Archive for July, 2017

Thairo Estrada: A Yankees Prospect You May Not Have Heard Of

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?).

Atlanta’s Shocking Triple-A Soft-Tossing Pitcher

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.

There Is Hope for Kevin Siegrist

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 TimesBaseball 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.

Estimating Team Wins With Innings Pitched

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):

Arizona 89 797 787 446 344
Atlanta 87 783 787.67 405 449
Baltimore 88 782.67 790.67 392 470
Boston 89 794.67 795 431 366
Chi. Cubs 88 785 787 399 399
Chi. White Sox 87 760.33 771.33 397 429
Cincinnati 88 781.67 786.67 424 463
Cleveland 87 768.67 763.67 421 347
Colorado 91 812.33 806.67 461 419
Detroit 87 762.67 766.67 409 440
Houston 89 800 784.33 527 365
Kansas City 87 775.33 775.67 362 387
L.A. Angels 92 817 824.33 377 399
L.A. Dodgers 90 806.33 786.67 463 300
Miami 87 771.67 777 410 429
Milwaukee 91 818.67 809.33 451 406
Minnesota 88 785.67 781 403 463
N.Y. Mets 86 773 775 406 455
N.Y. Yankees 86 768 765.33 477 379
Oakland 89 784 790.67 382 470
Philadelphia 87 775 790.33 332 424
Pittsburgh 89 800.67 802 378 403
San Diego 88 776.33 781 312 440
San Francisco 90 813.33 827.33 431 435
Seattle 90 800 797.67 354 453
St. Louis 88 798 793 402 389
Tampa Bay 90 805 802.33 428 412
Texas 88 783.67 783 444 415
Toronto 88 789 788.33 366 430
Washington 88 789.33 781.33 486 396

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):

Team EST W (IP) EST W (R) Actual W
Arizona 54.50 54.82 53
Atlanta 38.83 39.43 42
Baltimore 36.00 36.80 42
Boston 44.17 51.07 50
Chi. Cubs 42.00 44.00 43
Chi. White Sox 32.50 40.44 38
Cincinnati 39.00 40.48 39
Cleveland 48.50 51.07 47
Colorado 51.16 49.45 52
Detroit 39.50 40.61 39
Houston 60.17 58.84 60
Kansas City 43.16 40.86 44
L.A. Angels 38.67 43.63 45
L.A. Dodgers 64.66 61.90 61
Miami 38.17 41.71 41
Milwaukee 54.84 49.84 50
Minnesota 48.67 38.47 45
N.Y. Mets 41.00 38.56 39
N.Y. Yankees 45.67 51.87 45
Oakland 37.83 36.20 39
Philadelphia 28.17 33.97 29
Pittsburgh 43.17 41.91 42
San Diego 39.33 30.67 38
San Francisco 31.00 44.62 34
Seattle 47.33 35.07 43
St. Louis 49.00 45.32 43
Tampa Bay 47.67 46.56 47
Texas 44.67 46.70 43
Toronto 44.67 37.59 41
Washington 52.00 52.11 52

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:

0.8497 0.7147

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

Team MSE (IP) MSE (R)
Arizona 2.25 3.33
Atlanta 10.05 6.61
Baltimore 36.00 27.05
Boston 33.99 1.15
Chi. Cubs 1.00 1.00
Chi. White Sox 30.25 5.94
Cincinnati 0.00 2.20
Cleveland 2.25 16.60
Colorado 0.71 6.53
Detroit 0.25 2.60
Houston 0.03 1.34
Kansas City 0.71 9.86
L.A. Angels 40.07 1.88
L.A. Dodgers 13.40 0.81
Miami 8.01 0.50
Milwaukee 23.43 0.03
Minnesota 13.47 42.61
N.Y. Mets 4.00 0.20
N.Y. Yankees 0.45 47.20
Oakland 1.37 7.82
Philadelphia 0.69 24.74
Pittsburgh 1.37 0.01
San Diego 1.77 53.77
San Francisco 9.00 112.82
Seattle 18.75 62.92
St. Louis 36.00 5.36
Tampa Bay 0.45 0.19
Texas 2.79 13.70
Toronto 13.47 11.61
Washington 0.00 0.01
AVG 10.20 15.68

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.

Franklin Barreto’s Short Stop in Oakland

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.

Following Up on Jimmy Nelson

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.

Stop Throwing Fastballs to the Astros

Everything’s Gonna Be Aoki

I went to SunTrust Field on July 4th 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 26th 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.

WBC Player WAR as of 2017 MLB All-Star Break

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.
Distribution of WAR Differential

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

2017 WAR as a function of 2016 WAR

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.

Twenty player with highest 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.

The Cubs Should Be Buyers — Long-Term

The Cubs have struggled this year. They are two under .500 and 5.5 games back in almost mid-July.

There was a lot of talk about the Cubs’ defensive regression and worse pitching, but one of the biggest problems has been the hitting. Last year they had a 106 team wRC+ which has regressed to a below-average 94 wRC+ this year.

That is not good, but it also means there’s a lot of room for improvement. The Cubs’ struggles are mostly based on their .279 BABIP, as their .180 ISO and 10% BB rate are above league average and their K% is about average. According to those stats, they should be at least above average.

The Cubs should hit better than that, and they have, outside an abysmal May and early June.

Here is the monthly breakdown (month: wRC+, BB, K, ISO, BABIP):

April: 98 wRC+, 10,22.9,.162, .313

May: 83 wRC+, 10.1, 21.1, .177, .242

June: 96 wRC+, 9.7, 22.7, .194, .282

July: 112 wRC+,9.2, 21, .212, .293

As you can see, the peripherals are not that different; if anything the ISO was trending upwards during the season. What was different was the BABIP, and that especially in like six weeks in May and early June.

Now there is no reason to believe the Cubs’ low BABIP would be for real, and the Cubs’ season ISO if anything might be a little low. They won’t be ISOing over .200 like in July so far, but high .180s seem to be a realistic goal. But even if they stay at their current peripherals of 10, 22, .180 and their BABIP improves to around .300 in the second half they should be a good hitting team.

The Cubs’ young core can hit, and they will never be much better than they are now. They have Rizzo and Bryant at their peak, and Contreras, Happ, Baez and Russell aren’t bad hitters either. And even the struggling Schwarber should bounce back. He probably was overrated by Cubs fans as his contact and defensive issues are for real, but his 14% BB rate and .212 ISO are solid and he was a big time victim of BABIP, at .199. Now his BABIP was partly due to his 14% IF fly rate and his predictable pull tendency (shift), but .199 still is way too low. Schwarber might just end up being a poor man’s Adam Dunn, but while hardcore Cubs fans see that as an insult, peak Adam Dunn was a pretty good hitter and even if he is not the second coming of Babe Ruth, Schwarber should be decent at least offensively.

So from a hitting perspective, there is no reason to wait. The Cubs can hit now and the service time clock for the core is ticking, although there are still quite a few years left and this certainly is no now-or-never situation. Also, except for Eloy Jimenez, all major prospects have been called up so there is also no reason to wait for guys in the minors. This is the Cubs’ core and it is one that gave them two big years.

So why potentially risk wasting one of the control years of the core? The Cubs are a few games back, but realistically, it is a weak division, and they still have clearly the best postseason odds in their division. And once you make the PS any team can win anyway, so grab that playoff run if you can; the chances are not going to get better.

Now the Cubs probably win the division without a move, but there is a risk the Brewers pull through. Also the Cubs do have future issues. Arrieta is going to be a free agent and might be declining a little anyway, so it will be tough decision on whether to re-sign him as he still won’t be cheap. Lackey is getting old too, and Hendricks, while not bad, clearly had a fluke-ish season last year. Lester is still good, but how much longer will he stay an ace at age 33? And all the typical buy-low Theo signings for the fifth spot so far did not work this year.

The Cubs have some interesting pitching prospects but nobody of note is remotely big-league ready.

So if you can add a cost-controlled young ace, now is a good time. Next year, arbitration will start to kick in, and the core won’t stay that cheap forever. That means a cost-controlled ace would ease the salary situation. The Cubs at some point need to make a move for pitching anyway, and why not do it now, when you are a couple games back and a hot-at-the-wrong-time Brewers club could cost you a very valuable playoff run that you counted on before the season started?

The Cubs still are in a good position, but there has been a negative swing in playoff odds that the Cubs could counter with a big move. Of course, that big move will hurt the Cubs a lot in what it will cost, but if there is a chance to get a deal done, now might be a very good time, and since the Cubs’ window, while not eternal, is not closing anytime soon in the next years, it probably makes sense to go for a long-term solution rather then a rental.

The Cubs have a chance to do two things at once: get back some of the playoff odds they lost due to their mediocre two and a half months of baseball in a season that was seen as a lock to make the PS, and fix a future need in the rotation, and IMO Theo should use that situation to make the move this deadline. There really is nothing to wait for — the future is now for the Cubs.

What’s Wrong With Felix Hernandez?

So as we all know, the King of the north isn’t the same right now. His 2.14 ERA in 2014 turned into 3.53, 3.82, and now 4.44. It’s clear something isn’t right.


Felix Hernandez throws a 4-seam fastball, a 2-seam fastball/sinker, his signature deadly changeup, a slider, a curve, and on occasions, a cutter.

Many believe his lowered velocity is the cause of his troubles, particularly with his fastball. What may surprise a lot of people is his fastball really isn’t that much slower than it used to be. The average fastball during his career has floated between 90.3 MPH and 92.8 MPH

Bold indicates a top 5 CYA finish


Year Fastball Velocity
2007 98.64
2008 96.01
2009 95.16
2010 95.00
2011 94.14
2012 92.94
2013 92.71
2014 93.62
2015 92.83
2016 91.22
2017 91.94


Clearly Felix isn’t the fireballer he used to be, but this is nothing new. His fastball dropped dramatically between 2011 and 2012 and has changed by very little from 2014, his last elite season, to now. Finishing top 5 in CYA in 2012 and 2014 can show that Felix doesn’t need overpowering hard stuff to be an elite pitcher.

So if it’s not his fastball, what’s the matter?

I believe the problem lies in his changeup, that world-famous put-away pitch that haunted the AL for five years. In 2014, Felix Hernandez changed the way he pitched. He started using his changeup a lot more. From 2009-2013 he used his changeup 18% of the time. From 2014-present, that has shot up to 27%. That’s a good thing, right? His change is so good, he’d be dumb not to use it! Not quite; it seems with his increased changeup use, the league got pretty good at reading it. When he used the change ~18% of the time, he got swings and misses on 28% of changeups thrown. Ever since, that percentage has dropped down to 19% swings and misses. His sinker, a pitch with similar movement, has remained at ~5% even through his off years.

What has led to this dramatic decrease in whiffs? He’s not locating them well at all.  When using his changeup more sparingly, very few of them were in the zone.




Compare this to recently, where you can see a significantly higher amount of changeups find their way into the zone.



What does this lead to? It took his changeup from unhittable to terribly easy to hit hard, especially in that lower zone his changes find themselves in.


This dramatic shift is not seen in his curve and sinker when thrown in the zone. In both parts of his career, sinkers and curves left in the zone were hit hard, but not changeups.

The Yankees Game

For those who don’t know, in late 2015, there was a game where Felix slipped on the mound due to rain. Many believe this is the cause of his troubles. Here’s his performance before that day:

18 GS, 11-5, 117 IP, 2.84 ERA, 112 Ks

And after:

13 GS, 7-6, 84.1 IP, 4.48 ERA, 79 Ks

What changed about him after that start? We know it’s not velocity. One thing I’ve found is Felix threw a lot fewer breaking balls .


Last year, he was actually pretty good until June. 63 IP, 2.86 ERA, 53 Ks, compared to 90 IP, 4.48 ERA, and 69 Ks, when he returned. If you notice this graph again:


…his offspeed pitches stopped fooling people around the time he performed poorly. Him slipping on the mound is just a coincidence since his movement hasn’t been affected at all.


My conclusion

Felix no longer has an elite changeup. Whether or not it’s due to overuse or poor location, it’s getting hit hard, whereas his fastball, sinker, and breaking pitches have remained pretty much as effective as they have been his whole career.

His changeup usage continues to be just as high as it was previously. His performance might have little to do with an age-related decline. He is only 31 years old, and his velocities across the board haven’t changed by much since his elite 2014 season.