Archive for Player Analysis

Introducing Two New Pitching Metrics: exOUT% and exRP27

exOUT%

In the early 21st century, Oakland Athletics’ General Manager Billy Beane revolutionized baseball forever. He was the first general manager in baseball to heavily utilize sabermetrics in his baseball operations. This isn’t a history lesson though, I bring him up because of his idea that outs are precious, and as a hitter your goal is to not make out, thus him prioritizing OBP so heavily. In the following years, baseball statistics have seen phenomenal progress on both offense and for pitchers. While I believe FIP and xFIP are both very useful statistics in really measuring a pitcher’s skill, my problem is that they essentially ignore all the batted ball data that we have (GB%, FB%, LD%). SIERA and tERA have solved some of these problems, but are far from perfect, and I believe the more statistics we have, the better.

As I mentioned with Beane, while we largely focus on a hitter’s ability to not make out, we still don’t have a catch-all statistic to realize how effective pitchers are at getting batters out, because if the batter’s goal is to not make out, the pitcher’s goal is to get the batter out. So I present to you expected out percentage, or exOUT% (the name is certainly a work in progress). exOUT% sets out to answer a simple question: For any plate appearance, what is the likelihood that the pitcher will get the batter out? This can easily be found by just looking at a pitcher’s opponent OBP, but that is rather primitive, and we can get a better estimate by focusing more on pitchers’ skills to strike people out, not walk batters, and the type of contact they are giving up, and also trying to negate the effect of the defense by him, by just using league averages. So to calculate a pitcher’s exOUT%, I used K%, BB%, GB%, LD%, FB%, lFFB%, and 2014 league averages on ground balls, line drives, and fly outs. (HBPs are essentially ignored but can certainly be incorporated in a future version, this is pretty much exOUT% v1.0)

I want to give full disclosure, I am not a statistician or close to it. Math and statistics are an area of interest and I am currently pursuing a degree in math-economics, but I am far from a professional, so I recognize there are going to be errors in my data. This is an extremely rough version; there’s even a combination of data from this year and last year so there will be inconsistencies, as I don’t have the resources to gather all the data I need. If after reading this, you are interested in this and would like to take this further, please feel free to contact me if you have the skills necessary to advance this further (or even if you don’t).

I will first post a simple step-by-step breakdown of how to calculate exOUT%, and then get into more detail and take you through it with Clayton Kershaw, because well, he is awesome.

1- Add K% and BB%, subtract this percentage from 100%, this leaves you with a balls in play%, let’s just say BIP%

2- Multiply the pitcher’s GB% (make the percentage a number less than 1, for example 40% is .4) and BIP% (leave it between 1 and 100, ex 40%), this gives you a GB% for all PAs, not just balls in play, we’ll call this overall GB%, or oGB%… now multiply this percentage (in between 1 and 100) times the league average percentage of ground balls that don’t go for hits (league average is .239 on ground balls in 2014, so out percentage on ground balls is 76.1%, but make it .761…. this will give you a percentage you can leave between 1 and 100, if the number is 20%, that means that there’s a 20% chance that pitcher will induce a ground ball out that PA, assuming league average defense, we can assume this because we’re using the league average for batting average on groundballs… we’ll call this exgbOUT%

3- Now follow the same steps but with LD%, exldOUT%, the percentage chance for any given PA that the pitcher will produce a line drive out. (The league average on line drives last season was .685 (!) so that means there is a 31.5% chance a line drive will result in an out)

4- Same thing with FB%, sort of, because we also want to incorporate IFFB%. So multiply a pitcher’s FB% by their IFFB%, this gives you the percentage of balls in play that the pitcher produces an infield fly ball (bipIFFB%). Multiply this percentage by their BIP% to get his overall percentage of PAs that result in an infield fly, and this will also be their exiffbOUT%, because any infield fly ball should be converted to an out, and if not, it’s to no fault of the pitcher, so we won’t punish him. Next subtract a pitcher’s IFFB% from 1 or 100, whatever, and this is their balls in play percentage of fly balls that are normal fly balls, to the outfield. Multiply this number by their BIP%, this gives you the overall normal FB% for a pitcher, not just balls in play. Multiply this number by .793 (the league average on fly balls in is .207, so there’s a 79.3% that a fly ball will result in an out). This number is the percentage chance that for any given PA, the pitcher will produce a fly ball out to the outfield. Add this exnfbOUT% (n for normal) and his exiffbOUT% and you have his exfbOUT%, the percentage that for any given PA, the pitcher will produce a flyball out, to the infield or outfield.

5- Add K% + exgbOUT + exldOUT + exfbOUT

6- You have your exOUT%

 

The terms are not that technical or scientific so I don’t confuse anyone — I tried to simplify a very complicated procedure as much as possible. To clarify and give you an example, let’s go through Clayton Kershaw.

Kershaw profiles like this (I compiled this data on 8/21): 32.3 K%, 4.9 BB%, 52.8 GB%, 26 FB%, 11.8 IFFB%, 21.2 LD%.

So let’s look at the balls that don’t go in play, strikeouts and walks. Add the two and balls not in play percentage is 37.2, 4.9% are walks and thus won’t be an out, and 32.3% are strikeouts so will be an out. Thus far, Kershaw’s exOUT% is 32.3 (of a possible 37.2 so far)

Now let’s look at the balls in play. People will usually say that a pitcher can’t control what happens when a ball is in play, but I vehemently disagree, the type of contact the pitcher gives up can’t be ignored and largely effects what will happen to the ball in play. I will quote a FanGraphs article here to explain it, “Generally speaking, line drives go for hits most often, ground balls go for hits more often than fly balls, and fly balls are more productive than ground balls when they do go for hits (i.e. extra base hits). Additionally, infield fly balls are essentially strikeouts and almost never result in hits or runner advancement.” And FanGraphs also gives us this data from 2014.

GB: AVG- .239, ISO- .020, wOBA- .220

LD: AVG- .685, ISO- 190, wOBA- .684

FB: AVG- .207, ISO- .378, wOBA- .335

 

So this means that fly ball pitchers are most likely to get outs, although they may be less effective because when they don’t get outs, it’s more trouble than for ground ball pitchers. But remember, this statistic is just finding the chance that the pitcher will get a hitter out.

 

All right, so, let’s calculate Kershaw’s exgbOUT%, exldOUT%, and exfbOUT%; you can follow the numbers along with the steps I listed above.

 

GB%- 52.8

62.8 x .528 = 33.1584

(33.1584 x .761)=  25.23354424 exgbOUT

 

LD%- 21.2

62.8 x .212 = 13.3136

(13.3136 x .315) = 4.193784 exldOUT

 

FB%- 26

26 x .118= 3.068 bipIFFB%

26 x .882= 22.932 (bipFB%)

62.8 x .22932= 14.401296 (onFB%)

14.401296 x .791= 11.3914251 exnfbOUT%

62.8 x .03068= 1.926704 oIFFB% and exiffbOUT%

exnfbOUT% + exiffbOUT% = 13.3469317 exfbOUT%, if you followed my math exactly a decimal may be off, like 13.31 something, but this is the number the excel doc chugged out, so I’m trusting that, my iPhone calculator can’t carry all the decimals sometimes.

Now add them all up

32.3 + 25.23354424 + 4.193784 + 1.926704  + 11.3914251 = 75.07%

K% + exgbOUT% +  exldOUT% + exiffbOUT% + exnfbOUT% = exOUT%

The league average exOUT%, using league average statistics from 2014 for the ones involved, is 69.8%. Scherzer leads the majors (well the 89 pitchers I was able to export data from FanGraphs) with a 76.43 exOUT%. If you want to look at it as a more concise and better version of opponent OBP, his is .236, so, you know, good. Here is a picture of the data for the top 37 — the J column is what you are looking at. Betances is in their because I wanted to calculate one reliever. 

View post on imgur.com

All right, I’ve explained it a bit in the prologue, but now that you’ve seen it, let me explain more why I like this stat. Well first, I created it and calculated, so, well, yeah… but I also like this stat because it answers a very simple question “How good is a pitcher at getting people out?” Pitching in its simplest form, is exactly that, getting people out. The stat recognizes that there’s basically only these outcomes for an at bat: strikeout, walk, ground ball, line drive, and fly out, and looks at the pitcher’s stats in these categories to determine how many people he should be getting out. The stat is more predictive than evaluative in nature, because you can calculate a pitcher’s actual out percentage, but that doesn’t nearly tell the whole story, because a lot of luck is involved with balls in play, and other fluky outcomes.

This operates under the basis that a ground ball will perform the way the average ground ball does, a line drive performs the way an average line drive does, and a fly ball behaves the way a typical fly ball does. There could be guys getting very fortunate with ground balls: having a great infield behind them, balls not squeaking through the holes; with line drives: being hit right at people; and fly balls: staying in the park, having outfielders who cover a lot of ground. And there could be guys who are getting unlucky: the ground balls are getting through the holes, the infielders don’t have range; line drives seem like they are always going for hits, and fly balls are falling in. This says that a pitcher can’t control that, but they can control how much they strike out people, how much they walk people, and how often they give up ground balls, line drives, and fly balls, and if these balls in play behaved the way they should, the pitcher should be getting this percentage of people out.

I will address the flaws I have found with it. As much as getting people out is important, sometimes what happens in the plate appearances that don’t end in outs are almost as important. This only deals in batting average regarding balls in play, but wOBA is very important too. Fly balls are more likely to be outs than ground balls, but the wOBA on fly balls is over 100 points higher. Additionally, I’d prefer instead of ground balls, line drives, fly balls, to use soft contact, medium contact, hard contact, because that is a truer test of pitcher skill, however, I did not have this data at my disposal as far as league averages on what the batting average is for soft contact, medium contact, hard contact (if someone does, please contact me like I said). So what I have for now will do and this batted ball data is still a good measure. I set out to calculate what percentage of batters a pitcher should be getting out, and that is exactly what I found out. So while it’s not perfect, it has its use, and it’s something to build on.

 

exRP27

And build on I did. While the out percentage is nice, it doesn’t give us a measure like ERA or FIP or xFIP, that tells us how many runs a pitcher should be giving up. So using the data I used to calculate exOUT%, I present to you exRP27 (expected runs per 27 outs, a stupid name for a hopefully not stupid stat).

The basis for this stat is this data from FanGraphs, “Line drives are death to pitchers, while ground balls are the best for a pitcher. In numerical terms, line drives produce 1.26 runs/out, fly balls produce 0.13 R/O, and ground balls produce only 0.05 R/O.” (I don’t know how this was calculated, or when it is accurate for, but this is what I got). We don’t know this for soft contact, medium contact, hard contact, so again I’m sticking with ground balls, line drives, and fly balls. 

All right, so what I am going to do using this stat and the pitcher’s K%, BB%, GB%, LD%, and FB% is see how many runs the pitcher should be allowing over 27 outs, and then adjust it to get it on a scale similar to ERA, FIP, and xFIP.

Keeping Clayton Kershaw as our example, let’s take a look.

Kershaw’s K% is 32.3 — we’re multiplying this by 27 (for outs in a game), and we get 8.721 K’s, so 0 runs so far because a K will never produce a run

Now GB%. His exgbOUT% is 25.23354424, multiply this by 27 and we get 6.8 (ish, final number will be exact via the Excel doc). Multiply this by .05 (the runs per GB out he gets) and we get .34 runs.

LD%- his exldOUT% is 4.193784, multiply by 27 and get 1.13232168, and multiply this by 1.26 for LD runs/out and we get 1.43 runs

His exfbOUT% is 13.3181291, now multiply by 27 get 3.6 and then that by .13 and you get .47 runs

Add up all these exRUNS and Kershaw’s total is 2.24. However, we can’t stop here because the number of outs he’s recorded is only 20.3 (8.7+6.8+1.1+3.6) approximately. 20.3 is the rounded up total. So get this 20.3 (or whatever the pitcher’s exOUTS is) up to 27  by multiplying by whatever it takes, and then multiply his exRUNS by this same number. For Kershaw you end up with 2.97 exRP27. The league average would be 3.78. Last year’s average ERA/FIP/xFIP was 3.74, but when I adjust everything to that, everyone’s exRP27 just goes down slightly (Kershaw’s from 2.97 to 2.94), but I want it to be on a more realistic scale where everyone’s totals are lower and a really good exRP27 is comparable to a really good FIP, like in the low 2s. 

So I don’t know what the statistic’s correct way is, but here is what I did to make it work. I calculated what his “ERA” would be using by multiplying his exRUNS by 9 and then dividing that by his exOUTS. His was .99, the league average was 1.26. I then did .99/1.26 to get .78 or so, I then multiplied that by his exRP27 and got 2.34. I felt like this was more realistic and in line with his ERA/FIP/xFIP. Obviously, can’t be the same because they measure different things, but just got in in the area. And the same is done for all pitchers. Obviously, not everyone gets multiplied by .78 of course. The league average remains 3.78, between last season and this season’s average for ERA/FIP/xFIP.

Here is the leaderboard for that (S column):

View post on imgur.com

 I really like this stat a lot, and feel like it does what I wanted to accomplish: figure out how many runs a pitcher should allow per 27 outs given his K%, BB%, GB%, LD%, FB%, and the notion that balls in play will behave the way they normally do, as anything else is likely luck and not indicative of the pitcher’s performance.

I look at Sonny Gray as someone this stat is perfect for. His ERA is outstanding at 2.04, but his FIP is 3.00, his xFIP is 3.47 and his SIERA is 3.50. The problem is, at least with FIP and xFIP for sure, is that they ignore what happens when the ball is in play. He doesn’t strike out too many people, he has a good BB% but not spectacular, and he’s given up 10 home runs, a fair amount, so this hurts his FIP and whatnot. However, instead of saying “well he will regress, look at his FIP/xFIP/SIERA” this looks at why he’s having this success, and it has to do with the balls in play, which is getting ignored. Gray’s LD% is just 14.6! That is really good! Second best of the 90 pitchers I did this for. And his GB% is 54%, 9th best, also really good. The pitcher does have control over the type of contact he allows, and the fact that Gray is producing a ton of ground balls, and very few line drives, is why he’s been so successful. His 2.34 exRP27 suggests that he has not been as good as his 2.04 ERA suggests, but he’s not as far off as the other stats suggest. 

Obviously exRP27 is far from perfect, and is in no way supposed to replace FIP/xFIP/SIERA, but it is something to look at with them. I am a big believes in aggregation, so I think that averaging some combination of these 4 stats together or them all, is an even better way to evaluate a pitcher. We’ve got more data than ever, so it makes sense to use it, exRP27 and exOUT% are just more examples of utilizing this data to help better evaluate pitchers.  

I hope you guys enjoyed. Any feedback please comment or contact me. Next I will be looking at exWOBA against for pitchers using similar data, and exWOBA for batters using the data but for hitters.


The Improvement of the Indians Starting Rotation

Remember at the end of last season and before this season when we all foresaw an Indians rotation that could possibly feature somewhere between 2 and 5 really good, and possibly great, starting pitchers?  Don’t get bogged down on the slight exaggeration of that 1st sentence – To recap what we were looking at coming into this season for the Indians’ rotation:  Corey Kluber won the 2014 AL Cy-Young; Carlos Carrasco had a string of starts to end 2014 in which he seemingly (finally) figured out how to harness all of his powers in a bid to ascend his name to an echelon where only Clayton Kershaw’s name resides; Danny Salazar has always had elite swing and miss stuff and was also excellent in the second half of 2014;  Trevor Bauer and his Costco-sized arsenal of pitches have made some of us incredulously, if not warily optimistic since he was taken 3rd overall in 2011; and even T.J. House made us pause and take notice with his strong second half of 2014.

Then, like hype men with a special blend of Cleveland Kool-Aid being intravenously administered, Eno Sarris and Daniel Schwartz posted one of my favorite FanGraphs articles ever, Pitch Arsenal Score Part Deux, and the anticipation over the Indians’ rotation pulsated like a vein in the neck of John Rambo in the midst of fleeing from man-hunters.

The supporting cast, the lineup, looked poised to support the staff with plenty of runs.  Returning would be: break out star Michael Brantley; bounce-back candidate Jason Kipnis; now-full-time-first-basemen, Carlos Santana; a supposedly healthy Michael Bourn; an offense-first but totally-respectable-defensively, Yan Gomes; and an actually-not-that-horrible-in-2014, Lonnie Chisenhall.  Slugger Brandon Moss, and contact-happy-supposedly-glove-first Jose Ramirez had secured full-time spots as well in RF and SS respectively.  So even though it wasn’t without flaws, it seemed like they would allow the pitchers to rack up plenty of fantasy-relevant wins.

Note: This post isn’t about the disappointment of the Indians, though they have been disappointing; it’s more about what factors beyond luck have contributed to the numbers of the Indians’ starting rotation at various points throughout the year, and the disparity (big or small) between the pitchers’ rates and predictors at those points.

The Indians’ starting pitchers, or at least the top 4 (Kluber, Carrasco, Salazar, and Bauer) have, for the most part, been putting up good, albeit, inconsistent numbers all year despite posting some elite peripheral rates and ERA indicators.  A number of reasons have caused these numbers to grow apart (bad), come together, and then grow apart again (good).  Luck can work like a bit of a pendulum, swinging from one extreme, through the middle, and to the other extreme before evening out and that is at the core of what the Indians’ starting pitchers have experienced this year — although they have yet to experience the final stabilization phase.

We will examine plenty of numbers (Beginning of season to August 18th) based on this time frame: (Spoiler alert – this article is long and dense, and this timeline serves as a sort of cliff notes as to how the staff’s numbers have improved throughout the year – so if you’re the type of person who feels like looking at a bunch of data is superfluous when the bullet points are in front of your eyes, just read the timeline and be done with it.)

timeline

April 6th – May 23rd/May 24th – June 15th

One week into the season, before it was evident that the team’s defense was very sub-par, Yan Gomes hurt his knee and hit the disabled list for over a month.  Roberto Perez filled in quite nicely, and looking at just a couple numbers, could be considered the more valuable catcher (1.4 WAR compared to 0.5 WAR for Gomes).  Brett Hayes (0.0 WAR) was called up and was the secondary catcher during this period.  Behold, a table from StatCorner:

statcorner

 

 

 

 

 

 

Perez has had the least amount of pitches in the zone called balls and the most amounts of pitches out of the zone called strikes.  Overall, despite receiving fewer pitches than Gomes, he has saved more runs (4 DRS to Gomes’ 1) and their caught stealing rates are basically identical with a slight edge going to Perez – 38% to Gomes’ 35%.  Gomes was much better in terms of framing in 2014, and it’s possible the knee injury has limited his skills all around this season.  Anyways, from April 6th – May 23rd, the combined stats of Kluber, Salazar, Carrasco, and Bauer look like this:

ERA FIP xFIP SIERA K-BB% GB%
Kluber 3.49 2.16 2.46 2.51 25.3 48.6
Salazar 3.50 3.27 2.46 2.30 28.7 43.8
Carrasco 4.74 2.60 2.67 2.82 22.3 48.9
Bauer 3.13 3.23 4.09 3.94 14.2 35.7
3.75 22.7 44.7

Gomes returned as the primary catcher on 05/24, and from that point through June 15th, the cumulative numbers aren’t too different, although there is a dip in both K-BB% and GB% that we’ll have to look into.

ERA FIP xFIP SIERA K-BB% GB%
Kluber 3.67 3.26 3.20 3.19 19.8 43.8
Salazar 3.60 3.72 3.36 3.43 17.3 47.7
Carrasco 3.65 2.83 3.29 3.17 20.2 44.1
Bauer 3.96 4.72 4.47 4.30 11.5 36.8
3.74 17.2 43.1

So despite lower K-BB and ground ball percentages (leading to higher ERA predictors), the group’s ERA in the segment of the season when Gomes was reinstated is essentially exactly the same as from the first block of time with Perez.  Now, I am not a big believer in CERA because there is a high level of variation and too many unknown variables pertaining to how much of the responsibility/credit goes to the catcher, the coaching staff, or the pitcher; but I do think that it’s possible Gomes’ extra service time has enabled him to be more in tune with his staff as well as understand hitter tendencies better than Perez and Hayes.  I realize we’re getting into a gray area of intangibles, so I’ll reel it in with some results based on pitch usage%.

% Difference in Pitch Usage with Yan Gomes compared to Roberto Perez

Pitcher FB% CT% SL% CB% CH% SF%
Corey Kluber -9.0 8.8 -17.3 5.0
Danny Salazar 9.8 -12.6 -4.4 17.1
Carlos Carrasco -6.5 9.4 49.2 13.3
Trevor Bauer -2.9 -15.0 -8.9 78.5 25.8

Using BrooksBaseball Pitch f/x data, let’s painstakingly find out how different each pitcher’s pitch usage was in regards to different counts, or better known as Pitch Sequencing.  We’ll look at first pitches, batter ahead counts, even counts, pitcher ahead counts, and 2 strike count situations.  As good as pitch f/x is, the data still isn’t perfect.  There may be discrepancies if you look at usage at Brooks compared to the usage at FanGraphs, so for each pitcher we’ll split the pitches up into three categories: Fastballs (four-seam, sinkers, cutters), Breaking Balls (sliders, curve balls), and Change Ups (straight change/split finger) – I’m aware that splitters are “split fingered fastballs”, but I liken them to change ups more because of the decreased spin rate and generally lower velocity.

*Having a table for each pitcher in regards to pitch sequencing made this article quite messy, so I’ve included a downloadable Excel file, and briefly touched on each pitcher below.

Pitch Sequencing Excel Doc.

Corey Kluber

Looking at the data, Gomes stays hard with Kluber more than Perez until they get ahead in the count.  Perez swaps some early count fastballs for curve balls, but they both see his curve ball as a put-away pitch.  Gomes tends to trust Kluber’s change-up more than Perez later in counts and Perez likes it more earlier in counts.

Danny Salazar

Much like with KIuber, when Gomes catches Salazar, they have a tendency to stay hard early.  Gomes pulls out Salazar’s wipe out change up after they’re ahead whereas Perez will utilize it in hitter’s counts as well.

Carlos Carrasco

Carrasco has 5 good pitches and he’s pretty adept at throwing them for strikes in various counts which is why there is some pretty even usage across the board, at least in comparison to Kluber and Salazar.  There is quite a bit more usage of Carrasco’s secondary pitches in all counts and there are pretty similar patterns when Gomes and Perez are behind the plate.  With Hayes, it doesn’t look like there is much that changes in sequencing until there are two strikes on a hitter.

Trevor Bauer

Bauer is probably a difficult pitcher to catch because of the number of pitches he has and the constant tinkering in his game.  Side note: Gomes is the only catcher to have caught a game in which Bauer threw cutters, and in their last game together, Bauer threw absolutely no change-ups or splits.  Bauer’s highest level of success has come with Hayes behind the plate and perhaps that’s from their willingness to expand his repertoire in more counts than Gomes and Perez do, but there is no way I can be certain of that.

Pitch sequencing can effect the perceived quality of each pitch and therefore, can produce more favorable counts as well as induce higher O-Swing and SwStrk percentages (or less favorable and lower).  So despite the framing metrics favoring Perez, the group throws more strikes with Gomes and also induces more swings at pitches outside the zone – although, as previously noted, there is some regression with Gomes behind the dish in terms of SwStrk% and K-BB%.

swing tendencies

 

 

 

 

 

 

 

 

 

aaa0ide

 

 

 

 

 

 

 

 

**These graphs represent numbers through the entire season to garner a bigger sample size.

With lower line drive rates and more medium + soft contact, and (in the case of the Indian’s defense), more fly balls, a conclusion could be jumped to that the staff’s BABIP has trended downward since Gomes regained his role.  A look at BABIP throughout the course of the season:

babip

 

 

 

 

 

 

 

 

 

Woah!  It was well above league average in April and then plateaued at just above league average through mid June, but has been plummeting ever since.  Obviously a catcher is not responsible for this dramatic of a swing in BABIP, so the Indians’ defense must have improved.

June 16th – August 18th

The rotations’ traditional stats look even better if you use June 16th as the starting point:

Pitcher IP H K BB W ERA WHIP
Corey Kluber 84 61 82 16 5 3.11 0.92
Danny Salazar 71 46 69 23 5 2.79 0.97
Carlos Carrasco 77.1 56 77 13 3 2.91 0.89
Trevor Bauer 68.1 69 63 24 4 5.80 1.37
300.2 232 291 76 17 3.59 1.03

 

So let’s take a look at the Indians’ defensive alignment by month (Player listed is the player who received the most innings played at the position).

 

POS April May June 1 – 8 June 9 – 15 June 16 – 30 July August
C Perez Perez Gomes Gomes Gomes Gomes Gomes
1B Santana Santana Santana Santana Santana Santana Santana
2B Kipnis Kipnis Kipnis Kipnis Kipnis Kipnis Ramirez
3B Chisenhall Chisenhall Chisenhall Urshela Urshela Urshela Urshela
SS Ramirez Ramirez Aviles Aviles Lindor Lindor Lindor
LF Brantley Brantley Brantley Brantley Brantley Brantley Brantley
CF Bourn Bourn Bourn Bourn Bourn Bourn Almonte
RF Moss Moss Moss Moss Moss Moss Chisenhall

If you’ve paid attention to the Indians at all, you know they’ve made some trades and called up a couple prospects.  But just how different is the new defense?  Well, we only have a small sample with the current configuration, but it appears to be A LOT better. If BABIP wasn’t enough of an indicator, and it’s not, because there has to be some regression to the mean – it can’t stay that low – here are some numbers from the players who were playing the most in May compared to the players who are playing the most in August (again, numbers represent full-season stats):

 

MAY PLAYER FLD% rSB CS% DRS RngR Arm UZR UZR/150
C Perez .994 2.0 38.5 4
1B Santana .997 -6 0.0 0.7 1.2
2B Kipnis .988 4 4.5 3.6 7.0
3B Chisenhall .963 7 3.1 3.3 10.5
SS Ramirez .948 -2 -2.4 -5.2 -21.9
LF Brantley .992 1 0.3 -2.1 -1.4 -3.3
CF Bourn 1.000 4 -7.2 1.1 -5.8 -11.4
RF Moss .975 -4 1.7 -2.5 -1.1 -1.8
AUG PLAYER FLD% rSB CS% DRS RngR Arm UZR UZR/150
C Gomes .996 0.0 35.0 1
1B Santana .997 -6 0.0 0.7 1.2
2B Ramirez 1.000 1 1.1 2.8 23.2
3B Ursehla .973 2 4.5 6.0 15.7
SS Lindor .967 6 6.0 4.9 14.9
LF Brantley .992 1 0.3 -2.1 -1.4 -3.3
CF Almonte 1.000 2 0.4 -0.2 0.9 10.0
RF Chisenhall 1.000 4 1.6 0.5 2.3 27.3

What’s interesting is that the biggest difference in the infield is Francisco Lindor (Giovanny Urshela has been very solid, but Chisenhall was pretty similar this season at 3B).  I’m sure someone at FanGraphs could churn out a really cool article (if someone hasn’t already) that shows us a quantifiable difference an above average to well above average shortstop makes for a team even if you just keep the rest of the infield the same, as the control.  The 2015 Tigers come to mind – a healthy Jose Iglesias has made a difference for a team that still features Nick Castellanos at 3B and Miguel Cabrera at 1B.  Teams are willing to sacrifice offensive contributions if a SS has elite defensive skills (Pete Kozma, Andrelton Simmons, Zack Cozart to name a couple off the top of my head).  Lindor, to this point, has been an above average offensive player, too, so this could be special.

At this point the Indians are in last place and are out of contention.  Abraham Almonte is their starting center fielder and with Kipnis back from the DL, Jose Ramirez is not playing 2B, but is instead getting reps in left field while Michael Brantley DHs due to his ailing shoulder.  Perhaps all this means is that they don’t have better replacements; OR PERHAPS they’re planning to establish a more defense-oriented squad next year…

Now there’s no doubt that this research has led to some frustrating conclusions.  With Gomes behind the plate, the K rate and GB rate of the staff has trended in the wrong direction in regards to ERA indicators; so is the difference in the batted ball profile plus an improved defense enough to make up for these facts?  This small sample size thinks so, but it could 100% just be noise.  However, there are clubs that are succeeding by using similar tactics right now:

Team ERA FIP ERA-FIP GB% (rank) SOFT% (rank) OSWING% K-BB% (rank)
Royals 3.57 3.93 -0.36 42.1 (29th) 18.1 (16th) 30.9 (19th) 10.5 (26th)
Rays 3.63 3.79 -0.16 42.4 (28th) 18.7 (13th) 31.2 (17th) 14.8 (7th)
Indians (as a reference) 3.85 3.65 0.20 44.7 (17th) 18.2 (15th) 33.3 (2nd) 16.9 (1st)

Granted, the Royals and Rays have the 1st and 2nd best defenses in baseball, and their home parks play differently than the Indians, but they also don’t boast the arms the Indians do.

The Indians have their noses deep in advanced metrics and having rid themselves of Swisher, Bourn, and Moss during 2015’s trading period has allowed them to deploy a better defensive unit which has amplified their biggest strength – their starting pitching.  Furthermore, their unwillingness to move any of their top 4 starting pitchers also leads me to believe they see next year as a time for them to compete.  I’m not going to speculate what moves the Indians will make in the offseason, but I hope they stick with this defense-oriented situation they have gone with recently because it’s been working (and because I own a lot of shares of Kluber, Carrasco, and Salazar in fantasy).


Three Undervalued Hitters to Help Down the Stretch

We’re officially in the dog days of summer, which means a few things of note: NFL is almost upon us; the fantasy baseball playoffs have begun for many; and finally, whether you’re in a roto league without playoffs or otherwise, you’re still looking to find value on your waiver wire.

I define value as something like: Players who produce counting stats (and/or average), who, for whatever reason, have low ownership rates and thus can be found on waivers for free, or in my case, for a few FAAB dollars (of which, I have zero remaining). The players I’m referring to are generally valuable in deeper mixed leagues or NL- or AL-only formats, but some, like Dexter Fowler, whom I’ve written about in the past, can offer solid numbers for leagues of any size/format.

I’ve recently written about guys like David Peralta, Fowler, and Jung-Ho Kang, and my advice on these players remains the same as it’s always been: pick them up ASAP. Their low ownership rates on ESPN continue to leave me flummoxed; E.g., David Peralta and his .294 average, 48 R, 13 HR, 66 RBI, and 5 SB is owned in just 70% of ESPN leagues. Go figure. Better yet: Go pick him up.

Here are a few more hitters I like who can help you down the stretch:

Yangervis Solarte: Solarte hit his tenth home run on August 21 and third in as many games. A switch-hitter, Solarte has multi-position eligibility (1B; 2B; 3B) and is owned in just 34% of ESPN leagues. With a triple-slash line of .269/.325/.425, Solarte has 47 R, 10 HR, and 49 RBI. Those stats play in most leagues, and while he is a bit streaky and on a power surge in August, his ambidexterity keeps him in the Friars’ lineup on a near-daily basis. Solarte has solid on-base skills (29:46 BB/K), hits for decent power, above league-average batting average, and the vast majority of his AB’s come in the leadoff or 2-holes in the lineup (110 and 142 AB, respectively).

That said, hitting in front of a hot Matt Kemp and a hopefully-getting-hot Justin Upton should help keep his run totals healthy, and he’s showing some nice HR power in August. His .283 BABIP is in line with career norms, so I don’t expect much regression in terms of batting average; if anything, that number seems somewhat low for a player who runs well, but ZiPS projects a BABIP of .280 the rest of the way. At any rate, you could certainly do a lot worse than Solarte, a player who might be finding his stride in the second half.

Colby Rasmus: In short, Rasmus is who he is: He hits for power and not much else. His power, particularly against righties, is the real deal: Rasmus owns a .451 slugging percentage and a solid .222 ISO in 2015 (with a career-norm .297 BABIP); his 17 HR and .750 OPS suggest he can help in AL-only or deeper mixed-leagues.

Owned in just 6.5% of ESPN leagues, Rasmus has 44 R, 17 HR, 44 RBI, and 2 SB to his credit (along with an unsightly .228 batting average), with the two most recent of his 17 Colby Jacks courtesy of Detroit lefty Matt Boyd. While he does sit against most LHP, Rasmus’ OPS against lefties in 2015 is a respectable .815 across 80 AB’s (compared to a .726 OPS vs. RHP over 244 AB). That said, you will see him in the lineup against a few soft-throwing lefties, but that will likely stop when Springer returns.

For perspective, consider Brandon Moss relative to Rasmus:

Moss is batting .211 with 38 R, 15 HR, and 51 RBI. He was recently ranked OF number 52 and 49 by two CBS analysts, whereas Rasmus is ranked 63 and 88. Although Rasmus’ power is less proven than that of Moss, Moss has been miserable since June and Rasmus has been steady, if unspectacular, effectively all season. But despite hitting more HR—and being projected to hit just 3 fewer HR than Moss (8 HR projected for Moss ROS seems totally absurd, incidentally)—Moss is owned in roughly 8 times more leagues than is Rasmus. In short: Colby is either massively under-owned, or Moss is hugely overvalued; or, I guess, both.

ZiPS has another 5 HR and 13 RBI projected for Rasmus rest of season, but those number seem a bit soft in the absence of Springer for a player hitting at Minute Maid Park. Rasmus won’t win a batting title anytime soon, but his solid OPS vs. lefties this year (an outlier, to be sure) and strong defense at all three OF positions keeps him in the lineup on a near-daily basis, especially given the recent, albeit short-term, demotion of Preston Tucker. Colby is a funk since his 2-HR game on 8/16, but like most power hitters, Rasmus is prone to streaks; my advice to you is exactly the same advice I took myself: pick him up and enjoy the HR power, but don’t expect him to suddenly become Bryce Harper.

Asdrubal Cabrera: Arguably the hottest hitter in baseball since he returned from the DL on July 28, Cabrera is hitting .404 with an OPS of 1.078 since the All-Star break. Those are not typos, though his numbers are propped up by a massively inflated BABIP. Also since the break, Cabby has 20 runs, 4 HR, 13 RBI, and 2 SB across 89 AB’s. He’s on fire, no two-ways about it.

What we’re seeing here, I think, are two things: 1) a player out-of-his-mind hot and 2) a veteran with proven, decent power and a solid hitter regressing to the mean. Currently batting .264 with 49 R, 9 HR, 35 RBI, and 5 SB (.730 OPS), Cabrera has hit at least 14 home runs every season since 2011 (career high of 25), and he’s on pace for roughly 12 this year. A career .267 hitter, Cabrera was miserable in April, May, and some of June, and while he’s hitting an unsustainable BABIP of .320, he was certainly due for a few bloopers to drop.

With dual 2B/SS eligibility, his ownership rate on ESPN has spiked from sub-20% in mid-August to 39% at the time of this writing. If you’re looking for help at a very weak SS position, or a possible Howie Kendrick replacement, Cabrera can certainly help you out; and as a switch-hitter, you’ll find him in the 5- or 6-hole in the Ray’s lineup on a daily basis.


The Evan Gattis Triples Game

There are 13 qualified hitters in baseball with at least six triples.  12 of the 13 players have at least five SB and the average among those 12 players is 18 steals.  Among the league leading ranks in triples stands one man who defies the common narrative that triples hitters are speedy.  He’s known as ‘El Oso Blanco’, which translates to “The White Bear” for non Spanish-speaking readers, and listed at a whopping 6’4”, 260 lbs, it’s easy to see why they call him that.  His story is one of modern day folklore, and it’s fitting that his wandering days eventually would lead him to an Astros squad that have taken the American League West by surprise.  Evan Gattis, has as many stolen bases as he has batting gloves, or as many as he appears to have, which is zero, because if you’ve witnessed him hit at all, one of the first things you notice about him is that he does not wear batting gloves.  Yet there his name is, one triple ahead of the likes of Adam Eaton and David Peralta; Evan Gattis, with nine triples, the man in sole position of second place for the most triples in major league baseball.

Consider this: he had 1 triple in his first 783 PA (or even 1 in his first 928, if we want to include all of his career PA up to May 28th, 2015 – the date of his first triple this year), and that one triple was hit into Triples Alley at AT&T Park in San Francisco on May 13th, 2014 (No, this was not a Friday the 13th).  Triples Alley is aptly named for the high volume of balls that are hit there that result in triples (relatively speaking).  So that was Gattis’ one and only, and yet he’s hit 9 in his following 446 plate appearances (or even scarier, 9 in 301 PA).  Before delving too much into this, I thought, “Conditions for an Evan Gattis triple would have to be perfect.  I bet at least 6 of these triples are due to Tal’s Hill“, which is the 90 foot wide, 30 degree incline, that extends the area of balls in play about 34 feet beyond where the fence would normally end at Minute Maid Park.  It is a whopping 436 feet to the wall at the top of Tall’s Hill.  However, a quick peek at Gattis’ home/away splits would reveal that he has just 5 triples at home and 4 on the road.

Well then he must have hit his triples in “triple-friendly” parks; below is a table showing where he has hit his 9 triples this year:

STADIUM 3B FACTOR
AT & T Park 1.211
Minute Maid Park (5) 1.549
Kauffman Stadium 1.240
Comerica Park (2) 1.465

Okay, that was predictable and makes a lot of sense to me.  Now here is a spray chart that shows his hit types (if you don’t read keys, the red dots are the triples):

chart (3)

*There is a sneaky red dot signifying a triple hiding behind a home run dot in left center just to the right of the most far left red dot*

Looking at the plotting of the red dots and considering what stadiums he hit his triples at is where I got the idea for this article – and I will now switch to writing in present tense to portray the feeling of spontaneity I felt when I first started this writing. Considering the factors, I get the feeling that I can guess which stadium each of his triples have been hit at – an exhibition of frivolity to be sure, but this is just the kind of thing that we’re looking for while we’re at work, trying to look busy, isn’t it?  If you wanna play, keep reading and guess along.  I am going to take a liberty and use the pronoun “we” instead of “I” so this feels more like a group effort.  And I also have a disclaimer: If you continue reading, you are assuming the risk that this could be a jarringly disjointed, moderately sarcastic, and gif cluttered article – it is.

The Evan Gattis Triples Game

Let’s consider my first hypothesis – that Tal’s Hill is responsible for a majority of these triples.  Looking at the red dots it looks like 3 of them may have very well landed there.  In order to kind of stick with my original idea, we’ll take the five most centrally located red dots and say that those are the triples he hit at home.

chart home

For reference into this reasoning, here’s the stadium layout of Minute Maid Park (all ballpark layouts are courtesy of Clem’s Baseball).  Note the massive depth of center field.

MinuteMaidPark

Using FanGraphs’ Game Logs I’ll pinpoint the dates of his 5 home triples and then plug those dates into Gattis’ spray chart over at BrooksBaseball.

1st Triple at home; 3rd Triple of Season: 06/28 vs NYY

triple1

That ball is not hit to Tal’s Hill, but it is one of his 5 most centrally hit triples of 2015, so that’s 1/1 if you’re scoring at home.

Now here’s the GIF – and here’s where I have to pause and give credit to another article.  When I started to write this post I hadn’t planned on including so much media, but as the post evolved it really did call for GIFs of these triples.  When I searched ‘Evan Gattis triples’ on google, the first link that popped up is this SB Nation post by Murphy Powell, and it’s the source for 6 of the 8 GIFs here and is, by all accounts, VERY similar and a much better article than mine, so check it out.  Any other GIFs were created using Baseball Savant media and makeagif.com.

gattis_3.0

“ARGH!”  That’s the sound of Michael Pineda groaning as he grimaces and falls on to bended-knee while telepathically willing the ball to stay in the park, which it does, barely.  Pineda is groaning because that was not a quality slider.  This information could probably be an entirely new post altogether, but I did warn you about this post being disjointed, so let’s to a quick detour.

This triple took place at the end of June – a table tracking velo and movement of Michael Pineda’s sliders shows that Pineda was throwing sliders of a lesser quality during this period.

Date(s) Velo x-movement v-movement BAA
Pitch to Gattis (06/28) 87.9 2.15 1.25 1.000 (obviously)
April 2015 84.08 4.54 -0.30 .208
May 2015 85.76 4.00 -0.41 .191
June 2015 87.12 2.47 0.02 .250
July 2015 87.10 1.34 0.46 .231

Whether it has been a conscious decision to throw his slider harder or it is a product of his ailing elbow, the results have not been so good.

Anyways, at this point, three triples into the season – and 3 in his last 36 games – Gattis’ reputation as a triples machine is really starting to build momentum (I warned you about the sarcasm, too) and as soon as the ball bounces away from Brett Gardner and is left to be retrieved by a scurrying Garrett Jones, Gattis is off to the races.

2nd Triple at home; 4th Triple of the season: 06/30 vs KCR

triple2

Bingo! This is a Tal’s Hill special and would be a home run at 29 other ball parks.

gattis_4.0

Lorenzo Cain, who has to at least be in the conversation for the smoothest looking active baseball player, is rendered looking like a reckless drunkard, smashing head-first into the wall and then toppling over on to his side after heaving the ball in towards a cut-off man from his knee.  Nonetheless, Gattis has his 4th triple of the year and we are 2 for 2.

3rd Triple at Home; 5th Triple of the season: 07/17 vs TEX

triple3

That one is not quite as impressive as the last one in terms of distance, but he laid into this one pretty good, too.

gattis_real_5.0

This hit scoots up on to Tal’s Hill after it nicks off Leonys Martin’s glove and then bounces off the wall – are you already missing the antics that Tal’s Hill won’t be causing in 2016?  The main thing here is that we are now 3 for 3 in this game.  I knew this would be easy.

4th Triple at Home; 7th Triple of the year: 07-28 vs LAA

triple4

So we’re wrong on this one and that brings our tally to 3 for 4 – and I’ll take most of the responsibility for the ones we get wrong – my bad.  “My bad” suffices when a player makes an errant pass out of bounds in a professional basketball game, so it should be enough here, too.

gattis_5.0 (1)

This one hit just under the yellow line against the Papa John’s sign, and it had to careen off the wall in such a way that it caused the ball to bounce into another empty center field where Shane Victorino finally picks it up and hurls it in just in time for Gattis to pull in to third base with a stand up triple.

5th Triple at Home; 7th Triple of the year: 08-14 vs DET

plot_hc_spray

This is technically another one of the 5 most centrally located triples so we are 4 out of 5.

Gattis Triple 5 Gif

 

 

 

The ball comes off the bat hard enough (99.3 mph) and then takes a generously frictional hop and loses speed as it trickles up against the wall in the deepest part of right center field at Minute Maid.  I don’t care if even the great Roberto Clemente was in right field, that is a long relay throw and there is plenty of time for Evan Gattis to lock down his 9th triple of the season.  Gattis is immediately pulled from the game as he is probably completely out of juice at this point in the season, but fans rejoice over his exploits and even Evan Gattis can’t believe his recent output of triples:

7fx3An

 

 

 

So we are hitting .800 after the home stand, but now let’s take on the triples hit away from home.  Here are the triples that we have left to identify:

chart (3)

The media, for whatever reason, has started to get smaller, so I will point out the locations of the triples: there is one to deep, left center; one to deep center, one to right-center, and one down the right field line.

For reference, here are the stadium layouts for Comerica (where he’s hit 2 triples), AT&T Park, and Kauffman Stadium.

Comerica

ballpark

AT&T

triple7

Kauffman Stadium: has the largest outfield in major league baseball as measured by total square feet.

KauffmanStadium

Let’s start with the one triple hit to deep center that did not take place at Minute Maid and say that one took place at Comerica Park, since, like Minute Maid, Comerica has a cavernous center field.

1st Triple of the Year: 05/21 vs DET @ Comerica

triple6

Huzzah! That was kind of obvious and maybe shouldn’t have elicited a Tobias Funke jubilation, but the fact that we’re five for six does.

gattis_half_1.0

Let’s jump ahead to what should be considered the other obvious pick, his triple hit at AT&T park.  There’s a triple that was hit to right center and we’ll say this triple it was a throwback piece; inspired by his first triple in the bigs, in that it was hit to Triples Alley.

8th Triple of the Year: 08/11 vs SFG @ AT&T Park

triple8

This one is wrong and that stings because I felt like this one would’ve been obvious.

qzp-eX

I’m not sure how much of the ball Gregor Blanco gets when he leaps – he may have ultimately sandwiched the ball between his back and the wall – but it looks like he prevented an Evan Gattis HR; but still can’t prevent yet another Evan Gattis Triple.  We’re 5 out of 7.

So of the two triples left, there is one that goes to deep right-center, and one that scurries down a right-field line.  The ballparks left are Kauffman and Comerica.

We’ll play the odds and guess that the one down the right-field line is hit at Kauffman Stadium because it would make sense for the one to right-center to have ended up in that little enclave at Comerica.

6th Triple of the Season: 07/26 vs KCR @ Kauffman Stadium

Oddly enough there is no data for this on Brooks Baseball and there is also no GIF for this triple; Who’s padding the stats?? At least that builds some suspense…

2nd Triple of the Season: 05/24 vs DET @ Comerica

triple9

Wrong – which also makes us wrong on the triple hit at Kauffman so we miss the final 2 – “my bad”.

gattis_2.0

It looks like Rajai Davis was positioned towards the gap and therefore had to hunt this ball down while El Oso Blanco set the base paths aflame.

So our (my) final score is 5/9, which is good but not great considering my 100% accuracy prediction.  While I’m completely aware of the vast, expansive magnitude of my ignorance, I really did believe I could pick out where each of these 9 triples happened…it’s probably this same hubris that causes me to lose $3 daily over at Draft Kings.

Trying to elicit some meaning out of this article would be contrived, so I’ll just say (tongue-in-cheek-ly), Gattis is likely to experience some regression to the mean (whatever that mean is in regards to triples).  I can’t imagine a reality where Evan Gattis highlights aren’t home runs and continue to be centered around him tearing around the basepaths – his massive, rippling thighs simultaneously inspiring awe, terror, and a few chuckles among his teammates – but what do I know?  The last time I tried to predict something about Evan Gattis, I was only 55.6% right.


BABIP Aging Curves

At age 35, Albert Pujols is having somewhat of a resurgent season. Many wrote him off last year after he posted his second straight, for him, subpar season. This year, though, he has hit 30 home runs through 108 games with ZiPS projecting him to get to 40 on the season. But there remain two big differences between 2015 and prime Pujols. One, he is walking less, at 7.5% vs. his career average of 11.8%. And two, his BABIP is a minuscule .228, continuing a declining trend:

Pujols BABIP

It certainly makes sense that with a loss of footspeed, BABIP would decline as well. After doing a quick mental recall, I decided to look up Mo Vaughn as another power hitter who seemingly lost it overnight. And sure enough, he experienced a big BABIP decline late in his career as well:

Vaughn BABIP

He still put up a .314 BABIP in his last full season, but it was a step change from the average .365 (!!!) BABIP he put up from 25-30.

So, is this a larger trend that we should be paying attention to? Or are Pujols and Vaughn just confirmation bias. Thanks to FanGraphs’ excellently downloadable data, I expanded the datatset to include every season and every player. Grouping by age reveals:

BABIP by Age

Well seemingly a lot of nothing. The BABIP for all 20 year olds in that time was .301, while the BABIP for all 39 year olds was .295. Definitely a decline, but with a p-value of 0.7 is not statistically significant. So that’s disappointing for my thesis, but encouraging for all the old folks out there! Back to the drawing board.

Pujols and Vaughn were big, hulking guys. Maybe when they lost a step, it was a step that they could less afford to lose and the impact on their BABIP of a marginal slowing down was magnified. So what if we restrict the group to only power hitters? For this, I defined power hitters as players with career ISOs over .200. The results appear to support my hypothesis better:

BABIP by Age, Power Hitters

This is plotted on the same scale as the previous chart so we can appreciate the relative differences. For this sample, the BABIP for power hitters declined from .313 at age 22 to .296 at age 36. Interestingly enough, power hitters had higher BABIPs earlier in their careers than the general population (including the power hitters), which then dip lower than the general population later in their careers. Apparently hitting the ball hard does have some benefits.

This time, the science backs up the hypothesis! My engineering professors would be so proud. With a p-value of 0.0165, the difference in BABIP between a 36 year old power hitter and a 22 year old power hitter is statistically significant. Pujols and Vaughn were indeed the victims of a real trend.

There could be a number of factors behind this. The first one I highlighted is the loss of footspeed. Second, it could just be that as you get older you don’t hit the ball as hard. Looking at exit velocity or ISO by age would help us judge that. Finally, age and a loss of bat speed or reflexes could lead to a change in batted ball in a way that leads to less balls falling for hits. It would make sense that as his bat speed slowed, Pujols tried to hit more fly balls to recover some of the home run power. That is the next thing I will look at.


Stephen Strasburg Is Better Than You Think

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


Two Infielders You Should Be Talking About

I wish I knew why Jung-ho Kang and Ben Paulsen seem to get so little respect. It’s baffling. Regardless, people should be talking about these guys and their production — both have very legit numbers, yet few seem to have noticed. More to my point: fantasy baseball players should pick them up from the waiver wire ASAP. I mean, right this second.

Kang, recall, is the stud the Pirates signed from Korea. An unknown for the better part of the season, Kang is making his presence felt in the middle of the Pirates lineup, having just earned honors this July for NL Rookie of the Month. Kang, with dual SS/3B eligibility, is owned in just 57.9% of ESPN leagues and is slashing a highly productive .291/.365/.446 and, based on what he did in Korea, his .809 OPS could prove to be low in the long run.

Kang went through a bit of a power drought in June, but he caught fire in July. He’s now hitting .291 with 8 HR and 35 RBI. Consider that in the last week of July, Kang recorded multiple hits in five out of eight games with 6 R, 2 HR, and 3 RBI in that stretch. In his next game, on August 1, he hit his 8th home run of the season, a ball that traveled 412 feet. In 2014, Kang launched 40 home runs in 120 games in Korea, while also hitting .297. The kid can flat-out rake. With Jordy Mercer on the shelf (and not very good when healthy), Kang continues to occupy the 4–6 holes in Clint Hurdle’s lineup.

As many hitters have said before: As the summer heats up, so do they. I suspect we’re going to see Kang launch many more home runs before season’s end. If nothing else, even if the power is merely moderate, the fact that he hits for average, steals a few bases, and slots in the middle of a very potent Bucs lineup makes him worthy of a pickup in leagues of any size.

Ben Paulsen. What’s not to love about a guy who: 1) plays half his games at Coors Field; 2) made minor league pitching look like little league; 3) hits for both power and average; and 4) absolutely kills right-handed pitching? Answer: Nothing. His numbers aren’t dissimilar from those of Kang (in fact, they’re nearly identical), with a .300 average, 8 HR, and 34 RBI. His average is a bit buoyed by a .363 BABIP, though ZiPS projects a .333 BABIP the rest of the way. The only knocks against Paulsen are playing time and his ugly platoon splits, which are obviously related. But as with guys I’ve discussed before, who cares if he’s not an everyday starter; he’d just tank your average anyway. Instead, bench him against the few lefties he’s allowed to face, and you won’t be disappointed.

FanGraphs had this to say about him before the season started; it’s like these guys are clairvoyant or something. But they’re also very much wrong in the when they say that Paulsen’s game is made for just NL-only leagues. It’s much better than that (keep reading). Per FanGraphs:

The Quick Opinion: If Morneau starts the year on the disabled list as he recovers from knee surgery, Paulsen could be a sneaky short-term option in NL-only leagues, but that’s about it.

Paulsen, actually, is now effectively an everyday starter in the mercurial Walt Weiss’ lineup, thanks to the demotion of Wilin “Baby Bull” Rosario. Justin Morneau’s concussion symptoms are persisting, and he may have played his final game in the big leagues. Thus, the gig is Paulsen’s to lose, and with Corey Dickerson on the DL again, Paulsen has also been playing some corner outfield when called upon.

And when the 27-year old Paulsen is called upon, the numbers are a thing of beauty — against RHP, anyway, who he’s torturing to the tune of a .308/.361/.535 triple slash. Paulsen’s OPS of .896 isn’t just ‘productive,’ it’s downright fantastic. Frankly, it’s more than a little weird that just 19.7% of ESPN players own him. I’m happy to say I’m one of them, though I missed out on Kang, much to my dismay (and totally because of my stupidity).

There will be more blogs to follow, with similar themes in mind: finding value where there seemingly is none. There always is, you just have to look hard enough.


Matt Shoemaker’s Need For Speed

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

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

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

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

And then take a look at the data since then:

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

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

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

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

Matt Shoemaker 2015 fastball velocity (n=900)

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

Matt Shoemaker average fastball velocity

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

Matt Shoemaker Outcome Breakdown Through June 19

 

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

Matt Shoemaker Outcome Breakdown Post All-Star Break

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

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


Bud Norris: A $150,000 Band-Aid

Note: Norris has now signed with the Padres.

Hey, remember Bud Norris? The guy who was an opening day starter for the 2013 Astros (although that team lost 111 games, so that might not be something to brag about). He then was traded for prospect Josh Hader (who was just traded for Carlos Gomez), and a replacement level player in L.J. Hoes and a compensatory 1st round pick. The draft pick turned out to be Virginia’s Derek Fischer who has hit 19 dingers for the Astros single-A club in 2015. He won 19 of his first 35 starts with the Orioles. This O’s pitcher got released on August 8th after clearing waivers. He is now free to sign with any team willing to take on his services. Norris has been a huge disappointment in 2015 — actually huge disappointment would be an understatement. The Orioles signed Norris to a one-year, $8.8 million contract last winter to avoid an arbitration hearing. He was slated to solidify the middle/back end of the O’s rotation. A solid veteran who over his first five full years in the league averaged a WAR right around 2. He has never been flashy but always solid, until 2015. 2015 is the year of the Bud Norris Apocalypse. Norris sported an ERA of 7.06, and a Win-Loss record of 2-9. So is Norris this bad, or is he a victim of bad luck, and is picking him up for a pro-rated portion of the league minimum worth it?

What changed in 2015 versus the rest of Norris’ career that saw him deliver an average ERA of 4.20 over parts of six seasons? There’s a few factors that snakebite Norris in 2015. The first is Norris had a brutal increase in his FB/HR rate. For his whole career (2015 included), 11.4% of the fly balls hit against Norris went over the wall. This year that number ballooned to 17.7%. That is over a 55% jump. Why the huge jump in FB/HR rate? Well, it is not that his fastball velocity dipped, in fact his fastball velocity is over .6 mph faster than his career average of 92.9 mph. Norris is throwing the same rate of strikes vs. his career rate (63%). He has not been throwing in the middle of the plate any more than usual either. In fact, on pitches in the middle third of the strike zone he has thrown 0.7% less pitches than his career average.

Perhaps the reason behind the change in FB/HR rate is luck, but Norris is also throwing 7% more fastballs than the career average. Batters may have been sitting on his fastball more than usual and were teeing off. My thinking is that when a pitcher does not have a huge drop in velocity or major change in strikes thrown, the huge increase in FB/HR rate must be something of a fluke. Norris also got snakebitten by an awful LOB% of 59.5%. His career rate is 72%. Maybe this is just a product of being unlucky. But Norris has been miserable in situations with men on base; with runners in scoring position, batters were hitting .313. No pitcher on earth is going to have a good ERA when batters are hitting over .300 with RISP.

To recap, it seems that Norris may have been much more unlucky this year than other years in his career. He has not been good by any means, but he is not as bad as the 7.06 ERA he has this season. The xFIP and SIERA projections give Bud an estimated ERA of 4.55 and 4.48 much closer to his career mark of 4.20. It seems that Norris has been plagued this season by an inability to pitch with RISP and an awful FB/HR rate. I highly doubt anyone is going to confuse Norris for a top-tier starter, but he should still be a serviceable back of the rotation option.

Signing Bud Norris at this point in the season has practically no risk. If Norris signed for the league minimum, it would be pro-rated to roughly $150,000. Norris could serve as a $150,000 insurance policy in the event that a starting pitcher goes down. He could get picked up and put in the bullpen in a long-relief role with the capability of making a spot start. Having a viable long-relief man is huge during the late months of the season as teams try to save their bullpens. He could easily be picked up by a team like Minnesota who is 4 games back of the wild card. They could use back of the rotation help with the injury to Tommy Milone. The Giants could use rotation help with the recent injury to Mike Leake. And unless Kansas City feels comfortable running Jeremy Guthrie out to the mound every 5th day, Norris could be a good fit. Even a team like St. Louis or Tampa could use him for a spot start to give some rest to fairly young starting rotation. There could potentially be multiple landing spots for Bud. While Norris is not a flashy option by any means, he is a veteran who could easily be a band-aid for a team with a banged up rotation or just simply looking for someone to eat innings.

*Stats acquired from FanGraphs.com and Baseball-Reference.com.


Rendering Paul Goldschmidt a Mere Mortal

The importance of getting ahead of hitters is stressed to pitchers from the first time they play in a non-coach-pitch league.  It’s not what happens on the pitch immediately following a first pitch strike, it’s because the numbers for the rest of the at bat sway dramatically in the pitcher’s favor.

2015 AVG SLG ISO
FIRST PITCH .335 .539 .204
AB after 1st Pitch Strike .223 .338 .115

These are league averages, but for the most part they apply to individual hitters as well.  Paul Goldschmidt is not a “league average” hitter, in fact, he is at least in the conversation when discussing the best hitter in baseball right now (2015) – and I only say at least because I’m too afraid of the backlash I might receive if I declared him the best.  But regardless if a pitcher is facing an average hitter or an elite hitter, the law of getting ahead applies –  even if the numbers for Goldschmidt do look a bit different from the table of above.

2015 AVG SLG ISO
FIRST PITCH .545 1.152 .607
After 1st Pitch Strike .288 .465 .177

Paul Goldschimdt is just so strong, and so adept at making hard contact to all parts of the field that, even at his worst, he’s still so much better than other professional hitters.  The results clearly show that he’s a lesser version of himself throughout the duration of an at-bat that starts with a first-pitch strike, but here’s the thing: getting a first-pitch strike on Goldschmidt isn’t easy.  Not only is he discerning, but he is so devastatingly destructive when he sees something he likes.  Pitchers have gotten a first pitch strike against Goldschmidt 56.7% this season (league average is 61.1%).  In 471 PA, Paul Goldschmidt has only swung 126 times at first pitches, or 26.8%.  It could be said that Paul Goldschmidt “goes to bat with a plan”.  But it’s not like pitchers’ game plans will stand idle while Goldschmidt continues to pummel them; they will make adjustments, and one adjustment they have made, because the pay-off is so dramatic, lies in figuring out how to get ahead of him.

First, let’s consider two samples from Goldschmidt’s 2015 – through July 3rd of this year Paul Goldschmidt put up MVP numbers:

April 6 – July 3:

PA H AB R 2B 3B HR RBI SB BB K AVG OBP SLG OPS ISO
354 102 288 57 18 1 20 66 15 64 65 .354 .470 .632 1.102 .278

Since then, however, he has hit like someone who just might be mortal:

July 4 – August 4:

PA H AB R 2B 3B HR RBI SB BB K AVG OBP SLG OPS ISO
111 24 88 10 6 0 2 11 2 19 28 .273 .387 .409 .796 .136

So what course of action have pitchers taken to get ahead of him in the count?  The answer lies in the conveniently bolded numbers featured in the CB% column of the table below.

Numbers represent the usage of pitches in all first-pitch situations to Paul Goldschmidt.

Date FB% SINKER% CHANGE% SLIDER% CB% CUT% SPLIT%
04/06-07/03 40.18 23.46 3.52 14.66 8.21 9.38 0.05
07/04-08/04 36.04 24.32 0.00 14.41 18.02 9.38 0.90

Obviously there’s been an uptick of a larger percentage in split fingers for first pitches, but a hell of a lot more pitchers throw curveballs than splitters, so that value is not really important.  What is important is that 119.5% increase in first-pitch curveballs, because Paul Goldschmidt SPITS at first pitch curveballs.  He saw twenty-eight, 1st pitch curveballs in the sample size concluding July 3rd and swung at a grand total of 1 of them.  Since then, in a month, he’s seen 20, first-pitch curveballs and has swung at exactly 0 of them.

Goldschmidt is looking for something hard-ish (fastball/slider/change-up; league average change up velo is 83.3 compared to 77.7 for curveballs and 84.2 for sliders) that he can drive on the first pitch, and knows he can lay off curveballs to sacrifice a first-pitch strike and still be an above-average hitter.  For the record, it’s not like Goldschmidt is bad against curveballs; he owns a 3.31 wCB/C in 2015 (3.79 through July 3rd, and 2.16 after), it’s just that he’s committed to his plan.  Pitchers – or analysts – have noticed his disregard for curveballs as first pitches, and the pitchers – not the analysts – have twirled curveballs in to Goldschmidt on the first pitch at a much higher rate over the last month – again, that number is 119.5% more often.  While the strike percentage of these curveballs has only been 45%, that’s still up from the 28% of curveballs for first-pitch strikes through July 3rd.

Conjecture alert:  Perhaps expecting more first-pitch curveballs, Paul Goldschmidt has readied himself to not swing at the first pitch, as he has swung at just 25.3% of non-curveball first pitches since July 4th, compared to 32.9% through July 3rd.  Pitchers have been able to sneak their first pitch strike percentage up against Goldy from 55.9% to 59.5% in this past month – that’s a 6.4% increase.  So it seems as though the best way to beat Paul Goldschmidt is to try to find some way to make him swing the bat less, because when he does, bad things happen to baseballs.  For clarification, I’m talking about throwing him more first pitch curveballs, not walking him every time up.

Paul Goldschmidt is so good that he will probably adjust to this new approach fairly quickly.  I said earlier, “he knows he can lay off curveballs to sacrifice a first-pitch strike and still be an above-average hitter” – Paul Goldschmidt’s aim is not to be a player who is an above-average hitter – he’s a force at the plate and he will adjust.  Health permitting, Goldschmidt will likely finish the season with at least a .300 AVG, 100 R scored, 30 HR, 100 RBI, and 20 SB – a line we haven’t seen from a first baseman since Jeff Bagwell did it in 1999.

So as Goldschmidt adjusts to this new attack from pitchers, maybe the real number to take away from this research is that Goldschmidt is partying like it’s 1999.