Archive for Player Analysis

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Consider:

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

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

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

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

But Sean, the Defense!

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

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

Who do you take?

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

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

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

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

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

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

For the 2015 season

And for the 2016 season

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

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

2015

2016

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

2015

2016

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

2015

2016

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

2015

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

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

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

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

2015

2016

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

2015

2016

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

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

2015

2016

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

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

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

First, fastballs with two strikes

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

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

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

Finally, let’s check out breaking pitches from lefties

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

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

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

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

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

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

Since even before the Pirates began using a shift against left-handed hitters regularly in 2013, this tactic has slowly become more and more prevalent.  In fact, it has become so prevalent that it is now a fixture in Major League Baseball.  As the shift has gotten more popular, many variations of it have been invented for different instances.  In some extreme shifts, the second baseman is placed in short right field, while the shortstop is positioned slightly to the right of second base.  Another shift places the second baseman, shortstop, and first baseman in between first and second, creating an almost impenetrable wall of three fielders on one side of the infield.  And, in some cases with certain hitters, all four infielders are placed to the right of second base.

All of these shifts have been proven to be immensely effective.  In fact, when the Pirates first began implementing it regularly when nobody else was using it that much, it gave them a jaw-dropping advantage over other teams.  Since then, every defense in the league has used it routinely — but mostly only against left-handed hitters.  There are many pull-happy right-handed hitters who have benefited immensely from not having shifts implemented against them regularly.  There is no reason why shifts should not be placed against right-handed hitters.  Of course, there are some right-handed hitters who go the other way as or more often than they pull the ball.  But there are some right-handed hitters, some of whom are very good, who could be considerably hampered by a shift.  Let’s take a look at some examples:

Robinson Chirinos:

Although Robinson Chirinos is not an impact player for the Rangers, he is a player who could have a significant amount of hits taken away from him because of the shift.  In fact, Chirinos could be one of the players who is most impacted by a shift.  He pulls the ball a shocking 62.1 percent of the time, and goes to center 24.1 percent of the time.  That means that he only goes the other way 13.8 percent of the time.  That percentage is so minimal that there should be a shift against him 100% of the time.  This shift would take away much of his production.  Actually, I invented a calculation that determines exactly how much of his production the hypothetical shift takes away.  I call this calculation “Fixed Average”, and it is very simple:  Fixed Average (FA) equals hits (H) minus by hits that would have been outs with the shift (SHIFT OUTS) divided by at bats (AB).  Or FA = (H – SHIFT OUTS)/ AB.  In this calculation, “hits that would have been outs with the shift” are grounders in between short and third that got through, or grounders up the middle that got through (the second baseman would have been playing up the middle with the shift).  However, some of the SHIFT OUTS  would still get through even with the shift.  So in that case it can be assumed that 1/4 of those hits (in between short and third and up the middle grounders) would have been hits.  And if the number of hits that would have been taken away is not divisible by four, then the calculation assumes that less than 1/4 of the discussed hits would have been stopped.

Robinson Chirinos’ regular average is .205.  Using the aforementioned Fixed Average, his average is .170.  That difference should be more than enough to convince teams to shift against right-handed hitters.

Maikel Franco:

Right now, Maikel Franco is the premier outlet of production for the Phillies.  He has hit 18 home runs so far this year, by far the most by a Phillies player.  Of course, his home runs wouldn’t be impacted by a shift, but he does have a .257 average, which would be impacted by a shift.  That average is respectable, but he pulls the ball 44.9 percent of the time and hits it to center 35.1 percent of the time.  He only hits it the other way 19.9 percent of the time.  So with a shift hampering his production, how would Franco do?

Using Fixed Average, that .257 average drops to .214.  Watch out Franco.  If a shift comes your way, you suddenly become less productive than your teammate Ryan Howard.

Brian Dozier:

Brian Dozier is a hard-hitting Twins’ second-bagger who has been a mainstay in the rapidly changing Twins organization for four years.  He has put together good power numbers while maintaining a less than desirable, but still respectable, batting average.  He has a very good amount of patience at the plate, keeping his OBP steady with his walks, but that would all change if a shift were implemented on him.  Out of all the players on this list, none pull the ball and hit it to center more than Brian Dozier.  And none hit it the other way less than him.  He pulls the ball 52.9 percent of the time and goes up the middle 34.2 percent of the time.  That means he goes the other way less than 13 percent of the time.

Right now, Brian Dozier’s average is .249.  Using Fixed Average, his average with the shift becomes .214.

Albert Pujols:

It is sad to see what a pull hitter Albert Pujols has become.  Although he was never one to go the other way with consistency, Albert always went the other way enough so that a shift would not be implemented on him.  However, since Albert joined the Angels, he has started to pull the ball with alarming regularity in order to prolong his quickly fading career.  Because of his new approach, Albert has been hitting the ball hard and often despite his climbing age.  That could change, though, if he were faced with a continuous shift.  That’s not to say he hasn’t ever encountered a shift.  He has been sporadically shifted on by opponents for the past few years.  But it’s been too little to significantly diminish his hitting.  In the absence of a continuous shift, Albert has kept on pulling.  He pulls the ball almost half the time he’s up, going to left field at a 49.2 percent clip.  He goes to center 32.2 percent of the time and hits it the other way a paltry 18.6 percent of the time.  That may not sound as significant as the other players on this list, but he still owns one of the most lopsided pull percentages in baseball.

Albert’s regular average is .249.  Utilizing Fixed Average, that average drops to a paltry .208.  Suddenly, the number-four man in the Angels’ batting order becomes an expensive waste.

Evan Longoria:

To have Evan Longoria on this list is perplexing.  He is commonly referred to as the “laser show,” because he sprays line drives all over the field.  However, it seems that the “laser show” only hits lasers to one part of the field.  Indeed, he’s been pulling for a while, although not as much as he is now.  This year, he has started to pull much more than he has in the past.  It’s been working.  His batting average, mired at or below .270 for the past few years, has suddenly jumped to .290.  It’s not as if he’s getting younger, either.  He’s almost 31 years old, just a year removed from his prime.  Therefore, it’s a weird time for him to be getting better.  There is only one dramatic change in his statistics that would explain exactly what caused his production to change.  His other-way percentage has dropped eight percentage points from last year, from 26 percent to 18 percent.

As pointed out before, Longo’s average this year is .290.  His Fixed Average is .255.  Therefore, his production would drop to even lower than it was before this year if a shift were implemented against him.

Edwin Encarnacion:

Feared stalwart of the Blue Jays batting order, Edwin Encarnacion has consistently produced 30-40 home runs a year.  Also, unlike teammate Jose Bautista, he has been known to keep a respectable average while blasting baseballs into the stands.  But there is a reason why his wRC+ hasn’t dipped below 135 since 2012.  Since that year, his other-way percentage has never climbed above 20 percent.  This year, it is at an all-time low, as he struggles to maintain production as his age and career progress.  His production would grind to a halt much quicker, and his value would drop much faster, if teams would put a shift on him.

Encarnacion’s season average is at a respectable .264, but his Fixed Average is .239.  That is a difference between a formidable All-Star and a three-true-outcome type of hitter.

Adam Duvall:

Adam Duvall burst onto the scene this year, giving the depressed Reds fans something to cheer about.  His majestic homers earned him an invite to the Home Run Derby, and his wRC+ has remained steadily above 110.  These stats are especially amazing considering his former stats in the major leagues were not good at all.  This has left people wondering, though, what the cause is for Duvall’s sudden jump.  Why has he suddenly vaulted himself into the upper echelons of baseball players?  What has he changed?  The answer is, of course, because he has started to pull the ball with consistency.  In his first few years in the bigs, Duvall went the other way 27 percent of the time with bad results.  Now, he only goes the other way approximately 18 percent of the time, and he’s experienced very good results.

Duvall’s average so far this season is just hanging onto “not horrible” at .246.  With Fixed Average, it is well into the “bad” bracket at .213.

Kris Bryant:

I saved the best (and the most surprising) for last.  Ever since he arrived at the major leagues, Kris Bryant has been pulling more and more.  His pull percentage has risen to 47.5 percent, and his other-way percentage has dropped to 18 percent.  Although he joins a list which includes the likes of Evan Longoria and Albert Pujols, Bryant would by far be the most affected by the shift.  He would be most affected because of how good he’s become.  Presently, he has a WAR above 5 and a wRC+ of approximately 150.  Many people have predicted him to win the MVP, and if he continues producing at this rate, he has a fair shot at this prestigious award.

Bryant’s average is .284, and his power is off the charts.  However, his Fixed Average for the year is .245.  Nobody with an average of .245 or below (except for pitchers) has ever won an MVP award.  Of course, his stats would still be considered respectable with a .245 average, because of his 25 home runs.  He’d also probably begin to go other way if faced with a shift regularly, so that we could assume his average wouldn’t drop to .245.  But overall, his stats would most likely not be as good as they are now.

I may have left out some right-handed hitters known for pulling, but these were the players with the most drastic pull stats.  There are many right-handed hitters who go the other way just as much as they pull, but overall the evidence is pointing towards implementing a shift against select right-handed hitters.  It would drastically change their production and the way the MLB works.  It all depends, though, on if teams are willing to use it.  It would help them immensely, but as with the shift against left-handed hitters, it will take time for teams to adopt the strategy.  But soon, as they begin to see results, it will slowly become more and more prevalent to the point where it is used almost as often as the shift against left-handed hitters.  The Fixed Average calculation is based on some assumptions utilizing each player’s play-by-play data; it is my best attempt at forecasting what would happen to each player’s production if they were to face regular shifts.  All the statistical information in this article was acquired from the games prior to July 24th.

The Drew Pomeranz-Anderson Espinoza trade has plenty of unknowns. With Pomeranz in the midst of a breakout season and blowing by his previous innings totals and Espinoza at the age when you normally would be graduating high school it is hard to know what to expect. Will Pomeranz continue to dominate or will he return to his previous self where he was either injured or mediocre? Will Espinoza blossom into the pitcher everyone sees when they watch his stuff, or will he fail to get his command under control and turn into the pitcher that his 4+ ERA in Single-A might seem to predict? To evaluate a trade like this you would have to do a lot of guesswork on the futures of these players. So instead of digging into the details it can be best to zoom out, and play the probabilities.

Predicting the futures of prospects is one of the most difficult tasks. Not only do you have to deal with the small samples of short minor-league seasons, but you have to project how those statistics will translate one, two or even three levels above their current competition. Additionally, you have to predict how the player will grow and mature as he enters his prime years. While some try to discover the answers to this question on an individual scale, it can be more effective to embrace the randomness present in each individual human being and create an average performance level for similar groups of players. To do this I tried to think of ways to quantify what makes Espinoza such a noteworthy prospect.

First, I thought that maybe the combination of age and strikeout rate that Espinoza has produced at Single-A Greenville this year might stand out and place him in a small group of notable players. A quick look at even his same team however seemed to prove this hypothesis wrong, as his teammate Roniel Raudes has almost identical statistics to Espinoza this year and is the same age. Raudes however is ranked 24th in Baseball America’s Red Sox preseason rankings, demonstrating that Espinoza’s stats this year are not anything incredibly special by themselves, even when accounting for age.

Next I examined how starters who have debuted for at least 50 innings in a year by age 21 have fared in the major leagues, since Espinoza is predicted to arrive in either his age 20 or 21 season. There are 44 pitchers that meet these qualifications and debuted between 1990 and 2005, some of which are big names (such as Hernandez, Sabathia, Greinke and Kerry Wood) while others not so much. In the time before they were scheduled to reach free agency, these players accumulated on average a total of roughly 8.2 wins above replacement of production over that span. There are some problems with this calculation, however. For one, almost a third of starters debut by age 21, so it is not terribly extraordinary. For every Felix Hernandez in this group there in a Rich Hunter who had one good minor-league season, which prompted a promotion to the big leagues. In his case, his career lasted only that one season. There are also pitchers such as Bud Smith in this group who were once top prospects but faltered in the big leagues. In his case he was once ranked first in the Cardinals’ system, a spot ahead of Albert Pujols, which can work as a friendly reminder that not all big name prospects pan out.

Another way of looking at Espinoza’s value is just to take his prospect ranking for what it is. Kevin Creagh and Steve DiMiceli have done research to try to put a trade value on prospects using Baseball America’s prospect rankings. The following table outlines their findings.

Analyzing data on Baseball America lists from 1994 to 2005, the two men created this table to calculate the average surplus value of players from each tier of the rankings. (Their process is fairly complicated, so it is worth it to take a look at their process here in an earlier version of the study). This can be a very simple yet effective way to evaluate prospects based on both their stats and scouting report (since both are used to create the rankings) while also eliminating as many individual biases as possible (of course the prospect rankings are subject to those same problems).

In Espinoza’s case, he was just recently ranked 15^th on the Baseball America midseason top-100, a five-spot jump from his preseason rank (though six players ahead of him have now graduated to the big leagues). This ranking puts him in the second tier of pitchers on the BA rankings and in line to have an average surplus value of 39 million dollars with a projection of 8.3 wins. This is almost exactly the 8.2 wins calculated earlier, though found through a very different method. While these rankings are in no way perfect, it is about as close as you can get to putting a concrete value on Espinoza’s skills (especially since both methods seem to agree), so we will use the $39 million value as a benchmark to compare with Pomeranz.

Moving on to Pomeranz, it is important to find a way to factor in all the different scenarios. You might have multiple ideas about how to take into consideration both this year’s statistics and those of the past, to try to come up with some sort of middle ground. While this is the right idea, this method is going to rely on assumptions that are unlikely to be made completely accurately. Instead we can use projection systems are much better at doing these calculations for us. For Pomeranz, this is the best way to include all the information about the many aspects of his performance and boil it down to one number.

Based on the depth chart projection on FanGraphs (the average between ZiPS and Steamer weighted for projected playing time), Pomeranz is projected to be worth about 1.3 wins the rest of the way. This is considerably worse than he has been so far this year, though much better on a per-inning basis than previous years, and still a valuable player. Using this projection, you can also project out the final two years of his contract assuming that he will continue pitching up to the same standard, by just doing a little math.

With roughly 46% of the season remaining at the All-Star break, you can use his rest-of-season projection to estimate his value over a full season, which ends up being 2.8 wins. First, though, you must use the same process as in the prospect ranking analysis to discount future performance since production today is considered more valuable than years down the line. Multiplying the value of each subsequent season by 0.92 you can account for the future discount rate of 8% (used in the prospect evaluation). Performing this adjustment results in 2017 and 2018 being valued at 2.6 and 2.4 wins respectively for a total of 6.3 wins over the three years with the Red Sox. At eight million dollars per win this totals to be around $50.4 million worth of production.

Finally, we must account for surplus value by subtracting how much Pomeranz is projected to make in arbitration. This year he is only making $1.35 million, but that should see a substantial increase after an All-Star season. I have little experience projecting arbitration but it seems reasonable that he would see his contract jump up to around $6 million in 2017 and see a more modest improvement up to around $10 million after a decent but somewhat less valuable season.  These estimates would total to around $17 million going to Pomeranz from the Red Sox in the three years, and subtracting this from the $50.4 million, you end up with around $33 million in surplus value.

In the end these surplus values are very similar. Espinoza’s value comes in a little higher at $39 million compared to Pomeranz at $33 million, but the $6 million gap is nothing the Red Sox would have to lose sleep over. In reality though that gap is just the gap in average outcomes for both players and it is more likely to be much more lopsided toward one side or the other. While this seems to show that the Padres are getting a slightly better deal, you can easily rationalize this trade for the Red Sox by saying that wins today matter more now for the Red Sox than for the average team since they are in the midst of a tight wild card and division race where they are favored over the division leader in the playoff odds on both FanGraphs and Baseball Prospectus. That way of looking at it does make it much more appealing for Boston. It was a trade that they had to make given their situation. Not a great trade, not a bad trade, but an adequate trade that could turn out either way.

The real takeaway from all this however is on San Diego’s side of the deal. For them it wasn’t just an adequate deal, and it wasn’t even just a good deal. It was a great deal! For them, the pushing of wins down the road is a net gain for them as opposed to a net loss as it is for the Red Sox. They pushed Pomeranz’s average outcome of 6.3 wins (which was being wasted on an noncompetitive team) down the road to a time when they may be competitive and wins will matter much more. Not only that, but they also increased the projected output to 8.3 wins. While it is possible that Espinoza could flop and be a major bust, that is all part of the math that works in the Padres’ favor. For every underwhelming Espinoza, there is a great Espinoza; one that was acquired in exchange for a player that had little value to the club at this point in time and someone the team will get to watch for years to come.

(Originally written before last Sunday)

It was announced that Orioles pitcher Dylan Bundy will start this Sunday on the road against the Rays. The move makes sense — the Orioles need good starting pitching and Bundy could become a good starter. I think Bundy will do very well as a starter, and in this article I’ll talk about why.

Dylan Bundy’s career started with incredible promise. Drafted fourth overall, his first eight starts in the minors were punctuated by a 0.00 ERA and a 20/1 K/BB ratio. By the end of the year, he was considered the top prospect in all of baseball. The next few years were rife with injuries — first Tommy John surgery in 2013, followed by complications in his shoulder which caused him to miss almost the entire year in 2014. Bundy hasn’t looked like the same pitcher since. His fastball velocity this season started at 92 MPH, much lower than the high 90s we saw in the minors. But since the beginning of June, Bundy has made a remarkable turnaround. Since June 9, the numbers are beyond outstanding, with 14.1 IP, 19 SO, only 4 BBs, and 0 earned runs. But the peripheral stats are even better.

I am currently in the process of writing an article about how I think the most important skill of a starting pitcher is getting to two strikes quickly. Since June 9, Bundy has done this better than any pitcher in baseball. In the top 10: Clayton Kershaw, Max Scherzer, and Stephen Strasburg, arguably three of the best pitchers in baseball. This obviously is not to say that Bundy is one of the best pitchers in baseball; his track record is far, far too short to proclaim that. But it bodes well for Bundy that over the past month he is controlling the ball as well as baseball’s top pitchers.

Bundy’s fastball velocity is also encouraging. Bundy throws a rising four-seam fastball, which bodes well for his ability to miss bats. But at the low 90s, he wasn’t able to generate a lot of swings and misses, and as a fly-ball pitcher was susceptible to home runs. Last appearance, Bundy threw his fastball harder than he’s ever thrown it.

The chart may not look like much, but there’s a clear trend here: Up. His fastball velocity has increased over 4 MPH since the beginning of the season, which is a gigantic leap.

The Orioles desperately need starting pitching, and Bundy could be that answer. The Orioles do not have the worst starting pitching in the league. In terms of WAR, that is currently the Reds. But the Orioles’ staff is really bad, even if they look worse pitching in a hitter’s park. Chris Tillman is their only competent starter, while the rest of their rotation contain some of the worst pitchers in the league. So stretching Bundy into a starter seems appealing.

There is a risk that Bundy will be much worse as a starter. Pitchers are notorious for throwing harder in the bullpen than they would as a starter, and given that the majority of Bundy’s success has come at a higher velocity, it would be reasonable to assume Bundy will not be nearly as effective as a starter as he is as a long reliever. I think this is correct thinking; we should not expect Bundy to start and still average 11 K/9. But his numbers as a reliever have been elite, so there is a lot of room for Bundy to come down and still be a quality starting pitcher. Starting pitcher is where Bundy has the most upside, and the sooner he gains experience, the sooner we can expect him to improve.

Bundy will probably do well this Sunday, especially against a Rays team that strikes out the second-most in the league. Don’t think this is a mirage. Bundy has the stuff and command to succeed, and I think we will see that as a starter.

Interestingly enough, one of the major postwar genres of Anglo-American literature was the academic comedy. Popularized in large part by Philip Larkin and the “Movement,” authors strove to poke fun at academic institutions and the conventions followed by the terrifically aloof professors. The most famous novel to fall into this genre is Lucky Jim by Kingsley Amis. The book features Jim Dixon, a poverty-stricken pseudo-pedant with a probationary position in the history department of a provincial university. A veritable alcoholic, Dixon attempts to solidify his position by penning a hopelessly yawn-inducing piece entitled “The Economic Influence of the Developments in Shipbuilding Techniques, 1450 to 1485.” Short novel made shorter, it doesn’t help him retain his position, but it does succeed in illustrating the banal formalities that academic writing necessitates.

In sabermetrics, there is a heavy reliance on sometimes inscrutable jargon, acronyms that sound like baby words (“FIP!”), and Mike Trout’s historical comps (Chappie Snodgrass is not a very good one in case anyone is wondering) that quite understandably renders the average fan mildly frustrated and the average fan over sixty wondering how we will ever make baseball great again. Typically, I enjoy those articles very much because they communicate news efficiently and analytically. Occasionally, however, articles stray into the Jim Dixon range of absolute obscurity, examining the baseball equivalent of “Shipbuilding Techniques,” whatever that may be. Such writings form the cornerstone of sabermetrics as they mesh history, theory, and sometimes economics.

Fortunately or unfortunately, my article today isn’t quite Dixon-esque, but it retains some of that style’s more tedious elements. It falls more closely into the category of two-minute ESPN quick sabermetric theory update. I don’t think that’s a thing. Seemingly pointless introduction aside, please consider what you know about DIPS theory. I won’t insult your intelligence, but it was developed by Voros McCracken at the turn of the millennium and has served as one of the principal tenets of the pitching side of sabermetrics ever since then. The theory, in its most atomic form, essentially posits that pitchers should be evaluated independently of defense because it’s something they cannot control. Hence “defense-independent pitching statistics.”

Certainly, it was a revolutionary concept and one that has even gained quite a bit of traction in the mainstream sports media. Announcers talk about how a certain pitcher would look a lot better pitching in front of, say, the Giants instead of the Twins. Metrics like xFIP only serve to quantify that idea.

But every grand theory or doctrine (DIPS is essentially sabermetric doctrine at this point) requires a corollary to frame it. And so I propose something I like to call the “WIS Corollary to DIPS,” where WIS stands for Weather Independent Statistics. The natural extension of evaluating pitcher performance independently of defense is to evaluate players independently of weather because it also exists outside of player control.

The basic idea of this is that weather plays enough of a role in enough games to superficially alter the statistics of players such that they cannot be accurately and precisely compared with the other players in the league because all of them face different environmental conditions. Taking that into consideration, all efforts must be made to strip out the effects of weather when making serious player comparisons. Coors Field is why Colorado performances are regarded with such skepticism, while the nature of San Francisco weather and AT&T Park is supposedly why that location serves as an apt environment for the development of pitchers.

Think about it — it’s something we already do. We look at home/road splits, we evaluate park factors, we try and put players on +/- scales. We talk about this constantly even at youth games. I have heard parents say many times, “If only the wind hadn’t been blowing in so hard he might have hit the fence.” It’s honestly a commonly held, yet generally unquantified, notion that the general public has.

Player X hits a blooper at Stadium C that falls in front of the left fielder for a hit. Player Y hits a blooper at Stadium D with the exact same exit velocity and launch angle as Player X’s ball, but it carries into the glove of an expectant left fielder. Should Player X really get credit for a hit and Player Y for an out? Basically all statistics, striving to communicate objective information, would say yes. If this kind of thing happens enough times over the course of a season, it can make a significant difference. A couple of fly balls that leave the park instead of being caught at the fence would put a dent in a pitcher’s ERA, while changing a player’s wRC+ by no small sum.

For that reason, players should be measured as if they play in a vacuum. One of the biggest goals of sabermetrics is to isolate player performance in order to evaluate him independently of variables he cannot necessarily control. Certainly, this has some far-reaching consequences if the idea gets carried out to its natural conclusion. Someone would likely end up developing a model that standardized stadium size, defensive alignment for varied player types, and other things of that nature. I’m not necessarily advocating for that, just for stripping out the effects of weather.

WIS by itself isn’t radical, but the extent to which it’s applied could be considered as such. As of now, it’s something consciously applied a relatively small portion of the time, but I think that it’s something that should be considered as much as possible. Obviously, there are issues with this. You can’t very well modify “raw” statistics like batting average or ERA so that they reflect play in a vacuum. What you could conceivably do is create a rather complicated model that requires a complicated explanation in order to describe how the players should have performed. And that’s something which brings us to an important point; the metrics that would employ this information would not be for the average fan; rather, they would be aimed at the serious analyst.

This is something I’ve already tried to employ with a metric I created called xHR, which uses the launch angle and exit velocity of batted balls to retroactively predict the number of home runs a player should have hit. The metric is still in development, but I think it’s something that works relatively well and can be applied to other types of metrics. For instance, an incredibly complex and comprehensive expected batting average could utilize Statcast information to determine whether a given fly ball would have been a hit in a vacuum based on fielder routes and the physics of the hit. By no means am I trying to assert that I have all, if any, of the answers. The only thing I’m trying to do here is to bring debate to a small corner of the internet regarding the proper way to evaluate baseball players.

Probably the most crucial thing to understand here is that the point of sabermetrics is to accurately and precisely evaluate players in the best possible way. Sabermetricians already do an incredible job of doing just that, but perhaps it’s time to take things a step further in the evaluation process by developing metrics that put performances in a vacuum. I know that baseball doesn’t happen in a void, but the best possible way to compare players is to measure them* as if they do.

WIS Corollary — One must strip out the effects of weather on players in order to have the most accurate and precise comparison between them.

*Oftentimes it’s necessary to compare players while including uncontrollable factors, like sequencing, especially when doing historical comparisons. It’s important to note that the WIS Corollary is applicable only in very specialized situations, and would generally go unused.

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On June 25, this was David Price’s tweet to family, friends and fans.  It was a clear signal that he knew the patience of the Boston fans and media was wearing thin.

Fast forward to the All-Star break and his “Made for TV” stats (those that casual fans know best) are underwhelming: a 9-6 record with a 4.34 ERA, which is worse than the MLB average of 4.23.  It’s not so much his ERA that’s the problem to fans, but more his inability to be consistent from start to start.  Price has three starts of six-plus innings allowing two or fewer runs, but also has four starts of allowing six or more runs.  With the rest of the rotation producing an atrocious 4.86 ERA, the Sox desperately needed Price to be the one to stop the bleeding, something he hasn’t been able to do.  But that doesn’t mean his underlying skills have deteriorated and all of a sudden he’s become a league-average pitcher.  In fact, the advanced metrics say he’s been extremely unlucky and that he’s due for a big second half. 

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* Rank is solely being used to establish a baseline for Price as a top 10 pitcher.

In 2014 and 2015 combined, Price was ranked in the top 10 of all pitchers in four of the skill-based statistics: K%, BB%, xFIP and SIERA (the latter two being ERA estimators with a weighting towards more pitcher-controlled outcomes).  Through the 2016 All-Star break, Price has maintained or improved his top-10 rank in K%, xFIP and SIERA but dropped a few spots in walk rate.  Despite the move from 9th to 10th in K% rank, his K rate is actually up from 26.2% to 27.1%.  The reason for the drop in rank is that 2016 newcomers to the list Jose Fernandez, Noah Syndergaard and Drew Pomeranz did not meet the minimum innings qualifier for the 2014/2015 combined list.  On the flip side, Price’s xFIP and SIERA are higher than they were the past two years, but he has improved his ranking versus his peers.  This is because xFIPs and SIERAs are both up 10% league-wide versus last year (due to all the home runs being hit) while Price’s increases are smaller.

So what is happening?  If his base skills are fine, why is his ERA so high and his performance so inconsistent?

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So everyone is familiar with ERA and can easily infer that 4.34 is no bueno for a $217-million pitcher.  But there is a reason these stats are labeled “Non Skill-Based” — that’s because these stats are influenced by factors outside of the pitcher’s direct control (defense, luck, sequencing, variance, etc…) and therefore have wide variability over small samples.  Three of these stats (HR/FB%, BABIP and LOB%) explain why David Price is a great rebound candidate for the second half.

HR/FB%

Price’s current HR/FB (home runs per fly ball) rate is 15.2% — which is good for being ranked 76th out of 97 qualified starting pitchers.  The past two years combined he ranked 19th.  To put this in context, Price’s career average is 9.4% while the 2016 league average is 12.9%.  Price has never recorded a full season (>150 IP) HR/FB rate higher than 10.5%.  Also, on balls hit into play against Price this year, 31.3% of them are fly balls, the second-lowest rate of his career.  The only season in which he allowed a lower fly ball rate was in 2012 when he won the AL Cy Young award.  Price is giving up fewer fly balls this year, but of the fly balls he is allowing, they are going over the fence at the highest rate of his career.  Those that remember Price giving up a HR in 10 consecutive starts this year are nodding violently right now.  His HR/FB% will regress towards his career norm (9.4%) and this should be the main reason for a big second half.

BABIP

Price is also suffering from an unsustainable BABIP (batting average on balls in play).  His current mark of .321 is well above his career rate (.289) and even above his highest full-season rate (.306).  Once a ball is put into play it is out of the pitcher’s control what happens from there.  This is why defense and luck influence this stat more than skill.  And with that said, statistical outliers here tend to regress towards career norms.  Even though Price is allowing ground balls at a higher rate than the past two years, his 2016 GB% is still lower than his career average.  BABIP can be influenced by the number of ground balls a pitcher allows, but he’s not allowing vastly more than his career average.  His BABIP should have some positive regression in it, which is another predictor of improved second-half performance.

LOB%

Price’s Left-On-Base% (percentage of runners a pitcher strands over the course of a season) is currently 70.9%, which is also below his career rate (74.7%) and would be his second worst full-season rate (70.0%) if the season ended today.  Similar to HR/FB%, he is ranked 73rd out of 97 qualified starting pitchers.  The past two years he ranked 22nd.  A pitcher with a higher than average strikeout rate should be able to sustain a slightly higher than average LOB%, but it’s playing out the exact opposite way for Price.  This is partly due to his inflated BABIP and HR/FB%; as these statistics continue to regress towards his career norms, the LOB% will creep up to expected levels.

Much has been made of Price’s velocity being down this year compared to any point in his career.  At the start of the season, his velocity was over 2.0 MPH lower than his career average (94.1).  He has since closed this gap almost entirely.  Here is his average fastball velocity by month (with number of starts):

April: 92.0 (5)

May: 92.5 (6)

June: 92.9 (6)

July: 94.0 (2)

If this upward trend in velocity stabilizes somewhere at or above 93.5, then nearly all the performance metrics within his control — velocity, K%, BB%, xFIP and SIERA — will be at or near his career norms.

Let’s dive a little deeper into that early-season velocity issue.  Below are two charts.  The first shows combined performance of 2014 and 2015 for ERA-qualifying starters while the second chart is the same data for the 2016 season through the All-Star break.  The orange circle is David Price.  The red circle (if shown) represents Price’s career average.  The blue circles are a hand selected peer group of the top 10 pitchers in the game (Kershaw, Sale, Arrieta, Scherzer, Bumgarner, Greinke, Strasburg, Syndergaard, Salazar and Fernandez).  Remember those rankings where Price was right around the top 10 — these are the guys usually outperforming him.  The gray circles represent everyone else.  Note: For these first two charts the top-right quadrant is Good, and the bottom-left quadrant is Bad (unless you’re a knuckleballer).

2014-2015 K/9 vs FBv

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2016 K/9 vs FBv

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The first graph shows David Price clustered where you would expect him — right at the middle-to-bottom of his top-10 peer group, with a healthy average fastball velocity and K/9.  The second graph (2016) shows Price in a similar relationship to his peers, but with slightly lower velocity and a higher K/9.  Note the gap between the orange (Price’s 2016) and red (Price’s career average) dots depicting his improved strikeout numbers this year despite the slightly lower velocity.  This graph also shows what freaks Noah Syndergaard, Jose Fernandez and (to a lesser degree) Jered Weaver are.

The final two graphs show the relationship between ERA and xFIP where xFIP is the more predictive estimator of a pitcher’s skill.  The bottom-left quadrant is Good (think Kershaw) and the upper-right quadrant is Bad (think Buchholz).  Anyone in the upper-left quadrant (Price in 2016) is a candidate for positive regression.

2014-2015 ERA vs xFIP

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2016 ERA vs xFIP

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The first graph again shows Price in his usual place — at the tail end of the top 10.  In 2014 and 2015 combined he had a very similar ERA (2.88) and xFIP (2.98).  The second graph (2016) shows the disparity between his ERA (4.34) and xFIP (3.16).  Pitchers with this large of a gap between ERA and xFIP are great candidates for regression.  The important takeaway is that his xFIP, relative to his peers, has stayed in that top-10 range.  This supports the point that some bad luck is the main element depressing his ERA.

David Price can easily be the best pitcher in the American League over the next two and a half months.  He already owns the lowest xFIP in the AL at 3.16 — the next-closest is Corey Kluber, at 3.34.  The skills above show he can sustain the xFIP level, but with some change in luck and maintaining his improved velocity, he doesn’t need to “pitch better”; he just needs to keep pitching — and the results will follow.

The Yankees have been a beacon of mediocrity this year, currently sitting under .500 and in fourth place in the resurgent AL East. Pitching is often a soft spot for the Yankees, as Yankee Stadium routinely ranks in the top half of the league in terms of worst parks for pitchers, according to park factors on FanGraphs. The short porch in right field is especially troublesome for hurlers. For this reason, park factors on FanGraphs have listed Yankee Stadium (tied with others) as the easiest park for a lefty to hit a homer from 2013-2015. Thus, there are bound to be some interesting trends among their starters. One such starter is the always entertaining Masahiro Tanaka. Let’s explore!

Tanaka struggled mightily with the longball during his first two seasons with the Yankees, posting a 1.24 HR/9 ratio from 2014-2015, which would have been good (or bad) enough for the eighth-highest among starters, had he qualified. That includes a ridiculous 1.46 HR/9 from last year, which would have ranked sixth (a few spots behind teammate CC Sabathia, who ranked third with 1.51 HR/9). At the same time, that high HR/9 may have been because Tanaka was victimized by a 15.7% HR/FB ratio during those two seasons. Since Tanaka is a righty (opposing teams are more likely to play lefties against him) who pitches half of his games at Yankee Stadium (lefty heaven), his HR/FB ratio is bound to be high, but not that high. Thus, it’s understandable that his HR/FB ratio decreased to a more normal 9.5% this year. It will probably be higher than that in the future, but this is just the ebb and flow of things: sometimes stats are higher than they should be, and sometimes they are lower.

Tanaka actually allowed a palatable 0.99 HR/9 in his first season with the Yanks, despite a 14.0% HR/FB ratio. His groundball rate remained virtually the same from that first season to the second, but one thing that made his home-run total lower in his first season as opposed to the second (other than a slight uptick in HR/FB) was a 9.3 K/9. When you allow fewer balls in play, you will allow fewer homers. However, in Tanaka’s second season, that number sunk to 8.1 K/9, a substantial drop. It has dropped even further this season, considerably below league average now at 7.1 K/9. What has brought about this dip in Ks? Changes in velocity often coincide with changes in K-rate:

Interesting. His velocity has remained quite stable throughout his time with the Yankees (if you look year to year). Let’s look elsewhere. Another determining factor for K-rate is movement. Here is Tanaka’s horizontal movement over the years.

His slider, cutter, and curve have lost a bit of that typical movement away from righties, but at the same time, the four-seam and split have improved their arm-side run, inside to righties. The sinker has pretty much held steady. How about vertical movement?

All of his pitches are dropping less/rising more, which is good for the four-seamer and cutter, but not so good for everything else. There are some good and some bad movement trends, but nothing that I would think would completely evaporate Tanaka’s strikeouts. There has to be something else. Has Tanaka’s whiff rate changed at all?

Indeed it has. The splitter has seen a sharp drop in swinging strike rate. Why is this happening? Did Tanaka change something? Let’s see if his pitch mix has any answers.

Woah! Tanaka has completely scrapped his four-seamer in favor of his sinker. Everything else has remained relatively stable. Here’s where we come full circle — back to the homers. I have a feeling that his reasoning for this change was that he wanted to improve his groundball rate in order to allow fewer home runs. Sure, the groundball rate has improved a little (up 3.4% in since last year), but this pitch swap seems to be the biggest reason I can think of for the sharp decrease in swinging-strike rate for Tanaka’s splitter. My theory is that his four-seamer, a relatively straight, hard pitch with a bit of rise, can help set up the splitter better than the sinker. The sinker and splitter are almost the same pitch; the split has a bit more drop, a bit more run, and is a bit slower. The velocity difference and movement difference between the splitter and four-seamer is much more drastic than those differences between the sinker and the split.

So, Tanaka seems to have traded some strikeouts for a few more groundballs/fewer homers. I’m not sure I like this change; his HR/FB rate was bound to normalize anyway. However, one thing I certainly like from Tanaka this year is that he has stayed off the DL. An asterisk has been over his name on draft day since the UCL injury a couple of summers ago. Despite staying off of the DL this year, people have been pointing to his better record on extra rest as a sign of fatigue. In 10 starts on extra rest this year, Tanaka has a 1.72 ERA, versus a 5.28 ERA in seven starts on regular rest. However, this has never been a noticeable split before this year, and it’s also worth noting that seven of those 10 starts have come away from Yankee Stadium, which I have deemed a bad park for Tanaka to pitch in.

Graphs and tables are from Brooks Baseball.

This article originally appeared on the Pirates blog Bucco’s Cove.

“Speed kills.” –Al Davis

Starling Marte is having a hell of a season stealing bases and this is one of the things that should have propelled him into the All Star Game without needing the stupid final vote. He is the top baserunner in the NL this year, he’s second in the league with 25 stolen bases, and he has a better percentage than the leader Jonathan Villar.

A recent game against the Cardinals gave a strong piece of evidence of Marte’s preternatural baserunning ability when he stole second base with Carlos Martinez pitching in the sixth inning:

The Pirates’ announcers commented on the fact that people very rarely steal off of Martinez. I went to look at the numbers: in 399 2/3 innings since entering MLB, Martinez has yielded only 10 stolen bases in 19 attempts. I think the 19 attempts is really indicative of his abilities to control the running game; people just don’t even try to run on Carlos Martinez. Martinez ranks 36th among all pitchers in SB/IP who have thrown at least 200 innings since 2013, which is decent. However, if we limit ourselves to looking at only righty starters over this time period (since it is much harder to steal against lefties and game circumstances are a bit different between starters and relievers), Martinez ranks 13th among pitchers with the same innings restrictions. (I’m not including tables for all of these stats, but if you want to see for yourself, mosey on over to the Baseball Reference Play Index, where all of this data comes from.)

Martinez has really shut down the running game since becoming a full-time starter in 2015, however. Over that time period, he ranks fourth among all pitchers (lefty, righty, starter, and reliever alike) having at least 250 innings in SB/IP, yielding only four SB in nine attempts over 282 innings.

(Note: If you bump this down to 150 innings to get more relievers on the list, Martinez is still in the top 10 for SB/IP.)

Of the four pitchers ahead of him in the table, two are lefties. (Sidebar: How the hell is R.A. Dickey on this list given the fact that he throws a knuckleball? It seems like it should be really easy to steal on him given that.) Martinez is similarly ranked (fifth) if you look at SB/Total Baserunners over the same period; in short, Martinez is really, really good at controlling the running game.

Furthermore, reigning eight-time Gold Glove winner Yadier Molina was behind the dish attempting to throw Marte out. Molina ranks first among all catchers since 2002 in his ability to control the running game by the defensive metric rSB. Obviously Martinez’s ability to prevent the stolen base is helped by having Molina behind the dish, but the combination of these two has been deadly over the past season and a half, making Marte’s accomplishment all the more impressive.

The Martinez/Molina duo (and Martinez in general) has only allowed one other stolen base this season so far. Who was it? None other than Bartolo Colon! Actually, I’m kidding, it was Starling Marte, which is almost as crazy! He has the only two stolen bases this season against a guy who only gave up two all of last season. These are also the only two attempts against Martinez all season. On May 6, after a bunch of false starts, pickoff moves, foul balls, and laughs between Molina and Marte, he finally got his stolen base:

The thing I find most entertaining is how loose everyone seems, goofing off and laughing about the play that just happened, which seems relatively rare in this day of boring interviews and generic soundbites. Molina had a good laugh about that one, but he was pretty upset about the more recent stolen base.

There’s nothing to be mad about, though; Martinez and Molina simply got burned by the best baserunner in the game right now.