Earlier this month the Yankees took a chance on Brandon McCarthy, trading Vidal Nuno to the Diamondbacks for the sinker-balling right hander.  While McCarthy’s numbers in Arizona were ugly (5.01 ERA), his FIP was much better (3.82).  Through 4 starts the investment the Yankees made has paid off.  McCarthy has pitched to a 3-0 record with a 2.55 ERA.   Since the trade many of McCarthy’s peripherals have not changed much however, there have been a few differences.

Two keys factors for pitcher success — K% and BB% — have not changed much with K% decreasing slightly and BB% increasing sightly, although neither can be looked at as the reason McCarthy has been so much better since the deal.  Another important stat to look at is his BABIP, which, has improved a few percentage points since the beginning of the season but is still above his career average of .297.  However, a major difference that can be seen in McCarthy’s numbers since the trade is his GB%.  In recent seasons as McCarthy has began featuring his sinker more his groundball percentage has increased significantly.  the 55.3% he showed with the Diamondbacks was more than 7 percentage points higher than the career high he set in 2013.  Seeing this major change in GB% opens the question of what exactly McCarthy has been doing differently with the Yankees.

During a few of McCarthy’s starts with the Yankees, New York broadcaster Michael Kay has mentioned that McCarthy did not throw his cutter as frequently with the Diamondbacks compared to how often he has used it since the trade.  Looking at his PitchF/x pitch selection data does show an increase in the use of his cutter but it also shows several other interesting trends.

As Kay has noted McCarthy has used his cutter more frequently but the increase is minimal compared to several other big changes McCarthy has made.  With the Dbacks McCarthy used his curveball more that a quarter of his pitches making it his second most frequently used pitch.  However, once he was traded McCarthy had been using the cutter as his second most common pitch.  However, the significant drop in his curveball usage did not get added to his cutter usage it instead was added to a pitch he did not use in Arizona, a four seam fastball.

Since his trade to New York from Arizona Brandon McCarthy has been a completely different pitcher.  While his ability has not changed and the park has not been much of an improvement (103 for NY 104 for ARI) the biggest difference in McCarthy as a pitcher has been in his pitch selection, once again featuring a four seam fastball while reducing the usage of his curveball.  To this point the move to the Yankees may have been exactly what McCarthy’s career needed simply because it allowed him to change the way he attacks hitters.

Introduction

A couple months back, I introduced a new method of calculating pitch values using a FIP-based WAR methodology.  That post details the basic framework of these calculations and  can be found here .  The May and June updates can be found here and here respectively.  This post is simply the July 2014 update of the same data.  What follows is predominantly data-heavy but should still provide useful talking points for discussion.  Let’s dive in and see what we can find.  Please note that the same caveats apply as previous months.  We’re at the mercy of pitch classification.  I’m sure your favorite pitcher doesn’t throw that pitch that has been rated as incredibly below average, but we have to go off of the data that is available.  Also, Baseball Prospectus’s PitchF/x leaderboards list only nine pitches (Four-Seam Fastball, Sinker, Cutter, Splitter, Curveball, Slider, Changeup, Screwball, and Knuckleball).  Anything that may be classified outside of these categories is not included.  Also, anything classified as a “slow curve” is not included in Baseball Prospectus’s curveball data.

Constants

Before we begin, we must first update the constants used in calculation for Jule.  As a refresher, we need three different constants for calculation: strikes per strikeout, balls per walk, and a FIP constant to bring the values onto the right scale.  We will tackle them each individually.

First, let’s discuss the strikeout constant.  In July, there were 47,449 strikes thrown by starting pitchers.  Of these 47,449 strikes, 4,585 were turned into hits and 13,750 outs were recorded.  Of these 13,750 outs, 3,725 were converted via the strikeout, leaving us with 10,025 ball-in-play outs.  10,025 ball-in-play strikes and 4,585 hits sum to 14,610 balls-in-play.  Subtracting 14,610 balls-in-play from our original 47,449 strikes leaves us with 32,839 strikes to distribute over our 3,725 strikeouts.  That’s a ratio of 8.82 strikes per strikeout.  This is exactly the same as our from 8.82 strikes per strikeout in June.

The next two constants are much easier to ascertain.  In July, there were 26,244 balls thrown by starters and 1,328 walked batters.  That’s a ratio of 19.76 balls per walk, up from 19.36 balls per walk in June.  This data would suggest that hitters were slightly less likely to walk in July than previously.  The FIP subtotal for all pitches in July was 0.52.  The MLB Run Average for July was 4.17, meaning our FIP constant for May is 3.65.

The following table details how the constants have changed month-to-month.

Pitch Values – July 2014

For reference, the following table details the FIP for each pitch type in the month of July.

As we can see, only three pitches would be classified as below average for the month of July: sinkers, cutters, and changeups.  Four-Seam Fastballs and curveballs also came in right around league average.  Pitchers that were able to stand out in these categories tended to have better overall months than pitchers who excelled at the other pitches.  Now, let’s proceed to the data for the month of July.

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Overall

Pitch Ratings – July 2014

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Monthly Discussion

As we can see, Cole Hamels takes the top for this month due to the  strength of his overall repertoire.  Hamels was classified as throwing five different pitches in July (Four-Seam, Sinker, Cutter, Curveball, and Changeup) and managed to earn at least 0.1 WAR from all five.  The most valuable pitch overall in July was Ian Kennedy’s Four-Seam Fastball.  The least valuable was Drew Hutchison’s Changeup.  As far as offspeed pitches, Garrett Richards’s 0.5 WAR from his slider lead the way.  The least valuable fastball was Hector Noesi’s cutter.

On our 20-80 scale pitch ratings, the highest rated qualifying pitch was Chase Whitley’s changeup.  The lowest rated pitches were the changeups thrown by Drew Hutchison and Mike Minor, Hector Noesi’s cutter, and Josh Beckett’s curveball.  The highest rated fastball was Drew Hutchison’s four-seam fastball.

Pitch Values – 2014 Season

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Overall

Year-to-Date Discussion

If we look at the year-to-date numbers, AL FIP and MLB WAR leader Felix Hernandez still sits in the top spot.  Current MLB FIP leader Clayton Kershaw ranks ninth.  The least valuable starter has been Marco Estrada.  On a per-pitch basis, the most valuable pitch has been Ian Kennedy’s four-seam fastball.  The most valuable offspeed pitch has been Garrett Richards’s slider.  The least valuable pitch has been Marco Estrada’s four-seam fastball.  The least value offspeed pitch has been Marco Estrada’s changeup.  Needless to say, it’s been a rough year for Marco.  Qualitatively, I feel fairly encouraged by the year-to-date results so far.  The leaderboard is topped by two no-doubt aces, both of whom currently in the top two in their respect leagues in FIP, and Marco Estrada comes in at the bottom after posting the highest FIP among qualified starters so far.  For reference, the top five in the year-to-date overall rankings are currently 1st, 12th, 10th, 2nd, and 9th on the FanGraphs WAR leaderboards respectively.

Tip: Click on any acronyms for an explanation in the FanGraphs glossary of terms.

With his final turn in the rotation for July completed, we’ve now had almost exactly one full year of Sonny Gray – one year of the 24-year-old starting pitcher, the up-and-coming staff ace, the dueler of Playoff Verlanders. In that year, we’ve seen him do some great things, like going eight innings with nine Ks and no runs against the Tigers in Game 2 of the 2013 ALDS. We’ve also seen MLB Fan Cave forcing him to prank New Yorkers as a result of some unknown fine print embedded in his rookie contract. Above all else, the one thing we’ve always known is that Sonny Gray has a really good curveball. Let’s take a look at it for all of its 12 to 6, 80-MPH Uncle Charlie glory, from a game against the Astros in August of last year:

How good is his curveball? He has never given up a home run off of the pitch, with the only extra-base hits against the curve in his career being four doubles. In the past calendar year, Sonny Gray has saved more runs with his curveball than any other pitcher in baseball, and is behind only Corey Kluber and Yu Darvish in Runs Saved/100 curveballs. Having watched Kluber a lot, I suspect his slider/slurve is actually being classified as a curveball; I think it looks like a slider, but PITCHf/x doesn’t, so I will defer to the all-knowing pitch computer. Regardless, with the metrics we’re about to examine, Sonny Gray has one of the best curveballs in the game. What we’re going to focus on specifically are the advances in his curve’s effectiveness, spurred on by an adjustment in the way he throws the pitch.

To start, let’s take a look at the top-15 starters by wCB and wCB/C for the past calendar year:

As stated before, Gray is at the top in both of these categories. We should put a little more stock into wCB/C, as it normalizes all pitchers to runs saved per 100 pitches, taking away the advantage that one player might have due to throwing a certain pitch more frequently than another player. This is important for what we’re looking at, because Sonny Gray throws a lot of curveballs. How frequently does he throw curveballs? Here are the leaders for percentage of curveballs thrown over the last calendar year:

The words “second only to Scott Feldman” don’t come up very often, but here they are. Gray throws his curveball a ton. Not only has he always leaned on the curve as a major weapon in his arsenal, but he has actually increased his number of curves thrown since he came into the league every month except for May (when he maintained his % thrown) and June of this year, when he seemed to temporarily lose a feel for the pitch and threw more changeups. However, his first start of July had Gray saying this after holding Toronto to one run over seven innings:

“That was the idea, to really get (it) going again,” Gray said of the curveball. “I think the last five or six starts it’s been OK, but it hasn’t been a big factor. We did some things a little different this week and I was able to find that again.”

Over the last 30 days, Gray has thrown the curveball more than ever, up to over 32% for the month. Not only that, he has found more effectiveness in the pitch, with his whiff % on the curve up to a career-best 19.2% during July. There’s also reason to believe that this isn’t simply a good month for Sonny Gray’s curveball – what we are now seeing is the fruition of a change of approach with the way he throws the pitch that has been coming for some time now. Let’s take a look.

Here we have the release speed of Sonny Gray’s curveball for every start since he was called up:

He’s throwing the curve harder than he ever has, adding over three miles per hour since he started pitching in the majors. That’s not a small change. On top of the speed increase, he’s cut about 2.5 inches of vertical movement off his curve between his first start in the majors and now:

Finally, he’s added more three-dimensional depth to his curve in the form of a top-3 best horizontal movement over the past calendar year. Only Corey Kluber and Charlie Morton have had better horizontal movement on their curves in that time period.

Add all of that up, and we have this 84-MPH curve from his last start against the Orioles:

It now looks more like a slurve, with its high release speed and nasty late break away from right-handed hitters. As Eno Sarris included in his great article from October of last year, Gray said he “adds and subtracts” with the same grip on his curve to move between the 12-to-6 and slurve (which is sometimes classified as a slider) varieties. However, it seems as if he has leaned more toward the slurve option as time has gone on.

One question that arises out of this is “why throw the slurve more?”

Given his whiff % on the curve has increased as he has added velocity, I’d say that fact alone has supported the move to the slurve over the 12-to-6. However, there’s another potential reason that isn’t strictly rooted in statistics, and could be more about what goes into an elite pitching approach: by increasing his arm speed and flattening out the vertical movement of his curve, Gray can further deceive batters into thinking he’s throwing hard pitches before the bottom drops out. His struggles to find consistency with the changeup are well documented, so why shouldn’t he adjust his best breaking pitch to better fool hitters for whiffs and weak contact? As we’ve seen with Yu Darvish, the pinnacle of an ace approach may be one that includes a “great convergence” of arm slots and release points, in which every pitch looks hard until it’s not, or until it is.

Gray’s horizontal release points for all of his pitches are closer to one another than they ever have been during his major league career. Not surprisingly, his curveball and fastball were released on average at the almost identical horizontal point during his May and July starts, when he posted career-best whiff rates on his curveball (18.6% & 19.2%, respectively). June was an aberration, as Gray seemed to lose his release point in general and was tinkering with his delivery, leaning more on the changeup:

Sonny Gray has work to do on parts of his game before he takes the next step into the true elite of starting pitchers. His walk rate has actually increased this year to 8.5%, owing mostly to a lack of fastball command in deep counts, and his changeup is still very much a work in progress as a third pitch. However, his adoption of the hard curve and syncing of arm angles is a positive step toward dominance, and is a sign that he knows what works; he’s now perfecting it.

And now, my first go at a DShep Darvish-like GIF of Sonny Gray’s 12-to-6 curve from last August along with his harder slurve from his last start to compare:

Jose Abreu has seemed a virtual lock for the AL Rookie of the Year Award ever since Masahiro Tanaka went down with what could be a season-ending elbow injury. Abreu has done nothing but hit the hell out of the ball since storming into the Majors after fine-tuning his craft in Cuba. Rookies like Brock Holt and George Springer have had nice seasons of their own, to be sure, but they’re certainly no challengers to the might of Abreu.

Then, of course, there’s the best rookie that mostly nobody’s heard of. Let’s say hello to Mr. Kevin Kiermaier. A 31^st round pick in 2010, Kiermaier fought his way through the minors and finally, after a single game appearance last year, was called up for good on May 17^th. He’s since appeared in every game since May 31^st.  In 57 games (198 plate appearances), he’s hit .311/.362/.544. More importantly, he’s produced a .391 wOBA and 157 wRC+. Those are certainly pretty numbers. But can he keep it up? Kiermaier hit well at almost every level in the minors, but was always known for his superb glove work. Let’s look at his and Abreu’s numbers together, shall we? All stats herein are as they were prior to the inception of action on Monday the 29^th.

Abreu is numerically the greater offensive producer, if only by a slim margin. He’s only out-produced Kiermaier relative to the league (wRC+) by two points, but his wOBA is indicative of the fact that he’s driving the bar a lot further (.619 slugging percentage versus Kiermaier’s .544). Yet while chicks most certainly dig the long ball, it isn’t everything.

Kiermaier’s .362 OBP is a fantastic mark. It’s about twenty points higher than Abreu’s, and it’s incredibly beneficial for his spot in the lineup. For all his famous lineup tinkering, Joe Maddon has been primarily using Kiermaier out of the 9 spot of late. He even went as far as batting the pitcher (Alex Cobb) eighth on July 23^rd in an inter-league game against the Cardinals so that Kiermaier could bat ninth. Putting him there allows him to serve as what basically amounts to a second leadoff hitter when the lineup turns over, and there’s one more runner on base for the big boppers in the heart of the order.

Kiermaier also is much more of a two-way player. UZR and DRS are both very pleased with his defensive skill set. That’s not a surprise, as many scouting reports on Kiermaier always gave glowing reviews of his instincts and range in the field. It’s that defense that has allowed him to be right on Abreu’s tail in total value, in far fewer games. While UZR says that Abreu isn’t a total loss at first base, DRS says he leaves much to be desired. Naturally, first basemen traditionally aren’t employed for their gloves, but nobody complains when someone like Carlos Santana comes along and dazzles on both sides of the ball.

Now, the hard part. We want to extrapolate Kiermaier’s value over the same time span that Abreu’s accrued his, but how? It would be easy to simple do it over the same number of games played. However, “games played” doesn’t account for late-inning substitutions, or early exits. A better (but still not perfect) way of looking at it would be to extrapolate it over innings played. This is still not perfect, as a batter can hit in the top of an inning and then be replaced in the field in the bottom, and vice versa. However, I’m not a good enough statistician to develop my own metric for this (check back with me in a few months) and I can’t find anything out there to show just how much time a player has seen.

I used Baseball-Reference’s game logs for this, as they have a count of how many innings the player saw. Here’s how these numbers work out.

This is far from an exact science. Abreu and Kiermaier didn’t produce exactly that many wins every inning they plated, but it’s how the numbers work out. Now, if Kiermaier’s fWAR/I (fWAR per innings played) is multiplied by Abreu’s total innings played, the result is an fWAR total of 5.425. So far this season, Mike Trout leads all of baseball with 5.7 fWAR. Troy Tulowitzki is in second place with 5.1. That’s assuming, of course, that Kiermaier maintains his current level of play. And as I said before, “But can he keep it up?” The answer to that is “I haven’t a clue.” Here’s why.

There are two glaring realities. One is that Kiermaier hits a lot of ground balls. His BABIP would seem to indicate that’s he’s getting lucky, and his grounders are finding holes in the infield. For what it’s worth, here’s the league leaders in ground ball rate out of everyone who’s qualified for the batting title. It’s a mixed bag for sure. There are some good hitters around Kiermaier’s range, like Yasiel Puig, Alexei Ramirez and Melky Cabrera. Derek Jeter’s made a heck of a career out of a high ground ball rate and good BABIP. So while that isn’t an immediate cause for concern, it’s something worth watching to be sure. For reference, here’s his Brooks Baseball spray chart on the year.

The other is that southpaws have completely owned him. While right-handers are more common (and Kiermaier certainly has no problem with them), in the age of specialized bullpens it’s one awful quality to have. Managers (and the think-tanks in front offices) are surely catching on to this. Eventually, the scouting report will read to get a lefty reliever to face him in high-leverage situations, if they can. This also, of course, could be a result of small samples sizes. Kiermaier only has 43 plate appearances against lefties, and 155 against right-handers. Another thing to keep an eye on going forward.

However, what Kiermaier is doing is not wholly unsustainable. Players have made livings on putting ground balls in the right spots, and good players at that. However, the projections for the rest of the season aren’t too pretty. ZiPS predicts that he’ll produce a .259/.312/.385 line for the rest of the season. Steamer has him at .257/.311/.382. The projection systems are generally not too far off from reality, but it’s fun to think about what could be. Will Kiermaier end up being a 5-win player? The odds aren’t good. It’s certainly possible, though, and if it does happen prepare yourselves for a wonderful offseason debate on whom the rightful winner of the Rookie of the Year Award really was. In the meantime, let’s marvel at how darn good this former 31^st round pick has been.

Nicolas Stellini is a student, college baseball announcer, and amateur baseball writer. Check him out over at @StelliniTweets. 

Over the course of 162 games, there’s only so much influence a manager of any baseball team could have over their outcome. After 105 games the Mets actual record is 3 wins shy of their projected record of 53-52, making this a .500 team. Several factors contribute to this discrepancy like losing your ace pitcher to injury, scrambling for a closer to begin the season, developing a major league catcher, adapting to a new hitting coaches philosophy, and setting the most productive lineup possible just to name a few. What Terry Collins has done with this team to this point can only be admired, but help has arrived and changes must be made to maximize team production.

The move of Curtis Granderson from the cleanup to leadoff role proved to be successful as the team surged from June’s end through July. Daniel Murphy and Curtis Granderson’s slash line numbers are almost identical, batting average is the only big difference which Daniel Murphy leads Granderson by about.060 AVG points and make him a more ideal leadoff hitter. Curtis Granderson hit 6 home runs from the leadoff spot which minimized his RBI potential which essentially is the reason Sandy Alderson signed him. In moving Daniel Murphy into the leadoff spot, the Mets actually increase their leadoff OBP while putting Curtis Granderson into a role where his RBI opportunities increase dramatically.

Daniel Murphy’s SLG% is nearly that of Curtis Granderson with half as many HRs, meaning that Daniel Murphy is doing a better job of getting into scoring position than our current leadoff hitter. The only 2 reasons the Mets have kept Murphy out of the leadoff spot in the past were lack of speed on the basepaths and low OBP. Now Daniel leads our starting players in SB showing he has some speed and base running ability and his OBP is amongst the team leaders. David Wright being the best hitter on the team (despite struggles in 2014) deserves the 2nd spot in the order. His power has declined this season, however his OBP is still respectable and he should remain in a table-setting role followed by Granderson. Lucas Duda has earned his cleanup role as he’s hit over .280 in the past couple of months with at least 5 HRs per month. He is driving the ball to all fields and should be a key contributor to driving in runs once our table-setters do their jobs.

The top 4 lineup spots should be configured as follows:

1  2B Daniel Murphy        (.293/.340/.412) 28 2B, 7HR, 11SB

2  3B David Wright           (.278/.339/.401) 24 2B, 8HR, 5SB

3  RF Curtis Granderson  (.232/.339/.415) 18 2B, 15HR, 8SB

4  1B Lucas Duda               (.259/.356/.500) 22 2B, 18HR, 3SB

For the next spot in the lineup, this player has had a tale of 2 seasons. Travis d’Arnaud has adjusted quickly since his demotion to AAA on June 6th. Since being recalled on June 24th, d’Arnaud has a slash line of (.302/.337/.646). He has lengthened our lineup and has earned the spot of the 5 hitter.

5  C Travis d’Arnaud

Before June 6th demotion    (.180/.271/.320) 3 2B, 3HR

Since June 24th Promotion (.302/.337/.646) 7 2B, 4HR

Season Stats                            (.232/.298/.379) 10 2B, 7HR

Right after Travis d’Arnaud in the Mets order is when they begin to look thin offensively. Having early success in the season but struggling as of recent is Juan Lagares, the defensive wizard and minor league doubles machine. This kid showed an advanced approach to lead off the year and is capable of making the bottom of our order a productive one. He isn’t seeing the ball well like he was in the first half, but we need to remember he is in his first full season in the bigs and known primarily for his route to catch baseballs and cannon for an arm, any offense is a plus.

6  CF Juan Lagares (.271/.306/.375) 16 2B, 2HR, 2SB

7  RF Chris Young/Eric Young/Kirk Nieuwenhuis/Bobby Abreu/denDekker

Our right field position is a question mark. I’m not saying the Mets haven’t produced anything from the position, but they don’t have an everyday right fielder which is a need to be addressed in the off-season or via trade before Thursday’s deadline. Though not one player has stepped up and taken over this position, I still believe they have produced more than my “ideal” 8 hitter, Ruben Tejada. In every championship team there is that one scrappy player that is on the squad solely for defensive prowess. Through the course of the season I have seen many different Ruben Tejadas. I’ve seen the defensive shortstop, the slap hitter, the kid in way over his head, and the wanna-be slugger with warning track power. This player is undoubtedly our 8 hitter and those who look too dependently on his OBP must take into consideration how many times he has walked for the sole reason that the worst hitting pitching staff is just 4 pitches away.

Ruben has been intentionally walked 10 times, twice as much as any player on the Mets. Ruben Tejada hasn’t defended the way he has in the past which quieted his lack of offense. In a New York setting, he shouldn’t start and the Mets executives know that. Ruben is a bridge to the future, an inexpensive filler until we land in a position of contention where an offensive producer is necessary at the position. Until then we have a shortstop with a strong arm and instincts but lacks the speed to get too many balls up the middle or steal a base when we need him to. He has no power and is offensively irrelevant as his slash line below shows. A shortstop with any tools is an upgrade here.

8  SS Ruben Tejada (.226/.351/.281) 9 2B, 2HR, 1SB

Welcome to the fourth installment of xHitting!  As always, reader comments and feedback are super encouraged and appreciated.  (Links to parts one, two, and three)

Briefly recapping the method, the gist is to estimate the expected rate of each individual hit type based on a player’s underlying peripherals, and in turn recover all the needed components to compute expected versions of wOBA, OPS, etc.  The only real change to the model since last time is that I now utilize a “hybrid” predicted home run rate, that averages between actual and (raw) predicted home run rate, with the weight given to actual HR rate increasing in the number of plate appearances.  (This is explained in part three, for those curious.)

Perhaps the more exciting change, though, is that this time I actually have results for an ongoing season, which potentially can help for fantasy purposes.  (Not that most readers need my help necessarily.)  Related to fantasy usage, there were a few requests to see a full spreadsheet of past results (2010-2013 seasons), which I have posted here.  Again feel free to take it or leave it at your leisure.

Note: I collected most of these data at the All-Star Break, so numbers may be a few weeks behind, but they’re still mostly true.  Also, for time considerations I only fetched 2014 stats for qualified leaders.  This even leaves out a few big names, but I couldn’t justify time to fetch every player.

So far, I’ve typically posted the biggest “over-” and “under”-achievers for a given season.  And I suppose I’ll continue that tradition today.  But while these lists are useful for highlighting which players seem most likely to regress, it overlooks another main use of the model, which is to assess the realness of a player’s apparent “breakout” or “decline;” at least in-sample.  (In some cases, the model may think that a player’s breakout is entirely justified, given peripherals, while others it may view more skeptically.)  Thus, today I’ll also post a second list, of players who seem to have taken a pronounced step forward/step back this season, and what the model thinks of their season-to-date performance.

Okay, time for results!  I’ll start with the list of “over-” and “underachievers.”

A general pattern I notice is that, having worked with this model for a while now, there do seem to be players that give the model some trouble and have a disproportionate tendency to appear on this list from year to year.  A few of these players appear on this list… more on that later.

Partly for that reason, I wouldn’t necessarily say to “buy low” the guys on the left, nor “sell high” the guys on the right; although you can if you want.  I won’t address every player, but I have some scattered comments:

• For readers who prefer OPS, .020 wOBA translates to about .050 OPS, on the margin.
• .397 predicted for Teixeira?  Not sure where that came from…
• Poor Segura.  All things considered, I think nobody deserves a big second half more than he does.
• Whatever happened to Casey McGehee’s power?  The guy once hit 23 home runs in a season, but now has ISO of .073, with surprisingly low fly ball distance.
• Although Chisenhall’s breakout is not as impressive if you take out what the model thinks is luck, it’s still a pretty impressive improvement.
• Chris Davis is sort of the reverse of Chisenhall.  Adding back in what the model thinks has been bad luck, he’s still way down from what he did last year, but not nearly as disappointing as he probably has been to many owners thus far.

As mentioned, certain players do seem to be able to over/underperform the model somewhat consistently; the same way we think some pitchers are usually better or worse than their FIP.  With now 4.5 years of data to work with, however, I think I can make educated guesses about which players systematically deviate from the model predictions.  I’ll term this deviation the “player fixed effect.”

(Requiring at least 1000 PA from 2010 through 2014 first half)

Comments:

• Again, .020 wOBA is equivalent to about .050 OPS, on the margin.
• Taking out their apparent fixed effect, Teixeira is only underperforming his xWOBA by about .020, and Brian Roberts is actually doing about par.
• On the reverse side, Mike Trout’s “adjusted” xWOBA jumps up to .408, where really it probably doesn’t surprise us that he’s outperforming even that, since he’s Mike Trout.  And although Giancarlo Stanton misses the Top 10 cutoff above, his apparent fixed effect of +.022 would be 11th; so his “adjusted” xWOBA is more like .385.
• Yasiel Puig (.058) would also be on the list of “positive fixed effects” if we relaxed the PA requirement (he has 826 during this time).  And Matt Adams (~.040) might also be well on his way to that list; although he has fewer plate appearances still than Puig.
• I don’t really have good explanations/know any common themes for players with negative fixed effects.  Maybe readers can help?
• For Trout, home runs are pretty clearly the area where the model underestimates him.  In any given season (2010-2014), he hits about twice as many HR as the model thinks he should in the “raw” prediction.
• And Trout’s not the only “HR rate defier,” either; just the most salient.  In general, the model has never done as well with home runs as it does with singles, doubles, and triples.  It seems there are other important determinants of home run hitting that really should be in the model, but currently are not.  Intuitively, I sort of would like velocity and angle of the ball off the bat, but so far have not found a good data source to actually include these.  (Maybe that will change in the coming years as MLBAM releases “Hit F/X” style data?)  Until then, reader suggestions are also super welcome here.

And now, finally, for the other usage: here’s a partial list of players who have taken either a pronounced step forward or back this season, relative to established norms.

* – To avoid inflation from Coors Field, for these players I’ve taken the total from 2011-13 seasons only

Comments:

• At least in-sample, Brantley’s breakout seems to be pretty much entirely justified.  Of course this doesn’t mean that he won’t regress somewhat, but if I were to guess, I’m a little more optimistic than ZiPS and Steamer (which currently project .341 and .333 RoS, respectively).  Similar deal for some others.
• “Yikes” for Billy Butler and Domonic Brown, whose declines this season seem (at least in-sample) to be entirely justified.
• I’m not sure why the model dislikes Casey McGehee so much.  Obviously his fly ball distance (mentioned earlier) isn’t doing him any favors, and his .369 first-half BABIP is probably unsustainable.  Still, .277 xWOBA?  Seems harsh.

As with any fantasy advice, don’t take any of this too literally…  Take it or leave it as you see fit.

Lastly, although I hyped this piece from a fantasy perspective, the overall goal remains that I would love to see more work done to de-luck hitter stats, the way people do so often for pitchers.  (FIP for pitchers, and xWOBA or xWRC+ for hitters! Is the dream.)

Reader thoughts on how to improve the model, or requests for players not already mentioned?

Over the last couple of weeks, I’ve been looking into how a players’ stats, age, and prospect status can be used to predict whether he’ll ever play in the majors. I used a methodology that I named KATOH (after Yankees prospect Gosuke Katoh), which consists of running a probit regression analysis. In a nutshell, a probit regression tells us how a variety of inputs can predict the probability of an event that has two possible outcomes — such as whether or not a player will make it to the majors. While KATOH technically predicts the likelihood that a player will reach the majors, I’d argue it can also serve as a decent proxy for major league success. If something makes a player more likely to make the majors, there’s a good chance it also makes him more likely to succeed there.

Things that were predictive for players in low-A and high-A included age, strikeout rate, ISO, BABIP, and whether or not he was deemed a top 100 prospect by Baseball America in the pre-season. However, a player’s walk rate was not significant in predicting a player’s ascension to the majors. Today, I’ll look into what KATOH has to say about players in double-A leagues. For those interested, here’s the R output based on all players with at least 400 plate appearances in a season in double-A from 1995-2010. Due to varying offensive environments in different years and leagues, all players’ stats were adjusted to reflect his league’s average for that year.

Unlike in the A-ball iterations of KATOH, a player’s double-A walk rate is predictive — albeit only slightly — of whether or not he’ll make it to the show. While walk rate is statistically significant, it still matters much less than the other stats: it takes 3 or 4 percentage points on a player’s walk rate to match what 1 percentage point of strikeout rate does to a player’s MLB probability.

This version is also different in that there are a couple of significant interaction terms, signified by the last two coefficients in the above output. The “I(Age^2)” term adds a little bit of nuance into how a players’ age can predict his future success. While the “ISO:BA.Top.100.Prospect” term basically says that if you’re a top 100 prospect, hitting for power is slightly less important than it would be otherwise. Hitting for power and making Baseball America’s top 100 list both make a player much more likely to make it to the majors, but if he does both, he’s a tad less likely to make it than his power output and prospect status would suggest independently. Put another way, a few top 100 prospects hit for power in double-A, but never cracked the majors — such as Jason Stokes (.241 ISO), Nick Weglarz (.204 ISO) and Eric Duncan (.173 ISO). But virtually all of the low-power guys made it, including Elvis Andrus (.073 ISO), Luis Castillo (.076 ISO), and Carl Crawford (.078). For non-top 100 guys, many more punchless hitters topped out in double-A and triple-A.

By clicking here, you can see what KATOH spits out for all current prospects who logged at least 250 PA’s in double-A as of July 7th, as well as a few that fell short of the cutoff — most notably Joey Gallo, Kevin Plawecki, and Robert Refsnyder. Topping the list is Mookie Betts with a probability of 99.95%, and of course the prophesy was fulfilled when the Red Sox called up the 21-year-old last month. Here’s an excerpt of the top players from double-A this year:

Keep an eye out for the next installment, which will dive into what KATOH says about hitters at the triple-A level.

Statistics courtesy of FanGraphs, Baseball-Reference, and The Baseball Cube; Pre-season prospect lists courtesy of Baseball America.

Last week, I looked into how a player’s low-A stats — along with his age and prospect status at the time — can be used to predict whether he’ll ever play in the majors. I used a methodology that I named KATOH (after Yankees prospect Gosuke Katoh), which consists of running a probit regression analysis. In a nutshell, a probit regression tells us how a variety of inputs can predict the probability of an event that has two possible outcomes — such as whether or not a player will make it to the majors. While KATOH technically predicts the likelihood that a player will reach the majors, I’d argue it can also serve as a decent proxy for major league success. If something makes a player more likely to make the majors, there’s a good chance it also makes him more likely to succeed there.

Things that were predictive for players in low-A included: age, strikeout rate, ISO, BABIP, and whether or not he was deemed a top 100 prospect by Baseball America in the pre-season. However, a player’s walk rate was not significant in predicting a player’s ascension to the majors. Today, I’ll analyze what KATOH has to say about players in class-A-advanced leagues. Here’s the R output based on all players with at least 400 plate appearances in a season in high-A from 1995-2009:

This looks very similar to what I found for low-A players: Walk rate isn’t significant, and everything else has very similar effects on the final probability. However, the coefficients from this model are all a tad bigger than those from the low-A version, implying that high-A stats might be a bit more telling of a player’s future. Intuitively, this makes sense: The closer a player is to the big leagues, the more his stats start to reflect his future potential.

By clicking here, you can see what KATOH spits out for all current prospects who logged at least 250 PA’s in high-A as of July 7th. I also included a few notable players who fell short of the threshold, namely Joey Gallo (who checks in at a remarkable 99.8%), Peter O’Brien, and Jesse Winker. Here’s an excerpt of the top-ranking players:

Next up will be double-A. Unlike A-ball, double-A tends to be a random mishmash of prospects and minor-league lifers, so it will be interesting to see how KATOH handles this wide array of players. And perhaps double-A is where a player’s walk rate finally starts to tell us something about his future success.

Statistics courtesy of Fangraphs, Baseball-Reference, and The Baseball Cube; Pre-season prospect lists courtesy of Baseball America.

In the 2008 season the Yankees started the year with two young pitching prospects in their rotation: Phil Hughes and Ian Kennedy. These two pitchers were expected to be the future of the Yankees rotation. That didn’t really go as planned. The two pitchers struggled, and they both earned demotions as they combined for an ERA of 7.44. Hughes and Kennedy were simply not ready for major-league action. They gave up too many walks, didn’t strike out enough guys, and didn’t keep the ball in the ballpark. That’s a recipe for disaster when it comes to trying to succeed as a pitcher at the major-league level.

Nonetheless, these two pitchers showed enough promise as prospects for the Yankees to actually wait on them. In fact, after their demotions, Hughes and Kennedy spent most of their 2008 season in the minors due to mediocrity and injuries. The  Yankees were patient for a year with their young talent, however there is only so much time that goes by before you go from being a developing prospect to struggling major leaguer. The Yankees quickly gave up on Kennedy, and traded him to Arizona, where he showed decent success as a starter. In three seasons with Arizona, Kennedy compiled a WAR of 10.2.

The Yankees saw something in Phil Hughes. Hughes showed some promise in 2009 as a reliever, and then in 2010 as a starter who compiled a WAR of 2.5. However, there was the problem of Yankee Stadium not suiting Hughes’s skill set. Hughes was a fly-ball pitcher in a stadium that was known for being a hitter’s haven. Hughes always struggled as a Yankee when it came to keeping the ball in the park. The lowest home run rate that Hughes posted as a full season Yankee starter was 1.28 in 2010.

Both Kennedy and Hughes had some success over the  years; one could even argue that Kennedy was one of the best pitchers in the league in 2011. However, for the most part their careers have been a mixed bag. But times have now changed. Kennedy is now with his third team, the Padres, and Hughes is with his second team, the Twins. After mediocre 2013 seasons, the two pitchers are actually performing well.

As of right now, Hughes is fourth in the league for FIP among qualified pitchers. The only pitchers who have been better are John Lester, Adam Wainwright, and Felix Hernandez. Hughes is third in the league for WAR, right behind Lester and Hernandez. For the first half of the season Hughes has pitched like an ace.

Hughes has had the second best walk rate among qualified starters. Any walk rate below two is considered to be good, and Hughes’s rate right now is downright ridiculous. We can’t expect Hughes to be this good at not walking people, however the ZiPs/Steamer projections have him finishing the year with a walk rate between 1.31-1.38. That’s a pretty good projection, considering Hughes has never had a walk rate lower than 2.16. Hughes has also improved his home run problem, as he isn’t letting an egregious number of baseballs leave the park. The main change in Hughes approach has been his implementation of the cutter. Between 2012 and 2013, Hughes had dropped his cutter. This year, he reintroduced the pitch — throwing it 23% of the time — and dropped his usage of a slider. The change has proven to be useful for Hughes, and he no longer needs to rely on his fastball.

Then there is Kennedy. Kennedy has turned himself into the ace of the Padres staff this year. The main difference in Kennedy is that he has actually gained velocity on his pitches. Throughout his career he has always been a soft tosser. For most of his career, Kennedy averaged 89-90 MPH on his fastball. In 2013 he was up to 90 MPH. This year he is averaging 92 MPH.

Not only does Kennedy’s fastball have more velocity, but he’s also throwing the pitch more than he ever has since 2009. He has thrown his fastball 48% of the time this year. The last time he threw it more than 40% was 2010.

While it may be good to have more velocity, it also could be a little bit of concern when it comes to Kennedy because his secondary offering don’t appear to be very good. In fact, all of his pitches have negative wRAA values except for his fastball, which has a wRAA of 12.8. Most of Kennedy’s strikeouts have come off of his fastball. Having a good fastball is nice, but when Kennedy gets older — and his velocity starts to decline — he’s going to have a hard time being successful if he doesn’t have good secondary offerings.

Overall, the changes for these pitchers seemed to have worked. They’re succeeding in their own environments. While the Yankees never were able to see their prized prospects come into fruition, these two pitchers have found success away from New York. Learning to pitch at the major-league level is a learning curve. Some pitchers dominate right away. Other pitchers struggle for their first couple of years, and then things somehow start to click for them. I’m not suggesting that Kennedy and Hughes have figured out pitching, nor are they the best pitchers in the majors. However, they have proved that they are  at least very average starters, or maybe even above average major-league pitchers. Only time will tell.

On June 3rd Marlins pitcher Henderson Alvarez threw an 88-pitch shutout against the Rays scattering eight hits while not issuing a walk. On July 11th Marlins pitcher Henderson Alvarez also gave up eight hits while not issuing a walk but only made it five innings after surrendering 6 runs. While the circumstances surrounding these two starts aren’t completely the same they do a good job illustrating the phenomena of cluster luck.

Cluster luck, originally discovered and coined by Joe Peta in his book Trading Bases, essentially tells us how lucky teams have been by measuring the difference in the expected number of runs scored by a team based on its power (total bases), and base runners (hits/walks) and its actual number of runs scored. In Alvarez’s July start above he was a victim of poor sequencing, allowing his hits in bunches rather than spreading them out over the course of his start. For a more complete (and easier to understand) definition and some real world examples check out this and this.

What I will be attempting to do in this article is figure out a way to accurately estimate how many runs a pitcher should have allowed, and subsequently what his run average should look like, and then pinpoint certain pitchers who have been lucky or unlucky so far this season. Basically I am trying to normalize a pitcher’s RA by adjusting for sequencing and cluster luck.

Fortunately for me the heavy lifting for part one has already been done thanks to Dan Smyth. His metric, Base Runs (BsR), was developed and popularized in the early 1990’s and is an extraordinarily simple yet accurate way of estimating runs allowed using standard box score statistics. Base Runs for pitchers takes four inputs, innings pitched, hits, walks, and home runs, which are converted into four factors, A, B, C, and D. The final formula looks like A*B/(B+C)+D. For a lengthier piece on Base Runs, it’s properties, and it’s pros and cons consult this and this.

I took these statistics, including run average, for every pitcher in the majors through July 12th and figured his expected runs allowed by Base Runs, then converted it to Base Run Average or BsRA and took the difference between BsRA and his actual RA. I also calculated the pitchers’ RA- and BsRA- by taking the pitcher’s RA or BsRA and divided it by the league RA or BsRA (for reference the league RA is 4.14 and the league BsRA is 4.19). By taking the difference between the two, (BsRA-)-(RA-), we can figure out the percentage of extra runs compared to league average the pitcher should have allowed.

In the tables below you’ll see I’ve given this stat the name Luck%, a poor name admittedly since we’re dealing with percentages and I’m sure the differences aren’t completely due to luck but the name will have to do until I think of something better. For example Max Scherzer’s RA- is 80.92 (RA of 3.35/league RA of 4.14) meaning he has allowed runs at around 81% of the league average, but his BsRA- is 88.62 (BsRA of 3.71/league BsRA of 4.19) meaning he should have allowed runs at around 89% of the league average. We then get a Luck% of 88.62-80.92=7.71, so Scherzer should have allowed 7.71% more runs compared to league average, he has a Luck% of 7.71.

Whew. Now we can get to the names.

First the top ten qualified pitchers who have had their numbers most positively affected by cluster luck.

I like this list since it is very diverse. We have pitchers who have been pleasant surprises this season but who we all know aren’t really that good (Vargas and Simon). Older pitchers experiencing a late career resurgence (Beckett and Buehrle). Great pitchers (Greinke and Tanaka) and not so great pitchers (Chen). Hard throwing (Alvarez) and soft throwing (Young). High strikeout and low strikeout etc. etc. It’s good to see that not just one type of pitcher is affected giving me confidence that cluster luck does play a factor in a pitchers numbers to such a degree even this late in the season.

Now on to the top ten pitchers who have had their numbers most negatively affected by cluster luck.

This is a slightly less diverse list. Most of these guys are having disappointing seasons, but perhaps they haven’t been as bad as we think. Four of these guys have a below average RA, but an above average BsRA (or perfectly average in the case of Kuroda). Then there’s Anibal Sanchez who might just be one of the most underrated pitchers in baseball as his BsRA is seventh in all of baseball.

So what does Luck% end up telling us about a pitcher? We know that pitchers have little control over what happens after a ball is put in play, but what we’re doing here is figuring out which pitchers have been victimized by poor sequencing. Perhaps we can look at Luck% the same way we look at BABIP. If the measure is abnormally high compared to a pitcher’s career rate and the pitcher hasn’t made a substantial improvement in his mechanics or pitch repertoire perhaps some regression is in order.

So is Anibal Sanchez due for a spectacular second half? Maybe not. A myriad of factors could be influencing his low Luck%. We know that in general offense goes up when runners are on base and Sanchez could be especially susceptible to allowing runs to score in bunches. He has a slow move to the plate potentially allowing more runners to steal and get in scoring position. Perhaps his stuff is less effective from the stretch due to a breakdown in mechanics. Maybe he focuses too much attention the runners on base and not enough on the one at the plate, I really don’t know.

I only have half a season of data on 100 or so pitchers so obviously more research is needed. One could find the correlation between Luck% and peripheral stats such as K% and BB%, or find year to year correlations for Luck% to find out how much variation is actually luck and how much is skill. I’d definitely be intrigued by those results and I’ll likely revisit these numbers when the season ends.

I’m still relatively new to performing this kind of analysis so any constructive criticism would be greatly appreciated or if you’ve seen something like this done elsewhere on the internet. If you have suggestions for any improvements (especially the name) or further research I’d love to here it. If you think I majorly screwed up somehow I’d love to hear about too.