Why Alex Bregman Will “Out Regress” Mookie Betts

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:

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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).

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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:

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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”.


Analyzing Underlying Factors Impacting Tickets Sold for Major League Baseball Games

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.


Examining 2018’s Biggest Pitch Repertoire Changers

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 29th)

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 29th)

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 26th 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 3rd 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 29th)

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 17th 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 3rd)

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 18th 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 4th)

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 17th 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.


Charles in Charge: Charlie Culberson Ain’t What he Used To Be

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.

Batted Ball

Previous Pct.

Pct. since May 27

Ground ball

51.0%

54.0%

Line drive

18.4%

23.9%

Fly ball

26.5%

15.0%

Pop fly

4.1%

7.1%

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.


Should Players Hit With Backspin? The Data Might Surprise You

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).

Ball Performance By Spin Quartile

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).

Player Level - Backspin vs. Performance

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:

Backspin Ball vs. Player Performance

 

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.

Spin Groups & Unexpected Distance

 

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.

Information in the Outliers

 

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).

Exit Velocity vs. Spin

 

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!

Player Size vs. Spin

 

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 Theoretical Attack of a Bullpen-Focused Felix Hernandez

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.


Eugenio Suarez Has Optimized His Brain, Results Have Followed

Last season, Eugenio Suarez was a pretty good Major Leaguer — 17% better than his peers, by measure of the runs he created. He was far better at home than he was on the road, as you may expect for a slugger who plays half their games in Great American Ball Park, but overall he had turned into a dude with Cincinnati in his third full year there.

2018 has been a different, even better story since jump street, though. Suarez has morphed again, this time into arguably the best hitting regular third baseman and the eighth best hitter in all of baseball, regardless where he’s playing. It’s even more impressive when considering his thumb was broken on an errant pitch in April and he hasn’t missed a beat since coming back. The whole thing is really curious.

Suarez rate stats

He’s walking and striking out a little less and he’s hitting a few more balls in the air. None of those explain how he’s driving the ball so much harder, as his ISO indicates, or why he’s been 23% better than last year when he was pretty good, though. Sometimes, seeing year-over-year differences in these numbers tells enough of the story. But looking at the surface doesn’t for Suarez doesn’t show us how he went from a dude to the dude. He leaves us with no choice but to wade into the water.

Suarez Contact and Discipline

Did your eyes pop going over the change in how Suarez attacks the ball like mine did? He’s dwarfed his lightly hit dinkers this season compared to last. He’s absolutely ripping the ball when he does hit it. He’s chasing the exact same rate of pitches and he’s going more at the ones in the zone. Throw in that he’s hitting the ball less to the opposite field and more up the middle, and the picture starts to clarify.

But not completely. We can see the What that’s driving Suarez’s production, but not the How. We don’t know how he went from just above average at generating hard contact to top two in the Majors, a half percentage point behind only Matt Carpenter, who has been Ares on a warpath for months.

Let’s wade into the Suarez water deeper and get to some gifs.

Suarezzz17

This is Suarez in 2017. He pulls a 94 mph fastball into left field for a single. He ended up driving in a run. An all around solid outcome.

Suarez 18 change

This is Suarez this season. He drives a 94 mph fastball into the right field seats for his 22nd tater of the year.

Suarez’s two swings are largely the same. But the big difference is that he’s gone from starting with his bat being parallel to his body in 2017 to starting with it parallel to the ground in 2018. His rate stats being so similar over the last two seasons suggest that he hasn’t drastically changed his approach. The tiny mechanical difference in his stance suggests that he’s found a way for his brain to make the same decisions in the mere milliseconds it takes for a pitch to reach the plate, but provide much more impressive results.

Frankly, what he’s doing this season is amazing. We don’t know where he’ll go next, but we do know that the new Eugenio Suarez is a strong representation of baseball in 2018: able, powerful, smart, and optimized.

Data from FanGraphs. Gifs made with Giphy from Statcast video


The Matt Carpenter U-Turn

Following a 0-for-4 performance against the Twins on May 15th, Matt Carpenter was hitting .140/.286/.272 with a 59 wRC+ over 140 plate appearances. That’s not, um, optimal. One of the Cardinals best players in recent memory was not starting the 2018 season on the right foot. At the time though, Carpenter’s underlying numbers indicated better times were ahead as his .371 xwOBA (expected wOBA) was much higher than his actual .257 wOBA. If you’d sort the largest underperformance difference between xwOBA and wOBA through May 15th for hitters with at least 75 plate appearances, Carpenter’s figure calls out for immediate attention.

Table 1. Underperformance by wOBA-xwOBA through May 15th, 2018 (Min. 75 PA)

Player Name               wOBA            xwOBA          Difference
Randal Grichuk           0.201             0.325                -0.124
Matt Carpenter            0.257             0.371                -0.114
Kole Calhoun               0.174              0.274               -0.100
Avisail Garcia              0.246              0.345               -0.099
Jason Kipnis                0.234              0.330              -0.096
Teoscar Hernandez    0.357              0.447               -0.090
Adam Duvall                0.284             0.371                -0.087
Ryan Zimmerman      0.298              0.384              -0.086
Jason Heyward           0.293              0.377               -0.084
Bryce Harper               0.381              0.463               -0.082

Despite xwOBA not being a “perfect” metric, it still provides a fair amount of insight into a player’s performance. Alas, the metric, to my knowledge, hasn’t accounted for speed data for every player. I am not as well-versed in the Statcast metrics as I would like to be, but that is one missing piece that would be critical in my opinion. At the same time, we can still make reasonable assumptions based on the data at hand.

Carpenter was clearly underperforming based on what one would expect from his batted ball data and historical results. This kind of rough start was surely not going to last an entire season. Eventually, the numbers would start to normalize. But could the shift have played a role in the early lackluster results? Perhaps as he has faced the highest percentage of shifts for a St. Louis hitter in 2018.

Table 2. Shift/Non-Shift Percentage Breakdown – 2018

Shift          Non-Shift
82.4%         17.6%

Look how opposing infields lined up against Carpenter when he stepped into the batter’s box this season. More times than not the infield shifted more towards the first base side, which makes sense against a left-handed hitter.

Screen Shot 2018-08-10 at 12.26.48 PM

Still, indications existed that pointed towards a rebound for the age-32 infielder as his 88.4 MPH average exit velocity through May 15th wasn’t too far off his yearly numbers per Baseball Savant. Starting on May 16th though, the sudden U-turn of Carpenter’s 2018 season took place.

Table 3. Top ML hitters by wRC+ since May 16th (Min. 100 PA)

Player Name               wRC+
Matt Carpenter             203
Mike Trout                     189
Mookie Betts                 188
J.D. Martinez                182
Max Muncy                    172
Jose Ramirez                 170
Paul Goldschmidt         165
Alex Bregman                164
Nelson Cruz                   163
Shin-Soo Choo              161

Holy expletive! Carpenter has been a better hitter, by wRC+, than Mike Trout. That’s something.

Since May 16th, Carpenter has arguably been the best hitter in baseball. His 203 wRC+ leads the majors since mid-May, and his .336/.434/.730 slash line looks darn right impressive. His 32 home runs is a new career-high. Since May 16th, Carpenter has an average exit velocity of 91.5 MPH. Yes, he started to hit the ball harder than he has in years past. In a National League without a clear-cut MVP candidate, I’d think Carpenter will receive plenty of attention. Honestly, he should.

Concerning xwOBA and wOBA, Carpenter went from underperforming to performing closer to what the batted ball data would suggest. In fact, he is now exceeding his expected numbers.

Table 4. xwOBA/wOBA since May 16th

xwOBA      wOBA
.453            .475

Carpenter has also found a way to excel against, or least mitigate, the shift throughout the summer months. For example, his wOBA against the shift was a measly .243 with a .356 xwOBA through May 15th.

Screen Shot 2018-08-10 at 5.15.43 PM

Since May 16th, he has posted a .467 wOBA with a .445 xwOBA against an infield shift.

Screen Shot 2018-08-10 at 5.15.18 PM

If you examine his batted ball data, it is clear that Carpenter has started hitting more fly balls and pulling the ball as the season has progressed.

Table 5. Monthly FB%, Pull%, Cent%, Oppo% – 2018

Monthly       FB%       Pull%       Cent%       Oppo% 
Mar/Apr       44.1%    45.0%       33.3%       21.7%
May               50.8%    47.7%       26.2%       26.2%
June              45.1%     40.9%      36.6%       22.5%
July               49.3%     52.0%      24.0%       24.0%
August          56.0%     64.0%      20.0%       16.0%

Carpenter adjusted to what the defense was giving him. The shift worked for a short while then he essentially shifted where he hit the ball. That’s awfully impressive. In turn, he now has a .401 wOBA against the shift for the entire season. His .320 isolated power would easily be a career-best and has already posted 4.9 wins above replacement with 46 games to go in the regular season for the Cardinals. His results could be a reason to point out why hitters can conquer the shift, yet it remains a difficult task actually to accomplish. Thanks to this adjustment at the plate, Carpenter has made a sudden U-turn into baseball’s best hitter since mid-May.

**Statistics and information as of August 10th, 2018, and courtesy of FanGraphs and Baseball Savant**


Brian Dozier: Regression, Desperation, and what the Dodgers Provide

As Brian Dozier began a new month with his new Los Angeles Dodgers, it was apropos that Dozier would hit a single, double, and home run in his first game. For Dozier, the Dodgers, and specifically Yasmani Grandal walking off in the bottom of the 10th, August 1 was a magical sort of night. The Dodgers broke a three-game skid to overcome the  Brewers 6-4. regarding momentum, an establishment of tone for the month of August. Having just passed the trade deadline and tied with the Arizona Diamondbacks for the National League West, August represents a fresh start amid a long season – the line between exhaustion and giving the remainder of dwindling energy.

Los Angeles now has Manny Machado, Arizona now has Eduardo Escobar; two players who plug glaring holes to add that last substantive energy. Los Angeles, however, also obtained Brian Dozier – a second baseman who has simply regressed in hitting. He is in a season-long lull, hitting .229 with a .415 SLG, and a 95 wRC+ after finishing at .271, .498, and 125 in 2017. He has never relied on the luck of high BABIP and lucky placement, always an extremely successful hitter for the Minnesota Twins on his own merit. He simply fell flat in a flat batting order.

While not an objective point of analysis, Dozier might just need a change of pace in a new town to start finding the ball again. Open comments to the media regarding Minnesota being ‘comfortable’ and Los Angeles being a team in the race ‘rejuvenating me [Dozier] as a player’ provide surface-level follow-up for the ‘new city – new player’ philosophy.

Considering that Dozier is in the last year of his contract makes him only more of an enigma. The ‘contract-year’ is traditionally when batters inflate their statistics on a bad team for a massive contract in their later years. (Tradition in free-agency, being broken and a topic much written about). With Dozier doing the complete opposite, assuming he has the ability to rejuvenate his career, Los Angeles might be able to hide his genius and buy-low in free-agency. They have attempted to trade for Dozier the past two seasons, hence trading for Dozier has given a subtle chess piece to the Los Angeles front office for the 2019 season.

The importance of Dozier in the Los Angeles lineup is not about dazzling power. While chess piece might be a degrading term, for Dozier, it is a complement to its procedural efficiency. Second base has been Los Angeles’ worst position and Dozier will plug what has been a sloppy turnstile in the batting lineup.

On a micro-level, Dozier’s enigma of a collapse is across the board. He is making 11 percent more contact outside and three-percent less inside contact but is still contacting around 82 percent on the fastball. The only difference, a fall to a .259 from a .298 average. Despite making more contact on sinkers, 83.2 to 86 percent and two-percent less swinging-strikes, he is only hitting at a 126 wRC+ from a 171 wRC+. (And, yes, that is still above average, but a fall for Dozier).

The slider, a pitch never hit for average, has seen seven percent less outside-swings (26.6 to 19.9 percent) and a subsequent drop in outside-contact (60 to 51.2 percent). The result has been 52.4 percent in-field fly-balls, up from 22.2 percent. The worst pitch for Dozier this year has been the curveball, seeing a pitiful .054 average and -50 wRC+. This comes even as he is swinging less at the curve (39.6 to 32.2 percent).

The only pitch Dozier has seen an improvement on is the changeup, hitting at a .333 ISO (.128 in 2017) and a 178 wRC+ (120 in 2017). The main emphasis has been him attacking inside the zone five percent more. This is the same strategy which Dozier held in 2016, when he attacked the inside changeup at 69 percent and hit for a .338 ISO.

The subtle change in Dozier, however, is evident in his attack of the changeup. In 2018, Dozier has been attempting to hit opposite field for changeup power, with incredibly precise hits to right field. He also has gotten lucky on three infield hits. In 2017, however, Dozier was comfortable in pulling changeups to the left-field. While this has been a positive trend for the changeup, on a meta-level, Dozier’s fascination with hitting opposite is putting him on pace for more outs.

The following is a side-by-side comparison of balls resulting in outs for Dozier (per Baseball Savant), with 2018 on the left and 2017 on the right. He is avoiding pulling the ball, and as a result, has patterned his hits into a persistent pattern within a strangely linear line. The same comparison done on balls hit into play with no-outs is further evidence of Dozier attempting to hit balls to opposite field. In short terms, he is attempting to create distinct power by aiming the ball instead of just continuing to comfortably pull.

Conjoining the theory that Dozier will be better on a better Los Angeles roster with what can be termed as ‘futile desperation’ in attempts to hit opposite the field leads back to the subtle change within each micro-pitch. While the meta-level comparison is little changed for swinging percentage, very small, but important tweaks, in Dozier swinging outside exist. Hence, he is trying to walk more.

In other terms, Dozier has been on a bad team, and knowing so, has been attempting to take less risks outside so he walks more while also trying to create more emphatic power by targeting opposite field. He has been trying to mitigate Minnesota’s inefficiencies by playing tighter himself. While one game is hardly a good sample size, there may be an underlying psychological shift in Los Angeles which allows Dozier to relax and comfortably attack the ball.


The Reds are Turning Their Players Into Joey Votto

Reshaping a team core comes with reshaping team analytics – or, at least the goal is to craft players within a certain subset of principles. Each player will have natural talent they are inclined to favor (power, getting on base, choice of stat here), and in that nature, the team principles taught might be best thought of as a way to control the random chaos of baseball. Creating an orderly lineup within a game of disorderly results.

Thus, one of the underlying theories of evaluating a team reshaping the core is to analyze those players they are shaping; players who have the talent to subtly manage their own game. The Reds are one of those teams rebuilding with a split between stark promise and those who, bluntly, are roster spots. In some regards, Cincinnati is a universe revolving around the Joey Votto-style of baseball – low rate of outside swings, high contact, the simplicity of getting on base. Overall, they have the fourth lowest swinging-strike percentage in the MLB (9.6 percent), are tied for the fifth lowest outside-swing percentage (29.1 percent) and are tied for the second highest-contact rate (79.5 percent) with the Boston Red Sox.

There is sensibility of principled baseball despite their slight record. Effectively, however, the main separating point between teams such as the Red Sox and Reds is visualized in their spray chart on base hits; Boston with a wide range, Cincinnati lacking viability of power. There is something to be said about Cincinnati having the MLB’s fifth-best batting average (.259), but that average is held back by a .144 ISO and a .401 SLG mark (bottom third of the MLB). Cincinnati’s range of contact is quaint, more akin to a peaceful breeze than a bombastic wind.

Philosophically, the peaceful wind of contact is bound to the style of pitches attacked, their underlying method of creating order. Further defining that order (and leading with summation), they have a knack to lead pitchers into throwing them a favorable pitch with foul-balls and an inclination for avoiding weak contact. And while Votto might be the veteran tangentially modeling an established career, the success of Scooter Gennett, Jesse Winker, Curt Casali, and Jose Peraza reflect effectiveness seasons from now.

To note, if any player can be removed from this group, it would be Gennett, who has intrinsic power and might be termed the most natural player. However, he still falls into the binding theme of an academic plate-approach under the adage of leading pitchers into contact and lowering swinging-strike percentage. Between all four players, they have only 724 swings outside of the zone. Not surprisingly, the aberration pitches far outside are mostly Gennett swinging – remove his partiality to power, and the chart loses wild swings and the knuckle curve.

Moving inside the zone, the Cincinnati four have only 10.3 percent whiffs, with 39.1 percent fouls, 32.6 percent balls batted into plays, and 18 percent hits. One of the most impressive aspects of the inside contact has been the lack of weak contact opposed to the flare, solid, or barrel contact. Hence, within their categorized principles, Cincinnati has shaped the type of pitches which need to be attacked inside the zone. Again, that point of avoiding handing a pitcher a quick-out, instead creating foul balls and probability in launch-angle. Although they overwhelmingly have better contact than average, their lack of deep power (exit velocity) is seen in the classic moon-shot of under contact.

Principled swinging might be best reflected in the evolved in-game attack. As the game progresses, these players make contact in a tighter range with higher exit velocity. The results in innings one through three have been a method to create more power contact, albeit, at the risk of weaker contact. Hence, a method of sorting through how to attack during the remainder of the game while taking a risk on some intrinsic fastballs. From innings four through six, the contact group becomes smaller, and thus the spray chart also becomes more oriented toward singles with less power. They become more principled to find the average rate of success. Even though power is lost, they are becoming better at capitalizing on simplicity.

Inning seven through nine are the most evident of how Cincinnati is shaping their players to hit for the average rate of success. Regard, this is a team which overwhelmingly has found a way to chip away late in games. (The Cincinnati four have combined for a .304 AVG; 128 wRC+ split in high leverage, .289 AVG; 111 wRC+ in innings seven through nine).

Their attack becomes tuned for breaking pitches (or finding the breaking pitches which do not break) and thus into making less powerful contact but creating more functional contact on average. They remove the risk from themselves by avoiding wild swings, and thus force pitchers to throw breaking balls into the zone. 28.7 percent of their swings in the late innings have gone into play, while another 37.8 percent have gone for fouls. That punctual ability to create foul balls and chip away at pitchers creates long-at bats to reveal weak-points, wears down relievers, and eventually lead to a swing-worthy breaking ball.

Cincinnati may not be the most successful or powerful team within the moment. But, this is only the moment of crafting. The Joey Votto way of baseball might be a grinding and dying way of baseball. The Votto way of baseball might even be a misnomer for power-hitters such as Gennett. Yet, in the end, the underlying philosophy of Cincinnati’s baseball is to remove intrinsic risk in swinging and create order by forcing the pitcher to make the first risk.