The Critical Importance of Dylan Bundy

The Orioles are a playoff contender. They also have a rotation than can best be described as “aspirational.” Their starters rank 19th by fWAR as I write this, and too many of them put more fear into Buck Showalter than they do the opposition. You may be asking yourself “Self, has a team with a rotation this bad ever won the World Series?”

Well, ever is a really long time, but I did check in on the World Series winners over the last 10 years, and the answer is: why yes. It’s happened twice in fact: The last two Worlds Series winners (Royals and Giants) also had mediocre starting-pitching production, ranked 22nd and 23rd by fWAR (respectively). The Giants, at least, had Madison Bumgarner, who amassed nearly 4 WAR, won all seven games of the Series, and hit two homers in each game. Ok, not all of that sentence is true, but the Giants clearly had an ace, a horse they could ride to victory.

The Orioles rotation is a lot less ace-y. Chris Tillman sits at 2.4 WAR right now, good for 31st in the majors. Kevin Gausman may reach 2.0. No other O’s starter will.

This resembles the Royals 2015 rotation more than that of the 2014 Giants. The Fighting Yosts had two 2+ WAR starters: Yordano Ventura and Edinson Volquez. Like those Royals, the O’s have a relentless offense (though relentless in a much different way), and a quality bullpen (both 5th in reliever WAR).

Both rotations also received a key midseason reinforcement. In the 2015 Royals’ case it was Johnny Cueto, who put up an unimpressive 4.76 ERA in his time in KC, but did contribute 1 WAR. With the Royals, Cueto went over 6 innings per start with a 4.06 FIP, giving the bullpen some rest and pushing the radioactive Jeremy Guthrie to the margins of the rotation. The Royals had three 1+ WAR hurlers in the second half: Ventura, Volquez, and Cueto.

The Orioles similarly received a rotation boost after the All-Star break, but via roster re-deployment rather than trade. On July 17 Bundy made his first major-league start. Against the punchless Tampa Bay Rays, Bundy surrendered four runs in just 3 1/3 innings. He struck out four, walked three, and coughed up three dingers. In the space of about an hour, Bundy’s ERA jumped more than half a run.

And it’s been heading down ever since. In his last five starts, Bundy has posted a 1.84 ERA, a .472 OPS against, 32 Ks in 29 innings, and just four walks. Those three homers the Rays hit are still the only ones he’s yielded. These aren’t joke teams Bundy’s been beating: of his last five opponents, only the White Sox’ offense serves comfort food.

In the second half, Bundy trails only Tillman in starter WAR for the O’s. He and Tillman will both reach 1. It seems unlikely any other O’s starter will. Using the 2015 Royals as a model, the Orioles can have success down the stretch and into the postseason with three decent starters. The only candidates are Tillman, Gausman, and Bundy. This puts a lot of heat on a guy with just six major-league starts.

Are there alarm bells? In moving to the starting rotation, Bundy’s velocity has actually increased. His home run rate (1.65/9) is worrisome, but in sample sizes this small it’s dangerous to draw any conclusions from that. He’s doing something new with his curve, probably a key part of his recent success. He’s a achieved a whiff rate in August with the curve that’s almost twice that of any other month in his career. He’s also using his sinker more. These are good things, but any time an injury-prone pitcher makes this many changes at once, it’s possible that he’s rolling the dice with his soft tissue.

The biggest warning sign is probably the innings. His 70+ IP this year amount to just under a third of all his innings in organized ball. In one sense this was expected: Bundy was supposed to get to the majors with relatively minimal minor-league time. However, it’s taken him nearly five years since being drafted to get to 241 career innings (across all levels). No one expected that.

There isn’t a lot of history to go on here; Bundy doesn’t have many comps. There are good reasons for that. Forty years ago the medical advances that have made Bundy’s continued baseball existence possible did not yet exist. Moreover, prior to free agency, it would not have made economic sense for a team to incur those costs even if it could have. Back then, baseball was like Verdun: throw people at the enemy’s trenches and maybe enough of them will survive to take the objective. If not, order up another division and try again. In the baseball context, that meant if a young pitcher’s arm failed, you sent the kid home with a positive reference for his future employer, and gave the next kid the roster slot.

No team can afford to be so cavalier with its pitchers today, at least the ones with significant ceilings. Bundy is, in theory, the unobtanium of baseball: a young, cost-controlled, electric arm. The Orioles’ patience with him to this point is thus admirable, but hardly visionary. It’s more a reflection of how baseball has changed than of the merits the organization.

But the interests of the young pitcher and his employer do not always coincide. Bundy finds himself in a situation similar to that of Steven Matz, a situation in which Bundy’s long-term future and the Orioles’ immediate future may be incompatible. He is critical to whatever hopes the O’s may have of reaching, much less going deep into, the playoffs. Bundy’s heart wants him to continue starting well into October; whether his elbow agrees remains to be seen.

But no matter what fate awaits the O’s and Bundy, 29 other franchises are watching the Bundy story unfold. He will be the comp for the brilliant yet jeopardized young arms of the future. For the front office there is the remote but tantalizing prospect of competitive advantage: The franchise that finds a reliable way to fix those wings will undoubtedly take flight.


The Domination of the Other Phelps

Coming off a season in which he pitched to a 4.50 ERA mostly out of the starting rotation with only 6.19 strikeouts per nine innings, the Marlins decided it was time for a change for David Phelps. They moved Phelps to the bullpen full-time to begin this year, and he was on his way to becoming a relief ace before the Marlins sent him back to the rotation. In 50 relief appearances before joining the rotation, Phelps averaged a whopping 11.43 whiffs per nine (69 strikeouts in 54.1 innings!). Since joining the rotation, Phelps has nine whiffs in 9.1 innings. It will be interesting to see if he can keep this up in the rotation (a la Danny Duffy), because the main culprit for this increase in strikeouts is a surge in fastball velocity:

Brooksbaseball-Chart-3

It’s a classic case of a pitcher’s stuff playing up in a move to the bullpen. Before this year, Phelps never even averaged 92 on the heater. Now, he’s close to 95. I’m hopeful that he’ll maintain this velocity surge because of what Danny Duffy has done this year. Last year, Duffy averaged around 94.5 on his heater and a mere 6.72 K/9. This year, he’s up to around 96 and 10 in those two categories, respectively, despite being shuttled to and from the bullpen his entire career (just like Phelps). There were plenty of concerns that Duffy’s stuff wouldn’t last in a move from the bullpen this year, but I think his 16-strikeout performance on August 1st quelled the last of those concerns.

Alas, a hard fastball isn’t enough to make a great pitcher, as the Yankees realized when they included David Phelps as a throw-in for Nathan Eovaldi. Now, they’re actually of a similar level of skill. Phelps’ four-seamer has decent arm-side movement, but Eovaldi’s has more. Both of their four-seamers generate close to a 20% pop-up rate, which is really good (average for a pitcher’s full arsenal is around 9.6%). Phelps’ four-seamer’s whiff rate has gone up: it was never higher than 9% (a mere 5.6% last year), but this year it’s nearly 13% (Eovaldi’s is at around 7.8% this year).

Here’s what Phelps has that Eovaldi doesn’t have: decent secondary stuff. Phelps’ second-most-frequent offering is his sinker, which has the eighth-best horizontal movement for sinkers of pitchers with at least 60 innings this year (according to FanGraphs). The whiff rate is up to its highest point ever (although only near 6%). He throws it hard, as it’s averaging over 94 this season. The GB% has never actually been good, but it’s up over 50% this year for the first time. The kicker is this: his sinker has allowed an incredibly low ISO this year (0.036). Who knows if the newfound power-suppressing/added groundball-getting ability will continue for the pitch, but even if it doesn’t, sinkers and four-seamers usually work really well together. This is part of how Phelps distances himself from Eovaldi — Eovaldi has no sinker!

Phelps also has a solid curve, which is generating its best whiff rate since his rookie year, at 11.3% this year. This year, it also has the most vertical drop and cutting action away from righties of his career. Of the 191 pitchers who have thrown at least 100 curveballs this year (sample from Baseball Savant), Phelps’ deuce has the 28th-highest spin rate (2616 revolutions per minute), well above that group’s average of about 2243. And boy, does that thing get grounders. Its average launch angle this year is -7.0! That’s good enough for the third-lowest among my curveballer sample. Brooks Baseball has its GB% at nearly 77% this year, and it has only dipped below 60% one season in his career.

His other pitch of note is a cutter/slider thing (he also has a change, but he’s only thrown it 14 times this year). It has good rise. It doesn’t get too many whiffs, but it’s at its highest whiff rate since his rookie year (close to 9%). The ISO against it has only gone above .100 one year, and is solid this year at .090. It gives him a bit of a new velocity band, as it has usually come in around 91 this year. It also gives him a different kind of movement from his other fastballs (FF=four-seamer, FT=sinker, FC/SL=cutter/slider thing, KC=curve, CH=changeup):

4754792016030120160813AAAAAmovement.png

The bottom line is this: Phelps has always had decent secondary stuff, but before this year, his fastball kind of sucked. Everything plays off of the fastball well, so as his fastball has improved, his secondary stuff has improved too. If Phelps can maintain this added velocity, I see a bright future for him.

Data from FanGraphs, Brooks Baseball, Baseball Savant, and Texas Leaguers. If you have a moment, read the awesome article that I linked from The Hardball Times: http://www.hardballtimes.com/pitch-sequencing/

Thanks for reading!


Dylan Cozens or Rhys Hoskins?

The Philadelphia Phillies are fortunate enough to possess the only two Double-A hitters with more than 30 home runs so far in the 2016 season. Besides Rhys (pronounced “Reese”) Hoskins and Dylan Cozens (pronounced “Cousins”), no other hitter in the Eastern League has more than 20 home runs. No other hitter in all of Double-A has more than 24.

But minor-league hitters with immense power are not a new phenomenon, and a vast majority of them never amount to much, if anything, in the major leagues. But these two, in my opinion, are a different story.

Cozens and Hoskins, rated the No. 18 and 19 prospects in the Phillies system, respectively, at the beginning of the year, have both made strides in 2016 that should have them both quickly ascending. While they both boast similar slash lines in Double-A Reading this year (Cozens: .283/.367/.611 with 32 HR and a .328 ISO; Hoskins: .284/.362/.591 with 33 HR and a .307 ISO), I believe that one of the two is much more likely to be an above-average major-league player and perhaps even an All-Star.

 

Approach:

Cozens, who has the less orthodox approach of the two, stands far away from the plate. He dares the pitcher to come inside, knowing that he has the arm length to cover any pitch that could potentially cross the outside of the plate. Essentially, any pitch thrown on the inner half to Cozens is akin to throwing a pitch down the middle to any other hitter.

Even less orthodox than his physical placement is the placement of his hands. Cozens, who stands very upright in the box, keeps his hands low and towards his back hip. This creates problems for him when he chases fastballs up in the zone, which he struggles to get his hands above. Since Cozens can’t to lay off of those pitches, this becomes especially problematic for the slugger. Additionally, his high leg kick leaves him frequently off balance and on his front foot, especially against offspeed pitches. With his freakish power, however, he can still drive the ball out of the park even when he’s fooled by and out in front of a pitch.

Cozens struggles to identify breaking pitches, leaving him even more susceptible to fastballs up and in. The upper, inner quadrant is easily the most glaring hole in his swing. That, coupled with his propensity to get out on his front foot and wave through offspeed pitches, has led to Cozens’ 29.3 K% in AA this year. Cozens does have a BB% of 11.8, which is encouraging, especially since it hadn’t topped 7.2 since Low-A in 2013. A lot of those walks, however, have been a result of Double-A pitchers not wanting to challenge the slugger. Needless to say, his approach will need to improve if he wants to compete against major league pitching.

Hoskins has a much better approach. His stance is more conventional, and his leg lift and stride are much shorter and controlled. With his back foot pointed slightly towards the pitcher, Hoskins lifts his front foot a few inches off the ground as the pitcher winds up and holds it there until he identifies the speed and location of the pitch, which he does well. When pitchers try to surprise him with changeups and breaking pitches in hitters’ counts, Hoskins is often ready to ambush. He stays balanced and doesn’t chase many unhittable pitches.

While most of Hoskins’ home runs are to the pull field, he doesn’t get too pull-happy, especially with pitches up in the zone. When he gets a fastball up and away, he’s not afraid to drive it to right field for a single. This explains Hoskins’ slightly lower ISO (.307 to Cozens’ .337). He is, however, susceptible to the fastball low and away, which he will try to pull.

Hoskins is able to differentiate between fastballs and offspeed pitches much better than Cozens. Hoskins will often check his swing on breaking pitches out of the zone, and he will stay back on changeups, even in hitters’ counts, and drive them. He doesn’t chase nearly as much as Cozens; his walk rate hasn’t been below 9.0% since Low-A (7.7%). Even more encouraging for Hoskins are his split stats. Most right-handed power hitters struggle against right-handed pitchers, but Hoskins’ split in 316 PA against RHP in 2016 is a robust .288/.365/.570 with 25 of his 33 home runs. Conversely, Cozens, a left-handed hitter, struggles mightily against LHP (.204/.286/.387 with 5 of his 31 HR).

Advantage: Hoskins.

 

Power:

While both hitters possess plus power, Cozens’ is elite and able to offset his below-average hit tool at times. The best metaphor for Cozens’ power is a flashy, new titanium driver: It is forgiving and doesn’t require perfect contact for a desirable result. Cozens doesn’t have to barrel up a ball to knock it out of the park, nor does he need to be balanced. He can be fooled by a breaking pitch and have a majority of his weight on his front foot and still easily clear the fence in right-center.

Hoskins, meanwhile, is a high-quality, old-fashioned persimmon three wood. He possesses above-average power, but needs to square up the ball in order to tap fully into it. But Hoskins’ power is more consistent and reliable, mostly due to his superior approach and hit tool.

Advantage: Cozens.

 

Athleticism:

Both Cozens and Hoskins are large men. Hoskins, listed at 6’4” and 225, is relegated to a corner infield position, most likely 1B, where he is adequate albeit unspectacular. Cozens is listed at 6’6” and 235 and offers more both in the field and on the basepaths. Cozens is an average defender in RF, although he has appeared in CF seven times in his minor-league career. He’s deceptively fast for his size, as his 23 stolen bases in 2014 and 18 this year attest to.

Advantage: Cozens.

 

Overall:

While both players possess above-average power and are thriving in Double-A at relatively young ages (Hoskins is 23 and Cozens is 22), Hoskins has the superior hit tool and approach. Cozens offers more as a defender and a baserunner, but not enough to offset his high strikeout totals, and his power is only marginally superior to that of Hoskins. Hoskins’ power is more translatable to the big leagues, where he has the opportunity to eventually thrive. This may seem strangely optimistic, but I would not bet against him reaching his ceiling as an All-Star first baseman with 30+ home run power and a .260/.340/.500 slash line.

Advantage: Hoskins


Tomlin Has a 3.81 ERA? You Must Be Joshing!

The Indians have an awesome starting rotation. They’re fifth in the MLB (first in the superior-hitting American League) with a 3.95 SIERA, seventh in ERA with a 3.96. They obviously have a solid top three in Carlos Carrasco, Corey Kluber, and Danny Salazar. Beyond them, the emergence of Trevor Bauer has grabbed headlines. But what about that last spot in the rotation? It is being held down, and held down steadily, by one Josh Tomlin. And he isn’t dragging down the staff’s ERA like most number 4s and 5s. In fact, he’s actually improved the ERA of the staff with a solid 3.81 ERA. However, he’s only averaged 6.39 K’s per nine innings, far below the league average for starting pitchers this year (7.72). He certainly doesn’t have overwhelming stuff. How has he been able to succeed?

Tomlin has impeccable command. He’s walking 1.15 guys per nine innings, in line with his career (1.45). He’s third in the MLB in K/BB ratio (first in AL). He’s 13th in first-pitch-strike percentage. He dots the corner with his main secondary offerings, a curve and a cutter, throwing them down and away to righties and down and in to lefties. He’s done this throughout his career:

 

And he’s continued to do so this year:

 

He owns that low and outside corner! Spotting his pitches on the corners has likely helped Tomlin to induce a solid Z-Swing percentage of 62.7% (according to FanGraphs plate-discipline data), which is 10th-lowest in the MLB this year. This means that Tomlin has been good at getting called strikes. He pairs this skill nicely with a 33.6% O-Swing percentage, which is the 11th-highest in the MLB this year. This means that Tomlin has been good at getting hitters to swing at pitches outside of the zone (pitches they usually can’t drive). This has been a skill for Tomlin throughout his career (33.2% O-Swing during his career).

In addition, he has a career BABIP of .274 (league average is around .295 every year). He has improved on that mark this year, allowing a .268 BABIP. This isn’t entirely surprising, given the high O-Swing percentage: If you swing at pitches outside of the strike zone, it’s much harder to make solid contact. Also of note is the fact that Tomlin’s Z-Swing percentage has really improved for him this year (66.3% career versus 62.7% this year).

What’s the driving force behind the improvement in these two plate-discipline stats? Tomlin’s cutter and curve offer a good explanation. According to PITCHf/x data on FanGraphs, Tomlin’s cutter has the 15th-best “rise” among qualified pitchers this year. It also has the eighth-most horizontal movement, darting away from righties and in to lefties. What’s more, the cutter has induced a 42.7% O-Swing percentage across his entire career. That number has held strong this year at a 44.5% clip. He’s decided to uptick the usage on the pitch this year to a career high, while throwing his four-seamer at a career-low rate.

Year

Four-seam%

Sinker%

Cutter%

Curve%

Change%

2010

36.45

13.51

29.28

10.34

10.43

2011

37.87

6.07

30.83

14.62

10.60

2012

33.55

4.57

35.54

13.59

12.63

2013

44.44

0.00

33.33

11.11

11.11

2014

45.06

2.01

30.81

14.96

7.16

2015

53.38

0.00

27.43

12.97

6.22

2016

31.07

5.68

40.97

14.94

7.34

The curve is the driving force behind the low Z-Swing percentage: This year, the pitch has a crazy low percentage of 44.7%. While that is lower than his career percentage on the pitch,  the curve is generating excellent vertical drop this year (15th-best in the MLB), and I wouldn’t be surprised to see him maintain a low percentage.

Tomlin isn’t flashy. He doesn’t pile up strikeouts. He doesn’t throw very hard. But, he spots the ball tremendously well and appears to have good contact management skills. Two pieces to the puzzle are his low Z-Swing percentage (fueled by the curve) and high O-Swing percentage (fueled by the cutter and its uptick in usage).

Data courtesy of FanGraphs and Brooks Baseball. Thanks for reading!


Will Cy Young Voters Like Zach Britton’s Year?

You probably know that Zach Britton is good at baseball, and that he’s having a great year. He was good enough last year that an article was written titled “How Zach Britton Blew His Saves”, and he’s been even more effective this year. Britton set a record last Wednesday night for consecutive saves by a left-handed relief pitcher to start a season, and has a number of other impressive stats:

  • he hasn’t allowed a hit since July 15 (a span of 8 appearances), a run since June 21 (18 appearances), or an earned run since April 30 (36 appearances)
  • he has allowed hits in just 18 of 47 appearances, and allowed multiple baserunners (via hit or walk) in just 10 of appearances

The ESPN Cy Young Predictor (CYP) shows Britton to be leading the current AL Cy Young race. He could be the first reliever to earn first-place Cy Young votes since Craig Kimbrel and Fernando Rodney received (single) first place votes in 2012. But is it really plausible to think that he could win?

Relievers and Recent Cy Young Voting

Let’s compare Britton’s stats with the four other relievers to receive first-place Cy Young votes since Eric Gagne’s 2003 victory, the last reliever season to actually win the Cy Young:

CYP finish Actual finish 1st place votes ERA SV BS IP H R ER HR SO WHIP
Gagne 2003 1 1 28 1.20 55 0 82.1 37 12 11 2 137 0.692
Rivera 2005 1 2 8 1.38 43 4 78.1 50 18 12 2 80 0.868
Kimbrel 2012 6 5 1 1.01 42 3 62.2 27 7 7 3 116 0.654
Rodney 2012 3 5 1 0.60 48 2 74.2 43 9 5 2 76 0.777
Britton 2016 1 ? ? 0.59 33 0 45.2 22 6 3 1 52 0.766

A lot of dominant seasons. A few notes:

  • Rivera’s 2005 season benefited from a transition time where many writers still prized W-L and voted for Bartolo Colon; Colon’s selection over Johan Santana looks silly in hindsight
  • Rodney’s 2012 and Britton’s 2016 season look really similar, except that Britton has been perfect in save situations (more on this soon)

Why Gagne Won

Gagne’s narrative of dominance that year, including his famous entrance and nickname “game over”, was corroborated by a combination of save records (55 saves and 0 blown saves, in the midst of a still-standing record 84-save-conversion streak), minuscule WHIP (.692), and an eye-popping 137 Ks (15.0 per 9 innings). Britton has the perfect save conversion rate and low WHIP that Gagne had, but faces additional obstacles in winning and constructing the name narrative.

The first is that Britton’s K rate, while great, is much lower. The second is that reliever seasons have become discounted recently, a sort of narrative goalpost shift in the sabermetric era. The perfect save conversion was repeated by Jose Valverde and Brad Lidge in 2011 and 2008, respectively, and I think no longer carries the same impression on voters. Gagne won convincingly even though there was no shortage of excellent starters that year (Mark Prior and Mike Schmidt both had WAR figures much higher than Gagne’s), because he was the story in NL pitching that year. Relievers tend to do worse on metrics like WAR compared to starters, and this makes constructing the same justification for voters to cast high votes to relievers much harder today. WAR was in its infancy in 2003, and I think if the 2003 vote were recast today, the result would be quite different.

This leads us to some sabermetric numbers:

ERA+ FIP WPA  (league rank) bWAR fWAR RA9-WAR
Gagne 2003 337 0.86 6.56 (1) 3.7 4.7 4.4
Rivera 2005 308 2.15 3.2 (6) 4.2 2.9 3.5
Kimbrel 2012 399 0.78 4.4 (1) 3.3 3.3 3.6
Rodney 2012 641 2.13 5.1 (2) 3.8 2.4 4.0
Britton 2016 749 2.00 4.37 (1) 2.7 1.6 2.5

A lot can be said here, but a few things I wanted to mention:

  • I think Gagne’s high WPA is based on his outstanding performance in high-leverage situations (a look at his splits shows that he gave up two extra-base hits against 63 strikeouts in 154 high-leverage plate appearances in 2003)
  • bWAR loves Rivera; he had three other seasons of 4 or more WAR, including his workhorse 1996 season with 107.2 IP and 5 WAR
  • Gagne and Kimbrel’s FIP are actually lower than their ERA, I think because of their K rates
  • Rivera, Rodney, and Britton had their ERA (and ERA+) figures benefit from allowing multiple unearned runs

 Lastly, let’s look at stranding runners:

LOB% IR IS BQR BQS
Gagne 2003 83.9 10 0 2 1
Rivera 2005 78.0 18 2 3 1
Kimbrel 2012 92.8 4 0 0 0
Rodney 2012 89.4 18 2 0 0
Britton 2016 86.3 14 1 2 2

All five stranded baserunners at an excellent rate, especially Kimbrel’s astounding 92.8% LOB. They were also extremely effective at preventing inherited runners from scoring, with a combined 55 of 60 inherited runners stranded. This takes us to:

Britton’s Luck

Britton has enjoyed both good and bad luck this year, and I’ll just mention two factors: defense and bequeathed runners. The good luck is in having a good infield defense behind him, which is obviously important for a sinkerball pitcher. Davis, Schoop, Hardy, and Machado are enjoying FanGraphs Fielding ratings of 1.1, 0.4, 1.9, and 4.1, respectively, and for what it’s worth, the Orioles are second in the AL in fielding percentage as well.

The (slight) bad luck is in his two bequeathed runners, both of whom scored. The first was on April 30, where Britton left with a runner on 1st and 2 out and Vance Worley allowed the runner to score, tagging Britton with one of his three ER this year. The other was on June 21, where Britton left with a runner at 2nd and 2 out, and Ordrisamer Despaigne allowed the runner to score. Britton was charged with 3 unearned runs but 0 earned runs, as Flaherty was playing 3rd instead of Machado that day and made an error early in the inning; this is one of only two errors committed behind Britton this year.

The Search for Perfection

If Britton ends up something like 55/55 in save situations (or blows one save) with his current rate stats, I think he’ll get at least a few first-place votes. But I think it is nearly impossible to be a reliever with a typical closer load and actually win the award in the WAR era, and perhaps tools like the Cy Young Predictor might be adjusted to reflect this.

This discussion also raises the question in my mind of whether we will ever see a reliever put up a perfect season of at least 60 innings with 0 runs allowed. It is really hard to throw that many shutout innings. Hershiser and Drysdale had streaks of nearly 60 scoreless innings, but all of the pitchers on the top 10 list of scoreless streaks were primarily starters. Reliever Brad Ziegler began his career with 29 scoreless appearances, but that’s only halfway to 60. Maybe it is like the chance of another .400 hitter.

We will see how Britton’s season turns out, and how the voters evaluate it. In the meantime, we will probably be seeing a lot more of this:


Hardball Retrospective – What Might Have Been – The “Original” 1997 Red Sox 

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the teams with the biggest single-season difference in the WAR and Win Shares for the “Original” vs. “Actual” rosters for every Major League organization. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.

Terminology

OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

AWAR – Wins Above Replacement for players on “actual” teams

AWS – Win Shares for players on “actual” teams

APW% – Pythagorean Won-Loss record for the “actual” teams

 

Assessment

The 1997 Boston Red Sox 

OWAR: 63.7     OWS: 317     OPW%: .583     (94-68)

AWAR: 41.4      AWS: 234     APW%: .481     (78-84)

WARdiff: 22.3                        WSdiff: 83  

The “Original” 1997 Red Sox cruised to the pennant by a ten-game margin over the Yankees. Jeff Bagwell delivered a 30/30 season (43 HR / 31 SB), drove in a career-high 135 baserunners, rapped 40 doubles and coaxed 127 walks. Brady Anderson followed his 50-home run campaign in ’96 with 39 two-base knocks and 18 dingers. A trio of “Original” and “Actual” Sox infielders provided additional firepower in Boston’s stacked lineup. Nomar Garciaparra (.306/30/98) merited the 1997 AL Rookie of the Year Award as he registered 209 base hits, 122 runs scored, 44 doubles, 11 triples and 22 stolen bases. Mo “Hit Dog” Vaughn slammed 35 circuit clouts and supplied a .315 BA. John Valentin (.306/18/77) led the League with 47 two-baggers.

1B Jeff Bagwell and 3B Wade Boggs placed fourth at their respective positions in the “The New Bill James Historical Baseball Abstract” top 100 player rankings. “Original” Red Sox teammates specified in the “NBJHBA” top 100 rankings include Roger Clemens (11th-P), Mo Vaughn (51st-1B), Brady Anderson (63rd-CF) and Ellis Burks (77th-CF).

  Original 1997 Red Sox                                                             Actual 1997 Red Sox

LINEUP POS OWAR OWS LINEUP POS OWAR OWS
Ellis Burks LF/CF 1.03 13.6 Wil Cordero LF -1.26 10.76
Brady Anderson CF 3.44 25.97 Darren Bragg CF 0.28 10.71
Phil Plantier RF/LF -0.02 2.24 Troy O’Leary RF 0.36 13.57
Mo Vaughn DH/1B 3.2 22.31 Reggie Jefferson DH 0.46 10.31
Jeff Bagwell 1B 7.47 30.58 Mo Vaughn 1B 3.2 22.31
John Valentin 2B 4.45 21.03 John Valentin 2B 4.45 21.03
Nomar Garciaparra SS 4.19 25.54 Nomar Garciaparra SS 4.19 25.54
Wade Boggs 3B 1.26 11.37 Tim Naehring 3B 1 8.1
John Flaherty C 1.26 12.67 Scott Hatteberg C 2.21 6.4
BENCH POS OWAR OWS BENCH POS OWAR OWS
Tim Naehring 3B 1 8.1 Jeff Frye 2B 1.43 12.16
Scott Hatteberg C 2.21 6.4 Mike Stanley DH 1.17 8.52
Todd Pratt C 0.63 4.46 Shane Mack CF 0.15 3.59
Ryan McGuire 1B -0.12 3.98 Mike Benjamin 3B -0.06 1.52
John Marzano C 0.05 2.39 Bill Haselman C 0.09 0.88
Jody Reed 2B -0.46 1.52 Rudy Pemberton RF -0.21 1.03
Danny Sheaffer 3B -0.71 0.79 Jesus Tavarez CF -0.59 0.56
Scott Cooper 3B -0.47 0.78 Curtis Pride 0.1 0.35
Michael Coleman CF -0.27 0.11 Arquimedez Pozo 3B -0.02 0.31
Jose Malave LF -0.08 0.04 Jason Varitek C 0.05 0.16
Walt McKeel C -0.04 0 Michael Coleman CF -0.27 0.11
Jose Malave LF -0.08 0.04
Walt McKeel C -0.04 0

Roger Clemens (21-7, 2.05) collected the 1997 AL Cy Young Award while posting a personal-best with 292 whiffs. Curt Schilling (17-11, 2.97) overpowered the opposition with a career-high 319 strikeouts. Paul Quantrill furnished a 1.94 ERA in 77 relief appearances. Tom “Flash” Gordon notched 11 saves for the “Actuals”.

  Original 1997 Red Sox                            Actual 1997 Red Sox

ROTATION POS OWAR OWS ROTATION POS AWAR AWS
Roger Clemens SP 12 32.22 Tom Gordon SP 3.72 15.2
Curt Schilling SP 5.93 22.29 Tim Wakefield SP 2.85 11.63
Aaron Sele SP 0.64 6.71 Aaron Sele SP 0.64 6.71
Frankie Rodriguez SP 0.93 5.97 Jeff Suppan SP 0.24 3.72
Jeff Suppan SP 0.24 3.72 Chris Hammond SP -0.23 1.7
BULLPEN POS OWAR OWS BULLPEN POS AWAR AWS
Paul Quantrill RP 2.64 11.66 Butch Henry SW 1.81 8.78
Ron Mahay RP 0.71 3.4 John Wasdin SW 1.23 7
Joe Hudson RP 0.42 2.93 Jim Corsi RP 0.78 6.01
Shayne Bennett RP 0.34 1.51 Ron Mahay RP 0.71 3.4
Reggie Harris RP -0.22 1.37 Joe Hudson RP 0.42 2.93
Erik Plantenberg RP 0.06 1.07 Ricky Trlicek RP -0.06 1.29
Josias Manzanillo RP -0.17 0.28 Robinson Checo SP 0.41 1.24
Cory Bailey RP -0.33 0.21 Mark Brandenburg RP -0.12 1.21
Greg Hansell RP -0.24 0 Derek Lowe RP 0.29 1.17
Brian Rose SP -0.17 0 Heathcliff Slocumb RP -0.52 1.14
Ken Ryan RP -1.09 0 Steve Avery SP -0.9 0.99
Kerry Lacy RP -0.76 0.75
Vaughn Eshelman SP -0.37 0.72
Rich Garces RP -0.1 0.43
Bret Saberhagen SP -0.15 0.01
Toby Borland RP -0.28 0
Ken Grundt RP -0.11 0
Pat Mahomes RP -0.39 0
Brian Rose SP -0.17 0

Notable Transactions

Roger Clemens

November 5, 1996: Granted Free Agency.

December 13, 1996: Signed as a Free Agent with the Toronto Blue Jays.

Jeff Bagwell

August 30, 1990: Traded by the Boston Red Sox to the Houston Astros for Larry Andersen.

Brady Anderson 

July 29, 1988: Traded by the Boston Red Sox with Curt Schilling to the Baltimore Orioles for Mike Boddicker. 

Curt Schilling 

July 29, 1988: Traded by Boston Red Sox with Brady Anderson to the Baltimore Orioles in exchange for Mike Boddicker.

January 10, 1991: Traded by Baltimore Orioles with Pete Harnisch and Steve Finley to the Houston Astros in exchange for Glenn Davis.

April 2, 1992: Traded by Houston Astros to Philadelphia Phillies in exchange for Jason Grimsley.

December 20, 1995: Granted free agency.

December 21, 1995: Signed by Philadelphia Phillies.

Honorable Mention

The 1927 Boston Red Sox 

OWAR: 32.6     OWS: 230     OPW%: .463     (71-83)

AWAR: 13.7       AWS: 153      APW%: .331    (51-103)

WARdiff: 18.9                        WSdiff: 77

The “Original” 1927 Red Sox tied for last place with the Indians yet managed to finish 20 games better than the “Actual” squad. Babe Ruth (.356/60/165) established the single-season home run record and paced the Junior Circuit with 158 runs scored, 137 walks, a .486 OBP and a .772 SLG. Tris Speaker sported a .327 BA and laced 43 two-base hits in his penultimate season.

On Deck

What Might Have Been – The “Original” 1904 Superbas

References and Resources

Baseball America – Executive Database

Baseball-Reference

James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at “www.retrosheet.org”.

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

 


Fantasy Metrics and xHR

RotoGraphs, in addition to several Community writers, have been posting about an “x” category of metrics for quite some time. They include things like Andrew Dominijanni’s xISO, Andrew Perpetua’s xBABIP, and more. The clear purpose of developing those statistical indicators was to measure and predict fantasy-baseball success, something we all aspire to in our hopefully low-priced leagues (although you probably found that using x-stats is a lot like overstudying for a test because the amount of effort you put into preparing yields diminishing returns, and you “over-Xed” the players).

One of the most prominent of the x-stats trotted out at the beginning of every season is xHR/FB, developed by Mike Podhorzer, and always accompanied by an amusing “leaders and laggards” piece. His version of xHR/FB is quite good, with a .649 R-squared value. In his regression analysis, Mr. Podhorzer utilizes somewhat exclusive metrics (hopefully public at some point), such as average absolute angle. Overall, it’s a pretty good predictor, and it becomes doubly understandable to the layman when it gets multiplied by fly balls to produce an expected home-run value.

The only real issue I have with HR/FB (and its prediction) is that it is HR/FB. While it is more stable for hitters than for pitchers, it still isn’t quite as stable as a stat I’d like to use for fantasy baseball. For my 1000 player-season sample from 2009-2015, HR/FB had a year-to-year R-squared value of .49. It isn’t terribly difficult to figure out why. There are numerous reasons, including weather changes, team changes, opponent changes, player development, and more. Moreover, it doesn’t take a very good picture of a hitter’s overall profile because it only looks at how many home runs a player hits per fly ball. A player might have a high HR/FB, but he may not hit enough fly balls for the metric to accurately describe his power (i.e. whether he actually hit a lot of home runs). On the other hand, it’s important to note that a high HR/FB generally goes with a higher FB%.

Perhaps a better metric for evaluating a player in the greater context of his hitting profile is HR/BBE. Home runs per batted-ball event is just HR/(AB+SF+SH-SO). It has a slightly higher year-to-year R-squared of .56 (from my sample), in large part because it takes into account more variables than does HR/FB. Under the umbrella of BBE fall not only fly balls, but line drives (and there can be line-drive home runs), and ground balls. In case you’re wondering why I included sacrifice hits, it’s because they tell a little bit about what kind of hitter a player is. Most modern managers are far more likely to ask a Ben Revere to lay down a sacrifice bunt than they are a Kris Bryant.

And so I thought it might be useful to run a linear regression analysis to develop an xHR/BBE (and from there, xHR). I’m a statistical autodidact, so I tried to keep things simple. Additionally, I thought it would be best if I utilized accessible variables like FB% so that a moderately literate sabermetrician could use it. After testing myriad variables, I came up with four that I’d use — average FBLDEV (Statcast), wFB/C, SLAVG, and FB%.

  • AVG FBLDEV – Average fly ball/line-drive exit velocity. The idea is that the higher this value is, the harder the player is hitting the ball, and so he will hit more home runs.
  • wFB/C – A rather obscure metric buried in the FanGraphs glossary, wFB/C is weighted fastball run values per 100 pitches. I use it because most home runs come off some form of a fastball, and home-run-hitter types are typically good fastball hitters.
  • SLAVG – “Slap” average, a metric of my own invention (although someone else has probably thought of it – I just haven’t seen it before), is singles divided by at-bats. It’s a bit like ISO in that it tells you about a player’s power distribution (or lack thereof). I figure that this is inversely correlated with power because the more singles a player hits, the fewer home runs he’s likely to hit.
  • FB% – Fly ball percentage obviously figures pretty heavily into a power hitter’s profile. It’s awfully difficult to hit a lot of home runs without hitting a plethora of fly balls.

It seems like a decent list of predictors in that they are understandable and accessible to the average fan, in addition to having a good relation to home-run hitters. I used all players that had at least 100 batted-ball events in 2015 and 2016 (Statcast only has data going back to 2015), which turns out to be close to 500 player-seasons. So let’s throw them into the Microsoft Excel Regression grinder and see what it spits out:

Note: To be clear, the end goal is not necessarily xHR/BBE, but rather xHR. xHR/BBE is just the best path to xHR because HR/BBE is a rate stat, meaning that it will have a better year-to-year correlation than home runs because that’s a counting stat. So if a player gets injured and only plays half a season, his HR/BBE would probably be similar to his career values, but his home-run numbers would not be.

The primary thing to recognize here is the R-squared value: a pretty good .78272. To the uninitiated, this simply means that the model explains 78% of the HR variance. If you’re interested (and you really ought to be), here are the coefficients for the variables and the overall formula:

xHR= (.114557524*FB% – .183885205*SLAVG + .006658976*wFB/C + .004075449*FBLDEV -.343193723) * BBE

With this information, it isn’t terribly difficult to look up a few pieces of data on FanGraphs and Statcast to see how many home runs a player “should” have hit. In case you’re wondering about its predictive value relative to that of HR/BBE, xHR/BBE has an R-square value that’s six points higher (.61). Nevertheless, it’s important to note that, based on the graph, the model struggles to predict home-run numbers for the players on the extremes – the Jose Bautistas of the world. Because the linear regression tends to underestimate rather than overestimate at the top, it’s likely that a quadratic regression would fit better. It’s something to look into, but this’ll do for now. Moreover, while there are some really crazy outliers, like Jose Bautista being predicted to hit 12 fewer home runs (Steamer does have him on pace for only 26 this year!), the model does work reasonably well for more average players.

Keep in mind that numerous improvements will be made. If anyone wants access to data or has a question, then just let me know. If not, then enjoy the tool and use it for fantasy, even though it’s getting a bit late for that. Maybe next year.


Jay Bruce, Matt Kemp, Perception, and Reality

Over the weekend, the Atlanta Braves swapped their bad infielder embroiled in a domestic violence incident, Hector Olivera, for nice-guy outfielder Matt Kemp. The deal was largely panned within the industry, and many felt the Padres benefited most by ridding themselves of Kemp while he still had value. Olivera’s involvement in the deal was a purely financial exchange, as he was immediately designated for assignment, and he may never play in the majors again amid the stink of mediocrity and domestic violence. But the complex financials of the deal effectively mean that to land Kemp, the Braves’ bank account will be light just $30M or so over the next three years. Forgetting for a moment the enormous misstep the Braves made in acquiring Olivera in the first place, this Kemp acquisition is unbelievably impressive considering the price other teams are paying for defensively-challenged power-hitting outfielders.

Take Jay Bruce. One of the hottest names on the hot stove this July, he got moved on Monday for Dilson Herrera and Max Wotell. The interesting thing here is that Bruce is due $13M and signed only through next season, and the Mets had to give up real talent to acquire him. Herrera, the headliner going back to the Reds for Bruce, is a 22-year-old second baseman with a .790 OPS in AAA. But the kicker here is that Bruce isn’t good. He’s been worth six wins below average over the last three years.

Consider:

  • Matt Kemp 2015/16: .263/.301/.460, 46 HR and an OPS+ of 109
  • Jay Bruce 2015/16: .240/.301/.481, 51 HR and an OPS+ of 108

Undoubtedly, Bruce has been the better player this year. His OPS+ is 20 points higher than Kemp’s, meaning he’s been about 20% better than Kemp. But let’s consider what that means for a moment. Purely in terms of slugging, it’s about 25 total bases over the course of 400 at bats. That’s an extra base every four games, or twice a week. I realize that baseball is made up of all those little differences — and that those differences are what separate the contenders and the pretenders — but we’re talking about a whole lot of luck when we’re talking about two extra bases a week.

So why does “the industry” value one of these guys so much more highly than the other? Perception. The Reds have spent the greater part of the last year building up Jay Bruce as a potential difference-maker for a playoff team desperate for power. They’ve subtly leaked rumors of his availability to the press. They’ve reminded everyone that he’s clocked 233 homers in his career, and they had to smile as Yoenis Cespedes proved last year that flawed players can bring teams over the hump.

But is Kemp really all that different from Bruce? Was Kemp available for 3/$30M to everyone? Do you realize what 3/$30M means in today’s baseball? Last offseason, Joakim Soria signed for 3/$25M while Gerardo Parra got 3/$27.5M. That type of money goes to 7th-inning relievers and 4th outfielders. Kemp doesn’t even have to be good to be worth that type of money; merely average.

But Sean, the Defense!

Eh. They’re both pretty bad at defense. Whether one guy is worth -20 runs while the other is worth -15 really doesn’t matter to me. Maybe it should, but it really doesn’t. That type of difference is similar to the white noise to which one can ascribe that one extra base per week.

So really, it comes down to a simple proposition. It’s not as glamorous as trying to pick between Nolan Arenado, Manny Machado, or any other young superstar.  You’ve got two guys. Both are power hitters and play bad defense. One might be better than the other this season, but he was way worse last year. That one costs a solid prospect, and is signed for one year at $13M. The other costs zero prospects, and is effectively signed through 2019 at ~$10M per.

Who do you take?


2016 Cubs Run Differential

In this post, I take a look at the 2016 Chicago Cubs though their first 100 games. I’ll start out by focusing on the Cubs’ run differential (Runs Scored – Runs Allowed). After a historic start, they reached their pinnacle after the 67th game of the year against the Pirates. At this point, the Cubs were 47-20 and had outscored opponents by 171 runs! Since then, the ball club is 13-20 and their current run differential is at +153.

Still, the Cubs’ +153 mark is 42 runs better than the next-closest team (Washington Nationals). The Cubs and Nationals are the only clubs to have a run differential that is greater than +100. The second-place Cardinals rank third in the league at +95 right now. While the Cubs dominate the top end of the spectrum, the Reds and Braves are running away with the worst run differentials in the league. The Reds have a -143 mark, largely due to the thrashings they have taken at the hands of the Cubs so far in 2016. The Braves have the second-to-worst differential at -134 runs.

Projected Runs to Wins

In another place, I introduced the “Pythagorean Theorem’s of Baseball” which basically tries to determine the number of games a team will win based on their number of runs scored and number of runs allowed. Here are the formulas for six of the most common win-percentage projection formulas:

I added up the Cubs’ total runs scored and total runs allowed after each game this year and compared their actual number of wins to the projected number of wins based on each formula. These charts visualize the differences between those numbers.

This matrix summarizes how accurate each of the projection formulas has been in predicting the Cubs’ winning percentage and total number of wins so far in 2016. The most accurate formulas was the James_1.83 followed by the James_2 and Soolman. Four of the six formulas were very good predictors, but the Cook and Kross formulas overforecasted the number of wins that they expected the Cubs to have. Notice that at one point this year, each of those formulas projected the Cubs to have over 15 more wins than they actually had. The R^2 value (coefficient of determination) is indicative of how well the projected win percentage matched up to the actual win percentage after each game this season.

All in all, the Cubs have should have at least six more wins this year based on these formulas. Scoring as many runs as they have (4th most in the MLB) and allowing as few runs as they have (T-1st in the MLB) should result in an even better record than 60-40. We knew it was unlikely that they would keep up their record-setting start in the run-differential category, but it will be interesting to see how these numbers match up as the season progresses.

@CubsAdvMetrics on Twitter


Should Bryce Harper Swing and Miss More?

Well, here we are: Over 100 games into the season and Bryce Harper has yet to break out of his slump. When Bryce came to the Majors back in 2012 he was one of the most hyped prospects since Alex Rodriguez broke into the bigs as a 19-year-old shortstop. The pressure, I’m sure, was immense, and through his first three seasons Harper had put up good numbers, but had yet to establish himself as the superstar we all thought he’d be. Something clicked in 2015 though, as he posted an amazing 9.5 WAR, 197 wRC+, and 0.461 wOBA, all best in the MLB by a fair margin. We all thought he’d done it, he had exceeded expectations and was ready to join Mike Trout as one of the most exciting, talented, and productive players in the game. His 1.5 WAR, 180 wRC+, and 0.443 wOBA through April of 2016 merely affirmed this sentiment.

Here we are. 2.8 WAR, 115 wRC+, and 0.346 wOBA. To be fair, these are by no means terrible numbers. He is still creating runs at a decently better rate than the average MLB player, with much of the credit going towards his MLB-leading 18.2 BB% and his 0.214 ISO. His defense has also been very good this year, helping to raise his WAR to 41st in the MLB. No, I am not saying Harper is a bad player, I’m just saying he is worse than the Bryce Harper we saw in 2015. We were all ready to call him a superstar (heck, we even voted him into a starting spot at this year’s All-Star Game), but now he’s taken this step back and we have no choice now but to start questioning his superstar status. Let’s take a look both at what might be causing this slump, and what Bryce could do to bust out of it (if anything at all).

The stat that jumps out at me most is his BABIP. The MLB average is exactly 0.300 this year, and Bryce has a career mark of 0.317. Bryce isn’t too far into his career, and while it’s possible that his 0.369 BABIP last season was an anomaly, it’s certainly safe to say that Bryce is definitively above average in this area. This season his BABIP has dipped down to 0.234, good enough for second to last in the MLB, ahead of only Todd Frazier (0.203). BABIP has a great degree of luck involved, in that some hitters with higher BABIPs might just get lucky (e.g. hit a little bloop into shallow right field that drops for a hit), or might be playing poor defenses (e.g. Jason Heyward would have caught that little bloop, but Jose Bautista was in right field and missed it by a foot). I believe, though, that going from 0.369 in 2015 to 0.234 in 2016 is enough of a differential to at least form the hypothesis that Bryce is struggling beyond just facing better defenses and getting less breaks.

One of the keys to figuring out this drop in production is figuring out what has changed from last year. Obviously his BABIP has declined, but why? For the  most part, pitchers are throwing him the same types of pitches at the same rates, and are throwing pitches in/out of the zone at the same rates as well. He has almost the exact same swing% on pitches outside the zone, but there’s about a 5% decrease in his swing% on pitches in the zone; nothing monumental, but something we ought take note of. The greatest changes that may be observed are in his batted ball numbers, shown here:

Year LD% GB% FB% IFFB% HR/FB GB/FB Pull% Center% Opposite% Soft% Medium% Hard%
2015 22.2 38.5 39.3 5.8 27.3 0.98 45.4 33.8 20.8 11.9 47.2 40.9
2016 14.3 41.4 44.4 11.0 16.9 0.93 40.9 33.5 25.7 22.7 45.4 32.0

We can almost construct a narrative from these numbers: He’s hitting balls soft significantly more often, and he’s also hitting less line drives. Soft ground balls and fly balls are easier to convert into outs, and his infield fly ball% increase implies that he is hitting fly balls with less power. This explains why his home run rate is down. Where he was previously hitting hard line drives and grounders, and turning fly balls into home runs, he is now hitting softer, more easily-fielded grounders and popups, resulting in a steep decline in BABIP.

But this isn’t a cause, it’s a symptom. Again, we are forced to ask why it is that Bryce isn’t hitting balls as hard, and why he’s hitting less line drives? Bryce has been known for having great plate discipline, something that generally hasn’t changed over the last two years. At the surface, we see that he still has a very high walk rate, lays off pitches outside of the zone, and is one of the more patient hitters in baseball. However, one stat that caught my eye was his contact% on pitches outside the zone (and even inside the zone). His O-contact% went from 60.9% to 67.4%, and even his Z-contact% increased from 84.4% to 87.7%. This can be visualized here:

For the 2015 season

Bryce Harper Contact% vs All Pitchers
Season: 2015-04-06 to 2015-10-04 | Count: All Counts | Total Pitches: 2619 | View: Catcher
100 %
44 %
50 %
39 %
51 %
61 %
75 %
72 %
80 %
88 %
100 %
70 %
59 %
66 %
78 %
80 %
88 %
91 %
95 %
77 %
77 %
78 %
84 %
87 %
91 %
98 %
97 %
71 %
71 %
79 %
83 %
87 %
90 %
90 %
93 %
96 %
75 %
85 %
88 %
88 %
88 %
92 %
88 %
82 %
76 %
80 %
83 %
84 %
84 %
85 %
81 %
77 %
81 %
74 %
79 %
76 %
79 %
78 %
75 %
52 %
73 %
64 %
66 %
68 %
70 %
73 %
60 %
25 %
27 %
26 %
0 %

And for the 2016 season

Bryce Harper Contact% vs All Pitchers
Season: 2016-04-04 to 2016-07-28 | Count: All Counts | Total Pitches: 1616 | View: Catcher
100 %
60 %
100 %
75 %
23 %
45 %
76 %
89 %
97 %
100 %
100 %
82 %
75 %
59 %
70 %
89 %
97 %
100 %
100 %
71 %
77 %
80 %
86 %
91 %
98 %
100 %
100 %
63 %
73 %
79 %
78 %
84 %
92 %
99 %
100 %
100 %
67 %
74 %
86 %
84 %
88 %
86 %
95 %
99 %
100 %
69 %
84 %
82 %
85 %
89 %
91 %
85 %
89 %
84 %
81 %
74 %
81 %
81 %
86 %
68 %
35 %
88 %
79 %
74 %
70 %
78 %
74 %
69 %
50 %
40 %
39 %
0 %

There are two ways to look at this: The types of pitches Bryce is seeing, and the counts he’s getting himself into. All of this revolves around where pitchers are throwing pitches, where he’s swinging, and where he’s making contact. As you can clearly see, Bryce has been making a tangibly higher amount of contact this season. Logically, it makes sense to say that he is taking more pitches in the zone, and making weak contact where he used to just swing and miss. But that can’t be the whole story, can it? In attempting to find differences between this season and last, I merely found that regardless of what the count was, Harper was always making more contact; it didn’t matter if he was ahead, behind, or even. He was also making more contact regardless of what pitches were being thrown.

Let’s start with the types of pitches Bryce sees. We’ll split it up into fastballs (which includes 4-seamers, 2-seamers, and cutters), and secondary pitches (curveballs, sliders, and changeups). With secondary pitches, pitchers have begun to come into the zone a bit more than they used to. These charts show where pitchers are throwing Bryce non-fastballs:

2015

Bryce Harper Pitch% vs All Pitchers
Pitches: CH, CU, SL
Season: 2015-04-06 to 2015-10-04 | Count: All Counts | Total Pitches: 877 | View: Catcher
0.5 %
0.2 %
0.2 %
0.6 %
0.4 %
0.2 %
0.3 %
0.4 %
0.3 %
0.2 %
0.1 %
0.7 %
0.7 %
0.5 %
0.4 %
0.5 %
0.5 %
0.3 %
0.2 %
0.8 %
0.9 %
1.1 %
0.9 %
0.6 %
0.7 %
0.5 %
0.2 %
1.2 %
1.0 %
1.4 %
1.6 %
1.6 %
1.1 %
0.7 %
0.5 %
0.3 %
0.1 %
1.5 %
1.6 %
2.0 %
2.0 %
1.4 %
1.0 %
0.7 %
0.3 %
1.7 %
2.1 %
2.0 %
1.9 %
1.7 %
1.2 %
1.0 %
0.7 %
1.8 %
2.1 %
2.3 %
2.3 %
1.8 %
1.3 %
0.8 %
0.7 %
1.6 %
1.9 %
2.0 %
2.0 %
2.1 %
1.4 %
0.8 %
0.5 %
2.9 %
3.0 %
2.1 %

2016

Bryce Harper Pitch% vs All Pitchers
Pitches: CH, CU, SL
Season: 2016-04-04 to 2016-07-28 | Count: All Counts | Total Pitches: 579 | View: Catcher
0.7 %
0.4 %
0.2 %
0.4 %
0.5 %
0.5 %
0.5 %
0.7 %
0.5 %
0.3 %
0.2 %
0.6 %
0.6 %
0.7 %
0.7 %
0.7 %
0.6 %
0.4 %
0.2 %
1.2 %
1.1 %
0.9 %
0.9 %
0.8 %
0.7 %
0.5 %
0.2 %
1.1 %
1.5 %
1.7 %
1.5 %
1.5 %
1.3 %
0.6 %
0.6 %
0.4 %
0.2 %
2.0 %
2.4 %
2.2 %
2.0 %
2.0 %
1.2 %
0.5 %
0.4 %
2.0 %
2.9 %
2.9 %
2.4 %
2.0 %
1.4 %
0.7 %
0.5 %
1.5 %
2.3 %
2.8 %
2.5 %
2.0 %
1.3 %
1.1 %
0.9 %
1.3 %
2.0 %
2.5 %
2.6 %
2.0 %
1.3 %
1.1 %
1.1 %
2.4 %
3.1 %
0.9 %

It is by no means a huge difference, but it’s still there. Obviously, pitchers are still mostly throwing him non-heaters down and away, they’re just getting them in the zone more frequently. How does Bryce respond to this change? Well, he’s been laying off the low pitch a bit more, and instead has attempted to hit the inside pitch. These are his swing percentages on secondary pitches:

2015

Bryce Harper Swing% vs All Pitchers
Pitches: CH, CU, SL
Season: 2015-04-06 to 2015-10-04 | Count: All Counts | Total Pitches: 877 | View: Catcher
0 %
14 %
0 %
19 %
30 %
33 %
37 %
13 %
40 %
43 %
0 %
22 %
44 %
47 %
52 %
42 %
41 %
53 %
22 %
27 %
60 %
71 %
60 %
61 %
61 %
50 %
27 %
12 %
33 %
69 %
79 %
76 %
73 %
83 %
78 %
50 %
0 %
50 %
67 %
82 %
77 %
77 %
84 %
88 %
58 %
57 %
65 %
81 %
88 %
77 %
72 %
75 %
69 %
45 %
64 %
75 %
82 %
79 %
67 %
53 %
49 %
25 %
53 %
61 %
65 %
60 %
68 %
43 %
44 %
20 %
33 %
13 %

2016

Bryce Harper Swing% vs All Pitchers
Pitches: CH, CU, SL
Season: 2016-04-04 to 2016-07-28 | Count: All Counts | Total Pitches: 579 | View: Catcher
8 %
0 %
0 %
7 %
19 %
24 %
22 %
50 %
80 %
67 %
0 %
16 %
28 %
30 %
34 %
60 %
75 %
85 %
33 %
36 %
41 %
52 %
56 %
71 %
89 %
89 %
60 %
8 %
40 %
55 %
66 %
76 %
72 %
83 %
93 %
60 %
50 %
39 %
60 %
73 %
75 %
73 %
58 %
64 %
77 %
44 %
56 %
73 %
69 %
72 %
67 %
56 %
53 %
52 %
50 %
67 %
70 %
71 %
59 %
56 %
47 %
54 %
52 %
50 %
58 %
54 %
46 %
51 %
53 %
13 %
30 %
18 %

This also means that those non-fastballs are being called as strikes more frequently (assuming that umpires are generally going to call pitches in the zone as strikes). As we can see in his contact% charts, this season Bryce has been making contact at an extremely high rate on those high and inside pitches, and softer pitches have been absolutely no exception. In fact, he’s been making contact with the high and inside non-heaters more than he is with high and inside fastballs. What are the implications of this? Let’s look at his slugging% against secondary pitches:

2015

Bryce Harper SLG/P vs All Pitchers
Pitches: CH, CU, SL
Season: 2015-04-06 to 2015-10-04 | Count: All Counts | Total Pitches: 877 | View: Catcher
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.044
.059
.065
.121
.091
.000
.000
.154
.151
.244
.222
.196
.217
.077
.000
.000
.254
.477
.357
.458
.173
.113
.062
.000
.000
.160
.469
.503
.503
.282
.111
.047
.000
.209
.218
.349
.475
.285
.109
.042
.000
.176
.207
.222
.353
.201
.061
.018
.000
.049
.098
.108
.246
.147
.024
.000
.000
.009
.040
.000

2016

Bryce Harper SLG/P vs All Pitchers
Pitches: CH, CU, SL
Season: 2016-04-04 to 2016-07-28 | Count: All Counts | Total Pitches: 579 | View: Catcher
.000
.000
.000
.000
.000
.000
.056
.100
.200
.333
.000
.000
.000
.030
.094
.143
.083
.308
.167
.045
.122
.214
.146
.146
.056
.056
.200
.042
.085
.084
.325
.203
.069
.029
.000
.000
.000
.039
.040
.065
.108
.062
.000
.000
.000
.093
.091
.105
.101
.175
.077
.000
.000
.190
.152
.113
.203
.167
.094
.000
.000
.086
.116
.078
.067
.092
.037
.000
.000
.000
.014
.000

Slugging% is by no means a perfect measure of a hitter’s ability. Yet, in this case, it gives us a decent idea of which locations a hitter is making solid contact. In his 2015 campaign he was able to get his arms extended and drive curveballs with great power. This season he is attempting to pull the ball more, and it’s resulting in weaker contact. While he is able to drive the inside breaking ball at a pretty decent rate, I suspect that he’s opening up his stance, which can occasionally result in a hard-hit ball, but will often result in a weak fly ball to the opposite field, or a weak grounder to the pull side. The fact that he’s swinging so much more frequently at inside pitches would also be reason to guess that as he’s swinging at breaking balls out over the plate he is still attempting to pull them, as opposed to going with the pitch. Further evidence of this comes from looking at how he hits breaking balls from lefties (curving away from him), versus how he hits them from righties (curving towards him).

2015

Bryce Harper SLG/P vs L
Pitches: CH, CU, SL
Season: 2015-04-06 to 2015-10-04 | Count: All Counts | Total Pitches: 287 | View: Catcher
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.067
.111
.182
.118
.000
.000
.000
.400
.238
.345
.450
.320
.067
.000
.000
.000
.667
.846
.417
.711
.345
.154
.000
.000
.000
.214
.727
.444
.450
.314
.300
.095
.000
.070
.174
.279
.417
.188
.182
.120
.000
.083
.042
.130
.508
.361
.133
.077
.000
.028
.020
.000
.219
.320
.071
.000
.000
.000
.000
.000

 

Bryce Harper SLG/P vs R
Pitches: CH, CU, SL
Season: 2015-04-06 to 2015-10-04 | Count: All Counts | Total Pitches: 590 | View: Catcher
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.033
.040
.000
.125
.222
.000
.000
.095
.115
.184
.116
.077
.500
.182
.000
.000
.164
.361
.333
.341
.077
.074
.182
.000
.000
.136
.379
.525
.520
.265
.000
.000
.000
.292
.248
.390
.500
.314
.068
.000
.000
.234
.310
.272
.278
.148
.048
.000
.000
.067
.145
.163
.255
.108
.015
.000
.000
.027
.048
.000

He even seems to do better against lefties. Against both of them, however, he clearly is able to see the pitch that will eventually break across the middle/outer half of the plate, and drive it with power. Let’s head over to 2016:

Bryce Harper SLG/P vs L
Pitches: CH, CU, SL
Season: 2016-04-04 to 2016-07-28 | Count: All Counts | Total Pitches: 180 | View: Catcher
.000
.000
.000
.000
.000
.000
.000
.500
.500
.000
.000
.000
.000
.000
.000
.100
.571
.200
.000
.087
.211
.125
.000
.000
.100
.333
.000
.000
.059
.312
.400
.118
.000
.000
.000
.000
.033
.071
.121
.059
.000
.000
.000
.029
.057
.096
.027
.000
.000
.000
.000
.029
.096
.109
.053
.000
.000
.000
.000
.000
.037
.077
.045
.000
.000
.000
.000
.000
.000
.000

 

Bryce Harper SLG/P vs R
Pitches: CH, CU, SL
Season: 2016-04-04 to 2016-07-28 | Count: All Counts | Total Pitches: 399 | View: Catcher
.000
.000
.000
.000
.000
.000
.167
.200
.000
.000
.000
.000
.045
.214
.294
.071
.000
.000
.083
.154
.217
.156
.222
.091
.000
.000
.053
.125
.102
.333
.102
.049
.043
.000
.000
.000
.052
.043
.062
.103
.065
.000
.000
.000
.150
.109
.109
.129
.222
.129
.000
.000
.393
.192
.115
.257
.205
.120
.000
.000
.158
.153
.078
.072
.108
.043
.000
.000
.000
.016
.000

While his production has decreased against both righties and lefties, it is clear that the disparity is much larger when it comes to lefties. This is because Bryce is able to get away with trying to pull the ball against righties, as the ball is curving towards him. This makes pulling the ball a much more natural motion. Against lefties, the only breaking balls he is hitting are the ones that start inside and break right to the inside part of the plate, and the pitches that break to be right down the middle. It is the non-fastballs that are low and on the outer part of the plate that he is unable to drive, especially the ones being thrown by lefties. He’s opening up more, which also explains why his pull% hasn’t gone up (in fact it’s gone down). When he’s open, it’s hard to drive the outside pitch even if you make contact with it intending to hit it to the opposite field. Instead, he’s making that weak contact that results in outs.

Looking solely at secondary pitches, the narrative becomes: Bryce is taking the pitches that are out over the plate, and is instead swinging at pitches that are high and inside. He has a tendency to attempt to open up to the ball, and while he can sometimes get away with it against righties, lefties have been able to essentially shut him down. He is also making much more contact with all of these pitches, meaning that he’s putting more balls in play, yes, but they are weak balls that are easy to field, and are thus resulting in outs. With this mindset, even trying to hit the ball to the opposite field becomes more difficult, and all of this culminates in a lower BABIP.

Next, let’s look at how he’s handling fastballs. One thing that quickly becomes evident is the fact that Harper has been swinging at fastballs a lot less this year, especially ones up in the zone.

2015

Bryce Harper Swing% vs All Pitchers
Pitches: FA, FC, FT
Season: 2015-04-06 to 2015-10-04 | Count: All Counts | Total Pitches: 1443 | View: Catcher
3 %
26 %
11 %
38 %
67 %
77 %
90 %
88 %
59 %
33 %
22 %
45 %
65 %
79 %
89 %
91 %
78 %
56 %
38 %
34 %
66 %
79 %
87 %
88 %
78 %
56 %
52 %
7 %
35 %
63 %
78 %
80 %
81 %
75 %
54 %
46 %
0 %
37 %
52 %
70 %
70 %
70 %
61 %
46 %
43 %
27 %
43 %
51 %
59 %
59 %
52 %
31 %
25 %
12 %
28 %
42 %
45 %
51 %
47 %
29 %
11 %
11 %
13 %
30 %
31 %
29 %
30 %
26 %
9 %
6 %
6 %
7 %

2016

Bryce Harper Swing% vs All Pitchers
Pitches: FA, FC, FT
Season: 2016-04-04 to 2016-07-29 | Count: All Counts | Total Pitches: 888 | View: Catcher
4 %
25 %
9 %
6 %
27 %
50 %
54 %
66 %
58 %
50 %
22 %
32 %
46 %
67 %
75 %
77 %
68 %
58 %
35 %
42 %
66 %
80 %
72 %
74 %
74 %
59 %
32 %
7 %
42 %
61 %
78 %
76 %
68 %
73 %
64 %
35 %
0 %
35 %
53 %
66 %
76 %
79 %
72 %
59 %
38 %
24 %
45 %
52 %
63 %
67 %
57 %
35 %
21 %
12 %
34 %
45 %
48 %
42 %
30 %
15 %
3 %
4 %
23 %
33 %
42 %
43 %
26 %
20 %
6 %
0 %
13 %
0 %

His swing% on fastballs in other areas of the zone is roughly the same; it’s really just those high and down-the-middle fastballs that he’s suddenly laying off of more. And yet, just as with non-fastballs, Harper still has been managing to make more contact this year, especially on pitches high and inside, as well as pitches low and out of the zone. How has that translated in terms of his slugging%?

2015

Bryce Harper SLG/P vs All Pitchers
Pitches: FA, FC, FT
Season: 2015-04-06 to 2015-10-04 | Count: All Counts | Total Pitches: 1443 | View: Catcher
.017
.070
.000
.013
.000
.037
.206
.155
.297
.154
.000
.094
.189
.136
.117
.234
.176
.187
.088
.046
.247
.466
.236
.257
.257
.140
.167
.011
.018
.088
.239
.319
.226
.236
.176
.162
.000
.034
.116
.227
.279
.303
.169
.176
.092
.027
.082
.246
.234
.265
.135
.050
.056
.055
.063
.181
.214
.217
.105
.011
.000
.022
.044
.118
.137
.095
.086
.000
.000
.016
.028
.000

2016

Bryce Harper SLG/P vs All Pitchers
Pitches: FA, FC, FT
Season: 2016-04-04 to 2016-07-29 | Count: All Counts | Total Pitches: 888 | View: Catcher
.000
.000
.000
.000
.000
.000
.000
.000
.060
.100
.000
.018
.169
.115
.000
.000
.038
.226
.176
.063
.145
.277
.067
.000
.012
.057
.107
.000
.090
.161
.103
.077
.068
.107
.062
.019
.000
.099
.143
.161
.101
.135
.336
.148
.042
.096
.107
.248
.318
.256
.318
.167
.032
.027
.040
.173
.352
.196
.101
.067
.000
.000
.000
.034
.170
.159
.018
.000
.000
.000
.000
.000

What immediately jumps out at you is the large hole in the top part of the zone this year where Bryce is generating virtually no production. His production on low fastballs is closer to on par with last season, but up in the zone (the same area where he isn’t swinging nearly as often) he can’t get anything going. Why is this? With fastballs it’s a little more simple than with breaking balls in some aspects: For whatever reason he’s laying off fastballs in the zone, and he’s making weak contact with fastballs both high and inside, and down and away (which is where pitchers throw him fastballs most frequently). He’s giving pitchers more opportunities to throw fastballs out of the zone too. The big question mark comes at why he can’t do anything with those high fastballs specifically?

The answer isn’t too straightforward, but I do think that a large part of it is what types of pitches Bryce swings at in which counts. See, there is a very large differential in Bryce’s swing% in counts with no strikes between last year and this year, whereas in two-strike counts his swing% is about the same. He is taking more pitches when he has no strikes against him, especially the high fastball:

2015

Bryce Harper Swing% vs All Pitchers
Pitches: FA, FC, FT
Season: 2015-04-06 to 2015-10-04 | Count: 0 Strikes | Total Pitches: 611 | View: Catcher
0 %
21 %
33 %
29 %
52 %
56 %
75 %
73 %
20 %
38 %
50 %
30 %
51 %
68 %
79 %
83 %
58 %
40 %
33 %
23 %
56 %
71 %
79 %
80 %
58 %
49 %
33 %
7 %
28 %
56 %
72 %
68 %
71 %
65 %
40 %
23 %
0 %
30 %
36 %
58 %
57 %
60 %
55 %
49 %
20 %
25 %
28 %
31 %
41 %
45 %
40 %
32 %
18 %
8 %
21 %
27 %
27 %
35 %
31 %
24 %
15 %
9 %
9 %
21 %
17 %
15 %
16 %
11 %
14 %
3 %
8 %
0 %

2016

Bryce Harper Swing% vs R
Pitches: FA, FC, FT
Season: 2016-04-04 to 2016-07-28 | Count: 0 Strikes | Total Pitches: 301 | View: Catcher
0 %
0 %
0 %
5 %
14 %
24 %
19 %
43 %
38 %
13 %
0 %
22 %
28 %
39 %
38 %
61 %
35 %
10 %
0 %
20 %
46 %
55 %
44 %
68 %
64 %
32 %
0 %
11 %
25 %
43 %
58 %
53 %
51 %
70 %
55 %
27 %
0 %
24 %
39 %
45 %
50 %
58 %
55 %
31 %
17 %
18 %
42 %
40 %
41 %
55 %
39 %
7 %
0 %
7 %
34 %
44 %
50 %
42 %
26 %
5 %
0 %
0 %
15 %
29 %
36 %
48 %
17 %
7 %
0 %
0 %
0 %
0 %

He seems to be swinging at less pitches overall, and his focus has shifted from the top of the zone to the inside part of the zone. It should be noted, too, that he is swinging significantly less at breaking pitches with no strikes as well, which highlights something that’s a little less tangible. With fastballs the narrative becomes this: Bryce is taking more fastballs early in the count, which means he isn’t capitalizing on those fastballs. Once he has two strikes on him, it would reason to guess that he would have more trouble making square contact, right? Well, not quite…

Against fastballs in two-strike counts Bryce is actually hitting decently, but he’s still missing the ones across the middle of the plate. One thing I noticed is that, in two-strike counts, he’s getting thrown more breaking pitches than before, and less fastballs. In 2015, 258 out of 719 two-strike pitches were breaking balls (36%). In 2016, the mark has been 189 out of 448 (42%). With two-strike pitches in 2015, 383 out of 719 were fastballs (53%), whereas 2016 has only seen 220 out of 448 (49%). Bryce has become more aware of the outside pitches, both fastballs and breaking balls, and this has something to do with it.

With two strikes, Bryce is swinging at around the same rate in 2016 as he was in 2015. The pitches he is hitting successfully are: High and inside fastballs, away fastballs, away breaking pitches from righties, all breaking pitches in the middle of the plate, and high and inside breaking pitches from lefties. Ok, that’s pretty tedious. Let’s show all of that visually, looking just at 2016:

First, fastballs with two strikes

Bryce Harper SLG/P vs All Pitchers
Pitches: FA, FC, FT
Season: 2016-04-04 to 2016-07-29 | Count: 2 Strikes | Total Pitches: 220 | View: Catcher
.000
.000
.000
.000
.000
.000
.000
.000
.400
.400
.000
.000
.333
.190
.000
.000
.143
.889
.667
.138
.258
.500
.114
.000
.000
.182
.250
.000
.195
.360
.209
.147
.000
.045
.071
.000
.000
.261
.242
.189
.093
.038
.038
.125
.045
.226
.182
.211
.179
.040
.000
.050
.045
.050
.077
.225
.720
.294
.000
.000
.000
.000
.000
.048
.300
.471
.111
.000
.000
.000
.000
.000

Again, he’s gearing up for away pitches, and he’s swinging at almost anything, so he has success against away fastballs. We know that he’s been very keen on high and inside pitches of all kinds and in all counts this year, and that is also the easiest pitch to see. He has a reactionary eye for that pitch, and is able to catch up and drive it. High fastballs out over the plate can be somewhat easy to react to but he a) isn’t as keen on hitting them, b) isn’t seeing them that often in two-strike counts anyways, and c) isn’t expecting them. Thus, he’s most likely popping them up, which explains his high increased infield fly ball%. This is supported by the fact that his ground-ball rates on high fastballs with two strikes is quite low:

Bryce Harper GB/P vs All Pitchers
Pitches: FA, FC, FT
Season: 2016-04-04 to 2016-07-29 | Count: 2 Strikes | Total Pitches: 220 | View: Catcher
0 %
0 %
0 %
0 %
0 %
0 %
0 %
0 %
0 %
0 %
0 %
11 %
0 %
0 %
0 %
0 %
0 %
0 %
0 %
17 %
6 %
0 %
3 %
8 %
8 %
0 %
0 %
0 %
12 %
14 %
5 %
9 %
25 %
36 %
21 %
0 %
0 %
13 %
16 %
11 %
14 %
31 %
35 %
31 %
9 %
3 %
7 %
11 %
11 %
28 %
22 %
15 %
9 %
0 %
3 %
13 %
24 %
24 %
24 %
5 %
0 %
0 %
0 %
5 %
15 %
29 %
22 %
7 %
0 %
0 %
0 %
0 %

Next, let’s look at slugging% against breaking pitches from righties with two strikes

Bryce Harper SLG/P vs R
Pitches: CH, CU, SL
Season: 2016-04-04 to 2016-07-29 | Count: 2 Strikes | Total Pitches: 126 | View: Catcher
.000
.000
.000
1.000
1.000
.000
.000
.333
.600
.714
.500
.000
.000
.091
.357
.429
.250
.000
.000
.200
.000
.000
.000
.063
.167
.125
.000
.000
.000
.077
.059
.190
.308
.000
.000
.000
.444
.143
.310
.294
.593
.500
.000
.000
.625
.313
.286
.656
.310
.286
.000
.000
.143
.133
.083
.200
.263
.000
.000
.000
.000
.045
.000

Again, it appears that because the ball is curving towards him it’s going to be easier to drive. He is then able to pull the breaking pitches that are up and out over the plate, and is able to drive the low and outside pitches with authority. His lack of success on up and away pitches is a little perplexing, but could be attributed to anything from bad luck, to him possibly not seeing that exact pitch as well, to the fact that the sample size here is pretty small and he hasn’t seen a ton of pitches in that area.

Finally, let’s check out breaking pitches from lefties

Bryce Harper SLG/P vs L
Pitches: CH, CU, SL
Season: 2016-04-04 to 2016-07-29 | Count: 2 Strikes | Total Pitches: 63 | View: Catcher
.000
.000
.000
1.000
1.000
.000
.000
.000
.000
.333
.800
1.000
.000
.000
1.000
2.000
.000
.000
.167
.500
.000
.000
.000
.286
1.333
.667
.000
.000
.000
.000
.000
.000
.400
.222
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000

Nothing monumental in this aspect, especially as it’s not too different from how he hits breaking pitches against lefties in all counts. Regardless, it still fits our narrative as the up and inside pitches are right in his wheelhouse, and the pitches that breaking out over the plate are easier to hit than any other breaking pitch coming from a lefty.

Whew. That is a lot of heat maps to take in. Let’s review a bit: Bryce has a tendency to take more pitches early in the count, something he hasn’t done before. He’s also opening up, which makes him susceptible to breaking pitches and causing him to make weaker contact. The fact that he’s making more contact on pitches outside of the zone doesn’t help much either. He’s taking more fastballs as well, and once he has two strikes on him he’s seeing less of them, and is most likely expecting them less. He then begins to swing much more frequently, which actually reaps pretty good rewards, though there are some holes in his swing against certain pitches. He can’t get the high fastball, and struggles with breaking pitches against lefties. The result of all of this? Lower BABIP, lower wRC+, lower wOBA, you name it.

Obviously there are factors involved with this that go far beyond what heat maps and stats can show us. Baseball is an incredibly mental game, and once you realize you’re in a slump it can sometimes just drive you deeper into that slump. Statistics also can almost never tell the whole story, and as I mentioned earlier the sample size here is small enough that none of this is much of a predictor for future behavior. There’s a good chance that, on many of the situations mentioned above, Bryce has just gotten unlucky (or heck, maybe even lucky) and thus the heat map doesn’t reveal much. Overall though, when looking at everything in a holistic manner it allows us to construct an idea as to why Bryce is failing where he previously succeeded. We can never know everything for sure, but we know more than we did.

I’ve been hearing for months now that Bryce will be just fine, slumps happen to everyone, he will soon return to form, etc., and I’m not here to disagree with that. Although, I will ask (and I ought add that I am a big supporter of Bryce’s): What if he doesn’t break out of it? Odds are his 2015 will be one of the best seasons of his career, and 2016 (if it continues like this) will be one of his worst, and he will find himself somewhere in between for the rest of his career. It’s just that the deeper he drives himself into this rut the more compelled I am to find the source of problem as best I can, from a purely analytical standpoint.

Love him or hate him, the more that Bryce (and the many young superstars like him) thrives, the more baseball thrives.

(Note: All statistics and heat maps taken from Bryce’s page on FanGraphs.com)