The Boston Red Sox almost romantic approach to the plate has been one of the major themes on their journey to be the first team with 60 wins. Last night’s expose of producing home runs and precise batting behind Chris Sale’s robotic approach to pitching gave the Red Sox a 10-5 victory over Kansas City Royals for their 60^th victory; another notch in a long-chain of accomplishments. More impressively, however, is the Red Sox micro approach to each game. They have not only revolutionized the average statistics played out through the tenure of a season but have revolutionized how they approach the plate inning-by-inning. The romantic plate approach is more than good batting – it is the beginning to a methodical introspection into opposing pitchers for an evolution in innings five and six.

In an interview with 710 ESPN Seattle’s Danny, Dave, and Moore, Seattle Mariners pitcher Marco Gonzales casually remarked of his struggles against the Red Sox on June 24 that they were “taking swings we haven’t seen before.” Gonzales lasted only six innings against the Red Sox, allowing seven hits and five runs on six strikeouts. The fifth inning was the instant the game changed in the Red Sox favor as they scored three.

Naturally, this observation may have been a microcosm dependent on Gonzales’ pitching, not so much the Red Sox. Yet, the observation was enticing enough to warrant investigation. The results were incredible, explaining why the Red Sox meta of plate patience is about more than being disciplined – they pedantically study batters through the first few innings, leading to innings five and six which are destructive.

Before delving into the data, two notations must be established. First, the Red Sox are, on average, destructive regardless of the inning. Their jump in innings five and six are not why they are good, but why the are atop the MLB this year. Second, analytic rise in statistics in innings five and six is a trend across the league; it might be easy to pass on the Red Sox rise as the best batters popping off on ‘third-time through the rotation’ deterioration. Again, however, the Red Sox are using the seemingly inevitable deterioration of pitchers throughout the game and exacerbating on that analytic.

Within innings one through three, the Red Sox hold a .270 batting average with a 20.5 percent strikeout rate, an 8.4 percent walk rate, a .467 SLG, and a 117 wRC+ – all rates which make the Red Sox a top MLB team intrinsically. Stopping here, the Red Sox would be a good team alone. However, as mentioned, the Red Sox jump to great in inning five and six. They post a .292 batting average, only 15.7 percent strikeouts, 7.9 percent walks, a .538 SLG (.240 ISO!), and a wRC+ of 139.

On a micro-level, the functional output has benefited Mitch Moreland and Mookie Betts the most; Moreland has a .808 SLG and Betts has a 234 wRC+. Even Rafeal Devers has a sharp increase in effectiveness in these innings, raising his egregious .198 average from innings one through three to a .304 average in innings five and six.

Mechanically, the Red Sox, as a team, change the type of pitches they attack. Produced from Baseball Savant, here is a graphic of the pitch movement attacked in innings one through three; here is the comparative graphic for innings five and six. The graphic shows most of the pitches they take at the beginning of the game have little horizontal movement and trend with more vertical movement – hence, pitches which are easier to see. As the game goes on, they dramatically increase their SLG by attacking pitches with sharp horizontal movement, even hitting low.

In application, it might be said the Red Sox study through the first few innings, waiting to see how pitchers will attack under the guise of movement. Their contact is more studied through this span, evidenced by J.D. Martinez’s expected SLG of .936, Bett’s of .843, and Andrew Benintendi’s of .757. Even Devers sees an increase from an xSLG of .389 to .545.

The Red Sox plate discipline is purposed, thoughtful, and intended for the length of a game and season. They literally improve the quality of swings and contact throughout the game; the maxim of why analytical discipline is important to success.

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

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

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

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

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

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

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

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(Data courtesy of Fangraphs and Baseballsavant)

There is beauty in the penultimate. While hit number 3000 will be the moment that is played at Albert Pujols’ inevitable Hall of Fame ceremony, that milestone could only be reached due to the 2,999 victorious battles waged before it. This is the story of Miguel Castro vs. Albert Pujols. The following article focuses on the complicated beauty of everything that surrounded the penultimate hit of a cherished milestone. The following piece is also showcase of how being in touch with batting analytics can and should help managers make the correct bullpen calls.

Miguel Castro is a young, below average reliever. Since his trade from the Rockies to the Orioles in 2017, Castro has posted an ERA of 3.25 and a WAR of -0.1. These numbers are far superior to the ones posted during his stint with the Rockies, but they are not anything particularly special. During his development, Castro has all but ditched the fastball, as he initially (2015) threw it 63% of the time. By 2017, when he would first duel with the aging Pujols, batters saw a fastball from Castro a mere 1.7% of the time, with even less of a fastball dish rate so far in 2018. Castro now makes his career on Changeups, Sliders and especially Sinkers. Castro threw batters a Sinker 58.8% of the time in 2017, this puts his Sinker rate at 6th among 2017 relievers. These numbers have stayed relatively the same so far in 2018, although Castro has thrown slightly less Sinkers in favor of more Changeups. As baseball writers have lamented the death of the Sinker, Castro has been one of the few pitchers that still rely heavily on the dying pitch.

The Albert Pujols of St. Louis needs no introduction, he is one of the most prolific hitters of all time, and a future Hall of Famer. The Albert Pujols of Anaheim is a different player altogether. Much has been written recently on FanGraphs about the decline of Pujols, so I will spare those details here. Instead, I want to focus on how Castro allowed hit number 2,999 to occur against a batter that had been unable to get on base in all their previous meetings. 

In 5 meetings at the plate that span from August 18th 2017 to May 3rd 2018, Pujols has hit on Miguel Castro one time. On May 3rd, Pujols hit a 96 mph sinker (Castro’s average sinker speed this year) and in doing so acquired his 2,999th hit. In all of their three previous meetings Pujols hit into an out, and on their subsequent meeting Albert was hit by an inside Changeup. So what was different about their 4th meeting? For the first and only time, Castro threw a sinker close to the center of the strike zone. In their previous 3 meetings, Castro threw Sinkers on the inside and outside of the plate, as well as mixing in Sliders that got looking strikes on multiple occasions. On Thursday night however, after a Slider that got called a ball and, just like in previous encounters, a Slider that got Albert looking, Castro threw a Sinker down the middle-right, and paid the price.

From 2016 to 2017, Pujols’ Batting Average slid across the board against every single pitch but two. One of those pitches just happens to be Miguel Castro’s specialty, the Sinker. (The other is the Curveball.)  In fact, of all the pitches that Albert sees on any given day, he has the best chance to get on base while facing a Sinker by a wide margin. In 2017, Pujols batted .338 against the Sinker, compared to .250 against the Changeup, his next highest batting average against a given pitch. Average is not the only thing Albert was better at while facing a Sinker. His stats across the board are at their highest in 2017 and now 2018 when facing the Sinker. Pujols has a higher SLG% and more HRs when facing a Sinker. He had the most doubles in 2017 against the Sinker compared to any other pitch. One of the three Triples in his entire career came against a Sinker. In short, Albert undoubtedly likes to see a pitcher that throws Sinkers.

Analyzing Pujols’ batting average in the strike zone with and without the data for Sinkers since June 1st 2016 shows just how effective Albert has been against the afformentiond pitch. Almost every area of the strike zone saw an increase in average when attempts at Sinkers were factored in. Of special note is the mid to upper right quadrant, where averages increased in every sector. This is the area in which Castro threw the Sinker that would create Pujols’ 2,999th hit.

To futher analyze Pujols’ batting preference for Sinkers, I also compared the heatmaps of Albert’s average against Fastballs compared to Sinkers.

Unsuprisngly, we again see a great disparity between Pujols’ performance when facing Sinkers and when facing other types of pitches.

The conclusion here is that on Thursday night Buck Showalter replaced Chris Tillman with the worst possible choice. With runners on and Pujols’ soon coming up to bat, Showalter subbed in Castro, a pitcher whose main pitch was the favorite of the upcoming batter, who then summarily hit the Sinker into play and scored runs on a breezy double. An event that would put the former St. Louis slugger one hit way from history. If Baseball Clubs would have teams of analytics people, those who could have warned Showalter before he sent out Castro, teams could make more informed decisions about who to put out in relief in high risk situations as seen on Thursday night.

• Data was sourced from Fangraphs and BaseballSavant

Thank you for reading! This is my first piece in the whole baseball analytics realm, and chances are this thing has logical fallacies or something of the like. Any helpful comments/critcism/pointers are much appreciated.

The best thing about Spring Training statistics for fantasy owners is that you can spin them whichever way is convenient for you, the owner. If you’re heavily invested in a certain player who is struggling in Spring Training, you can always say “It’s only spring, these numbers don’t count!” Or, on the other hand, you can use a hot spring to justify reaching for a player who you believe will breakout. So yes, largely spring statistics are meaningless. Except, Jeff Zimmerman wrote an article earlier this year highlighting batted ball data to spot potential breakouts. With limited Statcast data provided at many Arizona and Florida ballparks, the ground out/fly out ratio may be the best indicator for hitters to spot those breakouts. Luckily MLB.com provides the GO/AO ratio for all spring statistics, so we can put Jeff Zimmerman’s hard work to use now that 2018 Spring Training is in the books. Let’s look at three players that look poised to breakout in 2018. I’ll write a part-two portion including three or four players who had previously broken out (relatively speaking) in 2017 but are projected to regress some by the masses.

Let’s start with Brandon Nimmo, the young outfielder for the Mets. Nimmo had a hot spring and with Michael Conforto starting the season on the DL, Nimmo got the nod to leadoff and play centerfield for Opening Day. Conforto is progressing much quicker than expected and should be back before the end of the month. halting Nimmo’s playing time. Thanks to the Mets signing on Adrian Gonzalez, effectively blocking Jay Bruce from moving from right field to first base, Nimmo is left without a spot. I won’t speculate on injuries (too much) but Yoenis Cespedes rarely plays a full season and I don’t expect Adrian Gonzalez to be at first base all season.

Back to Nimmo, he hit .306 with three home runs and whooping nine extra-base hits in Spring Training. In addition to all those loud numbers, his GO/AO ratio sits at 0.87 for the spring. For context, his minor league ratio is 1.32 and so far in limited major league experience (250 at-bats) it’s 1.12. Based on Zimmerman’s conversion table, we are looking at a ground ball rate of between 42% and 43%. Throughout his minor league career his ground ball rates have ranged between 45% to 56%, let’s call it 50%. That difference in groundball rate could mean an improvement in fly ball rate to near 40%. Nimmo has never been considered a power hitter but he’s been graded with a 50 in raw power, so a change in approach may unlock 20+ home runs. His previous career high is 12 in 2016, mostly in AAA and one at the major league level. His plate discipline is already fantastic evidenced by his incredible minor league walk rates. If he were to unlock average to above average power, Nimmo could become a Matt Carpenter-type leadoff hitter for years to come.

Steven Duggar is a name I haven’t seen on many people’s radar this offseason. He performed well this spring and has impressed the coaching staff of the Giants. But alas, he was Optioned to AAA to receive everyday at-bats. The Giants believe he is the centerfielder of the future and given the health track record of players like Hunter Pence and the mediocrity of Gregor Blanco, I wouldn’t be surprised to see Dugger by June (if not sooner). Duggar is a good athlete with a good hit tool and above average speed. His raw power is only graded out as average but I’ve noticed an approach change that began in High-A last year where he, like many others began elevating the ball more. He missed some time last year but also saw a solid HR/FB% at about 13% along with the increase in fly balls. This is a good sign. So let’s compare some numbers for Duggar.

In his first two seasons of minor league ball, his GO/AO ratio was 1.52 with fly ball rates typically below 30%. In 2017, again he dealt with injuries and only played in 42 games, but improved on his GO/AO ratio and fly ball rate to the tune of 0.82 and 43% respectively. This spring he’s continued elevating the baseball with a GO/AO ratio of 0.92 along with 4 home runs and six extra-base hits. His patience at the plate is incredible, much like Brandon Nimmo and his outfield defense is good enough to play centerfield for the Giants right now. He’s been a doubles machine in the minors and it’s possible those doubles start turning into home runs. I don’t see the upside in terms of home runs compared to Nimmo but I think Duggar can steal more bases, so both can be solid fantasy contributors, especially in OBP formats.

Based on all the hype in Ozzie Albies’ direction this offseason, you would be under the impression that he already broke out. However, he was only up with the Braves for all of 57 games and 244 plate appearances. In that short amount of time, he performed admirably with a triple slash line of .286/.354/.456 with six home runs and eight steals at the ripe age of 20 years old. Impressive to say the least, but before 2017 he had hit a total of eight home runs in 293 games. So, should we just chalk up the 15 he hit between AAA and the majors in 2017 to luck or an outlier?

How about neither, you know better than that! Ozzie was a ground ball machine in the minors which is typical for a speedster with 70-grade speed and five foot nine inch, 160-pound frame. Prior to 2017, Albies’ minor league GO/AO ratio was 1.5. Last year between AAA and the majors, it was 0.9 which matches his approach this spring at 0.85. Albies has hit over .300 with three homers and six extra-base hits this spring. I realize that Albies only played in 57 games in 2017 but I set some parameters for comparison sake to Ozzie Albies’ short time in the Majors, because why not? It’s fun. Take a look. Not bad, right? I set the walk rate above 8%, the K rate below 17%, the flyball rate above 39%, and the Hard contact above 33%. The player I want to highlight of this group is fellow five foot nine inch Mookie Betts. Let’s compare Mookie’s 200+ PA cameo at age 21 to Albies’ 200+ PA cameo last year.

I should point out that Betts didn’t strike out as much as Albies did in the minors but still impressive, to say the least. New SunTrust Park plays much better in terms of power for left-handed batters and yes, Albies is a switch hitter, but should bat from the left side at least 65% of the time. Hitting from the left side should help his power production. The infatuation with Albies continues to grow. If he builds on his success from 2017, there’s nothing in his batted ball profile that would prevent him from hitting 20+ home runs as he reaches his peak. The kid’s a star! I envision multiple seasons of 20 home runs and 30 steals with a great average for Albies.

Trading away your team’s best players is never going to make you popular. You’ve probably read plenty about how the return for Marcell Ozuna was pretty good for the Marlins, while the return for Stanton was pretty thin. But savvy baseball fans understand that when you trade players, you’re not only trading their production, but also their contracts – so offloading an insane 13-year $325M contract might not return as much as a team-friendly contract for a lesser player. Add in the fact that Stanton had a no-trade clause (thus, a ton of leverage over to whom he was traded) the fact that the Marlins got anything in return for Stanton is actually impressive. The Yankees took on practically all of Stanton’s remaining contract; so in context, this was a fine deal for the Marlins. Dee Gordon, though contact-and-speed types typically don’t sustain a lot of value into their 30’s (as Gordon enters this year at 30), has put together 3.8 WAR/162 across his last 4 seasons, so maybe they could’ve gotten a little more out of that deal, but again – they were able to get rid of Gordon’s entire contract, which is guaranteed until his age-33 season of 2020.

The trade that stuck out most to me was the one for Christian Yelich. Yelich is an established star in the league who is still very young and has lots of upside, won’t be a free agent until 2023 (accounting for a team-friendly option in 2022), and seems like the type of player you might want to keep, even in a rebuild. They did receive top prospect Lewis Brinson and others in return, but of all the deals they made this one was, to me, the most indicative of “holy crap Jeter has no idea what he’s doing.”

And then, I realized, maybe he’s a genius.

Well, it doesn’t take a genius to recognize that Yelich is a future star, if he isn’t rightfully considered one already. It takes some genius (and perhaps a few gift baskets for your fans?) to say tear it all down. The Marlins could’ve kept any or all of Yelich, Ozuna, and even Stanton, but they’d still have been bad for the foreseeable future. The past four seasons they won 77, 71, 79, and 77 games. It’d have been easy to continue to toil in mediocrity, maybe even make a wildcard or two. But mediocrity is pointless in a business that overtly rewards losing.

You’re saying you want us to lose? No, we’ve BEEN losing. What I want is for us to finish dead last.
-Derek Jeter (probably).

It’s not a secret that tanking is now an actual strategy employed by “rebuilding” teams. I was surprised to learn in my research that tanking is probably not a new phenomenon (the percentage of teams who win 70 or fewer games is fairly consistent over the past several decades) but the game has changed so significantly in the era of free agency, “service time,” and revenue sharing, that the financial benefits of tanking should probably not be legal (but that’s for the CBA to determine). 2018 could be the worst year ever in terms of the number of teams not trying to compete.

Is that wrong? “Tank and bank” isn’t a purely theoretical exercise anymore. As you probably know, the past two World Series winners were responsible for some of the most blatant, disgusting, glorious middle-fingers-to-the-league you could ever imagine – and their paths coincide almost directly.

2008: the Cubs were an aging but solid team that led the NL in wins, with a dangerous lineup and a restored version of Kerry Wood, now a closer. They were bounced early in the playoffs however, in the same year Joe Maddon came up just short of an unlikely World Series title with the Rays. That same year, the Astros were competitive – winning 86 games – but came up short of a playoff birth.

Both teams achieved Marlins-esque mediocrity in 2009 and 2010, and that’s when the tanking rebuilding began. The Astros were the most aggressive and flagrant in their process, and many people forget just how bad they were. They won just 56 games in 2011, followed by campaigns of 55 and 51 wins (that’s three straight seasons of 106+ losses). Their payroll went from $77M in 2011 to $67M in 2012 to $25M in 2013 and then – somehow – cut it in half during the season by shedding even more salary. Notably, and not coincidentally, the Astros got a new owner in 2011. That historically bad 2013 for the Astros was actually historically great: they had the most profitable season in MLB history.

While the Cubs also lost a bunch of games during that same time period, they had a pretty big advantage over the Astros: they hired Theo Epstein (all due respect to Jeff Luhnow, whose roundabout career path is worthy of its own article). I’m not going to try and give Jeter or his staff a current/future grade as it pertains to winning lopsided trades but let’s just assume the Marlins are more like the 2011 Astros than the 2011 Cubs. Their “competitive advantage” over teams who may have better guys in analytics/baseball ops is that they can lose lots of games.

Currently, the Marlins are projected to win the fewest games in baseball which would of course net them the #1 overall pick. Picking first is certainly no guarantee of success (ahem, Kris Bryant went #2 to the Cubs in 2013 while the Astros picked up Mark Appel at #1) but it’s objectively better to pick in the top 2 or 3 than, say, outside of the top 5. There is also the correlated benefit of turning a bigger profit by fielding a lower payroll. To put it simply: if you’re going to miss the playoffs anyway, make as much money as possible while getting the best draft pick you can. It’s easy to say “I wouldn’t have traded Yelich/Ozuna/Stanton” in an attempt to appease your fan base (who aren’t coming to games anyway) while not having personally invested hundreds of millions of dollars into a team; but when your expensive team has little chance of even making the playoffs (never mind winning a World Series) the business side of things becomes even more important.

Based on the aggressive trades the Marlins have made to shed payroll, expect them to mirror the ’11-’13 Astros financially: they have about $80M committed this year, about $50M in 2019, but only $23M in 2020; 22M of that is to Wei-Yin Chen who I’m sure the Marlins hope can stay healthy long enough to generate a little interest from a contender. Righty-specialist and all-time home run preventer Brad Ziegler (making $9M) should have enough appeal to anyone who gets tired of giving up homers to the right-handed heavy Yankees or Angels lineups, and Junichi Tazawa (making $7M) might have a few buyers as well. Justin Bour (age 30, $3.4M, arb-eligible) should find a home with a competitor  – possibly best fit with the aforementioned Angels or even Yankees depending on how Greg Bird recovers, given their respective needs for some left-handed power options. Perhaps they can package the no longer desirable Martin Prado (2yr, $28.5M) with the very desirable J.T. Realmuto (age 27, $2.9M, arb-eligible) to shed some more salary.

By year 5 of their rebuild, both the Cubs and Astros blossomed into legitimate competitors, before winning their World Series in years 6 and 7 respectively (and being in great position to compete for years to come). Marlins fans probably don’t want to year “2022” as the best case scenario for their team to begin competing…but competing for a World Series doesn’t come easy. And as I’m sure Astros and Cubs fans could attest, it’s worth the wait.

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.

As I’d like to think I’m an aspiring sabermetrician, or saberist (as Mr. Tango uses), I decided to test my skills and explore this research question. How did starters, who had 25 or more starts in one season and an ERA of 6.00 or higher in their final 10 starts, perform in the following season? This explores whether past performance, regardless of intermediary performance, adequately predicts future performance. Mr. Tango proposed this question as a way to explore the concept of replacement level. From his blog: “These are players who are good enough to ride the bench, but lose some talent, or run into enough bad luck that you drop below ‘the [replacement level] line’.” Do these players bounce back to their previous levels of performance, or are they “replacement level” in perpetuity?

To explore this, I gathered game-level performance data for all starters from 2008 through 2017 from FanGraphs, grouped by season. I then filtered out pitchers who had fewer than 25 starts and had an ERA less than 6.00 in their final 10 starts. This left me with a sample of 78 starters from 2008 through 2016 (excluding 2017 as there is no next year data yet). I assumed that a starter with an ERA above 6.00 was at or below replacement level. Lastly, as some starters were converted to relievers in the following year, I adjusted the following year ERA according (assuming relievers average .7 runs over nine innings less than starters: see this thread).

Seems like the 10-game stretch to end each season is a bit of an aberration. The following year’s adjusted ERA is much closer to the first 15+ games than the final 10 games for pitchers in our sample. In fact, the largest difference between any first 15+ game ERA and its following year adjusted ERA is .58 runs, in 2011. The smallest difference between any last 10 games ERA and its following year adjusted ERA counterpart, for comparison, is 1.7 runs, in 2009.

Using adjusted ERA corrects for the potential slight downward bias in our following year totals. Following year games started fell by ~9%, while reliever innings increased from zero to each season’s value. Relievers, on average, have a lower ERA than starters. As mentioned above, I adjusted each season’s following year ERA by .3 runs per reliever inning pitched (my assumed difference in runs allowed between starters and relievers per inning pitched). Another source for potential downward bias is sample size – of the 78 pitchers who fit our sample qualifications, only 69 pitched in the majors the following season. A survivor bias could exist in that the better pitchers in the sample stayed pitching, while the worse pitchers weren’t signed by a team, took a season off or retired.

What is driving these final 10 game ERA spikes? It has been shown that pitchers don’t have much control over batted ball outcomes. Generally, it is assumed pitchers control home runs, strikeouts and walks – the basis of many defense-independent pitching stats. Changes in these three stats could explain what happens during our samples’ final 10 games. Looking at each stats’ rate per nine innings, however, would be misleading, as each season exhibits uniform change (such as the recent home run revolution, or the ever-growing increasing in strikeouts). I calculated three metrics for each subset (first 15+, last 10 and following year) to use in evaluation: HR/9–, K/9– and BB/9–. All three are similar to ERA– in interpretation – a value of 100 is league average, and lower values are better.

Further, not necessary math details: for example, a value of 90 would be read as the following. For HR/9– or BB/9–, a value of 90 means that subset’s HR/9 or BB/9 is 10% lower, or better, than league average.  For K/9–, a value of 90 means that the league average is 10% lower, or worse, than the subset’s K/9. To create these measures, I calculated HR/9, K/9 and BB/9 for each subset and normalized them to the league value for each season – including the next year’s value for the following year’s rates. Then, I normalized these ratios to 100. To do that, I divided HR/9 and BB/9 by the league averages and multiplied by 100. Because a higher K/9 is better (unlike HR/9 and BB/9), I had to divide the league average by K/9 and then multiply by 100, slightly changing its interpretation (as noted above).

As mentioned above, the issue of starters-turned-relievers within our sample likely influences our following year statistics. I was able to adjust the ERA, but I did not adjust the rate stats – HR/9, K/9 or BB/9 – as I have not seen research suggesting specific conversion rates between starters and relievers for these.

Interestingly, our sample of pitchers improved their K/9– across the three subsets, despite having fluctuating ERAs. They were below average, regardless, but improved relative to league average over time. Part of this could be calculation issues, as league K/9 fluctuates monthly, and I used season-level averages in calculations.

Both HR/9– and BB/9– drastically get worse during the 10 start end-of-season stretch. These clearly drive the ERA increase. In fact, despite seven of the nine seasons’ samples having better-than-average HR/9 in their first 15+ starts, every season’s sample has a much-worse-than-average HR/9 in their last 10 starts, where eight of the nine seasons’ samples HR/9 are 40%+ worse than league average. Likewise, though less drastically, our samples’ BB/9 are much worse than league average in the last 10 starts subset. Unlike HR/9–, though, our samples’ BB/9– is worse than league average in the first 15+ starts subset. The first 15+ games’ HR/9– and BB/9– are identical to the following year’s values, unlike K/9–.

It appears that starters with an ERA greater than or equal to 6.00 in their final 10 starts, assuming 25 or more starts in the season, generally return to close to their pre-collapse levels in the following year. This end of season collapse seems to be driven primarily by a drastic increase in home run rates allowed, coupled with an increase in walk rate. These pitchers performed at a replacement level (or worse) for a short period and bounced back soon after. Mr. Tango & Bobby Mueller, in their email chain (posted on Mr. Tango’s blog), acknowledge this conclusion: “they are paid 0.5 to 1.0 million$ above the baseline… At 4 to 8 MM$ per win, that’s probably an expectation of 0.1 wins to 0.2 wins.” We can debate the dollars per WAR, and therefore the expected wins, but one thing’s for sure – past performance is a better predictor of the future than most recent performance.

– tb

Special thanks to Mr. Tango for his motivation and adjusted ERA suggestion.

Spring is a time for optimism, in baseball and in life. Teams are starting to think about their opening day starters and more broadly, their starting rotations. Some rotations look “set” while some have a “battle for the 5th spot”. Some are toying with the idea of a 6-man rotation.

But here’s the thing: we know that (almost) every team will end up using a 6-man rotation, whether they like it or not. Eventually, your favorite team will need to call in reinforcements. This can happen because of poor performance or injury. But hey, we’ll cross that bridge when we come to it, right?

… when do you think we might come to it?

We know, as do those in charge that teams use something like 11 starters per year (in 2017: 11.3). In a six-month season, how long does it take before the first reinforcements arrive?

In a few words, not very long. Some pitchers have injuries, some get moved to the bullpen, some sent to the minors. Either way, at least one of them will be gone pretty soon, so don’t name the puppy.

Of course, fate comes at different paces. In 2017, the Cardinals didn’t use a sixth starter until June 13th. And even then, Marco Gonzales only pitched because they had a double-header. In contrast, Junior Guerra, the Brewers’ opening day starter, was injured that same opening day. He wouldn’t pitch in the majors for another seven weeks (and it turns out, not very well either).

Half of teams used a sixth starter before April 25th. 90% of teams used a sixth starter before their 50th game.

Some of those sixth starters, along with their full-season WAR: Alex Wood (3.4), Mat Latos (-0.3), Mike Clevinger (2.2), Mike Pelfrey (-1.0).

We know that teams need depth. Not only that, but life comes at you fast.

Data: Baseball Savant

As I’m sure you know, Major League Baseball is toying with the idea of putting a runner on second base sometime around the 12th inning. While I’m not doing this to argue its validity or lack thereof, I’m going to discuss and evaluate some scenarios that could happen under those conditions. It won’t be anything groundbreaking; I’ll be demonstrating the metrics involved with a team under the various circumstances I induce.

The following scenarios are played out to score at least one run in a given inning. Top or bottom of the inning, I envisage the same sort of conditions will play out for both teams. And because there is never any telling what part of the order will start with this setup, I speak in generalizations.

I’ve thought about what would be the likeliest of moves under this arrangement and I’m going to guess it would come down to the most boring events in baseball; the offense bunts the runner to third or the pitcher intentionally walks the first batter attempting to set up the double play. Of course, there will be times when the managers decide to simply attack the situation as-is. That’s more of a volatile situation and therefore much harder to work with.

First, the basics. From 2010-2015, having a runner on second base with no one out produces the following:

• The predicted number of runs scored is 1.100
• The percent chance of scoring a run under those conditions is 61.4%

So from the get-go, the offense is expected to score a run in three out of every five chances.

Play the bunt or a standard defense?

Let’s start off with the first of two scenarios; the bunt to move the runner over to third. I feel like this is the most likely action but also the most difficult to work with because of varying defensive strategy. Will the defense make an anticipatory shift for a bunt or will they be in ‘straight up’ formation? In 2011, Bill James found out that bunting in sacrifice situations produced a .102 batting average. Not like we needed that because we could have guessed that you’re going to be out roughly 90% of the time.

To bunt or to swing away?

So assume the hitter lays down a bunt that moves the runner while making an out at first. Run expectancy is now 0.95 with a 66% chance of scoring a run. Your run expectancy went down 0.15 runs BUT you increase your chances of scoring by a little less than 5%. Would bunting make sense to you as a manager? Taking out any sacrifice-type contact, if your hitter produces an out and the runner has to stay at second, your run expectancy drops to 0.664 and the chance of scoring a run plummets to roughly 40%. Still feel the same way (regardless of the hitters bunting ability)?

Walk or pitch to the next hitter?

Keeping with the initial decision, we have a runner on third and one out. Pitch to the next hitter or put him on to set up the double play? Our strategy could be further altered because at this point the defense might be inclined to bring out a ground-ball pitcher or create a split situation (lefty vs lefty and vice versa). But again, let’s go with the assumption that the team will do the safest thing by having the next hitter walked. That puts runners on first and third with one out. That decision causes run expectancy to jump back up 0.18 to 1.13 and but the probability you’ll score at least one run drops to 63.4%. Would you make that same call (remember, we are in a vacuum)?

Runners on first and third with one out produce the following expectancy:

• Average number of runs scored is 1.130
• The chance of scoring a run under those conditions is 63%

One of a couple of outcomes will follow should you elect not to intentionally walk the hitter. He will drive in the run by putting the ball in play various ways (sacrifice fly, fielder’s choice, hit, etc) and accomplish what the offense set out to do; score at least once to put the pressure on the home team. Or, the hitter could strike out, ground out (which could turn into a double play, an out at home, etc) or fly out.  If contact is made, this could alter our base-out states: two outs and runners at various bases (first and third, second and third, second or first should the runner somehow get thrown out at home). Due to the randomness of contact in this event, we’ll stay with the intentional walk.

To bunt or to swing away, pt II?

So what about the offensive strategy for first and third, one out? The options are much more vast. You could sacrifice bunt to move a runner over to second (assuming the runner on third is held up), thereby dropping run expectancy to 0.580 and dropping your scoring chances to 26%. The risk here is having the batter somehow bunt into a double play; runner at third is tagged/thrown out and the batter is thrown out at first. Do you, as a manager, take the initial risk that set up this problem? It is challenging to turn a double play on a bunt but if the defense is ready, it makes it easier to do so.

This time, let’s assume the hitter botches the bunt to the first base side and the overeager runner is thrown out at home (or caught in a rundown), runner safe at first. Now, with two outs, there’s a runner on first and second, we sit at a very poor run expectancy of 0.429 and have just over a one in five chance of driving in that run.

Walk or pitch to the next hitter, pt. II?

At this point, again with neutral context, you can walk the batter to load the bases, (if the hitter is too good and the next isn’t great, etc.) or you can just pitch to the batter (maybe bringing in a bullpen specialist). Walking the batter gives the offense a 10%better chance of scoring and a .33 increase for run expectancy.

If you elect to pitch to the batter either the final out is made or runs score. Walking the batter loads the bases and forces the defense to hope for the best. The latter situation would actually produce the most excitement; a crucial decision would need to be made. Either way, my tangent baseball universe will end; three outs, inning over or the needed run(s) score.

While I don’t necessarily agree with or enjoy the thought of the game being altered in this way, it could produce some interesting strategical decisions and test the maneuvering skills of team managers.

This post and others like it can be found over at The Junkball Daily.

Defensive shifts are designed to prevent hits. By placing fielders in spots of higher hit frequency, the logic follows, fewer batted balls will drop in as hits. Notably, though, as the number of shifts has drastically increased, the league-wide BABIP hasn’t changed. Since 2011, shift deployment has increased tenfold (though BABIP has actually increased 1.7% – .295 in 2011 to .300 in 2017). Better positioning could lead to teams utilizing fielders who have less range, as they’d be located closer to batted balls. Do teams who shift frequently employ worse-ranged fielders?

First, the recent MLB environment. Through a combination of enhanced analysis and deeper data, teams across MLB are increasing shift usage. Positioning fielders in locations of high hit density, for specific batters, allows them to field more batted balls. Every team is increasing their shift usage, driving the total shifts deployed up.

The intuitive result of this would be batters are recording fewer hits. As fielders field more balls, they should convert more of those previously-hits into outs. However, league-wide BABIP has actually increased as shift usage increased. Perhaps the quality of the batted balls has decreased, though – trading doubles and triples for singles. According to the league-wide wOBA, though, the overall quality of offense has increased.

Clearly, shifts aren’t having the effect one would expect them to have. Rather than explore what effect they do have (as if they had no effect, why would teams continue to shift?), I want to see if perhaps the defenders being used are worse. Perhaps shifts have allowed teams to mask poor defenders with better positioning.

After browsing the data, I thought it was best to compare year-to-year changes in range runs saved above average to changes in shift deployment, in attempts to analyze the effects of a large change in shift use on range runs above average (RngR). This variable doesn’t measure data for shifts — any shift-influenced batted balls are excluded. This exclusion is what makes RngR perfect for analysis — we can isolate plays which are standard and similar fielder-to-fielder and control for frequency of shifts.

To do this, I first prorated range runs above average to a 150 defensive game rate (RngR.150), as each team had slightly different innings totals. I then took the year over year difference in RngR.150 as RangeDiff, to analyze changes in range runs above average. Similarly, I took the year over year percentage changes in shift deployments. Due to the drastic increase in shift usage across the majors, comparing these absolute numbers would be meaningless here, so I scaled these percentage changes to each season’s average change in shift usage. This variable, ShiftScaleYOY, represents a team’s shift usage change as standard deviations above or below the season average change. All this data is from Fangraphs, 2011-2017 team defense statistics and shift deployment.

My hypothesis is that teams that have a drastic increase in shift usage between seasons, compared to league-average, would have worse defenders, as measured by range. The results:

First, notice the axes. Third basemen have a larger variance. Teams with larger increases in shift usage year-to-year, relative to the rest of the league during this same time periods, appear to have defenders at third with range values closer to zero. This is difficult to see through inspection, however. There doesn’t appear to be much of a relationship with 2nd basemen or shortstops.

When I regressed the between-year standard deviation measurement of shift changes on between-year range change, with dummies for position and season, the shift change variable was insignificant. In fact, there were no significant variables, and the R-Squared was merely .13%. Notice the symmetry in the above graphs, though. A team’s range values seem to converge as the team’s standard deviation of shift changes increases.

To explore this, I ran two regressions, with subsets where the dependent variable, Range.150, was positive and negative. The positive regression had an R-Squared of 9.2%, implying it poorly describes the variance in positive Range changes year-over-year. 2017, 3B and SS were all statistically significant, at the 99% confidence level. This implies that there is a 2.15 range per 150 defensive games decrease in 2017 versus the other seasons, that there is a 1.5 run increase for being a third baseman and a 1.4 run increase for being a shortstop over a second baseman. The negative regression had an R-Squared of 8.6%, again implying this model poorly describes the variance in the data. Here, however, 2017 and 3B both were statistically significant, at the 99.9% or greater confidence level. The values were greater, but the direction of implication was the same – 2017 implies a 2.7 run increase, and a third baseman has a 2.4 run decrease over second basemen. These analyses suggest that 2017 resulted in fewer outlier defenders and that third basemen were higher variance than second basemen.

There are a few issues or improvements with this analysis that could be made. First, publicly available data is limited – comparing shifted plays and non-shifted plays would be best for this analysis. What I did could be seen as cursory, at best an introduction. Secondly, the sample size of defensive shift data is small. Defense data for individual, full-time players is generally utilized in three-year samples, and I was using single-year measurements (albeit at the team level, slightly larger samples per position than individual players). Lastly, a deep analysis on shift impacts on player abilities would use individual players – comparing his or her defensive prowess on shifted and non-shifted plays. This would allow us to try to measure the impact of shifts on defensive performance, to better understand if teams would employ different-skilled players as they increase shift usage or if their players perform differently with shift usage.

There are suggestions in the data that certain years or positions differ with respect to defensive range. Nothing suggested relative increases in shift usage impacts range or quality of defenders on the field. All in all, I think this study can be summarized by the wisdom of Albert Einstein: “the more I know, the more I realize how much I don’t know.”

– tb