If you predicted that Chris Sale and Max Scherzer would rank among the top five pitchers in WAR through mid-May, you aren’t likely to receive much more than a perfunctory pat on the back from your peers. If James Paxton was among your predictions to join Sale and Scherzer in that group, you might just be Jeff Sullivan. But if you foretold that 34-year-old Kansas City Royals southpaw Jason Vargas would rank among the league leaders over a month into the season? Well, my friend, come join me in line for Powerball tickets…

Not many pitchers “find themselves” so late into their careers, and, as a pitcher who hadn’t exceeded ten starts in a season since 2014, Vargas wasn’t exactly on the top of anyone’s spring training Comeback Player of the Year Award list. With that being said, Vargas had never been a bad starter as a Mariner, Angel, or pre-2017 Royal. Between 2011 and 2015 (Vargas only made three major-league starts in 2016 due to Tommy John surgery), his FIP fluctuated between 3.84 and 4.30, and although his xFIP indicated a slightly worse underlying performance, Vargas demonstrated value as a solid back-of-the-rotation starter. This year, of course, he has been anything but mediocre; posting a 1.01 ERA and 1.6 WAR through May 16, Vargas has been one of the few bright spots in an otherwise uninspiring start to the Royals’ season.

What’s most interesting to me about Vargas’s recent ascendance onto the league leaderboard is that his pure “stuff” doesn’t appear to have changed much, if at all. Again, pitchers in their mid-thirties rarely “find themselves” — let alone pitchers who recently underwent Tommy John surgery — and with over 1,200 big-league innings on his arm, Vargas isn’t going to find an extra five miles per hour on his fastball anytime soon. None of his pitches’ horizontal or vertical movement has significantly changed this year, nor have their velocities.

What has been different this year, however, is the effectiveness of his changeup. Throughout his twelve-year career, Vargas’s changeup has consistently been his best pitch, but so far in 2017, the pitch has been far better than at any point prior. Opposing batters have achieved a slash line of just .109/.149/.125 and have struck out at nearly a 33% rate against the pitch. With a standardized linear weight of 4.63, Vargas’s changeup ranks third among all changeups in the majors, and with an unstandardized linear weight of 9.4, Vargas has been the owner of the most valuable changeup in the league.

As noted earlier, none of Vargas’s pitches, including his changeup, significantly differ this season in either movement or velocity. Further, according to PitchFX, the movement on Vargas’s changeup ranks favorably relative to other pitchers’ changeups, but not incredibly so; this season, Vargas’s changeup has the 14th-highest H-movement in the league, and has the 28th-highest V-movement. Therefore, while the pitch’s “stuff” is impressive, it doesn’t quite tell the whole story.

Instead, the secret to Vargas’s changeup transformation appears to be how finely he’s been able to command the pitch this year:

Compare that to his changeup in 2014, which had a much wider spread around the lower right-hand corner of the strike zone:

While batters haven’t swung at Vargas’ changeups any more in 2017 than they did in the past, they, put simply, haven’t been able to make consistent contact against it. When the pitch has been in the strike zone, batters have made contact only 55.7% of the time — even lower than their contact rates on Vargas’s out-of-zone changeups (57.5%). Combine this with the fact that Vargas has been throwing a higher percentage of his pitches for strikes (50.5%) than any season since 2007 — also five percentage points higher than the current league average — and perhaps the explanation for his success is simpler than expected. One should also note how Vargas’s delivery has changed within the last few years, which may also be contributing to his newfound success:

As many readers are already aware, Vargas probably isn’t going to continue pitching at such a high level throughout the season. As a pitcher without an elite strikeout rate, Vargas won’t be able to maintain anything resembling an 88.7 LOB%. Also, even in the vast expanses of Kauffman Stadium (the third-worst stadium for home runs), Vargas’s 2.0 HR/FB% is all but guaranteed to rise, especially considering that his 2017 FB% is actually in the upper third percentile of qualifying pitchers. As a result, xFIP offers a far more modest view of Vargas’s 2017 performance than does FIP (3.72 vs. 2.17).

All things considered, it’s not unreasonable to expect Vargas to keep posting strong numbers this season. While those who expect him to end the year with a sub-1.10 ERA will be disappointed, Vargas hasn’t shown signs of losing any of his command thus far, issuing a total of just six walks in his last three starts. If his changeup continues generating swinging strikes at such high rates, it’s not implausible that the Royals will possess one of the most surprising (and valuable) trade chips come July.

First, let me start with an excuse: I’m not able to pull launch-angle data from Statcast and thus I’m only using the data from MLB.com as of May 16th 2017 this year. I’m currently learning R for doing better analysis, so if anyone knows how to get a complete LA leaderboard, please let me know.

We all know the the best LA for a HR is around 28 degrees and a HR is the best result in baseball. But still, when looking at the leaderboards, the best guys are all around 13-15 degrees. I looked at the top 10 in wRC+ to date this year and the average was 15.7 +/- 3.9. Looking at BABIP, the average was 13.6 +/- 4.0 (admittedly there is a lot of noise in BABIP at this point of the year) and in ISO the average was 16.4 +/- 4.9. That gives a small hint that BABIP peaks lower than ISO.

This chart supports that.

According to those data, BA peaks between 10 – 14 degrees, while slugging peaks much higher.

But there are also other factors why so far in MLB the best LA is around 15 degrees. According to Alan Nathan, the average fastball, depending on pitch height and velo, goes downward around 5 – 10 degrees.

That means the optimum swing for making contact goes upward 5 – 10 degrees. If you want more lift, you either need to hit under the ball more, which decreases EV, or you have to swing up more, but that means you are in the hitting zone for a shorter time, probably costing you some contact. Some sluggers will go above that, but then it comes at a cost.

And then there is the factor of EV sensitivity. Around 8 – 15 degrees the BABIP is not very sensitive to EV; most balls between 80 and 100 MPH will be hits. At 20 – 25 degrees that is very different; we are seeing that donut hole where you get the bloopers at 75 to low 80s and mostly outs mid 85s to low 90s, and then again HRs in the high 90s. Not every ball will be hit hard, so at lower LAs you get more out of your softly-hit balls.

And lastly, the LAs will be distributed on a Bell curve.

It doesn’t seem like players are able to consistently hit under the ball. That means if your average LA would be 28 degrees, basically half of your batted balls would be useless fly outs above 30 degrees, while if you peak at 15, most of your well-hit balls will fall in the useful 10 – 30 range. That also explains why EV peaks around 10 degrees — that is where the attack angle and exit angle match, and thus balls are hit on the screws while HRs tend to be a couple MPH slower than the hardest-hit balls (and many of the 110+ HRs are hit around 20-25 and not 30+).

Practically, that means that every player with a swing attack angle of below 10 degrees could benefit from swinging up more without any cost for consistency. Swinging up like 10 – 12 degrees means you get some lift and good contact. In fact, at plus 10 degrees, you are longer in the zone than at a completely level swing, plus your BABIP at 10 is better than at 0 degrees.

But above that, it gets more tricky, because slugging goes up but BABIP and contact can go down. For certain hitters with low contact and high EV profiles, it might make sense to swing up at up to plus 20 degrees to maximize whatever contact they get, but it will make the profile more extreme. The swing revolution is a good thing, but above 15 degrees many hitters might reach a point of diminishing returns when they try to elevate more.

Thus I think we will see more elevation of the launch angle. The average LA of MLB is now just below 13 degrees, which is well higher than the last two years (I think it used to be around 11 degrees), and I could see it go up to 15, but then I think the end of the line is reached. I think we are quite close to seeing the LA optimized in MLB. There always will be players who benefit from more, but there is a limit.

I think guys like Joey Gallo might benefit from going to 20+ but Trout and Harper are basically average in their LAs; they get enough elevated balls with their LA profile to hit 30+ homers and still have a high BABIP. I think the best all-around hitters/sluggers will stay between 13 and 15; it is only the below-10 guys that will slowly adapt or die. Guys like Ryan Schimpf will never become the norm. When many players were chopping wood, almost anyone could benefit from swinging up more, but a point of diminishing returns might be reached soon.

On Sunday, the Astros were forced to play a double-header at Yankee Stadium after a rain-out on Saturday afternoon. In the first game, they scored six runs by way of nine singles and five walks, recording no extra-base hits. In the second, they amounted 10 runs, on nine hits again, but with five extra-base hits, including four home runs. After ending the second game late, they traveled to Miami for their fifth road game in a row. And scored seven more runs, by way of home runs, base hits, and walks.

The Astros have the best record in the league at 27-12, and are being paced by a great offense along with good pitching. Most important to their offensive success, though, has been their incredible balance. Here is a table of how the Astros compare to the MLB average in some major offensive categories, along with their rank in parentheses:

They are hitting for average and power, all while striking out at a very low rate. And it’s not like they are struggling to draw walks, either, as they are still a middle-of-the-pack team in that regard.

The past two seasons, the Astros have blinded us with home runs and strikeouts. Guys like Chris Carter, Luis Valbuena, and Evan Gattis made the ‘Stros a hit-or-miss lineup, but the Astros have completely transformed their offensive profile.

In a league that is striking out more every season, the Astros have dropped their strikeout rate immensely from their 2015 – 16 rate to their 2017 rate. With a 4.8% decrease, they have lowered their strikeouts more than anyone else. The next-best is the Rays at 3.7%. Behind the Nationals, who have increased their 2017 average .029 from 2017, the Astros are second, with a .025 positive increase. They have done this while continuing to hit home runs, sitting at sixth in home-run rate in 2017.

The Astros added balance to their lineup with their offseason additions of Josh Reddick, Carlos Beltran, Nori Aoki, and Brian McCann. And now, the Astros are looking like a top-three offense in baseball, and perhaps like the league’s most complete.

A little over a year ago, readers of this site were introduced to Hector Neris and his excellent splitter. Articles published early in the 2016 season by Craig Edwards and Jeff Sullivan explored how he was using the pitch, and Neris continued using his splitter to great effect for the rest of the year. His was the third-most valuable splitter in MLB, in a tier with Masahiro Tanaka’s and Matt Shoemaker’s as the only splitters with double-digit run values. He allowed just a .155 opponent batting average and had a 59.6% ground-ball rate with his splitter. It was also his primary punch-out pitch, using it to get 66 of his 102 strikeouts last year. Coming into this season, more cynical observers posited that only the value of saves and his upcoming arbitration negotiations were preventing Hector Neris and his splitter from dominating the ninth inning as the Phillies’ closer.

In case you forgot what it looked like, here he is striking out Bryce Harper with the pitch last September:

Just nasty. A lot can change in a year, however, and in the young 2017 season his splitter hasn’t been nearly as good.

Those numbers aren’t encouraging. He’s getting half as many called strikes while throwing more balls. His hit percentage has doubled, his LD% has jumped, and his GB% has dropped. He’s already allowed half as many home runs as in all of 2016, and we’re only halfway through May. Additionally, his O-Swing% has dropped, from 46% last year to 41% in 2017. Not the results you would hope for from your best pitch, let alone one of the best splitters in the Majors one year ago.

He’s not throwing it any more or less than last year and he’s not really allowing more contact or balls in play. What’s more puzzling is that his swinging-strike rate is almost identical to last year, just over a healthy 21%.

So what’s going on?

As Craig and Jeff wrote, the pitch was so effective because of where he was locating it. He located it down in the zone, ducking under bats for Ks or grounders and sneaking in for called strikes when batters laid off the pitch.

His 2016 heatmap shows a consistently executed pitch; one that gave opposing batters fits. What has happened so far in 2017?

Oh.

While the footprint hasn’t changed much, after 146 pitches there’s definitely a disturbing trend developing. His splitter is creeping up towards the middle and down under the the strike zone, instead of living right on the lower edge like it did last year. If the pitch is thrown too low, perhaps batters are able to lay off it more, which would explain the drop in O-Swing% and called strikes as well as the rise in balls. If the pitch misses up, well, this happens:

Where was that pitch located?

Right down the middle.

To develop into the dominant late-innings pitcher that his 2016 performance suggests he could become, Hector Neris is going to have to regain command of his best pitch. Here’s hoping that he finds it soon, because MLB is missing one of its best splitters.

Take a brief look at this leaderboard of the best seasons of all time, at least among position players. For those of you who like an easy reference without switching tabs or are too lazy to open up a link, here’s the part that you are going to look at.

Now your first thought is probably going to be something about how good Babe Ruth was. Ruth’s best season is 2 WAR higher than anything anyone else ever did. Babe Ruth’s fourth-best season is still better than anyone else’s first-best season. Not that those other three guys weren’t very good too, because they obviously were, but Ruth is clearly a step above the rest. But let’s add another column to that chart.

With the exception of Hornsby’s season — in which he hit .424 — all of these players had 40+ home runs. And 25 home runs isn’t too shabby. Hornsby was actually fourth in baseball in home runs that year. But really, this shouldn’t be much of a revelation. Home runs are the most productive thing a player can do in a single plate appearance. Hitting a lot of them is a good way to produce a lot of value.

As you might expect, we’re going to next look at the best seasons without much power. Specifically home-run power. I’m going to arbitrarily define 20 home runs as too much power for this next leaderboard. That’s about the threshold that people start to get considered home-run threats and it’s nice and round.

Well, the names aren’t quite as impressive as those on the first list, but they’re all in the Hall of Fame. And really, aside from Boudreau, all those guys are top 25 greatest position players of all time. Seven of the seasons are from the Deadball era when no one was hitting 20 home runs. Honus Wagner’s 10 dingers in 1908 was second in the league to the Superbas slugging first baseman Tim Jordan, who hit 12.

You’ll probably notice another pattern — most of these guys stole a ton of bases. Now this isn’t necessarily because stealing bases is such a valuable thing like home runs are — it’s more because guys stole a ton of bases in the most power-sapped era in baseball history. All the Deadball guys stole at least 50 bases, but we’re going to kick out all the guys who stole at least 20 — sorry, Joe Morgan.

Again, all these guys are Hall of Famers, but only Hornsby and Musial are really inner-circle guys. Hornsby and Musial actually had somewhat similar careers — both guys got their start in relatively low-power eras, but grew into their power as the ball livened up. While their career totals for home runs are astonishing to us now, they ranked 5th and 6th, respectively, in all-time home runs when they retired. It’s not really correct to call them no-power guys — more like guys who didn’t need power to beat you.

Going into this, I expected this list to be populated with slick fielders who had big offensive years. That description certainly fits Joe Gordon and Lou Boudreau. Total Zone says Gordon was a fantastic defender his entire career. That, combined with a BABIP spike in 1942, bumping up his typical 120 wRC+ to 152, sneaks him onto the list. For Boudreau, basically everything went right. he had career bests in home runs, BB%, K%, BABIP, AVG, OBP, SLG, ISO, and defense according to total zone. Oh, and he managed the Indians to a World Series victory.

Arky Vaughan was a shortstop, but Total Zone only considers his defense at that point to be serviceable. Instead, to make the list he hit .385/.491/.607, all of which were career highs. In fact, that .491 OBP is the best OBP since 1901 for players with less than 20 home runs.

Harry Heilmann was, well, definitively not a slick fielder. What he did do was crush everything that came his way, to the tune of .403/.481/.632. It was a phenomenal year, but it wasn’t the best that year, as Babe Ruth put up 6.8 more WAR than him.

And finally we come to Wade Boggs. While he might not rank that highly on the leaderboard there, he is the grand champion of the no power, no speed club. Not only does he have the only season there with single digits in both home runs and steals; he actually has the four best seasons with these parameters. In 1988, he put up 8.6 WAR, with only 5 home runs and 2 steals. Oh, and his defensive metrics that years are pretty average, so that’s all on contact, gap power, and walking.

Now, you’ve probably noticed that most of these seasons happened in the distant past. For all but the oldest of readers, Wade Boggs is probably the only guy on that last list that all y’all have seen in real time. What are the chances of seeing a season like these any time soon?

In 2016, the best season for a guy meeting both the power and speed thresholds was Francisco Lindor, who accumulated 6.3 WAR with 15 home runs and 19 steals. In order to make the top ten, he’d probably have to break at least one of those, if not both. That being said, Lindor making more contact and taking more pitches might be our best hope. Guys like Adam Eaton and Brandon Crawford — the next two guys down the list — probably aren’t good enough to hit 8 WAR. Guys like Dustin Pedroia and Buster Posey may have had the necessary skillset to pull it off, but it’s probably too late in their respective careers to put together an 8-WAR type of season anymore.

We’re probably not going to see a Wade Boggs-type season anytime soon — it’s just too hard to produce an incredible amount of value without hitting for home-run-type power or having the athletic ability to steal a ton of bases.  Appreciate weird players while they’re around.

Stating exactly what Luis Severino would be at the start of the season was a puzzle. He flashed such different versions of himself over the previous two years that there was no telling if he’d stick in the rotation or be relegated to the bullpen, whether because of his own lacking presence or a less deniable one among other in-house competition. But after six starts, he’s given us — and the Yankees — an emphatic answer.

Luis Severino is a starter. And maybe more.

We’re at the point where the basis for these numbers has largely become reliable for what we could expect moving forward. There are a couple key components. Austin Yamada explains how two-plane movement in Severino’s slider has been giving hitters fits. Matthew Mocarsky forecasted at the season’s start that Severino’s changeup could be critical to balancing his line drives and grounders, which is exactly what’s happened.

Pitches are rarely distributed dead equally. Acknowledging each one’s weighted value as if they were can provide a solid picture of just how much impact a certain pitch is having in a guy’s repertoire. And for Severino, his changeup has been crucial.

The caveat with weighted pitch values is how the amount thrown directly dictates how often a hitter has the chance to knock it around. Severino hasn’t thrown a ton of changeups and that certainly contributes to the offering’s weighted value. But the numbers suggest that when he has thrown it, he’s paced play with it.

We could wonder what would happen if he started throwing it just a little more and his fastball just a little less, but let’s zoom out. Let’s regard what Luis Severino is showing us in 2017 as his first well-planted foot in the majors. He’s already shown he can make adjustments, so let’s also consider he’s got one or two more in him that elevate his game.

What would that mean for the Yankees?

Severino could represent a home-grown anchor in their rotation, and that’s something they haven’t had in a long time. Chien-Ming Wang’s best work was a flash in the pan that wasn’t completely supported by his peripherals. Andy Pettitte was more impressive for his steadiness than his dominance. And before that? You’re going back to at least the 70s.

It would be interesting to see how the Yankees would approach Severino’s contract if he continues on his current course. They haven’t really been in a position to sign a young star to a sweetheart deal like, say, the Rays. They also have the financial wherewithal to not feel such pressure.

But the fact remains that he could be more important than any other player in their young core, and how they decide to go about keeping him in New York could have sizable implications for the franchise.

We can say it’s only six weeks into the 2017 season, but Luis Severino is a big reason the Yankees have one of the best pitching staffs in baseball. And he’s a big reason it could stay that good, too.

Zack Greinke is a really good pitcher. That’s why he signed a monstrous $200+M deal in Arizona before the 2016 season. Unfortunately for Zack Greinke and the Arizona Diamondbacks, he wasn’t really good during the 2016 season.

Something went wrong in the desert. Part of it could have been that Greinke was pressing, trying to live up to his big contract and the largest per-year salary in baseball history. Part of it could have been adjusting to a more hitter-friendly home park (he had a 4.81 ERA at home versus a 3.94 mark on the road in 2016 with FIP and xFIP showing similar spreads). Whatever his issues were, the Diamondbacks and their fans were hoping for more out of their expensive ace in 2017.

Through seven starts, the results have been encouraging. He has pitched to a 3.09 ERA (and a 3.15 FIP/3.05 xFIP) despite his GB% and HR/FB% remaining the same as 2016. One thing that has changed is that he’s allowing 30% fewer walks while striking out two more batters per nine innings. Another thing that has changed is that he’s throwing his slider more than ever before.

After his most recent start, a win at Colorado on May 5 where he threw seven innings and allowed two earned runs, Greinke was asked about his slider usage in the game. He replied:

“I threw a decent amount, I don’t know how much more than normal, against righties, but, it was working. Could have thrown more, might have had better results, it was just working really good. I throw it more to righties and they had a lot of righties in their lineup today.”

In fact, he threw a higher percentage of sliders than in any other start in his career, and the highest since 2011. According to Brooks Baseball, in 2017 he’s thrown a higher percentage of sliders than in any other year in his career (25.7%). In terms of raw pitch counts, he threw more sliders in April (144) than in all but two other months in his career. He is throwing more sliders than normal.

He was right about one thing, though: the pitch is working. Facing the Rockies at Coors, Greinke threw 38 sliders with a 26.3% whiff rate while only allowing one hit. So far in 2017, he’s getting a 26.1% whiff rate with his slider after only getting 21.5% in the last two years. He’s also getting the highest Swing% of his career, and had his second-highest month of raw whiff counts in April.

So what has changed about the pitch besides its usage?

He is not throwing it as hard as in recent years, while also seeing an increase in horizontal movement. Brooks Baseball has his slider velocity at its lowest and his horizontal movement at its highest since 2013.* Additionally, he is locating the pitch closer to the plate than he has in years, while maintaining the same height in the zone. He is also getting a higher percentage of called strikes this year than in 2015 or 2016, years in which his horizontal location moved away from the plate.

It is not a completely new pitch, but it has changed, and he is using it differently. Located closer to the plate, and moving more, the pitch is getting more swings and calls, leading to more strikeouts and fewer walks. There is little wonder that he has thrown it as much as he has.

*I omitted his Colorado results because as we know, Coors suppresses movement. If you look up his Brooks Baseball movement chart you will see a massive dip in horizontal movement for May 2017 as we only have the Coors start to draw data from.

Jacob deGrom has been one of the better and most consistent pitchers in the league across his short career, posting ERAs of 2.69, 2.54, and 3.04 in 2014-2016. He accumulated a 12.0 WAR in those seasons, 13th in the league and sandwiched between Dallas Keuchel and Stephen Strasburg. Good company.

He has been great, but he has not exactly been a strikeout maestro. He averaged 9.24 K/9 in 2014-2016, a good mark and solidly above the league average of 7.86 in that time, but also nothing to write home about. However, his K/9 has skyrocketed to 12.66 in 2017, ranking 3rd among qualified starting pitchers. What is behind the spike?

First, here is a table of deGrom’s pitch usage across his career:

In 2017, deGrom has thrown considerably fewer two-seam fastballs and made his curveball an afterthought, greatly increasing his slider usage. In 2016, deGrom’s most effective strikeout pitch was his slider, with a 27.5% K%. His two-seam fastball induced the fewest strikeouts by far, posting just a 13.1% K%. deGrom has also slightly increased his four-seam fastball percentage, which has been his second-most effective strikeout pitch.

The pattern here is obvious — deGrom is throwing more of his strikeout pitches and less of his others, explaining the strikeout spike. But the change is curious. As deGrom struggled through injury in 2016, his velocity fell. A change in approach to mitigate the velocity loss would make sense in that season, but that did not occur, and he struggled to some degree in 2016 because of it. deGrom’s velocity is back to old form this season, but now he has strayed from the approach that made him so effective in 2015.

deGrom’s infatuation with the slider began during 2016, but he did not throw it nearly as often as he has this season. It was by far his most effective pitch last season. It was sort of his savior that year — his slider allowed a minuscule .168 average while his other four pitches were hit to a .276 average. deGrom clearly decided that his slider was his best weapon, and chose to make it a more prevalent pitch in 2017.

However, whether by design or mistake, with the increase in sliders, deGrom has also altered the location of his slider this season. (Comparisons will be made with 2015 season because of deGrom’s 2016 health). Here is a heat map of deGrom’s 2015 slider location. Pounded low and away from the arm side, like a typical slider. But now, take a look at the heat map of deGrom’s 2017 slider. It’s all over the zone, but placed particularly often at his arm side.

In 2015, deGrom had three primary offerings. The four-seam, the two-seam, and the slider. This is a heat map of his four-seam/slider combo in 2015, and this is the heat map of the 2015 two-seam. He attacked almost entirely glove side with the four-seam and slider, up with the slider and down with the two-seam. To counter, he pounded the extreme inside of the zone with the two-seam for a balanced offering. But look at the heat map of his 2017 four-seam/slider combo, his two primary offerings this season. deGrom is attacking across the entire zone with his two main pitches, but does not have that same vertical variance that he did previously. Instead of using the slider as an edge/out-of-the-zone wipeout pitch, he is trying to establish it as an in-the-zone pitch, but it has not been nearly as effective.

deGrom is attempting to attack the arm side with the slider instead of the two-seam, while also trying to attack the glove side with the slider to create balance in his approach. While it’s reaping benefits in terms of missing bats, it is not keeping hitters on their toes. Look at this table of deGrom’s slider profile:

One can see the massive spike in strikeouts and whiffs, which looks great. But people are also offering on the slider less because of the lack of deGrom’s balance with the two-seam, and it is leading to a lot more walks than in 2015. We see the exact same thing with the four-seam fastball:

The strikeouts are nice and all, but they are coming at a hefty cost in other areas. deGrom is not commanding the strike zone like he did previously. He has not lost his pitch control, as his Zone% in 2015 and 2017 are nearly identical, but he has lost some of his authority over hitters and is not manipulating them.

But it is not just the walks that have been more of a problem. In 2015, deGrom’s Hard% of 26.3% ranked 19th among qualifying starters. In 2017, he is sitting at just 69th out of 94 qualifiers with a 35.6% Hard%. Also, deGrom has given up six home runs in his last five starts. He gave up 15 in 30 starts in 2015.

This could all be an overreaction, of course. We are only seven starts into 2017, and deGrom could just be getting acclimated to his new approach. And it is not like deGrom has been getting walloped — he is pitching all right. However, this could also be an overreaction on deGrom’s part. With decreased stuff and velocity due to injury in 2016, deGrom saw a dip in his strikeouts from 2015. He may have lost confidence in his previous approach after his minor struggles last year, and has overcompensated in 2017 by trying to miss bats all the time. The new approach has not been quite effective this season, as deGrom has sacrificed command and soft contact to create more whiffs.

Offense is at a seemingly unparalleled level in baseball this season: balls are jumping off of bats, folks are debating the juiced-ness of balls, swing paths have been all the rage, and pitchers have resorted to crashing motorbikes to avoid the mound.  There are 53 (!) players with 7 or more home runs at the roughly 1/5 mark of the season, despite a few sluggish sluggers dragging everyone else down (Mark Trumbo, Brian Dozier, Jose Bautista, and Edwin Encarnacion to name a few).  So, naturally, we’re here today to examine those hitters who missed the bus to wRC+ town and have instead flailed away to no avail.  Abandon hope, all ye who enter.

1. Dansby Swanson paces the undesirables with a .231 OBP, only .89 lower than the current MLB average of .320.
2. There are currently 25 players who have as many or more home runs than this group combined.
3. The group runs a combined .155/.216/.247 line, or put another way, roughly what one would expect out of Mike Trout if he became a full time left-handed hitter.
4. The 26.6 K% and 6.6 BB% might be respectable, were the group not worth -2.5 WAR in a combined 152 games.

Danny Espinosa

Espinosa has always been an enigma.  Two 3+ WAR seasons early in his career set the bar that he has yet to reach again, and this year is the most confounding of all.  He’s never been considered as necessarily a dangerous bat, but he has generally been able to post league-average offense with a little pop.  This season, nothing is doing at all.  Like the remainder of guys on this list, his BABIP is ludicrously low, well below his career .288 average.   However, a possible explanation of his lower BABIP is an increase in the number of fly balls he’s hitting.  Espinosa has seemingly jumped on the fly-ball bandwagon, with a FB% of 46%, well above his career average of 38.4%.  That, juxtaposed with his underwhelming average exit velocity of 85.86 mph (league average this year is 87.76 mph) isn’t enough to give him a significant power spike.

Dansby Swanson

Swanson arrived to the big show last year and performed well, getting on base at a .361 clip and being worth 0.8 WAR in essentially a quarter of a season.  Expectations were fairly conservative, with ZiPS and Steamer projecting him for 2.4 and 1.7 wins, respectively.  That Swanson, however, has not arrived this year.  It would be unfair to not mention the .195 BABIP he is running, but there is still cause for concern among Braves fans who were hoping for a franchise cornerstone.  Swanson has stopped hitting the ball as hard as he did in 2016, with his hard-hit percentage dropping from 34.7% to 26.6%, and he too is hitting more fly balls than last year.  His line-drive rate is down 5% from last season, and his overall contact profile is much more meh than projections expected.  On an optimistic note, Swanson is walking in 9.1% of his plate appearances, salvaging his performance to an extent and reflecting a good control of the strike zone.

Alcides Escobar

Escobar does not hit the ball hard.  His average exit velocity sits at a paltry 83.44 mph, but given his glove-first profile, that has generally been acceptable to keep him on the field.  But Escobar spits in the face of consistency, and he has has, intentionally or not, made a tragic mistake with regards to his ball-in-play profile this year: he has attempted to join the fly-ball revolution.  Until this season, 30.2% of his balls in play have been fly balls, with his highest being 34.2% in 2010.  But not this new Alcides, no sir.  Say hello to the Alcides Escobar who hits 41.8% fly balls, the man whose most comparable ball-in-play profiles this season have been Anthony Rizzo and Nomar Mazara.  Not to mention, he’s hitting more balls than ever to center field, and there is perhaps some doubt that Escobar has anything but warning-track power to center field.  Needless to say, it isn’t particularly working in Escobar’s favor.

Curtis Granderson

The Grandyman can’t.  In this context, ‘can’t’ refers to hit the ball out of the park.  Or out of the infield.  Or anywhere hard.  He’s been a mess.  His career hard-hit percentage of 33.3% has dropped to 29.4% this year, and he has also, regretfully, been drawn to the dark side of the fly ball.  A former career 44.1% fly-ball hitter, Mr. Granderson now hits 57.1% of his balls in the air, where no amount of slow-motion camerawork can push them over the fence.  A staggering 16.7% of his balls in play have been infield flies.  There’s not a lot to love about how Granderson is hitting the ball right now, and it unfortunately looks like Father Time is winning another battle.

Devon Travis

I like Devon Travis.  It may be because he’s a fellow short guy, or it could be because he’s just a fun player to watch.  Well, he was a fun player to watch.  This year most Blue Jays fans have probably looked away when he steps to the plate.  In 2015, his first taste of the bigs, he looked like a potential star, running a 135 wRC+ to 2.3 wins in only 62 games.  2016 was a bit of a reality check for him, but he still managed to rack up 2.5 wins in 101 games.  2017 is going…differently.  Travis, of all the players listed here, appears to be hurt by his low BABIP the most, given that his career BABIP entering the year was .354, which has effectively been cut in half this season.  In 28 games he has managed to be worth -0.7 wins, with little to no value having been produced at all. His contact profile and batted-ball results are comparable to his career averages, and he hits the ball just as hard as your average major-leaguer, with an average exit velocity of 87.36.  Add to that an 8.6% increase in line drives this season, and Travis should, of all the worst hitters, be fine.

There you have it.  These guys have all, at one point or another, been excellent major leaguers, but this year have been the absolute worst hitters of all qualified batters.  Baseball is a fickle sport, and it wouldn’t surprise me at all if these guys go on to make everything I’ve said seem dumb in retrospect and finish their seasons strong.  But for at least a little while, we can horrify ourselves with some scary numbers and dread the thought that our favorite team has an irredeemable scrub on the roster.

In the modern MLB, the record for the longest losing streak sits at 23 games, set by the 1961 Philadelphia Phillies, while the longest winning streak sits at 21 games, set by the 1935 Chicago Cubs.  In recent memory, the 2002 Oakland Athletics come to mind, with their Moneyball-spurred 20-gamer, taking them from 68-51 to 88-51 and first in their division.  Winning streaks captivate a fan base, and attract league-wide attention, but little is understood about their nature.  How much luck is involved?  Are certain teams or players more inclined to be streaky?  Are teams really more likely to win their next game if they’ve already won a few in a row?  In this piece, I’ll outline a simple model for what legitimate team-level streakiness might look like, and see if any interesting behaviour arises.  I was able to do this after reading the section on Markov Chains in Linear Algebra by Friedberg, Insel and Spence.

The Model

This model only requires two inputs: the probability of a team winning a game given that they won the previous game (hereafter P(W|W)), and the probability of a team losing a game given that they lost the previous game (hereafter P(L|L)).  Admittedly, this assumes ballplayers have very short memories, but.  The first thing we need to generate is what’s called a transition matrix:

The first row contains the probabilities that a team will win a game based on what happened in the previous game, and the second row contains the probabilities of losing.  Notice that the entries of each column sum to 1, so we can rewrite this as

Without going into too much detail, all we need to do is multiply matrix A with itself a lot, and find the limit as we do this infinitely many times.  This will give us another matrix which will contain two identical columns, each of which will correspond to the long-term probabilities of winning given a team’s P(W|W) and P(L|L) values.

For example, if our team has P(W|W) = 0.6 and P(L|L) = 0.5, we’ll have
,
and the limit of A^m as m goes to infinity is
.
So our long-term probability of winning will be around 0.56.  Over the course of a full season, then, this team would expect to win around 90 games.

Now we can examine various cases.  It may not be surprising to find that if we have P(W|W) + P(W|L)  = 1, we’ll have a long-term probability P(W) = P(L) = 0.5.  That is, no matter how streaky a team is, if their probabilities of winning after a win and after a loss sum to 1, their expected win total over a 162-game season is 81.  But what if we look at a given long-term probability P(W), and see what conditional probabilities P(W|W) and P(L|L) give us P(W)?  In the table below, pay special attention to the boxes with P(W) values of 0.5, 0.667 (our incredible team) and 0.333 (our really really bad team).

(Pardon the gaps in the table — my code had a bug that made it output zeros for those parameters, and I didn’t feel like the specific numbers were integral to this article so I didn’t calculate them manually.)

For P(W) = 0.5, we notice a straight line down the diagonal – which makes sense, given that we know P(W|W) + P(W|L) = 1 for these entries.  For P(W) = 0.667 and P(W) = 0.333, we have the following pairs of P(W|W) and P(L|L):

P(W) = 0.667 — (P(W|W), P(L|L)) = (0.5, 0) or (0.6, 0.2) or (0.7, 0.4) or (0.8, 0.6) or (0.9, 0.8)

P(W) = 0.333 — (P(W|W), P(L|L)) = (0, 0.5) or (0.2, 0.6) or (0.4, 0.7) or (0.6, 0.8) or (0.8, 0.9)

So our two-thirds winning team could just never lose two games in a row and play at a .500 clip in games following a win.  Or they could lose a full 80% of their games after a loss, but be just a little bit better at 90% in games after they win!  How could a team that never loses two games in a row be the same as a team that is so prone to prolonged losing streaks?  It’s because we selected this team for its high winning percentage, so even though P(W|W) and P(W|L) actually sum to less in this case (1.1 instead of 1.5), the fact that this team wins more games than it loses means it’ll have more opportunities to go on winning streaks than losing streaks.

Likewise, our losing team could never win two games in a row but play at .500 in games following a loss, or they could be the streaky team who wins 80% of games following a win but loses 90% of games following a loss.

These scenarios are illustrated below.  The cyan dots correspond to the following pairs of points (P(L|L), P(W)) from top left in a clockwise direction: (0,0.667), (0.8, 0.667), (0.9, 0.333), (0.5, 0.333).  These are exactly the scenarios discussed above.

These observations indicate a more general property, which will sound trivial once we put it in everyday baseball terms.  If your long-term P(W) is above 0.5, and you have to choose between two ways of improving your club – you can improve your performance after wins, or you can improve your performance after losses – you should choose to improve your performance after wins.  And if your long-term P(W) is below 0.5, you should choose to improve your performance after losses (up until you become an above-average team through your improvements, of course).  In other words, if you expect to win 90 games (and hence lose 72), you want to improve your performance in the 89 or 90 games following your wins rather than in the 71 or 72 games following losses.

Conclusions, Future Steps

I don’t have anything groundbreaking to say about this experiment.  It’s obviously an extremely simplified model of what real streakiness would look like – in the real world, the talent of your starting pitcher matters, your performance in more than just the immediately preceding game matters, as well as numerous other factors that I didn’t account for.  However, I feel comfortable making one tentative conclusion: that the importance of the ace of a playoff contender being a “streak stopper” (i.e. one who can stop losing streaks) may be overstated, simply because the marginal benefit from such a trait is smaller than the marginal benefit from being a “streak continuer.”  I have never heard of an ace referred to as a “streak continuer,” even though this model indicates that on a good team, this is more beneficial than being a “streak stopper”.

I don’t think it’s worth examining historical win-loss data to compare with this model, as this was not intended to be an accurate representation of what actually happens; rather more of a fun mathematical exploration of Markov chains applied to baseball.

Thank you for reading!  Questions, comments, and criticisms are welcome.