose the Texas Rangers or Seattle Mariners or Miami Marlins—come on now, stop laughing and pay attention, this is kind of important—or any other team in a no-income tax state, he would not have to pay any state income tax at all. That’s not true. In every place that levies income tax in which his team plays games, a ballplayer on a big league roster is responsible for paying them. Regardless of whether he plays 81 games at home as, for example, a member of the Astros in Houston, located in a state with no income tax, when the Astros play their 19 road games in California (top income tax rate: 13.3%), he would owe the Golden State tax on (19÷162 =) 11.7% of whatever his total salary for the year is. Same goes for all the other games in all the other taxing places his team plays in. So no matter what team Bryce Harper plays for, he’ll be paying a boatload in state income taxes to a bunch of states.

The other point is that, regardless of which team Harper ends up playing for, he’s going to pay the same in federal taxes no matter what, which makes sense. Why would his federal tax burden be any different playing in New York versus paying in Tampa Bay? Of course it wouldn’t be. Although you might be surprised to learn that even though one team plays in Canada, which obviously has a different federal income tax scheme than the US does, the tax brackets of the two countries are surprisingly similar, with Canada being even a touch lower at the top end than the US is. So, for this exercise, let’s assume that regardless of what team Harper ends up playing with, his federal tax burden to his team’s country will be the same. As such, our focus is not federal income tax; it’s only local income tax.

OK, now: to get at what the difference in local tax burden would be based given each of the thirty teams Harper could possibly play for, we had to determine how many games each team would be playing in each state in 2019. There are seventeen different states, one province and one District of Columbia (let’s call them all “states” for this piece) that are home to big league teams. Three of these states (FL, TX, WA) collect no income tax at all. Five others (CA, NY, DC, MN, Ontario) impose very high state income tax rates of at least 9% at the top end. The remaining eleven range from about 3% (PA) to 6% (GA). In addition, ten teams play in cities that levy their own income taxes, ranging from 1% (KC, STL) to a top end of 4.25% (NYC). It’s a veritable hodge-podge of Thanks For Playing Here, Now Pay Up.

To put actual numbers on this, let’s assume that Bryce Harper signs on to play for a base salary of $35 million next year. That seems as good a guess as any at this point. Personally, I think he’s probably going to sign a one-year deal for $35 million, tops. Given his inconsistent performance of the past three years, as well as testing positive for injury history, I don’t think he’ll be getting the terrain-adjusting 10/400 deal he and his agent have been craving. I think recency will make teams think twice before committing to him for so much money and so many years, so I can see Bryce wanting a shot at putting up an MVP level season in 2019 so he can get his 10/400, or even better, next winter. I won’t belabor this point anymore, since it belongs in another article anyway. Let’s just say in 2019, one way or the other, Bryce is going to sign for $35 million.

That’s not the income he’s going to be paying tax on—there will be write-offs and exemptions and deductions and the like that his first-rate tax attorneys will find, so let’s say all of that leads to an adjusted gross income for tax purposes of $30 million. That’s the number we’ll use as the basis of Bryce Harper’s state and city tax burden for next year.

Lastly, these tax estimates are based on the actual tax rates each of these states and cities levy on income earners in their jurisdictions, including the application of graduated rates where appropriate (mostly in high tax states, and in New York City). Even so, with only a few exceptions, the vast, vast majority of Bryce Harper’s income will be taxed in all these states and cities at the highest rate available.

Here’s the result, rounded off to the nearest $10,000 to make it easier to take in. Remember, these are not taxes he pays only to the city or state his team calls home—they’re taxes paid to all the jurisdictions he plays in during the season:

Holy Toledo, is this ever a huge difference. Choosing a California National League or a New York City team, just by itself and independent of anything else, will take an additional $2 million out of Bryce Harper’s pocket than choosing the Houston Astros. Are you scared yet, Yankees/Red Sox/Indians/Angels/A’s fans?

Now, to be clear, it’s not as though we should cry for Bryce Harper because of his heavy tax burden. Bryce Harper has been a multimillionaire ever since he was 17 years old. He is a very rich young man who is on his way to becoming a very wealthy young man. (If you’re unclear on the difference between being “rich” and being “wealthy”, here’s a very entertaining and very NSFW tutorial.) So let’s not feel as though we need to light any novena candles on behalf of Bryce Harper.

By the same token, even for a man as rich as Bryce Harper, $2 million is not nothing. It’s decidedly something. And remember, that’s $2 million based on one year of Bryce’s salary alone. Over the course of the kind of ten-plus year contract he’s looking for, that could well add up to over $20 million in wages lost just to local taxes, for no other reason than he might choose playing for the Dodgers over playing for the Astros. Or over playing for the Rangers or Mariners or Marlins.

You can laugh about that last one now, because math class is over.

A significant challenge in baseball research is identifying when a player has made a transformational adjustment that results in a step-change in playing level (i.e. J.D. Martinez in 2013) vs. a player who has a great, yet unrepeatable year. Mookie Betts and Alex Bregman both had excellent years in 2018 and a call for regression would be expected. However, this research note presents data which suggests that Mookie Betts did indeed make a transformational mechanical change and will likely perform at high levels going forward while Alex Bregman’s improvement does not share the same solid underpinnings.

I recently examined the relationship between backspin and performance in this post. One of the key takeaways from that research was that no player in the highest backspin quartile (since the data started in 2015), has consistently put up “superstar” numbers. In fact, Mookie Betts was in the high backspin group and had the second highest wRC+ of 122 over the 2015-2017 time period – not “bad” but far from a super-star level. With Betts’ phenomenal 2018, I was curious if he was the only high backspin hitter to “break out” or if he made a significant change to his swing mechanics to hit the ball more “square.”

After reading that he and J.D. Martinez were working together on mechanics, I was curious if his backspin profile changed from prior years. Not only did it change, Betts had the largest reduction in backspin of all Qualified Hitters in 2018! Here is a list of the top and bottom ten backspin changers over last year:

Alex Bregman, on the other hand, had the sixth largest increase in backspin of all Qualified Players. Take a look at a comparison of Exit Velocity (EV), Launch Angle (LA) and Distance for the two players on well-hit fly balls (EV>=90, LA>=15).

Both Betts and Bregman had an EV increase of approximately one MPH. The change in the launch angle profile between the two hitters is significant – Betts added five degrees of launch angle compared to Bregman’s two-degree reduction. Betts should have had a distance gain; however, the fact that he didn’t is actually a positive based on the data. Thus, while Betts is showing a 13 ft. distance decrease over last year, Bregman had a 14 ft. increase. Most of Bregman’s distance increase is from backspin – a very unhealthy source based on the data.

While beyond the scope of this research note, the mechanical drivers responsible for changes in spin are Vertical Bat Angle, the amount of Explicit Swing Loft (also referred to as “Attack Angle), and the ball contact point (above or below the ball equator). Backspin increases with lower levels of Vertical Bat Angle and Explicit Swing Loft (Attack Angle) while “square” contact increases with larger values. More to follow on this in a future post. Because of the link between swing path quality and backspin, using distance as a performance metric in isolation is highly problematic – and can lead one to the opposite conclusion in projecting performance. In other words, it matters where the distance change is coming from.

In addition to the amount of backspin, other metrics such as the Standard Deviation of Launch Angle and a player’s IFFB% also have a strong relationship to the quality of a player’s swing path. Using a quartile ranking system for each of the three metrics, four players were in the top and bottom quartiles for all metrics in both 2017 and 2018. The difference in performance of the two groups is quite telling:

Wow! Considering only swing path quality metrics, the performance between the two groups is worlds apart.

To get a sense of the magnitude of the change for Mookie Betts in 2018, he was in the fourth quartile for all three metrics above in 2017. He moved from the fourth to the second quartile in backspin, fourth to first in Standard Deviation of Launch Angle, and fourth to second in IFFB%. Alex Bregman, on the other hand, moved into the fourth quartile for all three swing path quality metrics in 2018.

I have followed Bregman’s swing for some time and have made some timely performance predictions (in both directions) based on video. The backspin and swing path quality data, on the other hand, point to longer term issues that may not surface immediately. After all,  backspin improves the performance of balls hit but is inversely related to player performance given sufficient frequency (i.e. PAs). Thus, getting the precise timing of a performance shift based on the above data is difficult. However, without a swing path change for Bregman, the odds suggest that significant regression is not a matter of “if” but “when”.

I. Introduction

In 2017, Major League Baseball exceeded 10 billion dollars in total revenue for the first time. Ticket sales were a major component, making up 29.84 percent of this revenue (Statista.com). Due to the fact that fans continue to spend money once inside the stadium, 29.84 percent is merely a lower bound on revenue from ticket sales. For example, the average 2017 ticket price was 31 dollars; however, once inside the stadium, fans spent an average of 16 additional dollars on food (Statista.com).

II. Data

The data for this project are in an unbalanced panel format and contain 60,705 observations from 35 teams spanning from 1992 to 2017. Other than the 2017 season data, which I collected myself from baseballreference.com, the data from 1990 to 2016 were scraped from baseballreference.com by Troy Hepper, a consultant at Morgan Franklin Consulting, and shared on his github.com page.

Descriptive statistics of my game by game data are displayed in Table 1. The dependent variable is the percentage of tickets sold relative to a stadium’s capacity (PERCENTSOLD). PERCENTSOLD ranges drastically from a little bit under 2 percent to over 150 percent with a mean of around 66 percent. PERCENTSOLD is sometimes greater than 1 because for certain important games ticket sales exceed stadium capacity; however, only 76 out of 60,705 observations exceed 110 percent and these outliers have almost no effect on the estimated coefficients in the models.

The explanatory variables in this model are designed to control for the time effects of when a baseball game was played, the quality of the home team, and the quality of the opponent. To control for the time that a game was played, indicators for the month and year are included in the model. To control for day of the week and whether or not the game was played at night or during the day, four dummy variables were created indicating whether or not a game was a night game during the week (NIGHTWEEKDAY), a day game during the week (DAYWEEKDAY), a night game during the weekend (NIGHTWEEKEND), or a day game during the weekend (DAYWEEKEND). Due to the immense popularity of the first game of the season, an indicator variable for Opening Day is also used.

The quality of the home team is assessed using both information on payroll and playoff chances. Better teams have better players and since players are paid based on skill and production, better teams consistently have higher payrolls. The payroll variable created here is the percentage deviation from league average payroll (HOMEDEVIATION). The minimum percentage deviation is a little under 20 percent of the league average while the maximum is over 280 percent of the league average. A standard deviation of a little under 40 percentage points shows the consistent variability of team payroll throughout the data. The playoff chances of a team are weighted by the number of games back or up they are on the guaranteed divisional playoff spot.

The quality of the visiting team is assessed using information on payroll and the opponent’s relationship with the home team. Fans want to come to the park to see good teams play so more attractive visiting teams will consistently have higher payrolls. The visiting team’s payroll variable (AWAYDEVIATION) is constructed the same way as the home team’s payroll discussed above. Because fans want to see their teams make the playoffs and the best way to do this is by beating the teams in your division, an indicator variable to assess the draw of a divisional game is used as well.

III. Regression Specification and Results

To better understand the relationship between the explanatory variables and the long-run demand for tickets, the data were analyzed using three panel data estimation techniques: one-way fixed effects, two-way fixed effects, and random effects models. For these data, it is clear that a fixed effects model is a better fit due to the fact that the unobserved metric of fan loyalty, which is constant over time, correlates very strongly with the two explanatory variables that control for payroll. The reason that fan loyalty is constant over time is that it is clear that for some teams, like the Chicago Cubs, the teams are deeply engrained in the culture of their cities and the fan bases remain loyal to these teams no matter what. On the other hand, for certain teams, like the Oakland Athletics, fan bases consistently disregard their teams and never become engaged. Because loyal fans spend more money and demand higher quality teams, owners of these teams must spend more on players. For this reason, payroll is correlated highly with the omitted variable, fan loyalty, making the use of a fixed effects essential for unbiased coefficient estimates.

The results of the three separate panel estimation techniques are recorded in Table 2; however, this paper will focus on the results of the following two-way fixed effects model:

In this model, T represents the team, S represents the season, and G represents the gth home game for each season. An interesting conclusion is that except in the case of DAYWEEKEND, both the fixed and random effects estimation have the same sign and approximate magnitudes for each coefficient.

In the two-way fixed effects model, all variables except the time fixed effect for 1996 are significant at any standard level. The largest coefficient is that of the Opening Day dummy, which causes an estimated 38.7 percentage point increase in percentage of tickets sold. Interestingly, the year dummy variable shows an approximate 11 percentage point drop in PERCENTSOLD in 1995 in comparison to 1994. This drop is most likely due to the disdain towards baseball fans developed following the players’ strike of 1994. Another interesting league wide trend is the approximate 4 percentage point drop in PERCENTSOLD from 2007 to 2009 during the Great Recession. For the average sized stadium, this sized drop would result in a decrease of a little over 1,700 fans per game. According to statista.com, the average ticket price in 2009 was 26.6 dollars. Thus, the resulting setback of losing 1,700 fans paying 26.6 dollars per game over the course of 81 home games would be around 3.7 million dollars. According to the Hardball Times, league average revenue in 2007 was 171 million dollars so for the average team, a 3.7 million dollar drop in revenue in 2009 would result in around a two percentage point decline in revenue from ticket sales alone. This is economically significant for a profit maximizing firm like a baseball team.

Using April as the base case, the coefficients of all other month dummies are positive. This indicates that the first month of the season is the weakest month for maximizing PERCENTSOLD. Notably, July and August dominate the percentage of tickets sold with an estimated 13 to 14 percentage point increase in PERCENTSOLD in comparison to April. Economically, maximizing games played in July and August while scheduling off days during April would result in increased revenue; however, if three more games were scheduled in July and August, the increased number of fans paying the 2017 average price of 31 dollars per ticket would result in a little over 500,000 dollars in increased revenue, which is an economically insignificant increase of .2 percentage points.

The indicator variables designed to control for game time and game placement during the week also shed light on what type of games maximize PERCENTSOLD. In the model, NIGHTWEEKEND was left out and the coefficients of the other three dummies were negative. This tells us that weekend games played at night are the most popular. DAYWEEKEND seems to have the least effect decreasing PERCENTSOLD by around 1 percentage point, while NIGHTWEEKDAY has the most effect decreasing PERCENTSOLD by 14 percentage points.

The coefficient of HOMEDEVIATION can be interpreted as a 50 percentage point increase would result in a 14 percentage point increase in PERCENTSOLD. The other assessment of the home team, games back from the playoffs, predicts that for a five game lead on the division a team will see an approximate 2.5 percentage point increase in PERCENTSOLD while with a ten-game deficit a team will see a 5 percentage point decrease in PERCENTSOLD. This variable is particularly effective because on Opening Day everyone is 0 games back from the playoffs so it has no effect, but as the season continues and the games back variable becomes smaller or larger, its increased effect over the course of the season is naturally weighted in the model.

The coefficient AWAYDEVIATION has a smaller coefficient than HOMEDEVIATION, but is also positive and statistically significant. The effect of opponent is also shown in the divisional game dummy which tells us that if an opponent is in a team’s division, the percentage of tickets sold increases by a little under 1 percent. Although the divisional dummy is statistically significant, even if in 2017 the MLB had scheduled 40 more games against divisional opponents for each team, this change would have added under 500,000 dollars in revenue and increase total revenue by less than .2 percentage points, which is an economically insignificant change.

Overall, the data seem to tell the story that one would expect; however, it is always nice to attempt to quantify these relationships. For further information, the author can be contacted at marinojc@kenyon.edu.

Every season, pitchers and pitching coaches across the league tinker with pitch arsenals, with varying effectiveness. This series examines the pitchers who have most significantly changed their arsenals in 2018, beginning with starting pitchers who have added new pitches.

This season, a handful of big-name pitchers have added a new weapon to their arsenal, including Nationals ace Max Scherzer. Below, we’ll look at Scherzer and his pitch adding companions in detail, in order of their new pitch.

Cutters

Max Scherzer, Nationals (Statistics through July 29^th)

Even in the midst of a relatively down season for the Nationals, Mad Max has put together another terrific season, currently sitting at a 2.30 ERA and 4.8 fWAR. Already a three-time Cy Young Award winner, the ace righty has added a new weapon to his pitch mix this season. Scherzer has added a cutter, a pitch he toyed with in 2015, to his arsenal, and used it fairly regularly this season after not employing it at all last season. Scherzer’s pitch mix for the last two seasons is displayed in the following table:

As the table shows, Scherzer’s fastball, curveball, and changeup usage have remained about the same (as have the velocities on each pitch) while he’s shifted his focus away from his slider and towards his cutter. The new pitch is averaging 88.4 mph, a few ticks faster than the slider. Scherzer’s located both pitches similarly, throwing both pitches primarily low and on the left-hand batter’s box side of the plate. The cutter (pitch usage chart below on the left) has been used more inside to lefties, while the slider (right) has been kept low and away to righties.

However, there is one key difference between Scherzer’s cutter and slider usage: of the 242 cutters he’s thrown, only 7 have been to righty hitters, while only 3 of the 375 sliders he’s thrown have been to lefties. The development of his cutter has allowed Scherzer to avoid using his slider against lefties (who have slugged .368 off it career, compared to .270 vs righties) while keeping a four-pitch mix in play. While the pitch has only been about league average this season (with a weighted pitch value of 0.11 wCT/c), it may provide him a better weapon against lefties than his slider has. To this point, lefties have actually slugged .450 off the cutter this year (in a small sample), although they’re only hitting .183 against the pitch, and his overall line vs LHH is much improved this season (.189/.252/.349 with a .260 wOBA compared to .213/.299/.392 with a .299 wOBA in 2017). Only time will tell, but Scherzer’s been even better this season, and it may be in part due to his new pitch.

Carlos Martinez, Cardinals (Statistics through July 29^th)

Another NL fringe contending team’s ace, another new cutter. Martinez began tinkering with a cutter this spring and has carried it over into the regular season. The hard-throwing righty has turned the cutter into a significant part of his arsenal, ranking 26^th out of the 148 starting pitchers to throw at least 50 innings this season in cutter usage. Martinez has shown one of the most drastic changes in pitch mix of any starter over the past two seasons, with the cutter being the largest catalyst for this change.

As you can see in the table above, Martinez’s new pitch has largely come at the expense of his fastball, which has dropped in usage by 12%, marking the 3^rd largest decrease in fastball usage between 2017 and 2018 of any starter in the sample. The Dominican righty has also leaned less on his slider since developing the cutter, which sits between 90-91 mph, about three ticks below his usual fastball and seven up from his slider. Martinez utilizes both a sinker and a four-seam fastball in addition to the cutter and uses each fastball in a different part of the plate. As illustrated in the pitch usage charts below, he tends to stick low and away (to a righty hitter) with the cutter (left plot), locates the sinker mostly down and inside (middle plot), and lives up in the zone with his four-seamer (right).

Martinez’s newfound cutter (an above average pitch with a wCT/c of 0.73) has also given him a nice complement to his slider, which he primarily uses low and away to righties and down and into lefties, but as mentioned sits at a much lower velocity than the cutter. It is worth noting that Martinez has rarely used the cutter against righties but has, in fact, used it as his go-to pitch versus lefty hitters, according to Brooks Baseball. Thus far, CarMart’s cutter has been an effective weapon against southpaw swingers, who are batting only .220 with a measly .305 slugging percentage on the offering. Additionally, Martinez has done a much better job overall of handling lefties on the season, holding them to a .228/.350/.332 slash (good for a .307 wOBA) after allowing a .260/.342/.441 slash (.337 wOBA) to opposite-handed opponents last season. Although Martinez has taken a step back against righties this season (allowing a .301 wOBA in 2018 compared to .263 last year), it seems that Martinez has an effective new weapon in his arsenal.

Sonny Gray, Yankees (Statistics through July 29^th)

While both other pitchers discussed so far have had success this season, Gray has struggled to a 5.08 ERA on the campaign and has slipped down the depth chart in a Yankees rotation he was brought in to stabilize at the deadline last year. It’s worth noting that Gray is the most recently moved of the pitchers discussed, and his deal to the Yankees may well play into his changing pitch mix. Since the start of last season, the Yankees rank last in the major leagues in fastball usage, with just 39.3% of deliveries recorded as fastballs. Following this trend, Gray has seen a drastic shift away from using his fastball since donning the pinstripes, with his FB% dropping more than any other pitcher from last season. A comparison table of Gray’s pitch mix the past two seasons illustrates the change in pitch mix for the former Vanderbilt Commodore:

There’s a lot to unpack here, obviously starting with the cutter usage. Gray’s cutter usage ranks 17^th in baseball among starters with 50+ IP this season, after not being utilized in 2017. As discussed earlier, the FB% is way down, as is the changeup usage, giving way to an increase in curveball frequency. Gray’s slider usage remains largely unchanged, holding steady in the mid-to-upper teens. In 2017, all of his pitches graded positively, with the slider standing out the most (1.06 wSL/c), but this season has been an entirely different story, with every pitch besides the curve (0.94 wCB/c) grading as below average and the cutter grading especially poorly at -1.77 wCT/c (to say nothing of the changeup, which has graded poorly but not terribly in the past, clocking in at -6.60 wCH/c, although small sample size should be noted here). There’s been plenty written about Gray’s struggles already this season, but it’s probably worth noting that his shift toward the cutter may not be helping him.

Slider

Jameson Taillon, Pirates (Statistics through August 3^rd)

After missing a portion of the 2017 season to battle cancer, the Pirates righty is in the midst of a very solid season, running a 3.58 FIP/2.1 WAR through his first 22 starts. Some of this success may in part be chalked up to Taillon’s new slider, which he debuted in earnest during his May 27 start (written up here by Rotographs’ Paul Sporer) against Martinez’s St. Louis Cardinals after dabbling with it in a few earlier starts. Taillon has since made the slider a significant portion of his arsenal, utilizing it 13.5% of the time thus far in 2018. His pitch mix across the past two seasons is displayed below:

As Taillon has added a slider to his repertoire, its usage has come primarily at the expense of his curveball and sinker, which has seen the most dramatic drop in usage. Despite this decline, Taillon is still carrying a solid 49.2% groundball rate (up slightly from last year’s 47.3%). This may be in part due to the fact that Taillon’s new slider has generated a high rate of grounders (generating a GB% of 52.38%, per Brooks Baseball). The new pitch stands out in another way as well: thus far in 2018, Taillon’s slider ranks third in the majors (among qualified pitchers) in slider velocity at 89.9 mph. Taillon has thrown 174 sliders against right-handed hitters this season and has primarily located low and away, while most of the big righty’s sliders against opposite-handed hitters have been down and in, as shown in the zone profiles below (vs left on the left, vs right on the right):

Although it’s impossible to discern exactly how much of an impact the pitch has had on Taillon’s improvement this year, it is worth noting that the pitch ranks 18^th among qualified pitchers in Fangraphs’ Pitch Value among sliders at 1.40 wSL/c. Although right handed hitters have had success against the pitch thus far (.263 BAA with a .491 SLG), the new pitch has devastated lefties this season, who are hitting a measly .160 with a .240 slugging percentage off the pitch. Taillon’s overall line against lefties is also much improved compared to last season (.321 wOBA this season vs. .355 in 2017), although it’s worth noting that Taillon’s numbers from last season are likely distorted by a rough second half following his return from cancer treatment. He’s also been more effective vs. righties and seen improvements in pitch value on both his fastball and curveball as well, possibly due in part to the new threat of his slider. After displaying strong talent and remarkable perseverance last season, Jameson Taillon has added a new weapon to an already strong arsenal en route to a very strong 2018 season.

Curveball

Patrick Corbin, Diamondbacks (Statistics through August 4^th)

Coming off a solid but unspectacular 2017 season (4.08 FIP), Patrick Corbin seems to have taken a major step forward in his walk year, compiling 4.3 wins above replacement on the back of a 2.56 FIP through 141.1 innings pitched this season. The lefty has seen his strikeout rate jump more than nine percent from last season (21.6% in 2017, 30.7% in 2018), and has done so with a new weapon in his arsenal: a curveball he seems to have debuted in his April 17^th start against the division rival Giants. Corbin has used the pitch a little over a tenth of the time (10.6% of his deliveries to be exact) and has seen it become his third option in a pitch mix that heavily features sliders and fastballs. Here is Corbin’s pitch mix over the past two seasons:

Corbin’s fastball (averaging 90.5 mph this season) and slider (81.6 mph) usage have remained largely the same, as the addition of Corbin’s curve has come largely at the expense of his changeup, which the lefty rarely uses. The new curve has averaged 73 mph on the season, coming in about nine ticks slower than Corbin’s primary breaking ball. Per Brooks Baseball, Corbin’s curveball seems to be of the 12-6 variety and has been used almost exclusively against opposite handed hitters, who have seen 218 of the 219 deliveries registered as curveballs by Brooks. Additionally, it is worth noting that over half of the curves Corbin has thrown have been to start an at bat, and that the pitch has resulted in the highest percentage of strikes of any pitch Corbin throws (although the slider isn’t far behind). Corbin has located most of his curves down and away to righties, further contrasting to the slider (below right), which the soon-to-be free agent has buried down and in against righty opponents.

Although opponents have batted .294 and slugged .471 against the pitch in a small sample this season, it appears to be a more effective weapon against righties than the now-infrequently-used changeup, against which righty opponents have batted .339 and slugged .617 over the course of Corbin’s career. Corbin has also subdued righties much more effective overall this season than in the past, having held them to a .245 wOBA in 2018 compared to a career (including 2018) .324 line. It certainly seems plausible that Corbin’s shift away from changeups to opposite-handed hitters and towards early count curveballs (161 of the 218 curves to righty batters have come in 0-0, 1-0, or 0-1 counts) has helped him to more effectively dispatch opposite-handed opponents than ever before. Fangraphs’ Pitch Values also seem to offer support for his idea, grading Corbin’s curve as a positive pitch (0.38 wCB/C), whereas the changeup has graded as negative in every one of Corbin’s six seasons (with a net value of -2.27 wCH/C). Additionally, both Corbin’s slider and fastball have played up this year to the tune of career-best pitch values, possibly due to the threat of a third positive pitch against righty hitters. This has allowed Corbin to become a more well-rounded pitcher during an excellent season and helped pave the way to a potentially lucrative offseason deal.

Data courtesy of Fangraphs and Brooks Baseball. Zone profiles all from catcher’s perspective, courtesy of Brooks Baseball. In instances where pitch type disagreements existed, Fangraphs pitch data was prioritized over Brooks Baseball. Pitch Mix tables based on Fangraphs data.

The Braves and Dodgers completed one of the more intriguing trades of the offseason in December when Matt Kemp was sent to L.A. for a host of aging veterans with bad contracts — Adrian Gonzalez, Scott Kazmir and Brandon McCarthy — and utility man Charlie Culberson.

The trade was certainly mutually beneficial, putting the Dodgers under the luxury tax threshold while the Braves opened up more money for the 2019 roster by paying Kemp’s money up front instead of over two years. The Dodgers also got a svelte Kemp who slashed a .310/.352/.522 to the All-Star game, while the Braves only have McCarthy’s 78 2/3 sub-replacement-level innings to show from the veterans.

But then there’s Culberson, who has surprisingly been the trade’s saving grace for Atlanta. Culberson went into Thursday’s series finale against the Nationals standing at 0.7 WAR, a stark contrast to his -1.3 WAR entering 2018.

Culberson was expected to be a versatile, defensively minded bench piece, coming into the season with a career .231/.272/.324 line, an OPS that was 43 percent below league average over that span.

Going into Thursday, Culberson’s line stood at .283/.329/.493 line with a 122 OPS+ and 119 wRC+. This comes despite an April in which he had a .324 OPS and 0 wRC+ and was nearly cut when the Braves needed space for Johan Camargo and Ronald Acuña. Through May 20, Culberson slashed .200/.273/.300 with just three extra-base hits through 50 at-bats.

Then Culberson took seven days off and everything changed.

Since May 27, when Culberson returned to action, he’s slashed .310/.348/.555 with eight of his 14 career homers, a 141 wRC+ and a .383 wOBA over 164 plate appearances. Certainly, Culberson is not that good, with those numbers being propped up by a .381 BABIP and just a .283 xwOBA.

But there’s reason to believe that Culberson is becoming a better hitter.

His xwOBA may seem low, but that’s deflated by a high strikeout rate (25.6 percent) and low walk rate (5.5 percent). Looking solely at his balls in play, we can get a better picture of where he’s made his improvements.

From 2016, his first season in the Statcast era, until May 20 of this year, Culberson thrived on balls hit between 0º and 25º, the upper end of the line drive range as defined by Statcast. This is visualized below, using Statcast’s estimated wOBA based on launch angle and exit velocity.

It’s clear that Culberson saw his best results on the lowest level of line drives, around 10º, and had about equal success with higher-launch-angle grounders and line drives. But as his launch angle jumps from line drives to fly balls, the decline is precipitous.

That leaves Culberson with two options: either hit the ball harder or optimize his approach to hit fewer fly balls. He certainly hasn’t done the former. From 2016 through May 20, Culberson’s average exit velocity was 85.0 mph, below the 87.3 mph league average. Since then, it’s been exactly the same at 85.0 mph.

Coming out of his time off, he has, however, appeared to change his swing plane. Before mid-May, Culberson’s career average launch angle was 7.7º. Since May 20, that has dropped to 6.1º, a 20.8 percent decrease. That’s resulted in a serious change in his batted ball profile.

By dropping his launch angle, he’s managed to drastically increase his line drives while cutting his fly balls almost in half.

This translates well to his estimated batted ball wOBA as well.

In addition to cutting his fly ball rate, it appears that whatever changes Culberson made have also improved his batted balls in the 25-35º range. Even if that’s just noise, Culberson is hitting more balls at his optimal angle, increasing his percentage of batted balls between 0º and 25º from 30.6 percent to 33.6 percent. An additional 9.7 percent of his batted balls have been hit between 25º and 35º.

Certainly some of this has been luck. Even on batted balls, his wOBA has well outpaced what would be expected. His xwOBA on batted balls sits at .355 while his wOBA on balls in play has been an exorbitant .501.

Going forward, Culberson will need to improve his plate discipline if he wants to find a spot as an everyday player. But even if he isn’t going to become the next Ben Zobrist, it’s not hard to see him filling a role similar to the one Brock Holt played with Boston from 2014-2016, posting a respectable 94 OPS+ while playing every infield and outfield position.

I’ve been researching connections between hitting mechanics and data for a while and wanted to share some surprising findings that I thought you might find interesting.

Hitting with backspin has been a popular, “conventional” objective in hitting for some time. We know from basic physics that a ball hit with backspin travels farther than a ball hit flat or “square.” I developed a model to assess the distance impact from spin based on Statcast data (the method and model are included at the end of this post ). As shown in the table below, high backspin balls result in high BABIP. It is important to note that the data in the following table is based on ball, not player performance (the dataset is balls hit with Exit Velocity >=90MPH and Launch Angle of >=15 degrees).

At the player level, however, square-hitting players significantly outperform high backspin players as evidenced by higher levels of BABIP (.324 vs. .300) and wRC+ (129 vs. 105). The following table is based on Qualified Hitters from 2015-2017).

Wow! So high backspin balls by themselves outperform, but the players who hit high backspin balls more often actually underperform? That seems crazy! Actually, when you consider that hitting a ball with backspin requires greater precision in order to hit the bottom half of the ball just right, it’s really not all that surprising. The distance difference between the groups is considerable. The square hitting group had slightly higher EV as well as three degrees of additional loft and should have had a distance advantage of approximately 20 feet; however, the average distance of the square-hitting group was actually eight feet less than the high backspin group. This opposite performance relationship between balls and players is shown in the chart below for each backspin quartile:

Clearly, at the player level, there is a “cost” side of the equation that needs to be considered. Thus, players cannot simply choose to hit only the “good” backspin balls – they must accept the full distribution of results that come along with that strategy. The spin impact can be seen in the following chart of hits for both player groups over the 2015-2017 seasons.

The spin impact for both player groups as shown above indicates that there is a spin-type “tendency” at the player level. Additionally, over the examination period, only one player switched groups, confirming that the player/spin relationship is not random. As suggested in the chart above, the horizontal angle of the hit reflects the type of spin (i.e., backspin vs. sidespin) which has a significant influence on distance (see model here for additional detail).

Although the R2 between spin and wRC+ is not very high maxing out at .17 (for the Qualified Player dataset), the outliers are quite remarkable. In fact, of all the extremely high performing players (wRC+ >135), none are hitting with high levels of backspin. Similarly, of all the very low performing players (wRC+ <80), none are hitting the ball with low levels of relative spin. The dataset below includes players with at least 200 PAs each year for 2015-2017.

I was curious how spin compared to exit velocity as a performance factor. After all, EV is widely considered as one of the best performance related metrics. It turns out that spin-related performance for players with high levels of plate appearances (PA) is indeed significant based on an examination of the top and bottom quartiles for both EV and spin (inclusive player membership required for all years from 2015-2017).

Not only did players in the top quartile, flat-hitting group outperform the top quartile, high EV hitters given high plate appearances (PAs), the performance difference between the top and bottom quartiles was greater for the square-hitting group. As PAs increase, the “noise” of the short-term outperformance of backspin is essentially extracted, revealing the greater value of a square hitting approach.

Without question, EV has a strong connection to performance; however, the ability of players to influence EV is limited due to physical size, strength and swing speed; consequently, players likely have more upside by switching from a backspin to “square” approach than attempting to increase EV.

I had a hunch that smaller players might be tapping into the backspin-driven distance gains – indeed they are!

This is quite remarkable. The smaller players are consistently utilizing more backspin and are hitting the ball farther despite both lower launch angles and exit velocities. In terms of why the smaller players are paying such a high “price” for the incremental distance, I’d be interested to hear your thoughts. Here are just a few that I’ve come up with:

• Whether consciously or subconsciously, players learn that hitting with backspin increases distance. Since the larger players generally have more natural power, they haven’t needed to use backspin to “keep up” with their peers in terms of distance. The data suggests the smaller players may be blinded to the “cost” side of the equation, and are focused more on the extra distance. Maybe human nature in seeing what we want to see?

• It could also be a selection issue where distance is being incorrectly viewed as “power” for the smaller players and those players are being promoted through the various levels of baseball.

• Is the typical pre-game batting practice where many players go for home runs causing or contributing to the issue? Ego is a very real issue and the typical batting practice sessions may be unknowingly changing the swing paths of the smaller hitters to generate more backspin. I noticed the other day that Tony Kemp with the Astros (a smaller player) is now avoiding all pre-game, on-field hitting because he doesn’t want to be tempted to “swing for the fences”. Without spin data at lower levels of play, however, it is difficult to know when, in the course of the smaller player’s career, spin is being added.

Conclusion

Given that “hit with backspin” has been part of consensus views for some time, this advice is not merely ineffective but it is actually performance-detracting. What’s more, significant improvement may be possible for players who are in the high backspin group and simply reconsider the “truth” of backspin

If there seems to be interest in the topic, I will submit a follow-up post regarding the specific mechanical differences, based on data, of “how” players are hitting the ball square – the findings are equally surprising.

The slow progression that leads to a self-acknowledged decline was a process Felix Hernandez, unfortunately, entirely skipped. His career arch was a natural regression to average, injury, failure to adjust; sudden, poignant, and ridden of organizational, cognitive bias. The stark drop-off resulted in a split between a player who once meant everything being the roughest point in a rotation embattled in a playoff race. The hope that Hernandez could return to a balanced tactician on the mound was probably maintained one game too long – the last time he held a team to no runs was on opening day. Even more egregious, there was no subtle change to change his approach. The long leash of hope allowed him to stay stagnantly desperate.

His last outing against the Texas Rangers was the final capitulation to put him into the bullpen, no longer scheduled to make his start on Sunday August 12. The seven runs he allowed were built off his consist frustration leading to a parochial process. He no longer worked through counts with cognition for how batters were attacking – he was simply throwing. Analytically, Hernandez works from a fastball to a breaking ball; speed leading to mistimed swinging later in the count. Simply put, his fastball is necessary for leading into the breaking ball, and with his fastball dead in the water, his breaking ball is also dead. Batters no longer deceived now look forward to teeing off on a very predictable and forced breaking ball.

As the arm dies, the fastball dies. The changeup and breaking ball, however, does not always die. Furthermore, spin may die on the curveball, but spin rate makes an average curve deadly. Henceforth, Hernandez, does not need an incredible fastball to work toward an average changeup/curve. Yet, as a starter he has failed to figure out how to work into his changeup; he is beholden to a fastball which no longer averages 90 MPH. Experimenting with velocity and pitch utilization reached a maxim in 2018, leading to nowhere even after dropping the four-seam fastball. It is a crutch he has been unable to move past.

The following charts display how Hernandez has attacked batters based on the count from 2012, 2016, and 2018. As stated, in 2012, he used his fastball to work into a changeup on ahead counts ahead or a sinker when behind. Through 2016 and 2018, injury forced him to drop the fastball on the opening pitch, instead of using the fastball sinker. This creates two problems: the sinker is no longer effective to land a strike when behind counts because it is used as the opening pitch and the sinker becomes exposed to each batter, leading to the predictable approach. His sinker now owns a 1.64 BB/K ratio with a 1.001 OPS.

Moving to the bullpen should not necessarily be a point to fix Hernandez for an eventual transition as a starter. He is, for better or worse, an abbreviated pitcher, and as of right now, cannot endure multiple innings. His limited arsenal establishes him as a stretch reliever for two innings at best. To become an endurance starter, he needs to improve his curveball to break across both sides of the plate – or, McCullerize himself.

The focus for 2018 Hernandez is bullpen effectiveness and no more. Unfortunately, precedent for a pitcher in the Statcast era who utilizes a slow fastball and curveball/changeup is limited; limited to Sean Marshall. (This is assuming that Hernandez continues to forego his fastball in the bullpen. Most curveball relievers have a fastball which averages 93-97 MPH. Fernando Rodney is another reliever who is comparable with a sinker/changeup arsenal, but even he has maintained a 94 MPH sinker at age 41.) Thus, there is some new paths to be paved with Hernandez in the bullpen, making the transition even more intriguing.

The main goal in the bullpen is inducing ground balls; that was the magic of Hernandez’s changeup in his prime. Marshall achieved this in his prime with ground ball rates of 52.2, 57.5, and 56.3 percent from 2010 to 2012. He opened his counts with a slider, moving to a curveball when ahead and staying with his slider when behind. His curveball broke left (right from Marshall’s view) and was best when slyly placed out of the zone.

Hernandez breaks his curveball in the same style, just the opposite direction. In fact, while the spin rate has dropped, without injury, there is a clear improvement in control (2018 curve map versus 2015 curve map). Using the curve to introduce batters can theoretically be complemented by a changeup which paints the other side of the plate. Even so, his changeup is falling more in the zone as he ages (implicative of lack of a fastball to paint the inside, 2012 changeup versus 2018 changeup).

Putting the different strings together, a bullpen focused Hernandez would utilize a curveball to specialize for attacking the right side of the plate, with an increase to break across both aspects. His changeup then becomes the quick out option to force quick ground balls. If he slowly beings to move that pitch into the corner of the zone again, he can end at-bats on weak contact and topped contact. Despite his demise, the changeup is a quality pitch for inducing topped contact if he is ahead of counts. (Emphasis on if, and, there is a base loss of control which cannot be ignored; again, the point he is a limited inning pitcher with an onus on control). Thus, control with the curve to land a strike or foul from the corner can lead to an inning of what might remain of a magical changeup.