Archive for spring training

Yoan Moncada has frustrated talent evaluators over the past two years. He’s about as physically talented as a baseball player can be; while still a prospect, the team here at FanGraphs thought he merited future grades of 60 hit, 60 power, 70 speed, 50 field, and 70 throw, with an OFP of 70 good for No. 1 overall prospect status. Prospects don’t get evaluated much better than that; in fact, a 70 OVR on a position player is as good as it gets. He was the kind of prospect that could headline a trade for a top-five starting pitcher, a bonafide ace, in his prime on a team-friendly contract with three years left.

Flash forward two years, about a year and a half into Moncada’s major league career, and he hasn’t performed quite as billed. Instead, in 901 career plate appearances before Opening Day 2019, he posted a 97 career wRC+ and 3.1 total fWAR, almost exactly league-average or slightly below. His defense at second base has not impressed, and so he’s being moved to the hot corner in the wake of 1) the White Sox whiffing on Manny Machado, and 2) the White Sox drafting “future Gold Glove second sacker” Nick Madrigal with the 4th overall pick in 2018. If nothing changes, he’s be in danger of becoming a utilityman.

Moncada’s offensive struggles are a little unusual. He has two traits required to be an offensive monster — power and patience — in abundance. Last year, his average exit velo of 90.6 mph was in the 86th percentile, while his 4.12 pitches seen per PA was in the 81st percentile. However, those positive traits were offset by the modern game’s bugaboo — strikeouts. Moncada struck out in an ugly 33.4% of his PAs last year, behind only Chris Davis and Joey Gallo, and his career K rate sat at 33.6% this offseason. This is very concerning, as contact issues are a flaw that are difficult to resolve.

The profile above seems to describe a three-true-outcomes hitter like the aforementioned Gallo. Dig a little deeper, though, and you’ll find that how Moncada struck out that often is not normal, and in a sense he doesn’t actually have contact issues, at least not 33.4% bad. He didn’t chase many pitches out of the zone last year — only 23.3% — sitting in the 87th percentile of qualified hitters. Neither does his whiff rate of 12.2% (league average in 2018 was 10.7%) jibe with that huge strikeout rate. Taken together, we can conclude that while Moncada’s contact ability may be somewhat below-average, he limits how much he swings-and-misses by rarely chasing pitches out of the zone. So if Moncada doesn’t chase much, and doesn’t swing and miss that much, how is he striking out so much? Read the rest of this entry »

Matt Davidson was not good last year. He got 443 plate appearances in his first full MLB year on a rebuilding White Sox club, and it didn’t go well as he posted a WAR of -0.9. That mark was seventh-worse in MLB for position players with at least 400 PA. There’s little mystery how he got there, as he combined DH-only caliber defense with a paltry 83 wRC+.

Davidson achieved that uninspiring number by hitting like a three-true-outcomes guy without the walks, more or less a poor man’s Chris Carter. Good news first: last year, he ran a pretty decent ISO of .232, putting him close to good-to-great hitters like Francisco Lindor, Anthony Rendon, and Anthony Rizzo, cracking 26 homers along the way. His raw strength is very real: he blasted a tape-measure 476-foot moonshot out of Wrigley with a 111MPH exit velocity in July. Big power is a good trait to have, but it’s been devalued in today’s game, where guys like Carter and Logan Morrison can hit 35+ homers in a year and then can’t find contracts of even $5M the following offseason.

Still, significant pop is necessary for a high offensive ceiling, so what’s holding Davidson back? In a word, strikeouts. He struck out a horrifying 37.2% of the time in 2017, second-most in the majors.  Unsurprisingly, his whiff rate was a scary 16.3%, sixth-highest among his peers; for reference, that’s identical to how often hitters swung and missed against Andrew Miller last year. The walk rate that keeps most K-prone sluggers’ OBP somewhat afloat wasn’t in evidence, as Davidson walked only 4.3% of the time. You won’t be shocked to find that he finished second-worst in K/BB with an ugly 0.12. Although he did hit the ball hard (we’ll come back to that), his flyball-heavy batted ball profile and below-average speed kept his BABIP suppressed to .285. That mark was in close agreement with his xBABIP of .283.

The astronomical K% and below-average BABIP held him to an ugly .220 AVG, which combined with the poor BB% led to a truly abysmal OBP of .260, second-worst among hitters with 400+ PAs. The only guy worse in that column was Rougned Odor, who has a similar offensive profile, but at least he can partially blame a particularly unlucky .224 BABIP.

Looking at last year’s stats, there appears to be approximately zero reason for optimism for Matt Davidson. He hit for power well, but was near the top of all the peripheral leaderboards that you really don’t want to be at the top of.  So why is this post being written at all? In short, Davidson seems to have turned over a new leaf this spring.

Now, I know the sabermetric kneejerk reaction to that last sentence: spring training means nothing and spring training stats mean less than that. But that’s not entirely true, as this excellent piece in the Economist way back in 2015 details. If you don’t want to read the whole piece, that’s fine, because it can be summed up very briefly: a hitter’s strikeout rate in spring training actually has a pretty high correlation with their strikeout rate in the regular season. Of course, one of the chief objections to drawing conclusions from spring training stats is the tiny sample sizes with which we’re working. Fortunately, strikeout rate is one of the fastest-stabilizing peripheral rates there is; Fangraphs itself puts the threshold for stabilization of strikeout rate at about 60 PA.

That piece was linked somewhere recently and I read it for the first time. A couple days later, being entirely starved for any form of baseball through this long winter, I reached the rock bottom of scouring the spring training stats of the team I supported, the White Sox. To my own surprise, there was actually something interesting buried there; as you might guess, it was in Matt Davidson’s stat line.

Luckily for us, and this piece, Davidson’s played the most of any White Sox this spring, totaling 60 PA as of March 20. He’s struck out twelve times, a K rate of 20%. He has walked seven times, for a walk rate of 11.7%. In this small sample, he’s almost halved his strikeout rate and nearly tripled his walk rate from 2017. On the one hand, that sounds like an insane improvement that cannot possibly be maintained; on the other, those rates from spring training are by themselves quite unremarkable for a major league hitter. Using BBRef’s summed 2017 stats to calculate league-wide rates, 20% K and 11% BB would have both been slightly better than average league-wide in 2017.

A significant walk rate improvement wouldn’t actually be terribly surprising. If you peruse Davidson’s player page, you’ll find that before last year he never posted a BB% worse than 9.1%, ranging up to 12.0%, from Double-A onwards, a total of five seasons spent mostly at Triple-A plus a month in the majors with Arizona. His walk rate at least doubling this coming year wouldn’t be coming out of left field; rather, it would be him returning to the player he has been in that sense for pretty much his entire professional career minus last year. It will probably come down from 11.7%, given that MLB pitchers likely have better control than those he’s faced this spring, but still, a big jump in walk rate seems likely for him this year.

That strikeout rate is a different animal, though. He’s always struck out a lot, never posting a K rate below 20% at any stop in the minors, and the whiff rate mentioned previously supports that. On the other hand, the sample size is now at the point where this being a complete fluke is pretty unlikely. Is this a real improvement or a mirage? I don’t know, and we don’t have plate discipline numbers in ST to see underlying patterns, but according to Davidson himself, making more contact is exactly what he’s trying to do. It sure seems like he’s succeeding in that thus far. As another small data point, he doesn’t seem to have a pattern of ST flukes in K rate, as in 58 PAs during last year’s spring training he struck out in 37.8% of his plate appearances, a number that echoes his full-season 37.2%.

This wouldn’t be as interesting a case if Davidson did nothing well offensively. He’s a large and very strong man, which is why he hasn’t just been released by the White Sox years ago. Take a look at his contact profile. Basically, last year, he pulled balls, hit more fly balls than ground balls, and vaporized balls in to play, with a quality-of-contact triple-slash line of 15.7% Soft/46.1% Med/38.2% Hard. His HR/FB% was a robust 22.0%, rubbing statistical shoulders with established sluggers like Nelson Cruz and Edwin Encarnacion. In short, when he actually did hit the ball, he looked for all in the world like a poster child for the fly ball revolution. Those underlying numbers hint at a lot more offensive potential than anyone outside of the White Sox organization sees in him, if he could just reduce that giant 32.9 K-BB%.

Now he’s showing signs of significant improvement in that fatal flaw of plate discipline. It doesn’t seem like the improvement in K% and BB% thus far in spring training has cost him much in power, considering that he’s demolished ST pitching to the tune of .358/.433/.679 (1.113 OPS & .321 ISO). Obviously, he’s not going to keep hitting quite that well, but the still-rebuilding White Sox aren’t about to outright bench or demote him either. Maybe it’s all a lot of noise, and he’ll be bad again this year. Or maybe Matt Davidson, at the age of 26, is about to be the Next Big Breakout™. Just as a reminder, it took J.D. Martinez until 26 to figure it out and become the “King Kong of Slug”; Justin Turner was 29-year-old replacement-level utility infielder who suddenly blossomed offensively in 2014; Jose Bautista was almost 30 before he turned into a nightmare for AL pitchers in 2010. So, here’s an prediction I would have laughed off for 2018: Matt Davidson is about to bust out in a big way.

UPDATE 3/29: Davidson hit three homers on a cold day in Kauffman Stadium, every single one of them with a 114+ MPH exit velocity. He also walked and did not strike out. Jump on the bandwagon now while there’s still room.

Spring-training games in the Cactus League are a unique joy, especially for baseball fans (like me) who hail from colder climes. Unlike the Grapefruit League, which features stadiums separated by hundreds of miles of humid Florida air, the Cactus League consists of a compact cluster of stadiums bathed in sunshine and desert-dry air. Spectators and players alike can enjoy the spring conditions (and for some, including myself and Carson Cistulli, Barrio Queen guacamole and sangria) in the Valley of the Sun for weeks before teams return to their home stadiums across the country in late March.

Figure 0: Your author enjoying the 82-degree sunshine (and probably a juicy IPA, not pictured) at Hohokam Stadium, March 2017

Some teams will return to relatively warm and dry climates (Arizona Diamondbacks, who have to trudge the 20 freeway miles to Chase Park), but others will return to retractable domes (Seattle Mariners) or cold conditions where snowed-out games are certainly not out of the question (Cleveland). Given that the point of spring training is to get players ready for 81 games at their home ballpark, are two months of baseball in dry, sunny paradise the best way to prepare players for opening day at home? Short of building exact climate-controlled replicas of Kauffman Stadium and Wrigley Field in the Phoenix Metro, how could teams better prepare their players for the start of the season at their own home ballpark? Enter an unlikely hero, the great “Rocky Mountain equalizer”: the humidor.

Figure 1: Climatology of Phoenix, AZ (Feb-Mar) and the home locations (ICAO Airport codes) of the 15 Cactus League teams (Apr-May)

Just by eyeballing the graphs in Figure 1, without wading into the different lines and the specific airports (some lines switch to larger airports with RH), no stadium’s meteorological conditions are close to those in the Phoenix area. With the exception of the Rangers, no team plays in a stadium with an average May high temperature greater than the average March high temperature in Arizona. And only the “high desert” of Colorado comes close in RH to the dry air in Arizona March. Clearly, the opening day meteorological conditions will be significantly different from those Cactus League players see during spring training (Figure 2).

Figure 2: Changes in climate between April (major airport nearest home stadium) and March (PHX), with larger markers indicating larger temperature differences (dotted markers indicate increased T) and blue markers indicating more humid conditions (orange being drier)

This drastic change in temperature and humidity (Figure 2) is likely to have a major impact on how the ball plays once teams leave Arizona. Like many baseball physics researchers before me, I will once again heavily rely on the work previously done by Dr. Alan Nathan to inform my physical exploration herein. As shown in Nathan, et al. (2011), the two crucial meteorological factors of temperature (T) and relative humidity (RH) have a strong impact on both aerodynamic factors (such as drag) AND contact factors (such as coefficient of restitution, COR) that determine how far a batted ball travels. Rather than run afoul of the copyright of the American Journal of Physics by reproducing the figures here, I highly encourage you to check out Figures 2-4 in Nathan, et al. (2011) to see these relationships.

Equation Block 1: Calculating the effect of COR changes on “effective” exit velocity of a batted ball

The eternally relevant Baseball Trajectory Calculator developed by Alan Nathan has the ability to adjust aerodynamic factors associated with stadium altitude, barometric pressure, temperature, and relative humidity. Combined with the equations from Block 1 above, the changes in COR as a result of meteorological changes can be simply approximated in the Nathan Calculator as a manual change in the rebound (exit) velocity of the ball off the bat.

Great, simply smash aerodynamic and COR changes together and we’re in business, right? Well, almost…it seems every baseball physics article could have all the baseball-specific details stripped out and what would remain is a meditation on linearity and covariance. This example is no different. While we might expect meteorologically-induced aerodynamic and contact factors to vary independently, in real on-the-field situations, balls will be affected by not only their current conditions but also their recent history of past conditions. Absent experimental data on the time scale of such internal ball changes, we can still get a general sense of what could happen when multiple changes overlap. Let’s dive into some colorful 3-D contour plots of results using the default batted ball parameters of the Trajectory Calculator (100 mph pitch, 100 mph exit velocity, 30 degree launch angle) and see what happens!

Figure 3: Effects of meteorological T and RH on fly ball distance, including COR effects equal to ambient conditions (as if balls were kept in the same conditions)

We aren’t too far afield from the basic variables one can change in the Nathan Calculator, so the results from Figure 3 aren’t terribly surprising. Baseballs travel further through warm and dry air. In addition, dry/warm baseballs are bouncier than cold/wet baseballs. It’s unlikely that equipment managers are keeping baseballs outside, so they probably aren’t going to actually experience changes in COR associated with extreme conditions due to the time necessary for water vapor to diffuse into the guts of the baseballs and soften them. But absent a sense of how equipment managers store baseballs, let’s explore the possible impact that a spring training humidor could have.

Figure 4: Effects of humidor-like T and RH on fly-ball distance, with aerodynamic effects equal to PHX March average but COR changing with humidor conditions

Figure 4 shows what would happen if we changed the internal ball T and RH but continued to play in the average Phoenix-area meteorological conditions in March. The weakness of the temperature effect compared to the strength of the humidity effect can be predicted with the slope of each experiment in Nathan, et al. (2011). It’s unlikely, though, that T and RH both have, when combined, a linear effect on COR. For example, it’s unclear whether this linear model captures the hot/wet and cold/dry combinations correctly. This indicates the need to inspect the covarying relationship between T and RH on COR (and therefore, fly-ball distance) more deeply than the simple linear combination I used in this model.

Table 1: Monthly climate, elevation, default fly ball distance using the Nathan Calculator and monthly climate, and scale factors for conversion of March fly ball distance (at PHX) to April fly ball distance (at home).

With the data from Figures 3-4, we can figure out an appropriate scaling factor (Table 1) to translate the dimensions of each team’s spring training stadium and compare them to the dimensions of their home stadium (Figure 5).

Figure 5: Surprise Stadium (KC) and Scottsdale Stadium (SF) scaled to April climatology in KC and SF (no humidor)

After comparing the “effective dimensions” of the Cactus League stadiums to the home stadiums of each team, one can’t help but wonder if the teams had a hand in the way the stadiums in Arizona were constructed. Some teams, such as the Royals, share a stadium with another team (Texas Rangers); therefore, this clearly can’t explain all of the similarities between stadium shapes.

Figure 5 shows that in Arizona during the month of March, the spring training stadiums play much “smaller” compared to other stadiums than their physical dimensions might indicate. By slightly lowering the COR of the ball by using a humidor, teams could cause their spring training stadiums to play with effective dimensions approximately equal to those of their home stadiums. If the Royals were to store their spring training baseballs in a humidor at approximately 70% RH, the differences between the distance up the lines (longer at Surprise than Kauffman) and the distance to straightaway center (shorter at Surprise than Kauffman) would yield around the same “effective surface area” of the scaled outfield.

This analysis, much like my earlier piece on fly-ball precession, neglects many physical variables that would impact the actual games being played. In this example, I have neglected the effects of wind and day-to-day changes in barometric pressure. Prevailing winds due to stadium orientation and location would make this experiment much more realistic. For variations in pressure due to synoptic weather systems (cold fronts, warm fronts, etc.), however, “averages” over an entire month inform us less in terms of the baseline environments of each stadium than monthly averages of temperature and relative humidity. The model also assumes that the balls are essentially stored in temperatures and humidities equal to the ambient conditions in the home stadiums; equipment managers likely store them in some indoor location, but it’s unclear whether they are treated to the exquisite RH control seen with the humidor at Coors Field. Such confounding factors will be explored in future follow-ups to this piece.

In addition to physical assumptions made here, it’s quite possible that baseball operations departments in teams have goals in spring training other than closely approximating the hitting conditions in their home stadiums. But if they want to see who will have power that plays well in their home stadium, the humble humidor could play a key role in moderating the enhanced fly-ball distance that comes naturally with the warm, dry spring air of paradise (Cactus League baseball, that is).

The Red Sox’s third-base problem was not solved by a veteran rental. No, it was solved by a sweet-hitting 20-year-old Dominican named Rafael Devers.

But before I explain Devers’ spectacular rise, I must set the stage for his entrance.

~~July 24th~~

It’s July 24th and the Red Sox have ground to a halt. Baseball’s non-waiver trade deadline is just eight days away and nearly the entire baseball community expects the Sox to trade for Todd Frazier.

Frazier, the third baseman for the White Sox, is in the midst of the worst season of his career. He’s hitting just .210 and his contract expires at the end of the year.

The Red Sox haven’t been able to gain traction since the All-Star break, going just 5-6. The Yankees, their ever-present rivals, are creeping up on them in the standings and have swooped in on a trade for Todd Frazier, even though many executives and analysts were sure the slugger would join the Red Sox.

Third base has been a huge issue for Boston, who has used eight (!) different players there. Collectively, Red Sox third basemen are slashing .227/.280/.320, marks that rank 27th, 29th, and 30th in the league, respectively. They have not only been terrible hitters, but they also lead the league in errors.

Dave Dombrowski decides to rectify the Red Sox’ third base issue by promoting top prospect Rafael Devers to the big leagues.

~~A Rafael Devers Profile~~

Rafael Devers was born on October 24th, 1996 in Sanchez, an aging port city in the Dominican Republic. He first started playing baseball at the age of five, inspired by his father, who played amateur ball. Devers grew up with baseball all around him and quickly showed immense talent.

In 2013, Devers signed with the Red Sox at just 17 years old. He was ranked as the number three international prospect in his class, and he signed with the Red Sox, his childhood favorite team, for $1.5 million. Devers entered the Red Sox organization as their 20th ranked prospect in a deep farm system.

Upon joining the Red Sox, Devers was placed in the Dominican Summer League (DSL), a place where new international signings go to work on their skills. Devers took the DSL by storm, batting .337/.445/.538 with three home runs in 28 games. He impressed everyone, by his ability to hit for both average and power, and also by his great batting eye — Devers walked more times than he struck out.

After tearing up the Dominican League, Devers was sent to the States, where he played in the Gulf Coast League. The Gulf Coast League, or GCL, is where first-year minor-league players are sent after being drafted or signed by their teams. Most of the players in the GCL have been drafted out of college or have just finished high school, meaning that at age 17, Devers was one of the youngest players in the league. Devers carved up the GCL, batting .312 with 11 doubles and four homers in 42 games.

After Devers’ wildly successful first year, he was rated as the Red Sox’ sixth-best prospect, and baseball’s 99th-best, all at just 18 years old. This was an incredible accomplishment, as Devers was the youngest player on Baseball America’s top-100 list that year.

In 2015, Devers was promoted to the Red Sox’ Low-A affiliate, the Greenville Drive, where he experienced full-season ball for the first time. There, he was matched up against much older opponents, being one of just seven position players under the age of 19 in the South Atlantic League. Devers played well in Greenville too, batting .288 with 38 doubles and 11 home runs in 115 games. During the 2015 season, Devers was selected to the Futures Game, an event during All-Star weekend that showcases baseball’s best young talent. After a season in Low-A, Devers was ranked as Boston’s second-best prospect, and baseball’s 18th-best. Devers jumped 81 spots on Baseball America’s top-100 in just one year, a remarkable achievement.

In 2016, Devers was promoted to the Red Sox’ High-A affiliate, the Salem Red Sox, at the age of 19. However, Devers hit a bump in the road in Salem. Among players much older than him, it appeared that Devers had finally met his match. In the first half of the season, he scuffled to a .233/.300/.305 line with just four home runs in 63 games.

However, Devers bounced back brilliantly after the All-Star break. He slashed an incredible .326/.367/.539, with seven home runs and 11 steals in 64 games. After this second-half breakout, Devers has not looked back in his meteoric rise to the majors.

In 2016, Devers’ defense finally started to catch up with his offense. Early on in his career, scouts considered moving him to first base, because of his heavy build. But Devers has worked hard on his defense, and has stayed at the hot corner. In High-A, Devers led all Carolina league third basemen in fielding percentage (.960), putouts (104), and assists (258).

After his outstanding second half in High-A, Devers earned a non-roster invitation to 2017 spring training with the Red Sox. This was a big step up for the 20-year-old Devers, but he wasn’t ready for it, batting 3 for 22 against big-league competition. Nevertheless, he earned a promotion to Double-A Portland, where he played for most of this year.

Devers was the Portland Red Sox’ standout player this year, socking 18 homers in addition to achieving an excellent .296/.366/.571 slash line. In 77 games, Devers jumped to number six in Baseball America’s most recent prospect rankings. He was also selected to participate in the MLB Futures Game for the second time.

Devers was promoted to Triple-A on July 14th, and continued to hit for both average and power while in Pawtucket. Devers became the third-youngest player ever to be promoted by the Red Sox to Triple-A, yet another reminder that he was playing extremely well for his age. The Dominican lefty hit an astounding .400 for the Pawtucket Red Sox, and he earned a promotion to the big leagues after just nine games in Triple-A.

When Devers debuted on July 25th, he was the youngest player in the major leagues, but you’d never know it. His first major-league hit was a home run (!), and during his 16 career major-league games, Devers has surprised everyone.

Scouting report

Devers has a very promising future, thanks to his ability to hit for both average and power. He has incredible raw power, and can spray the ball to all fields. His opposite-field power is unsurpassed among players his age. For example, when Devers hit two homers against the Indians on August 14th; one was a laser into the Green Monster seats in left field, and the other was a high drive into the Red Sox bullpen in right field.

Devers also has great bat speed, and he is able to hit pitches very far, and to any part of the field. On August 13th, Devers hit a 102.8 mph pitch into the Yankees bullpen, the fastest pitch ever hit for a home run in the pitch-tracking era.

Devers is not as polished as other recent Red Sox prospects like Andrew Benintendi, but he has a higher ceiling. I project that in his prime years he will hit around .285 with 30 home runs, 40 doubles, and five to ten stolen bases.

He has improved his defensive skills, but don’t expect him to be a Gold Glove-winning third baseman. I believe he will stay at the hot corner, as he is becoming more reliable and is improving his range. Overall, Devers projects to be an All-Star with a dependable glove and a reliable, middle-of-the-order bat.

Conclusion

As of August 15th, Devers is hitting .339 with six home runs, incredible statistics that show his ability is way beyond his years. I don’t mean to read too closely into Devers’ 62 career at-bats, but he has a very promising future.

Pairing Devers with other young Red Sox stars like Mookie Betts, Jackie Bradley jr. and Xander Bogaerts should help Boston stay at the top of the AL East for years. Devers gives Boston an entirely homegrown lineup, the dream of every major-league team.

Special thanks to Baseball Reference, Baseball America, and milb.com for the statistics I used in this post.

I would also like to thank NESN.com, the New Haven Register, and SB Nation’s Minor League Ball blog.

Prospect rankings are from Baseball America

Fenway Park Photo Credit: User: (WT-shared) Jtesla16 at wts wikivoyage [CC BY-SA 1.0 (http://creativecommons.org/licenses/by-sa/1.0)], via Wikimedia Commons