Dodgers superstar ace Clayton Kershaw has already cemented himself as the greatest starting pitcher of this generation and could go down as one of the best of all time. Despite all his tremendous regular-season success, an ongoing narrative has haunted him throughout most of his career, a well-known theory that Kershaw chokes in the postseason and can’t pitch in big games.

But in reality, this actually hasn’t been the case, and the fact that so many people consider Kershaw to be a choke artist speaks more to his amazing regular-season dominance than any struggles he’s had in the playoffs. Through 282 starts in the regular season, Kershaw has an outstanding 2.35 ERA and 0.998 WHIP, so anything worse than that in the postseason is going to feel like a disappointment.

The main argument defending Kershaw’s postseason woes for awhile now has been lack of sample size. As Kershaw has reached the playoffs more and more this argument has weakened a little bit but is still relevant, as his 89 total postseason innings pitched is less than half of what Kershaw pitches in a typical regular season. It’s a large enough sample size that we can make some conclusions about how Kershaw has pitched in the playoffs, but not enough that we can judge his true-talent level. We have 1892.1 innings of regular-season data to judge his true-talent level.

Let’s start with the basic statistics. In 18 games (14 starts), Kershaw is 4-7 with a 4.55 ERA and a 1.16 WHIP. At first glance these numbers seem not horrific, but very underwhelming for what we’ve come to expect from Kershaw. This ERA is a mix of some very good starts and some not so good ones that evens out to a mediocre 4.55.

But as we start delving into the advanced statistics, Kershaw doesn’t look so bad. His FIP is a very good 3.13, with his xFIP about the same at 3.17. These stats take into account the things the pitcher can mostly control — strikeouts, walks and home runs — in an attempt to gauge a pitcher’s true-talent level in the sample size given, and are on the same scale as ERA. So in a sense, Kershaw has had some bad luck in the playoffs, and while the results still haven’t been as great as his regular-season results, he has still mostly pitched like himself.

But where does this FIP come from, and why is it so much lower than his ERA? FIP takes into account strikeouts, an area in which Kershaw has actually performed better in the postseason than in the regular season. In the regular season, he has averaged 9.88 K/9, while in the postseason, he has averaged 10.72 K/9. He has also kept his walks down in the playoffs, averaging 2.73 BB/9, which is only a little bit worse than his regular season 2.37. As a result, his 21.5 K-BB% in the postseason is nearly identical to his 21.2 regular season K-BB%. So the problems he’s had in the postseason haven’t had to do with walking too many hitters or not striking out any batters. In that regard, he’s still pitched like the Clayton Kershaw we know and love. So where have his issues come from?

The answer to that is a higher average on balls in play, a higher HR/FB%, and a bad bullpen coming in to relieve him. FIP also takes into account home runs, and he has allowed more home runs in the postseason, averaging 1.01 HR/9 (which is still good, just not Kershaw good) versus an outstanding 0.58 HR/9 in the regular season. It’s really not fair to criticize him too much for this since his postseason sample size is still less than half of a regular season. In fact, that 1.01 HR/9 is actually better than his 2017 regular season HR/9 so far, which is a very uncharacteristic 1.22 in a year where he’s been neck-and-neck with Max Scherzer for the Cy Young award. Kershaw has allowed more home runs in the postseason as a result of not only a slightly higher fly ball% but also a higher HR/FB%, 10.9 versus 7.7 in the regular season. While this doesn’t mean that he’s been unlucky, it does mean that his HR/FB% is likely to regress closer to his career norms. xFIP takes this into account and the number ends up being virtually the same as his FIP.

In addition to the extra home runs, Kershaw hasn’t been as lucky on balls in play as he has in his career. In the regular season, he’s held a .269 BABIP, which for most pitchers would be thought to be unsustainable, but Kershaw’s pitched for so long now that it’s become clear that he’s just that good. He hasn’t been quite as lucky in the postseason, where he’s allowed a .295 BABIP. And it’s not like Kershaw has allowed way more hard-hit balls in the playoffs than in the regular season, although he has allowed slightly more. He has a 20.1 line-drive rate in the playoffs, which is just slightly higher but very similar to his 19.8% in the regular season. Pitchers obviously try to prevent line drives, as they often result in hits, and Kershaw has prevented line drives from being hit about as well in the playoffs as in the regular season. So that’s not the problem.

Kershaw has allowed slightly more fly balls — 40.2 FB% versus 34.3% — and this, paired with the higher HR/FB%, makes for a bad combination and more home runs. He’s still allowed ground balls at a similar rate, only slightly less, at 39.7% versus 45.9%. So has Kershaw allowed more well-hit balls in the postseason than in the regular season? Yes, but only slightly, and not enough that he should be considered a choker. The only slight increase in line drives shouldn’t result in as big a gap in BABIP as it actually does, meaning that luck has not quite been on Kershaw’s side the way it has been in the regular season. He’s struck people out like regular-season Kershaw, he’s prevented walks like regular-season Kershaw, and he’s prevented balls from being well hit only slightly less than regular-season Kershaw. That, in addition to slightly more fly balls leaving the ballpark, has resulted in a really good pitcher that maybe is not quite as good as regular-season Kershaw, but still very good, and it certainly doesn’t warrant calling him a “choke artist.”

It can also be argued that Kershaw has been overused and over-pressured to do well. He’s been so ridiculously good in the regular season that the expectations are for him to be just as good in the playoffs and to do it practically every three or four days against the best teams in baseball. Anything less and he seem like a disappointment. People often overlook the great moments he’s had in the playoffs, like when he came out of the bullpen against the Nationals to save a tight game or when he dominated the eventual World Champion Cubs in Game 2 of the 2016 NLCS. As a result of high expectations and trust in Kershaw, he has perhaps been left in games slightly longer than he maybe should have.

An occurrence that has plagued Kershaw in the postseason a few times is going deep into games and then getting hit around before his exit from the game. He’s often left with men on base, and the relievers coming in after him haven’t exactly been kind to him, allowing nine of the 14 runners he’s left on base to score. Let’s say the bullpen comes in and dominates, stranding all 14 of those runners, and his postseason ERA drops from 4.55 all the way down to 3.64.

Also remember that in the playoffs, teams are in their full strength and effort, doing everything they possibly can to try and win. These are the best teams in baseball, the teams that had everything working well enough for 162 games to make it past all the other teams and into the playoffs. The offenses Kershaw has to face in the playoffs are going to generally be better than the average offense he might face throughout the season. It is not uncommon for great pitchers to have slightly worse results in the playoffs. Madison Bumgarner, a famous “postseason hero” for the Giants, has a postseason FIP only 0.02 better than Kershaw’s and an xFIP 0.43 worse than Kershaw’s. Luck can go in very different directions for some pitchers in small sample sizes, and this is a perfect example.

Look at Pedro Martinez. In more postseason innings pitched than Kershaw, he has a significantly worse FIP/xFIP (3.75/4.31) despite an unsustainable low BABIP of .257, lower than his regular season .279. And no one thinks of him as a postseason “choker.” Greg Maddux, another all-time great, also has a worse FIP/xFIP (3.66/4.45) than Kershaw in even more innings pitched (198). And nobody considers him a postseason choker. Roger Clemens is the same deal. 3.52 FIP, 3.91 xFIP in 199 innings pitched. These pitchers are still considered all-time greats despite having postseason numbers that are arguably worse than Kershaw’s.

This really goes to show just how good Kershaw has been in the regular season. He puts up godlike numbers and then when he puts up “only” good numbers in the playoffs, it seems like he’s bad in comparison. When you look at the aforementioned fellow all-time greats, it’s clear that Kershaw is not the first great pitcher to have a little trouble in the playoffs.

So has Kershaw been as utterly dominant in the playoffs as in the regular season? No. But has he been a choke artist who gives up eight runs every time he’s put under pressure? No, not at all. He has had some rough outings in the postseason, particularly against the Cardinals, where he hasn’t been able to dominate and take control of the game quite like normal, but he has also had plenty of good moments of great pitching and when he’s left with runners on base, his bullpen has mostly let him down. All he really needs is one great World Series run to erase this ongoing narrative once and for all. No matter what, these small hiccups in the playoffs shouldn’t diminish the legendary career that Clayton Kershaw is in the midst of.

The panel on ‘The Changing State of Sabermetrics: at the 2017 SABR convention in NYC with panelists Joel Sherman, Mark DeRosa, Vince Gennaro and Mike Petriello claimed that fewer balls are going into play and singles are actually down. They posed the question, “Are singles still a thing?”

With that in mind, we aimed to verify if these claims are true and what makes people feel that players are hitting fewer singles in today’s game.

We used data that’s current as of July 2, 2017.

NOTES:

• Data being visualized is from http://www.baseball-reference.com/leagues/NL/bat.shtml and http://www.baseball-reference.com/leagues/AL/bat.shtml on July 1, 2017.
• BABIP definition is taken from  http://www.fangraphs.com/library/pitching/babip/

Below you will see two charts illustrating the number of hits, home runs and strikeouts per game.

You can conclude three things from these graphs:

1. Over the past 10 seasons, strikeouts have been increasing dramatically — 1.94 K/Game in the AL and 1.52 per game in the NL.
2. Over the past 3 seasons, singles per game have dipped.
3. Over the past 3 seasons, HR per game have spiked higher than ever before.

In 2017, the New York Yankees have one of the best minor-league farm systems in all of baseball along with others such as the Braves, White Sox, and Astros. As a result, there are some talented players who get lost among the shuffle, and one of them is Thairo Estrada. Estrada has been splitting his time between shortstop and second base this season in Double-A Trenton, but more recently has made second base his everyday position since top prospect Jorge Mateo was called up to play shortstop. Despite getting an All-Star nod for the Eastern League this season, Estrada still does not get talked about as much as other Yankees infield prospects including Gleyber Torres, Miguel Andujar, and even Mateo. Overall, Estrada is definitely worth taking a second look at alongside these other prospects, as someone who could be a solid middle infielder in the majors one day.

Estrada’s line of work speaks for itself this season. While the minor leagues do not have as much access to advanced stats, having seen Estrada play every day this season has given me a unique perspective into the facets of his game. Estrada has proven he can make adjustments, as evidenced by his strikeout percentage dropping roughly 4% from last season. As a result, his BABIP has skyrocketed to .344, and he has a slash line of .320/.375/.418. I attribute his lower slugging percentage as well as his low home-run total of 4 to the dimensions of the ballpark in Trenton. Not only is it 330 feet down each line, but the ballpark sits on the banks of the Delaware River, which as a result creates high winds that knock down potential home runs. If Estrada played in Yankee Stadium every day, he has the potential to hit 20 home runs, as evidenced by Brett Gardner, who in his two years in Trenton (2006-2007) hit as many home runs as I did (0).

Estrada also has a knack for base-running. This may come as a surprise to some given that he has only stolen three bases and been caught stealing nine times. However, on balls hit into the gap or down the line, Estrada has the ability to take the extra base, which has resulted in his wRC+ being 121 this season. Additionally, his spray chart shows that he has the ability to hit the ball to all fields, which makes it tougher for defenses to scout him, and gives him more opportunities for hits. There may not be many stats on Estrada’s defense, but after struggling somewhat at shortstop, he has become far more comfortable at second base, and has not made an error in 19 games.

If Estrada can continue this performance, we might see him in the majors soon, and he could potentially create a great middle-infield combo with Jorge Mateo if Torres’ recover from Tommy John surgery doesn’t go according to plan. So far through 14 games in Trenton, Mateo has a slash line of .396/.508/.755 and a BABIP of .486. The high OBP is a result of Mateo walking in 15.2% of his plate appearances. If Estrada does not play for the Yankees, then the Yankees should be smart enough to utilize his value and include him in a trade package for a big-name player (Sonny Gray, anyone?).

If you take a look at the leaderboards on FanGraphs for all triple-A pitchers this year, you’ll find a surprising pitcher in the lead in FIP who is above two Rays pitchers, MVP of the Futures Game Brett Honeywell and Yonny Chirinos, along with surprising pitcher Buck Farmer. It’s Andrew Albers, with a 2.58 FIP in triple-A in 77.1 IP, 20 appearances, and 11 starts, with a less impressive 3.61 ERA, along with a sterling 2.77 xFIP.

What’s driving this 2.58 FIP? A strikeout rate of 9.54 per 9, with a measly 1.40 walks per 9 and .58 homers per 9, which is shockingly low, even for him. The home runs will likely increase as he isn’t getting too many ground balls; 46.2% is all right, but not elite. He is also getting a ton of infield pop-ups, with a shockingly high 21.9%. He has had very high infield pop-up numbers in the minors before, which make it easier to do as well as he had, although some negative regression should be expected.

Why his ERA is too high: He generally runs a high BABIP as it has usually been above .330 in the minors since 2015. This year his BABIP is a ridiculous .372 which is inflating his numbers above where can can truly perform at. It should regress to normal levels, maybe even a .320 BABIP perhaps, since minor-league defenses are worse than big-league defenses are (even the A’s pitiful defense).

His strikeout and walk rates are exceeding previous levels; last year in triple-A his walk rate was a good but not great 2.17 per 9, while his strikeout rate was a disappointing 6.08 per 9. I think he’ll likely negatively regress in his K/9 to around 7.5 per 9, walks to around 1.9/9.

But, there’s a chance that Albers could just be a second coming of Jamie Moyer, which could be useful for a big-league team looking for a cheap player to be their fifth starter, since he wouldn’t cost much on a minimum MLB contract or in prospects, and for all intents and purposes is a poor man’s Jason Vargas, who has been surprisingly good this year and is a Comeback Player of the Year candidate. It seems like Albers has made a serious adjustment in performance. Quite an interesting buy-low opportunity for a playoff hopeful that is tight on prospects (Angels, Royals), or tight on cash (Brewers, Rays, Twins, Royals). The Braves should have an extra selling chip that they didn’t know about before. Granted, they might get a lottery-ticket prospect for him, but the Braves are rebuilding, so they need prospects to try out at the big-league level eventually since a lot will flame out. Another pitcher who is similar to Albers is Wade LeBlanc, who I feel should be a starting pitcher for the Pirates, especially considering their rotation issues. But it seems like the thought of him starting is scarier to them than being in a saw trap.

It’s an idea that teams like the ones above should use to get underrated players cheap, while teams that have players like that should sell them for more value than they invested in the player. His best comp is of a right-handed pitcher who is with the Blue Jays: Marco Estrada. They have similar velocities, similar lack of performance till they got older, and get lots of pop-ups. Essentially, he is a left-handed version of Marco Estrada, and Marco Estrada received $26 million over two years after the 2015 season — quite an interesting thought. Especially considering his unimpressive stats in the majors so far. Let’s see if anyone will be willing to give him a chance as a swingman, as he could be an amazing fit on the Nationals; way better than Jacob Turner, and he could start in place of Joe Ross if he performs the way he has so far.

All stats from FanGraphs as of 7-13-2017. I do not own any stats or pages used to help me write this article.

To say that Kevin Siegrist has really struggled in 2017 would be an understatement. After allowing 15 earned runs in 31 appearances through June 22, he was placed on the DL with a cervical spine sprain. With an ERA near 5, Cardinals fans have been left wondering what happened to the player who led the league in appearances (81) and finished third in holds (28) in 2015.

At first glance, Siegrist has an obvious issue — a very clear and very serious velocity problem. Take a look at this graph.

The velocity of his fastball has decreased every year since 2013. It hovered around 95.8 mph at one point, but more recently it’s dropped well below 93 mph. That’s a significant decrease, as the steep slope indicates. And for the first time, Siegrist, who is a reliever, has a fastball velocity well below a league average that includes starting pitchers.

If you have ever looked at aging curves, for hitters or pitchers, then you know that skills decline with age. Certainly, pitching velocity is no exception to this rule. Still, Siegrist is an extreme case.

Velocity very clearly declines with age and Siegrist has fallen right in line with this trend. For the first two or three years of his career, his changes in velocity pretty closely matched the aging curve. However, for the last two years, there has been a marked decrease.

In case you haven’t gotten the point, here’s one more graphic that shows Siegrist’s velocity problem.

This slope looks more like something I would ski down than data you want to see from a pitcher’s velocity. Clearly, Siegrist had an excellent stretch in 2015 and he produced the numbers to back that up. Other than that, we see a pretty consistent decline.

So, is that it for Kevin Siegrist? A slow decline into oblivion? I don’t think so. I actually expect him to far surpass expectations in the second half of the year.

What if I told you, Siegrist has actually improved this year? He’s not telegraphing his pitches. He has improved his tunneling. (For extra reading, here are primers on tunneling from The Hardball Times, Baseball Prospectus, and FanGraphs.)

Essentially, tunneling is the ability of a pitcher to repeat his delivery with similar, if not identical, release points. If a pitcher is able to do this, a batter has less time to recognize the pitch and a lower chance of getting a hit. If a pitcher’s release points are completely different, say for his fastball and changeup, a hitter can more easily distinguish between the two and put a better swing on the ball.

These are Siegrist’s release points from 2015 (his most successful year).

And here are the release points from the first half of 2017.

Let’s keep in mind we’re talking about inches here, not feet. Still, the differences between these two years are significant. The release points from 2015 are more spread out than the data from 2017. Siegrist has improved his ability to replicate pitch deliveries. Unfortunately, due to his decreased velocity, this hasn’t resulted in any type of noticeable success.

In 2015, the changeup and the slider release points overlapped nicely, but the fastball release points stick out like a sore thumb. In 2017, with the addition of a cutter, there is much more overlap among the pitches. If he can keep this up, it should translate to long-term success.

Moving away from release points, pitch virtualization data confirms the same hypothesis: that Kevin Siegrist has improved his ability to replicate his delivery.

This is the data from 2015. To the average viewer, and even probably to you and me, this doesn’t look too bad. At the 55-foot mark, the pitches have pretty similar locations. Even at the 30-foot mark, it’s probably pretty difficult to distinguish between five of his six pitches.

If we compare it to the 2017 data, we see a considerable difference.

It’s pretty clear, right? At 55 feet, the release points aren’t “pretty similar,” to use my own wording, they’re practically identical. And the trajectories remain extremely close to one another until about the 20-foot mark, when they break. 20 feet at 93 miles per hour (an all-time low velocity for Siegrist) gives the batter about a tenth of a second to decide what to do.

There is no denying that Kevin Siegrist has a velocity problem that he would do well to fix. And if the first half of 2017 is any indication, it needs to happen fast. It is unfortunate that he has not been able to reap the benefits of an improved delivery. The consistency in release points that Siegrist has shown during an abysmal 2017 is encouraging and should provide a source of hope going into the second half of the season.

Throughout the baseball season, I like to estimate teams wins, but I don’t do it in the traditional way. Some time ago, I discovered that I could use innings pitched to get a close estimate. Here’s what I do:

1) Take team games played and divide by 2;

2) Take the team’s innings pitched and subtract the team opponents’ innings pitched;

3) Add 1 and 2.

For example, the Washington Nationals, as of the All-Star break, have played 88 games. They have 789.33 IP, and their opponents have 781.33 IP. So I take 88 divided by 2, which gives me 44. Then I take 789.33 minus 781.33, which gives me 8. Then 44 plus 8 gives me an estimate of 52 team wins. Checking the standings, I see that Washington indeed has 52 wins.

How does my method compare with the traditional Pythagorean? (The Pythagorean method, of course, takes runs scored squared and divides by runs scored squared plus runs allowed squared.) I’ve set up some charts to demonstrate. First, let me present the relevant statistics for all teams as of the All-Star break (all statistics courtesy CBS Sportsline):

Now let me present a chart showing how many teams wins are predicted by my method and the Pythagorean method (for the Pythagorean method, I’m using 1.82 as my exponent, as shown by MLB on their Standings page):

My method appears in the second column, and the Pythagorean method appears in the third column, with actual team wins in the last column. My method, as shown above, gives estimated wins directly. The Pythagorean method actually computes winning percentage. To get the estimated wins for the Pythagorean method, I multiplied the team’s estimated winning percentage by the team’s games played.

The methods are pretty close! On a couple of teams, though, the methods miss by a wide margin. I’m way off on the Angels, for example, while Pythagoras is off on the Giants. But which of these methods is closer overall? I did an r-squared between each of the estimated win columns and the actual wins and got these results:

Mine’s a little higher, but let’s use mean squared error (MSE) as a cross-check. Here are my numbers:

I’m not a numbers person, so if I’ve made made errors in my calculations, please let me know, and I will never, ever trouble you fine readers again with another post. But I’ve published previous studies of both methods (in other places, under other names) and have found each time that my method edges out the Pythagorean in both r-squared and MSE.

If my method works at all, it’s because better teams typically have to get more outs to finish off their opponents. If the Dodgers, say, are at home against the Phillies, chances are they’re already winning when they go to the bottom of the ninth, and so the Dodgers don’t have to come to bat. That means the Dodgers had to get 27 outs and the Phillies had to get only 24. Conversely, on the road, if the Dodgers are leading the Phillies, the Phillies have to come to bat in the bottom of the ninth, and the Dodgers have to get the full 27 outs to end the game.

One caveat: my method tends to be more descriptive than predictive, so it’s a better measure of how a team has performed, not a good predictor of how a team will perform in the future. The Pythagorean method is much better as a predictive tool.

So there it is! My estimated team wins method. I hope you find it useful.

On July 8, the Oakland A’s sent Franklin Barreto back to AAA. In his major-league cup of chai latte (just 46 plate appearances), Barreto slashed a relatively unimpressive .190/.261/.381. He struck out at a horrific 39%, though he did pop two homers on the way to an ISO of .190, pretty robust for a middle infielder. Word is that his stay on the farm will be a short one, and there is reason to believe that given the collection of very movable objects that stands in his way.

The A’s almost certainly won’t pick up second baseman Jed Lowrie’s team option for 2018, and may well shop him before the trade deadline now that they are firmly in win-then mode. (Did you know that Jed Lowrie leads the A’s in WAR, regardless of which brand of WAR you use?) At short, the A’s have a have a variety of players who have trouble either hitting (Richie Martin) or fielding (Marcus Semien). For Barreto, it must look a bit like an E-Z Pass lane.

But the most fearsome demons we confront are often our own, and Barreto still has work to do before he’s fit for purpose. Here are three guys:

_____       K%        ISO         wRC+

Guy A       29.8        .147           91

Guy B       28.9        .160          83

Guy C       28.7        .158           83

No points for guessing that one of these guys is Franklin Barreto. That would be Guy A, and those are his numbers from AAA this season. Guy B is Javier Baez, and those are his career numbers. We’ll get to guy C in a minute.

Baez, like Barreto, hits the ball hard but misses the ball often. The approach seemed to work for him in the minors; he slugged a merciless .638 even while whiffing nearly 30% of the time at AA in 2014. The approach worked, that is, until it didn’t. In 2015, those crafty AAA pitchers still struck Javy out 30% of the time while feeding him a lot fewer cookies. And then the Cubs called him up. He proceeded to slash .169/.227/.324 while striking out at a horrific 42%. He did pop 9 homers on the way to a .155 ISO, which is fair to middlin’ for a middle infielder. Barreto did not repeat Javy’s first call up, but he did rhyme with it.

The Cubs responded to Baez’s 2014 by sending him back down to AAA in 2015. For a while. Quite a long while, actually: over 300 PAs among the Iowa cornfields and endless bus rides over the featureless Midwestern steppe. That was how Baez spent the first part of 2015, and when he came up again … he hit the ball less hard and missed it less often, shaving his strikeout rate to 30% whilst shaving his ISO to an unthreatening .118. A .412 BABIP softened the blow, superficially making him look like a useful offensive player. It wasn’t a breakthrough, but it was kind of like progress.

Baez held his gains in 2016, dropping his K rate to 24% while ramping up the power. His 95 wRC+ that year was well-earned. This year the numbers aren’t quite there, but the K rate has only inched upward — while the power is rising. The kid might be learning to hit. This represents a hopeful comp for Barreto. He’s three years younger than Baez, so he still has plenty of time to learn. If, that is, the A’s will let him.

The A’s trail back to the post-season is not well-marked, at least on publicly available maps. They currently have four of the MLB Pipeline’s Top 100 prospects, but just one in the top 50 (Barreto, at #42). Baseball America thought little of the system at the beginning of the year, ranking it 17th, just behind the then-recently depleted Cubs system. Two of the four (Barreto and third baseman Matt Chapman) graduated this year, with Barreto now sent back and Chapman still searching for answers in The Show. The other two top prospects, pitchers A.J. Puk and Grant Holmes, are both struggling at AA this year, though Puk pitched quite well at hitter-friendly Stockton, from which he was only recently promoted.

These are good players, but Beane will need more if he wants to bring a pennant to San Jose (er — I mean — oh, nevermind). Accordingly, Beane went against form in the 2017 draft by selecting a high-school bat, outfielder Austin Beck, with the 6th overall pick. Beck had helium before the draft, although the MLB Pipeline crew worried about his complex swing. Next year, Oakland will likely have a top-five pick, allowing Beane to grab another high-ceiling bat. Beck and this unmet friend will have a large say in determining when Oakland next plays October baseball.

But that won’t be soon, likely not in 2018 and probably not in 2019. So from a baseball standpoint, there is very little reason to rush Barreto. Oakland’s long-suffering and very knowledgeable fans are probably aching to see the future, given the bleakness of the present. And I suspect the front-office types share that yearning more than they would ever dare admit publicly. But Oakland plays in a land of giants (no, that’s not another San Jose joke): The two Texas teams are well-run and well-resourced, and the Angels brains will eventually match their wallets. And the Mariners … well … it’s complicated. The point is that the AL West contains a number of wily and dangerous opponents, and the A’s being generally well-run and perpetually under-resourced, need to build a roster that will be explosively good for a handful of years, rather than modestly successful for many. They can’t afford too many talent misses.

Which brings us to Guy C. That’s Danny Espinosa, and those are his career stats. Now 30, Espinosa is entering the twilight of a career that never really caught fire. And that’s because the strikeouts ate it.

At least by the numbers, you wouldn’t have seen it coming. Espinosa’s highest strikeout rate in the minors was 22.6%, and over his three years on the farm he hit with power and solid plate discipline. In the majors, he never had a K rate less than 25.2%. When he could keep it near there, he was a solid starting middle infielder (mostly at second, with a healthy helping of short). But when the K% slid upward, Espinosa was doomed, a bench bat at best. This year, he has a .513 OPS with the aforementioned Angels, and a staggering 35.8% strikeout rate. He is by no means a close comp with Barreto since the Ks only began to plague him at the major-league level, but his failure to bring the strike zone under control once there is a cautionary tale.

It’s easy to criticize Espinosa, and a bit unfair; he’s had a better career than the overwhelming majority of players in professional ball, most of whom will never set foot on a major-league field unless they’re taking the guided tour. Espinosa is a good defender at the two toughest infield positions, and for much of his career made pitchers all too cognizant that the wall behind them wasn’t nearly behind enough.

That said, the Oakland A’s can ill afford to produce many Danny Espinosas, at least from their top prospects, of whom (for now, at least) Barreto is the toppiest. Javier Baez hasn’t figured it out yet, but the Cubs gave him extra time in AAA to help that process along. While the jury is out, the signs are at least guardedly encouraging. The A’s should consider doing the same for Barreto, to ensure that his development, and theirs, isn’t stopped short.

The All-Star break brings a chance to reflect on what’s happened so far this season. For me, that means going back to examine a series of offseason pieces on various players. It’s fun to see what I might have been right or wrong about.

One such piece was about the struggles of Jimmy Nelson in 2016. In short, there were too many walks, not enough strikeouts, and too much disappointment. Specifically, I said that if Nelson “was your probable starter it was probable you’d sigh.”

But I also cited Nelson’s propensity to adjust through his career and made two suggestions as to how he could bounce back this season:

1. Set up on at a different position on the rubber.
2. Wrangle additional spin he gained from 2015 on all pitches, which could feed into sequencing.

Where a pitcher sets up on the rubber is one of those things that seems so utterly simple that it might not even feel like a real suggestion. But the change does do something fundamentally critical, which is influence the path of the baseball to the plate.

These images come with a caveat: Milwaukee’s camera angle at home was different last year. I had to find one that looked as straight-on as possible, which was a game against the Cardinals in St. Louis. That said, it appears as though Nelson has moved his back foot in to meet the edge of the rubber this year, whereas it hung off a bit in 2016. The difference seems to border on negligible and just enough to matter. Being more centered can help throwing toward the middle of the plate and letting the spin on each pitch speak for the movement.

Jeff Sullivan broke down Nelson’s full motion, though, and found that, regardless of setup on the rubber, he’s driving more directly to the plate. That aids the ball’s path, too — maybe even more — and still contributes to letting the spin on his offerings do the talking rather than trying to command a part of the zone every time.

Like Sullivan, The Sleeper and the Bust posited that Nelson has embraced something new. Paul and Eno focused on his arm angle on his slider, enabling two-plane break that distinguishes it from his curveball. Feeding into the arm angle would be a different grip that accommodates it.

The uptick in slider spin would seem to back all that up. In 2017, Nelson’s added 124 revolutions to it. (Dang!) The truly fascinating detail here is just how little the arm slot and grip changes might be to provide that kind of jump.

Driveline Baseball has detailed how spin could be put on a ball in 6 milliseconds. For perspective, consider that a baseball generally reaches the plate in 40 milliseconds, or four-tenths of a second. The way it moves is determined more than six times as fast.

The new break on Nelson’s slider may also better facilitate fastball use this year. Last year, his heat started leaking over the heart of the plate as the season wore on. Sullivan noted how Nelson’s change in motion this season has seen him throw less across his body. By being more direct now he can more readily attack up and down in the zone. Combined with how his new slider bites to the glove side, his mechanics allow his strongest pitch to augment his ability to sequence.

Nelson’s performance isn’t the only reason this is all relevant. After his final start of a miserable 2016, he told reporters that “I know that [pitching coach Derek Johnson] and I are doing the right things.” In the winter I said that may have been true, but that he might not have put it all together optimally.

Sometimes that work can take time to make an impact. His 2017 is a testament to the split-second nature of baseball, and how blinking at the wrong time means you might miss something big. Nelson was close to becoming an afterthought. Now he’s a major reason Milwaukee sits atop the NL Central halfway through the year.

Everything’s Gonna Be Aoki

I went to SunTrust Field on July 4^th to see the Astros versus the Braves, and little did I know the fireworks during the game were going to be far more explosive than the ones afterward. I got to see the most productive offense in baseball put up 16 runs against the Braves (and their usually stellar rookie pitcher Sean Newcomb). It’s undeniable how remarkably talented the Astros offense is. So, I set out to answer a seemingly-simple question: “why?”

I got the idea for this article while I curiously flipped through their team statistics to try to see what clicked. My first reaction when looking at batted ball data, hard hit rate, K%, BB%, etc. was “wow…they look entirely human.” It was strange how normal the advanced metrics show the Astros to be. They walk a decent amount and don’t strike out a lot, but they’re tied with the Pirates and behind the Red Sox in BB/K, so while that helps, it can’t be the only reason they’re this good. They lead the league in ISO, which tells us that their slugging percentage is pretty high, but doesn’t really explain why.

The table that jumped out at me was this:

In my article about Brad Peacock, I talked a little bit about the utility (and disutility) of using pitch-type linear weights to predict future pitching success (in short, the variation from year-to-year for pitchers is pretty high). Well, this is certainly more than noise. The Astros hit the absolute crap out of fastballs. For me, the only question is whether this is simply a result or if it can be used as a predictor.

I think there are two important distinctions to draw between the way we’re using pitch-type linear weights here and what we did earlier.

First, these are team-wide as opposed to pitcher-specific. The problem with these is that they capture a lot of context. That’s really the whole point of the stat. If you hit a fastball for a single and score the two runners from scoring position, your wFB is going to be much higher than if you begin the inning with one. However, because we’re looking at team-wide statistics, we should be getting measurements from enough distinct contexts that the noise begins to fade slightly. This is much different than examining Peacock’s statistics after four starts (two of which happened against the same high-strikeout team).

Second, we’re looking at change in offensive run expectancy, not defensive. This could be a metric like BABIP, where pitching is highly dependent on external factors, while it is a bit more focused on skill for hitters. This raises the question “is there such a thing as a fastball hitter?” The linked article was the closest I could find to directly answering the question (I’ve looked all over the place and can’t figure out who the author is, but as far as I can tell he’s decently connected with the baseball analytics community). Either way, if we take this guy at his word, his model does suggest that some hitters do perform significantly (statistically) better against fastballs.

This doesn’t completely solve all the problems with just looking at wFB (like the fact that the Astros are a naturally good-hitting team and will have many at-bats with runners in scoring position where the expected run value will be high), so we will have to devise a way to control for the context and just look at performance on a pitch-by-pitch basis.

Instead of reinventing the wheel with trying to prove the existence of fastball hitters, this article seeks to prove a strong relationship between fastball-hitters and overall success. If so, this could have drastic implications for how to pitch to teams like the Astros, or even which players to target when trying to replicate the “worst-to-first” transition as the Astros have.

Do “Fastball-Hitting” Teams Succeed More?

Here’s the relationship between fastball linear weights for 2017 teams and their weighted runs above average:

This should be an obvious relationship. For starters, they’re both weighted measures of how many runs a team is expected to produce. So, if you produce more runs on fastballs relative to other teams and hold everything else constant, you by definition are going to increase your total runs relative to other teams. However, the R-squared value does raise some eyebrows. Basically, this means that about 80% of the variation in “weighted runs above average” can be explained only by looking at the wFB statistic of each team. In a sport with as much variation as baseball, that’s actually fairly decent. But, we can do better.

I decided to look at the fastball linear weight for each team and divide that by the total wRAA. This should give us a decent idea of how many of those runs created came on certain pitches. This is kind of a weird comparison, because wRAA calculates the expected runs based off of weighted on-base average (wOBA), while pitch-type linear weights scoop up all of the situational context as explained above. However, it can still give us a general idea of how many expected runs a team generates off of a certain pitch relative to league average.

Here’s the relationship between fastball run percent versus weighted runs above average (please excuse the shaky highlighting):

This surprised me quite a bit. What this says is that about 76% of the variation in wRAA (in 2017) can be explained solely by looking at the wFB/wRAA. I should also mention that even though the dataframe is called “astros” it includes the stats for every major-league team in 2017. In short, the percentage of runs you generate off of fastballs correlates pretty strongly with the total amount of runs you score. Weird stuff.

Closing Thoughts

There are, of course, many problems with latching on to this one observation as the basis for total change in team management. For one thing, some teams may be good at hitting fastballs just because they see them more than every other pitch. A complete 180 in the way teams pitch could bring with it a heavy response by the league’s hitters that mitigates some of the advantage, making this a self-denying prophecy.

That being said, I have a hunch that the Astros are a good fastball-hitting team by design. I think the fact that roughly 60% of pitches seen by hitters are fastballs is an inefficiency that the Astros are effectively exploiting. I also believe this is part of the logic in the recent curveball revolution. Theo Epstein’s short and sweet reaction to the pitch usage of the 2016 World Series between two of the most data-driven organizations in baseball was simply “More breaking balls!”

It’s worth noting, however, the Astros haven’t always been this dominant with respect to hitting fastballs.

The expected run change this year is almost three times as much as last year’s value. If this is by design, why didn’t it happen last year? I’m not entirely certain, but the ridiculousness of the 95.6 value they put up in the first half this year should be a major tip. That doesn’t just happen by accident. If they have a wFB of 0 for the entire second half of the 2017 season, they would still be good for the 26^th best fastball-hitting season of all time (…since 2002). That is absolutely absurd. If you’re a fan of the game, please recognize just how good the Astros are. If you’re a pitcher, you better hope they don’t make an example out of you. And for your own good: don’t throw them too many fastballs.

Many of the talking heads on radio and TV have commented on how playing in the WBC and skipping part of spring training negatively affects player performance during the regular season. As a Texas Rangers fan who has wondered the same thing, I decided to do a quick and dirty analysis.

The Ground Rules

• WBC rosters were pulled from Wikipedia 2017 World Baseball Classic rosters.
• Player WAR data was pulled from FanGraphs on July 10, 2017.
• Only MLB players were included.
• Only players with MLB statistics in both 2016 & 2017 were included.
• A WAR differential is defined as the difference of the 2017 WAR and 2016 WAR (2017 WAR – 2016 WAR)

The Results

Here’s the RAW data as I compiled it from the above sources.

The last column in the spreadsheet is the difference of the 2017 WAR and 2016 WAR and has a mean of -1.1 for all the players in the list.

The histogram below shows how the data is skewed to the negative, which is easily seen in the list just scanning visually.

Another interesting chart depicts the correlation between 2016 and 2017 WAR. The slope of that trend line is 0.59.

Here are the top (bottom!) 20 players, and two of my Rangers are in the list. Rougned Odor is 36th on the list with a -1.8 WAR differential.

There could be many other reasons for the decline in WAR and it very well could have nothing to do with the WBC.  It was an interesting exercise and the numbers make me wonder if MLB has really looked at the WBC and how it affects the MLB players that participate.