Author Archive

Searching For Overvalued Pitchers

A little while ago, I created a post here about finding undervalued pitchers by looking at improvements between the first and second halves of the season. I had created a linear regression model for the predictions using data from 2002 to 2015, but when trying to use the same model to find overvalued pitchers, it didn’t exactly work as expected (I use the word “work” loosely here — in all likelihood, my predictions will fail as badly as the new Fantastic Four movie). It did find pitchers who suffered massive setbacks, but the majority of those were primarily due to increased — and probably unsustainable — home-run rates.

For example, Matt Andriese had an extremely successful first half of 2016. He put up a 2.77 ERA in 65 innings, backed up by a 2.85 FIP. But those numbers were much like my ex-girlfriend: pretty on the surface, but uglier once you get to what’s underneath. He struck out a lower percentage of batters than the average pitcher during that time while giving up more hard contact. The biggest sign, though, was his deflated home-run rate. He allowed just 0.28 home runs per nine innings, with only 3.2 percent of his fly balls going over the fence. This righted itself in the second half, where his HR/9 increased to 2.15 and his HR/FB to 17.4 percent. On the other hand, he improved his strikeout and walk rates, actually leading to a drop in his xFIP from 4.04 to 3.92 from the first half of the season to the second.

So then what should we expect from Andriese in 2017? The model I created predicts a 5.56 ERA from Andriese, leaning toward his 6.03 ERA from the second half of last season. While it’s unlikely he will allow fewer than 0.3 home runs per nine innings next year, it’s equally as unlikely that he’ll allow over 2 — after all, no qualified pitcher did so over the course of the 2016 season. Andriese’s full-season FIP of 3.78 actually closely aligned with his xFIP of 3.98, so it’s fair to guess that his home-run rates will level out and his ERA in the coming year will be in that range. That would signify an improvement from his 2016 season, rather than his decline predicted from the model.

So, instead of using the model, I took a simpler approach. Here are the players with at least 50 IP in each half of the 2016 season whose xFIP increased the most from the first half to the second:

xFIP Splits
Name First Half xFIP Second Half xFIP Increase
Tanner Roark 3.64 4.83 1.19
Drew Smyly 4.07 5.10 1.03
Hector Santiago 5.05 5.94 .89
Aaron Sanchez 3.41 4.29 .88
James Shields 4.82 5.70 .88
David Price 3.12 3.98 .86

For the purposes of this article, I’ll ignore Santiago and Shields since it’s unlikely that either of them will be relevant in 2017. That leaves four other pitchers whose skills declined dramatically over the course of the season and who you might want to avoid in your drafts.

Tanner Roark

Believe it or not, Roark’s already 30 years old. He’s actually had pretty decent success in his four years in the majors, with a 3.01 career ERA in over 573 innings. On the flip side, over that same time he has a 3.73 FIP, 3.96 xFIP and 4.06 SIERA. That’s not to say he’s a bad pitcher — just perhaps not as good as his ERA would have you believe. The same can’t be said for his second half of 2016. Despite actually bringing his ERA down from 3.01 to 2.60, his already-inflated FIP and xFIP numbers got even worse. His strikeout rate declined by 2.5 percent while his walk rate rose by about the same amount, leading to just a dismal 1.87 K/BB in the second half. His HR/9 nearly doubled as well, but not due to a substantial increase in his HR/FB rate — rather, his fly-ball rate rose from 26 to 37.6 percent, more in line with his pre-2016 career average of 33.9 percent. Why, then, was he able to continue to be successful? A .230 BABIP and a 86 percent strand rate offer an answer. Don’t expect another sub-3 ERA season from Roark — instead, look more toward his Steamer projection of 4.15.

Drew Smyly

For many last year, Smyly was a popular target. He was a high-strikeout guy who was able to limit walks and generate infield flies, prompting Mike Petriello to write this ringing endorsement for him. In his 114 1/3 innings for Tampa Bay before 2016, Smyly had maintained a 2.52 ERA and was among the best at generating strikeouts. But it all went wrong last year. As Tristan Cockcroft points out, Smyly’s season was marked by a first half of bad luck and a second half of deteriorated skills but better luck. His first-half 5.47 ERA was likely undeserved, as he continued getting strikeouts and limiting walks, but was plagued by a .313 BABIP, 63.2 percent strand rate and a 15.0 HR/FB rate, which corresponded to a 4.45 FIP and 4.07 xFIP. His ERA dropped to 4.08 in the second half, but nearly all of his peripheral stats worsened. A move to Seattle won’t fix all his problems, as Safeco Field was actually more hitter-friendly than Tropicana Field in 2016. The sky is the limit for Smyly, but there’s reason to be cautious. It’s possible he bounces back, but this could be who he is now.

Aaron Sanchez

This guy is good, don’t get me wrong. It took a while for some people to catch on, but I was always on his bandwag…all right, so I was one of the guys who didn’t buy in right away. That’s why I don’t do this for a living. Anyway, seeing his name on this list surprised me. After some digging though, it turns out that in my ignorance, I may have been onto something. In 2015, in Sanchez’s trial run as a starter, he was all right. A 3.55 ERA hid a 5.21 FIP and 4.64 xFIP before he got injured and was subsequently moved to the bullpen. When he returned on July 25, he was a completely different pitcher. This time, while he may not actually have deserved his 2.39 ERA, a 3.10 FIP and 3.33 xFIP showed he had made some kind of improvement. Or had he? After all, he only threw 26 innings in the second half of last season. And while there was undoubtedly a huge improvement for him in strikeout and walk rates, something else caught my attention. Take a look at Sanchez’s batted-ball type percentages from 2015:

Pretty clearly, Sanchez improved his batted-ball profile after becoming a reliever. His 2015 second-half ground-ball percentage of 67.6 percent would be the greatest of all of the 1281 qualified pitcher-seasons since 2002, when the statistic started being tracked. His fly-ball percentage of 18.3 percent, while not as extreme, would still rank as the ninth-lowest since 2002. That begs the question: would he be able to sustain those rates when he moved back to the rotation? The answer, as it always is with historically extreme rates, was no:

Both of his rates came crashing back to historically-accurate norms pretty much right away, and they continued to trend in the wrong direction as the season progressed. This, consequently, caused Sanchez’s xFIP to skyrocket. His strikeout and walk rates got worse from the first half of the 2016 season to the second, but only slightly. What really moved his xFIP was his fly-ball rate, which soared (pun intended — maybe I should do this for a living) from 21 percent to 31.8 percent. It’s difficult to say where Sanchez will go from here — after all, this was his first full season as a starter. If he can keep his fly-ball rate at last year’s 25.1 percent — which ranked fourth-lowest among qualified starters — he could still be a pretty decent starting pitcher, even with regression to a league-average HR/FB rate. What’d be even more impressive, though, is if he could keep his batted-ball rates at his numbers from the first half of 2016, which were among the league’s best. Perhaps with a full season under his belt, Sanchez may now have the stamina and endurance to achieve this feat. If he does, look out. If he doesn’t, you’re looking at an average guy.

David Price

Now that I’ve written nearly an entire article’s worth about one guy, let’s talk about another player from the AL East. Price, for much of his career, has been among the elite at the position. Before last season, the only time he had had an ERA above 3.50 was his first season as a starter back in 2009. Every year of his career, he’s been an above-average strikeout guy, but he topped even his own lofty standards when he struck out 27.1 percent of the batters he faced in the first half of 2016. He was unable to sustain that rate, and in the second half of the season he managed to strike out just 20.3 percent of batters, which would have been his lowest full-season rate since 2009. So what changed? Actually, it might have been the first half that was the fluke. Price allowed a 74.2 percent contact rate in the first half, contrasted with a 79.1 percent rate in the second. Those numbers don’t necessarily mean much on their own, but the difference is easy to spot when looking at his career rates:

Price’s whiff rate was higher than ever in the first half of 2016, but it’s tough to figure out why. Per Brooks Baseball, Price was generating swings and misses on his changeup at a career-best rate in the first half, but I couldn’t find any obvious changes to his velocity or movement on the pitch or any other. It’s fair to wonder, then, if his second-half numbers are what we should expect from Price at this point in his career, since his contact rates during that time were much more sustainable. He probably won’t be as bad as his 2016 3.99 ERA, but I wouldn’t be shocked to see it end up above 3.50 for the second year in a row.

Of course, this is not a comprehensive way to find overvalued pitchers. It’s a crude approach, but one that’s meant to highlight guys who fell off in the second half, as they’re the ones more likely to carry over those declined skills into 2017. That being said, xFIP obviously isn’t perfect, and these players all showed that they were capable of posting above-average results over half a season. Take a risk on them if you want, but be warned that they may not be worth the price.


Searching For Undervalued Pitchers

When looking to the future, there are countless ways to try and find undervalued pitchers.

One such way is to look at which pitchers’ FIPs outperformed their ERAs last year. This is a good approach, but it isn’t enough. For one, there will be players who consistently underachieve on their metrics, like the ever-teasing Michael Pineda. He sits second on the 2016 leaderboard in ERA-FIP, but his ERA is more than half a point greater than his FIP for his career and over a point greater each of the past two seasons.

The other problem with this approach is that FIP has become mainstream enough that everyone will be doing this same thing. Players who outperformed their FIP will be be common targets on draft day, driving up their prices and eliminating any sleeper potential that they had. This, too, is the downside of projections and other easily accessible data.

A different approach is then needed. In that spirit, I decided to create a linear regression model to predict a subsequent year’s ERA based on the difference in first- and second-half splits from the previous year, as well as that year’s ERA. This would help find the players who improved the most from the beginning of the year to the end, and perhaps players who are likely to carry over those improvements into the next season.

The model was generated using data from 2002 to 2015 obtained from FanGraphs’ splits leaderboard, with only pitchers with at least 50 IP in each half-season being considered so as to remove potential outliers. Non-significant variables were removed, and a final model was created. The resulting model was then used with 2016 data to predict ERA in 2017. The following graph shows those predictions, after being rescaled, plotted against 2016 ERA:

For the most part, the predictions line up with their 2016 counterparts. The labeled data points, though, are the ones I want to focus on. Based on this model, each of them are expected to see their ERA drop significantly from last season to this one and could help provide value in the latter rounds of drafts.

Jeff Samardzija
2016 ERA: 3.81
2017 Projected ERA: 3.40

The Shark has had a rough career. Since becoming a starter in 2012, he’s only had one season in which he’s beaten last year’s mark of a 3.81 ERA. He’s played for four different teams in those five years, he’s on the wrong side of 30 and his name is at the same spot on the pronunciation scale as Jedd Gyorko’s. But he does have a few things going for him. He’s struck out over 20 percent of the batters he’s faced in all but one year since 2011, and he’s pitching in a park where home runs go to die. His average fastball velocity is holding steady above 94mph and it was only two years ago where he had a sub-3 ERA with the estimators to back it up. He’s proven he can put up solid numbers, so the predicted improvement isn’t unreasonable. He had a 3.66 FIP in the second half of 2016 that exactly matched his ERA, a substantial drop from his first half numbers. The biggest contributors were his strikeout rate, which rose from 18.9 to 21.9 percent, and his HR/9, which dropped from 1.15 to 0.94. There’s no reason to think the rates are unsustainable either — his HR/FB dropped to a near-league average (in a normal year) 10.8 percent, and his strikeout rate improved almost directly with an increase in his O-Swing%:

Samardzija was able to get batters to swing at pitches out of the zone more frequently as the season went on, and consequently was able to produce more strikeouts. Steamer projects him for a 3.66 ERA, which isn’t all that far off this model’s prediction. If he can bring his strikeout rate back to what it used to be, and AT&T Park does its job, Samardzija could provide some sneaky value in 2017.

Ivan Nova
2016 ERA: 4.17
2017 Projected ERA: 3.72

Moving to the NL seemingly agreed with the former Yankees second-round pick. After posting an unsightly 4.90 ERA in 21 games (only 15 starts) in pinstripes, he turned his season around in Pittsburgh with a 3.06 ERA and 2.62 FIP in his final 11. Switching leagues undoubtedly helped, but there are more reasons behind his improvement. For one thing, he increased his strikeout rate while decreasing his walk rate — just doing those two would be reason to expect a lower ERA. Perhaps more significant, though, is that he halved his HR/9. Much of this is due to a change in scenery — his HR/FB dropped from 21.3 percent before his trade to just 7.8 percent afterward. Of course, he can’t be expected to repeat his performance. He walked just three batters in 64 2/3 innings, good for a 1.1 percent walk rate and a 17.33 K/BB. While Nova is probably better than Phil Hughes, it’s unlikely that even he can replicate that kind of walk rate. Look for Nova to improve on his ERA from last year, but don’t expect him to be as good as his second half. He’ll fall somewhere in the middle, but even that will be more than useful.

Wily Peralta
2016 ERA: 4.86
2017 Projected ERA: 4.35

Don’t look now (unless you promise to come back), but Peralta had a 2.92 ERA in the second half of 2016. Part of this was admittedly due to an inflated 81.7 percent strand rate, but even accounting for that, he managed a 3.75 FIP and 3.59 xFIP during that stretch. His success can be due largely in part to his increase in strikeout percentage, which jumped from 13.6 percent to 20.8 percent. It’s difficult to determine the exact reason behind this, but one explanation might be his increase in velocity. At the start of the year, his fastball was only averaging under 95 mph, a continuation of his 2015 trend and a disgrace to fireballers everywhere. By August, he was closing in on 97 mph, and presumably striking out batters as a consequence. Here’s his velocity by month since 2014, via BrooksBaseball:

Not only did Peralta see an increase in his strikeout rate, but his walk rate improved as well from 8.7 percent to 6.5 percent, which is the lowest to reasonably expect given his career numbers. His WHIP dropped from 1.88 to 1.15, his HR/9 from 1.64 to 1.02 and his wOBA against from .421 to .295 — seemingly everything improved except his age, but I’ll give him a pass on that account. The secret behind his success? His ability to limit hard-hit balls and induce soft contact. Take a look at the trends for each type of contact rate:

In case that doesn’t do it for you, here’s his Statcast exit velocity broken down by game date, via Baseball Savant (with a linear regression line added for those last few skeptics who aren’t convinced):


Peralta’s not an ace, but he has the potential to help out teams this season. Monitor his velocity during spring training, and buy him for a discount on draft day.

Clay Buchholz
2016 ERA: 4.78
2017 Projected ERA: 4.02

Of all pitchers who threw at least 50 innings in each half of the season, Buchholz improved his FIP the most — his first-half FIP was 6.02, so he gave himself quite an advantage, but he still brought it down to 3.74 following the Midsummer Classic. He’s already proven himself to be a capable pitcher, with four sub-3.50 ERA seasons in his past seven seasons, and now he goes to Philadelphia, where pitchers go to be reborn (see: Hellickson, Jeremy). Also, he moves from the AL East to its NL counterpart. Besides going up against a pitcher instead of a designated hitter, he will be facing the likes of the Braves and Marlins instead of the Blue Jays and Yankees.

Despite the difficulty of his former division, Buchholz still managed to improve as the 2016 season wore on. He marginally increased his strikeout and walk rates, doubling his K-BB% to a still-mediocre 9.3 percent in the second half of the season. While that’s not exactly comforting, it’s worth noting that his walk rate in the first half the season was higher than anything he’s put up since 2008, so it’s not likely to approach that number anytime soon. Furthermore, he was able to bring down his bloated HR/FB rate, despite the league’s general struggle to do so. In the first half of the season, 15.9 percent of Buchholz’s fly balls resulted in home runs, which would have been higher than any single season in his career. In the second half that number improved to 5.1 percent, which was much more reasonable given his average rate of 6.5 percent over the previous three seasons. Steamer projects him for a 4.07 ERA, but it’s not difficult to envision a scenario where he does better than even that.

With all that being said, not all of the pitchers on this list are going to live up to their projections. No model is perfect, and none of these guys have exactly had exemplary careers. But they all showed significant improvement over the course of last year, and that’s a strong indication for what to expect from them in 2017.


Buying or Selling Carlos Gomez

What are you to do with a former fantasy superstar who hasn’t lived up to expectations? For some, the answer’s easy; Carlos Gomez has already been dropped in over 25% of leagues on both ESPN and Yahoo.

Now that I’ve driven half my audience away with my use of a semicolon, let’s start the real analysis. Gomez certainly disappointed his owners through the first month and change of the season, sporting a minuscule .486 OPS through May 15 before being placed on the DL. For reference, out of 324 batters with at least 100 plate appearances, just two (2) have a lower OPS as of June 24. Both are on the Braves (one hit fifth in the lineup as recently as June 21, while the other has batted second 13 times this season).

So yes, one could see why owners would have lost patience with Gomez. But this was also a player who hit 66 home runs and stole 111 bases while hitting .277 between 2012 and 2014. If anyone deserved patience, it was him.

So when he hit two home runs in his first six games back from the DL, it was hard to be too surprised. Since then, he’s put together five multi-hit performances, and has brought his season line back up to at least non-Atlanta-ish numbers.

While it’s obviously a small sample size, Gomez’s 76 plate appearances in 19 games since his return have shown immense improvement over his horrendous start to the season. To demonstrate this, take a look at each of the different areas in which he’s bounced back:

Plate Discipline
2012-2014 April 5 – May 15 May 31 – June 24
BB% 6.2% 5.3% 10.5%
K% 22.8% 34.8% 30.3%
BB/K .27 .15 .35
SwStr% 13.9% 19.4% 16.7%
O-Contact% 59.5% 42.4% 45.9%
Z-Contact% 84.4% 74.4% 80.5%
O-Swing% 37.4% 32.1% 35.7%
Z-Swing% 79.3% 79.9% 65.8%

I could bring up more player comparisons and show you just how bad the Atlanta Braves are this year, but that’s not the point of this article. Instead, let’s just focus on Gomez’s numbers and how they compare to earlier in the year and during his prime years. He’s nearly doubled his walk rate while striking out more than 10% less often than before, leading to a BB/K that is no longer painful to look at. He’s missing less frequently on pitches he swings at, both in and out of the zone, and has fewer swings-and-misses as a result. The one worrisome spot here is his swing rates, where the trend is the opposite of what we’d generally expect when we see favorable results. However, his O-Swing% is still lower than it was between 2012 and 2014, and it seems as though swinging less at pitches in the zone is leading to more walks and less bad contact, so it’s not truly a terrible result.

Batting and Power
2012-2014 April 5 – May 15 May 31 – June 24
AVG .277 .182 .294
BABIP .329 .293 .405
OBP .336 .238 .368
SLG .483 .248 .471
ISO .206 .066 .176
OPS .819 .486 .839
wOBA .356 .216 .364
wRC+ 123 28 129
HR/FB% 14.6% 0.0% 33.3%

I already referenced Gomez’s OPS above, but it’s still almost unbelievable to see that his post-injury slugging percentage is nearly as high as his OPS once was. Besides that, there’s improvement across the board. His average is up over 100 points, as his OBP, SLG, ISO, OPS, and wOBA. He’s gone from being 70% worse than the average hitter to 30% better. What’s good to see her is that he’s not outpacing any of his career stats by a noticeable amount — an indication that his current run is very much sustainable. Okay, maybe not the .385 BABIP, but as you’ll see next, keeping it over .300 shouldn’t be an issue.

Batted Ball Breakdown
2012-2014 April 5 – May 15 May 31 – June 24
GB% 39.3% 47.1% 44.2%
FB% 40.6% 35.7% 20.9%
LD% 20.1% 17.1% 34.9%
Pull% 42.7% 36.4% 62.2%
Cent% 33.9% 41.6% 13.3%
Oppo% 23.5% 22.1% 24.4%
Soft% 16.7% 29.9% 31.1%
Med% 48.0% 45.5% 28.9%
Hard% 35.3% 24.7% 40.0%

Let’s take this one at a time. First, Gomez has seen a drastic increase in his line-drive percentage, unfortunately at the expense of hitting fewer fly balls. While it’d be better to see him hit fewer ground balls and get some more balls in the air, he’s certainly making this approach work for him right now. He won’t hit 30 home runs with this approach, but with the increased line drives, he should have no problem continuing to hit for extra bases.

Then comes the confusing part. He’s increased both the percentages of balls he hits to the pull side and opposite of the field, now hitting just 13.3% of his balls to center. He was definitely spraying the ball better beforehand, although the bloated Pull% will undoubtedly help him to put up some better power numbers. If the numbers stay in this region, I’d definitely expect his BABIP to regress, but it’s more likely that they regress closer to his career norms. A lot of those pulled balls will end up going to center field.

Finally, there’s the stuff that’s easy to analyze. Hit the ball harder, get better results. Gomez apparently believes in that approach as well, now hitting the ball hard over a third of the time and showing over a 50% increase from his previous rate. He needs to work on hitting the ball soft less often, which should happen if he continues to be selective and wait for his pitch.

Statcast Data
2015 April 5 – May 15 May 31 – June 24
Exit Velocity (mph) 88.5 84.8 86.4
Exit Velocity on Line Drives and Fly Balls (mph) 92.7 91.2 96.4
Fly Ball Distance (feet) 315.2 309 359

Ah, Statcast. What would we do without your infinite wealth of knowledge? The data here was obtained through Baseball Savant, and confirms that Gomez is indeed hitting the ball harder than he was before his injury. His overall average exit velocity remains low, but his velocity on line drives and fly balls is actually higher than it was last year. He can hit all the slow ground balls he wants and still be successful, provided he can keep up this increased velocity on balls in the air. Of course, he’s not going to continue hitting his fly balls over 350 feet — that’s reserved for people like Byung Ho Park (and apparently Tyler Naquin?). But he’s at 323 feet for the season now, and which should easily suffice for him to begin putting up some rejuvenated power numbers.

If you’re looking for a tl;dr, here it is: Carlos Gomez is performing much better than he was earlier in the season. He’s taking more walks, striking out less, making more contact, and hitting the ball harder and farther (further?). It’s obviously a small sample size, and he may not put up another 20/40 season, but he’s more than capable of hitting 10 home runs and stealing 15 bases the rest of the way. While it’s not elite production, it’d be better than he did last year, which would be quite an achievement after his start to the season.


Flying High

As a whole, Elvis Andrus’s 2015 season was quite unremarkable. In his seventh year in the bigs, he set career lows in batting average and OBP while finishing with his second-worst wRC+ season of his career. He also stole his second-fewest amount of bases while scoring fewer runs than ever before.

One thing that he can hang his hat on, though, was his power output. Andrus finished 2015 with the second-highest ISO of his career, setting a new career high for home runs in the process. Now, he still only hit seven, but we’re talking about the player who hit zero in 674 PA in 2010. Elvis Andrus hitting seven home runs in a season is like Barry Bonds hitting 85, or Ben Revere hitting three.

Reaching seven home runs was actually quite an extraordinary feat for Andrus, not because of the total itself but because of how it compared to his 2014 season. Andrus hit just two home runs that year, which tied him for second-fewest in the MLB among qualified batters. By hitting seven the next year, he more than tripled his previous total. Only three hitters who qualified both years achieved the same feat:

Player 2014 HR 2015 HR
Adam Eaton 1 14
Matt Carpenter 8 28
Elvis Andrus 2 7

What’s even more impressive is that two of those players, Carpenter and Andrus, had fewer plate appearances in 2015 than 2014. So how did they manage to do it?

I’ve been focusing on Andrus, so let’s continue with him. His HR/FB% went up a little in 2015, but it was only 1% higher than his career average and lower than his output in two of his previous seasons. Since that clearly wasn’t the change, it must’ve been something else. Looking at his batted-ball breakdown, something shows up.

Andrus finished 2015 with a 31.8 FB%, the highest of his career. This was an increase of 10.9% from 2014, which represented the largest increase in FB% of any player between the past two years:

Rank Player 2014 FB% 2015 FB% FB% Change
1 Elvis Andrus 20.9% 31.8% 10.9%
2 Todd Frazier 37.1% 47.7% 10.6%
3 Jay Bruce 34.0% 44.2% 10.2%
4 Adam Eaton 20.2% 27.3% 7.1%
4 Jose Bautista 41.7% 48.8% 7.1%
6 Albert Pujols 35.4% 42.2% 6.8%
7 Daniel Murphy 29.4% 36.0% 6.6%
8 Matt Carpenter 35.2% 41.7% 6.5%
9 Gerardo Parra 23.9% 29.4% 5.5%
9 Jose Altuve 29.7% 35.2% 5.5%

Eaton and Carpenter also both make this list, explaining their power outburst (at least partially). Some of these players aren’t very surprising, only making this list because their 2014 FB% was much lower than their career norm and they were simply regressing to where they should be (see: Pujols, Albert). Others, like Altuve, are only just beginning to explore their power potential.

Regardless of the reasoning, the most important question that comes from this list is whether or not those on it can duplicate their performance. Without looking at individual swings and searching for differences, I decided the easiest way to determine this was by looking at historical data. Since batted-ball data became available in 2002, there have been 19 different qualified players to increase their FB% by 10% or more between consecutive seasons, and then play another qualified season the following year:

Player / Years Year 1 FB% Year 2 FB% Year 3 FB% Y2-Y1 FB% Y3-Y2 FB% Percent Regression
Hideki Matsui 2003-05 23.8% 39.9% 36.3% 16.1% -3.6% 22.36%
Grady Sizemore 2005-07 31.0% 46.9% 46.6% 15.9% -0.3% 1.89%
Bill Hall 2005-07 34.5% 47.9% 41.3% 13.4% -6.6% 49.25%
Aaron Hill 2009-11 41.0% 54.2% 42.0% 13.2% -12.2% 92.42%
Carlos Beltran 2003-05 32.7% 45.9% 37.0% 13.2% -8.9% 67.42%
Jhonny Peralta 2009-11 30.6% 43.4% 44.2% 12.8% 0.8% -6.25%
Derrek Lee 2008-10 33.7% 45.7% 37.6% 12.0% -8.1% 67.50%
Mark Kotsay 2003-05 29.1% 40.8% 35.5% 11.7% -5.3% 45.30%
Jason Kendall 2006-08 25.9% 37.6% 36.6% 11.7% -1.0% 8.55%
Mike Trout 2013-15 35.6% 47.2% 38.4% 11.6% -8.8% 75.86%
Brad Wilkerson 2003-05 36.0% 47.5% 45.0% 11.5% -2.5% 21.74%
Daniel Murphy 2012-14 24.9% 36.3% 29.4% 11.4% -6.9% 60.53%
Derek Jeter 2003-05 21.5% 32.7% 20.7% 11.2% -12.0% 107.14%
Garrett Atkins 2005-07 30.2% 41.1% 44.1% 10.9% 3.0% -27.52%
Adrian Gonzalez 2006-08 33.3% 43.7% 36.6% 10.4% -7.1% 68.27%
Brian Roberts 2003-05 28.7% 39.0% 37.3% 10.3% -1.76% 16.50
Brandon Crawford 2013-15 31.8% 42.0% 33.5% 10.2% -8.5% 83.33%
Bobby Abreu 2003-05 26.7% 36.8% 28.9% 10.1% -7.9% 78.22%
Lance Berkman 2005-06 31.7% 41.8% 37.6% 10.1% -4.2% 41.58%

Only twice did the player make even further gains in their FB%, and the average regression among all 19 of the players was 46.01% toward their first-year numbers. With this in mind, it’s difficult to envision players like Andrus and Frazier repeating their performances from last season. And even if that means we won’t be seeing a double-digit home-run season for Elvis Andrus anytime soon, I think that we’ll be all right without one.


Started From the Bottom, Now We’re…Average

2015 was the year of Bryce Harper. He led qualified hitters with a 197 wRC+, the highest since the turn of the century among players not named Barry Bonds. This was a vast improvement on his already-impressive 2014 season, in which he totaled a 115 wRC+.

Depending on how you look at things, you could say Bryce Harper was the most improved batter in 2015. I choose not to for two reasons: 1) it’s too easy, and 2) it makes this article more fun. There’s also another more objective reason: with only 395 plate appearances in 2014, Harper didn’t qualify for the batting title.

This poses a question: what minimum do we set to determine who improved the most between 2014 and 2015? If we say that the player needed to qualify for the batting title each year, we get Chris Davis as the most improved batter, who increased his wRC+ from 94 in 2014 to 147 in 2015. If we set no minimum, our wonder-boy is none other than notorious slugger Carlos Torres, the Mets pitcher who upped his wRC+ from -100 to 491.

Clearly, there needs to be some minimum. For the purpose of the article, I’ve decided to set it at 100 PA. This seems a reasonably small enough number to include a wide array of players, but large enough to get rid of anomalies (I’m looking at you Carlos). When we set this minimum, we discover that the batter whose wRC+ increased the most between 2014 and 2015 is… Ryan Raburn. However, since Jeff Sullivan already talked about Raburn, I decided to go with the next name on the list: J.B. Shuck.

If you don’t know who that is, I don’t blame you. I didn’t until I started this research. If you do know him, I’m going to guess that you’re either a White Sox, Indians, or Angels fan. Either that, or you have more time to watch baseball than a college student taking a full course-load of credits. Who’s to say?

The reason the casual fan might not know Shuck is because, well, he’s not exactly a star player. Here are the players with the lowest wRC+ in 2014 of those with at least 100 PAs:

That’s right, he was literally the worst batter that year. Almost as bad as if I were to join the majors. It should be no surprise, then, that he was able to improve so much — he had the lowest starting point. Even so, he still had needed to improve quite drastically in order to surpass Harper’s wRC+ improvement. And that’s exactly what he did:

In 2015, Shuck improved so much that he almost managed to be an average player. But how did he manage to do it? Was it a matter of luck, or did he actually get better?

The number that stands out the most in Shuck’s 2014 season is his .146 BABIP (batting average on balls in play). For those of you that don’t know, that number is quite bad. Like, less than half of what it should be. His BABIP in other seasons is right around league average, so something must have gone amiss last year. Looking at the underlying numbers, some things showed up:

So. His FB% and Pull% numbers were way up as compared to other years. For some context, the league-average FB% has been approximately 34% the past two years, while Pull% has been approximately 40%. These numbers suggest that Shuck spent too much time trying to pull the ball over the fence two years ago, and the video suggests the same thing. Here’s an example of him trying to do just this to a pitch on the outside corner, but instead weakly grounding to first. You can see how he opens his hips before he even starts his swing, forcing him to simply slap at the ball if he wants to make any contact:

And here he is in 2015, driving a similar pitch into left field:

The cause of his change in approach is hard to say. He did get a new hitting coach to start off the year, switching from Jim Eppard to Don Baylor. From 2013 to 2014, the Angels as a team increased their FB% from 33% to 34% and their Pull% from 37% to 42%, so that argument does have some merit. Regardless of the reason, it’s clear that it had an effect. Here’s Shuck’s ISO by zone:

 

 

 

 

 

 

 

As can be seen on the left, Shuck had trouble hitting anything not on the inside edge of the plate in 2014. This past year, he learned to control more of the strike zone, and even though there’s less red than there was in 2014, there’s also a lot less dark blue. Shuck drove the ball from all parts of the zone to all parts of the field, and his numbers improved because of it.

While Shuck may not be an All-Star anytime soon, his year-to-year improvement is truly remarkable. If he can go from being the worst hitter in baseball to an average one, anyone can. And if that doesn’t inspire the Brendan Ryans of the world, I don’t know what will.


Examining Three True Outcome Percentage

Take a look at Chris Davis‘s stat line in August: 11 games, 45 PA, 14 Ks, 7 BBs, 6 HRs. Nothing really jumps out; it’s pretty typical for Chris Davis. Looking deeper though, this selection of plate appearances is actually quite remarkable. 27 out of the 45, or 60% of them, ended with a strikeout, walk, or home run, known as the “three true outcomes” where the ball does not end up in play.

As Baseball Prospectus explains in its definition of TTO, the statistic actually gained relevance with the introduction of DIPS, FIP, and other pitching estimators that ignored the outcomes of balls in play. While still not commonly used, it’s certainly interesting to take a look at once in a while to see what players are taking luck into their own hands.

Chris Davis is actually not the most extreme three true outcome player. Despite his 60 TTO% August, his season-long percentage through August 13 stands at 48.9%, good for 5th in baseball of those who have at least 300 plate appearances. The rest of the top-10 leaderboard features both good names and bad. On the good side, we have Giancarlo Stanton, the only player to feature a HR% over 8% (his is 8.5% , and he actually leads second-place Nelson Cruz by 1.4%). Other names you might associate with quality players are Bryce Harper, Joc Pederson, and George Springer, all of whom have a K% under 30% and a HR% of over 4%. The players who might not be as happy to be on this list include the aforementioned Chris Davis, Chris Carter, Steven Souza, Kris Bryant, and Colby Rasmus, who all feature a K% of 31% or higher. Mike Zunino, who comes in at 10th, sports a walk rate and home run rate of just 5.6% and 2.8%, respectively, but more than makes up for it with a 34.2% strikeout rate, second only to Souza.

Now that we’re done with the fun facts, let’s get into what it really means. TTO players are swing-for-the-fence players, those who aim to hit the ball over the wall every time they make contact. This is the cause behind their multitude of strikeouts. It also accounts for their walks, with the reasoning that pitchers are simply afraid to throw them hittable pitches.

The real question becomes “Are these TTO players valuable?” Looking at a graph comparing TTO% to wRC+ over the past 15 years, there is little correlation. It seems as though it is slightly more productive to be a TTO player, mainly because of the home runs and walks. This is far from a correlation though, as many bad players have a high TTO% and vice versa.

If we split it up into its parts, we might get a better view. League average TTO% has risen over the last decade, from 27.3% in 2005 to 30.3% this year (with a high of 30.5% in 2012).

We know the overall percentage has risen, but what’s driving it? If you’ve been following baseball, you know that the quality of pitchers has improved in recent years. Predictably, this has led to a decrease in walk rate and home run rate.

 

If 2/3 of the TTO% has decreased, but TTO% has still increased, that must mean the change in the third category must be drastic. This happens to be exactly the case. While BB% and HR% have fallen approximately a combined 1% over the past 10 years, league wide K% has risen by 4%.

What this means is that nowadays, if you are a TTO player, it’s likely much of that is coming from your strikeouts. In fact, out of the top-25 TTO% players with at least 200 PAs, only Paul Goldschmidt has a K% under 20%. Does this make high TTO% players bad? As I said before, there really isn’t a correlation, You’ll see players like Bryce Harper and Mike Trout with a high TTO%, while Buster Posey has one of the lowest because of his low K%.

The reality is, there are many different kinds of players. Some have adopted this TTO mentality, but others have stayed with a more conservative contact-focused approach. Without further information, it’s difficult to say which strategy is better. As a fan of statistics, I prefer the TTO players because it’s much easier to predict their performance. I don’t think they care much about that though.

Also, if you were curious, here’s a list of the top TTO% players with 200 PAs, created using FanGraphs data through August 13.


Stephen Strasburg Is Better Than You Think

To a casual baseball fan, Stephen Strasburg‘s numbers are not pretty. The owner of a 4.76 ERA and a 1.38 WHIP, Strasburg is clearly having the worst season of his career. But how bad has he been, really? Not as bad as you think. Take a look at these 2015 stats:

Player A: 3.48 xFIP, 22.8 K%, 5.5 BB%
Player B: 3.31 xFIP, 24.1 K%, 5.3 BB%
Player C: 3.18 xFIP, 24.9 K%, 6.0 BB%

Player A is none other than Johny Cueto, recently traded to the Kansas City Royals. 12th in ERA among qualified pitchers, Cueto is widely considered among the best, and perhaps deservedly so with five straight years of a sub-3 ERA. While he has consistently outperformed the above metrics, they are still indicative of general pitcher performance and should not be overlooked when comparing the quality of different pitchers.

Player B actually has the fifth lowest ERA among qualified pitchers and was also traded at the deadline. He’s been one of the most reliable pitchers over the past five years and has been an ace on every staff for which he’s pitched. Player B is David Price.

Player C is obviously Stephen Strasburg, and as you can see, his peripheral stats stack up against the best in the game. In addition to these 2 players, Strasburg also compares positively to others like Sonny Gray and Scott Kazmir, both of whom have better ERAs but a worse xFIP, K%, and BB%.  Strasburg is pitching like an ace, and xFIP shows that, so why have his results been so poor?

Well, first of all, there’s his .345 BABIP. Not only is this high compared to the league average (.296), it’s well above his career mark of .302. Considering he’s not giving up any more line drives or hard contact than usual, his BABIP should fall back to around the .300 mark and bring his ERA down with it.

Not only is his BABIP at an all-time high, his LOB% is at an all-time low. Currently at 65.3%, it figures to inch back up to his career 73.2% mark, or at least to the league average of 72.4%. Considering his strikeouts have not dropped off, there’s no reason for his drop on LOB%, and it can simply be chalked up to bad luck, something that he’s had plenty of this year.

Looking at these stats, there’s nothing that suggests Strasburg is anything but unlucky. However, as Jeff Sullivan pointed out here, Strasburg’s problem could stem from the injury he suffered in the spring. He had apparently adjusted his mechanics to compensate for the discomfort, and even though it appears as though he has fixed this, it’s possible that when pitching from the stretch and in higher leverage situations, he returns to this altered motion by default. When looking at the difference in Strasburg’s stats between pitching from the windup and the stretch, this is what we see:

K% xFIP
Bases Empty 30.1 2.73
Runners on Base 17.0 3.98

Evidently, this claim has some ground. Strasburg is clearly having some problems with runners on base, particularly in striking batters out. Before we deal with the strikeout numbers, let’s take a look to make sure that he’s not just getting killed during the at bats that don’t end in strikeouts.

GB/FB Batted Ball Velocity (mph) Hard Hit % Infield Hit %
Bases Empty .98 89 29.7 4.5
Runners on Base 2.05 88 28.7 12.2

Strasburg is actually generating more ground balls and weaker contact with runners on base. His infield hit percentage is triple what it is when the bases are empty, something that can be attributed to luck. With such weak contact, it’s safe to say this isn’t the problem. So it must be the strikeouts. If we take a look at his whiff rates, the results are intriguing:

2010-2014 2015
Bases Empty 20.1% 17.5%
Runners On Base 17.9% 8.6%

OK, so there’s definitely a problem here. With runners on base, he’s only whiffing batters at half the rate he’s done previously in his career, as well as half the rate that he does with the bases empty. So what’s the issue? Well, it’s not his pitch velocity:

4 Seam 2 Seam Changeup Curve Slider
Bases Empty 95.1 mph 95.4 mph 88.4 mph 81.3 mph 86.7 mph
Runners on Base 95.2 mph 94.9 mph 88.0 mph 81.5 mph 87.2 mph

Strasburg’s average velocity with runners on base is 91.5 mph, compared to 91.0 mph with the bases empty, so he’s actually throwing the ball harder when there’s runners on base. That can’t be the problem. He’s also not walking a significant amount more batters when there are runners on base, so it’s not like he’s sacrificing control for increased speed.

Without any numbers to provide a reason, it appears Strasburg’s struggles when striking out batters with runners on base are either based purely in luck or are completely mental. This is not necessarily a good thing, as we have no idea if or when he will sort it out. With his skill, Strasburg has the potential to be one of the best in the game. He just needs to get out of his own head, and maybe get just a little bit luckier.


Matt Shoemaker’s Need For Speed

If you look at the ERA leaders over the past 30 days with at least 20 IP, you’ll see some familiar names. Clayton Kershaw tops the list (apparently going 37 straight innings without letting up a run isn’t too shabby), and is followed by Scott Kazmir, who has allowed just one run in three starts with his new team. The third name might surprise you though, or maybe not, depending on whether you read the title of the article and how good your inference skills are.

The last time Matt Shoemaker allowed more than two runs in an outing was June 19. Since then, he’s pitched 37 1/3 innings, allowing just seven earned runs. He has 35 strikeouts compared to just 11 walks, leading to a 2.88 FIP. He’s been even better when just isolating the numbers in his three starts since the All-Star break, with 27/6 K/BB and a 1.36 FIP, although, to be fair, that is an incredibly small sample. For comparison’s sake, his FIP through June 19 was 4.70.

So has there been a change in Shoemaker’s game, or has his streak been a fluke? Well, I wouldn’t be writing this if it was the latter, as I’m sure you could’ve guessed (although if you weren’t able to guess who the article was about after the first paragraph, perhaps I’m overestimating you). There’s been a significant change in the way Shoemaker has approached batters. Take a look at his pitch type chart through June 19, courtesy of Baseball Savant:

Matt Shoemaker pitch selection through June 19 (n=1088)

And then take a look at the data since then:

Matt Shoemaker pitch selection since June 19 (n=652)

Through June 19, Shoemaker threw his fastball (four-seam and two-seam) 51.6% of the time. Since then, it’s been 56.9% of the time. Comparing these two proportions with a two-tailed Z test yields a p-value of .034, significant at the .05 level, showing that there has indeed been in a difference in the amount of fastballs he’s thrown.

Of course, throwing more fastballs doesn’t translate to a drop in FIP of over 3 points. That is, unless, those fastballs are of higher quality. And, class, what’s the most important aspect of a fastball? Hopefully you were at least able to guess this one: the velocity. Which, naturally, is the next thing I looked at.

Again, I used Baseball Savant’s PITCHf/x data. Narrowing the results to just fastballs, here are the velocities of Shoemaker’s pitches this year:

Matt Shoemaker 2015 fastball velocity (n=900)

At the beginning of the season, Shoemaker’s average fastball velocity hovered right above 88 mph. Since then, it’s steadily risen, and there’s a clear jump about two-thirds of the way into the season (note that this time would be remarkably near June 19). After the jump, his average velocity has hung closer to the 92 mph range, further away from Jered Weaver status. FanGraphs data shows the same thing:

Matt Shoemaker average fastball velocity

Note, this data also shows Shoemaker’s average velocity from 2014, when he had a 3.04 ERA and a 3.19 SIERA. This image confirms the steady increase in velocity of Shoemaker’s fastball, as it has recently resided at or even above its value from last year’s productive season. There have been clear results from this change, especially in the form of whiff rate, and predictably, strikeouts. Through June 19, Shoemaker’s whiff rate sat at a mediocre 10.5%.

Matt Shoemaker Outcome Breakdown Through June 19

 

Since the All-Star break, this is what that breakdown looks like:

Matt Shoemaker Outcome Breakdown Post All-Star Break

You might notice that his whiff rate sits at 13.7%, which would be top-5 among starters if he managed it for an entire season. Now, I’m not naive enough to think that number is where is true value lies after just 3 games, but he’s certainly improved off his 10.2% mark he had earlier in the season.

I’m not suggesting Shoemaker is the next coming of Clayton Kershaw. I’m not even sure if he’s the best pitcher on his own staff. But one thing is for sure: Matt Shoemaker is throwing the ball harder than he has in the past, and it’s working. And while it may not continue at this level, there’s no reason it should stop.


How Legit Is Carlos Correa?

Hearing Carlos Correa’s name can lead to polarizing reactions. If you’re one of the lucky few who managed to snatch him up in fantasy, then you celebrate every time he is mentioned. If you’re an Astros fan, I’d imagine you’d do the same, although being from New Jersey, I can’t say I actually know any Astros fans. However, if you’re not a part of one of those two groups, you’re probably asking “He can’t actually be this good, can he?”

Fortunately for me, I’m part of the group that owns him in fantasy. Because of this, I just want to enjoy the ride and not worry about whether it will end or not. With the fantasy trade deadline coming up though, it is something that I decided to look into. On a pace of 98 runs, 43 home runs, 115 RBIs, 17 steals, and a .297/.344/.573 slash line over a 162-game season, it’s hard to believe that he can keep that up.

First let’s take a look at the average. In 2014, at A+, Correa hit .325 with a .373 BABIP. You don’t expect a BABIP that high, but someone of his quality can certainly carry one over .320, so it’s at least not worrisome. This year, at AA, he actually improved on his average from a year ago, hitting .385 with a this time unsustainable .447 BABIP. He’s good, but not that good. This was evident upon his promotion to AAA, where he hit .276 with a .286 BABIP over 24 games. For someone only 20 years old and moving through the minors so fast, struggling (at least for his standard) was to be expected. In the majors though, he’s hitting a cool .297 with .312 BABIP, both seemingly in line with his career minor league numbers and looking like they will stay where they are.

Then there’s the OBP. Correa is reaching base at a .344 clip, which is actually lower than what he’s had at every level in the minors except for his 17-year-old debut season. His walk rate has decreased at each level, from 12.3% to 11.3% to 10.6% to the 6.7% it’s at right now. That’s concerning, but to be expected for such a young hitter moving up the ranks so quickly. His strikeout rate has also gone up to 19.1%, leaving his BB/K at an ugly .35. Without taking walks, it’ll be hard for Correa to continue getting on base at his current rate, but with the way he hits the ball and the lineup protection he has behind him, it’s hard to see his OBP dropping much below .340. Furthermore, if he keeps that high OBP and continues to bat in a top-4 spot (it’s hard to tell where he’ll bat in the lineup once George Springer returns from injury), his counting stats should have no problem continuing at their torrid pace as well.

It’s hard to believe anyone would have a question whether he could keep up his stolen-base production. He stole 18 bases earlier this year in the minors over 53 games while only being caught once. The year before that, he stole 20 bases in 62 games being caught 4 times. If anything, you’d expect Correa to actually have more stolen bases, but it’s hard to complain if he reaches the 15-steal mark.

The one thing that is probably the most in question is the power. His 24.5 HR/FB% would rank him 8th among qualified hitters, right below Mark Teixeira and above hitters like J.D. Martinez, Jose Abreu, Paul Goldschmidt, and Albert Pujols. Fortunately for Correa and his average, he sprays the ball around the field better than any of those players (even Martinez!), but that may not actually be helpful for his power as pulling the ball will generally produce more power. He also makes less hard contact than those above him on the HR/FB% leaderboard, which makes us question the number in the limited sample size we’ve seen.

In order to get a more accurate picture, I looked into the PITCHf/x data from baseball savant. Only 11 of Correa’s home runs were tracked this way, but that’ll have to do. According to the data, Correa actually had a higher average angle off the bat on his home runs, as well as a higher exit velocity (30.7 compared to 27.6 and 102.8 compared to 102.7). His batted ball distance, though, was shorter, calculating to 389 feet as opposed to the league average on home runs of 397.9 feet. While 10 feet is certainly meaningful, when combined with his better-than-average angle off the bat and exit velocity, it’s hard to credit too many of his home runs to luck. Even giving him 11 instead of 13 for the season, he’d still be on a 37 home run pace.

Getting away from the fancy numbers, the good news about all this is that Correa actually has an ISO that would be 6th best in the majors, due in large part to the 14 doubles he has collected alongside his 13 home runs. Correa’s power seems to be legit, and it wouldn’t be surprising to see him challenge for 30 home runs by the time the season is done.

After looking at the numbers, everything from Correa seems to check out, and it’s clear that he’s not just benefiting from luck. If he could achieve numbers even close to his pace, he already deserves to be called the best shortstop in the game. Over the past 10 years, the best offensive season by WAR for a shortstop came from Hanley Ramirez in 2008 when he had 125 runs, 33 home runs, 63 RBIs, 35 SBs, and a slash line of .301/.400/.540. Based on his prorated numbers, Correa could easily have that season next year, maybe with a few less stolen bases, a slightly lower OBP, and double the RBIs. Oh yeah, and he’s 20. Take that, Bryce Harper.