Five batters who are outperforming their xOBA.
Last week I wrote about eight players who are under performing their xOBA, so it seems only natural to talk about a few over performers today. There are a bunch of great cases to draw from, including Odubel Herrera and Xander Bogaerts, and below you will find a table that the players with the 15 largest differences between their wOBA and xOBA. Whereas many of the under performers last week were generally slow runners, many of the over performers are fleet of foot. I have tried to single out the guys who are closer to average runners to talk about today.
name | team | G | PA | AB | xAVG | ΔAVG | xOBP | ΔOBP | xSLG | ΔSLG | xBABIP | ΔBABIP | xOBA | ΔOBA |
Odubel Herrera | PHI | 52 | 225 | 188 | .251 | .068 | .367 | .060 | .315 | .132 | .314 | .071 | .321 | .061 |
Josh Harrison | PIT | 48 | 187 | 174 | .256 | .072 | .295 | .065 | .357 | .074 | .293 | .074 | .283 | .060 |
Jacoby Ellsbury | NYY | 43 | 182 | 164 | .234 | .047 | .302 | .042 | .315 | .100 | .280 | .048 | .272 | .060 |
Jackie Bradley | BOS | 50 | 203 | 178 | .288 | .044 | .371 | .038 | .472 | .129 | .314 | .059 | .366 | .057 |
Jonathan Villar | MIL | 51 | 223 | 185 | .241 | .062 | .354 | .055 | .325 | .091 | .324 | .091 | .313 | .052 |
Daniel Murphy | WSH | 52 | 213 | 198 | .340 | .054 | .380 | .047 | .578 | .058 | .344 | .067 | .397 | .051 |
Travis Shaw | BOS | 53 | 220 | 200 | .254 | .041 | .323 | .036 | .437 | .073 | .317 | .063 | .325 | .047 |
Marcell Ozuna | MIA | 52 | 215 | 198 | .279 | .049 | .338 | .043 | .518 | .053 | .305 | .080 | .358 | .046 |
Dexter Fowler | CHC | 49 | 223 | 182 | .269 | .044 | .395 | .040 | .395 | .133 | .336 | .053 | .370 | .046 |
Eduardo Nunez | MIN | 41 | 175 | 164 | .279 | .050 | .311 | .045 | .457 | .037 | .313 | .061 | .318 | .046 |
Xander Bogaerts | BOS | 52 | 242 | 222 | .304 | .043 | .359 | .038 | .452 | .057 | .352 | .047 | .346 | .045 |
Jay Bruce | CIN | 48 | 193 | 177 | .241 | .030 | .293 | .028 | .440 | .119 | .284 | .010 | .323 | .045 |
Eric Hosmer | KC | 52 | 217 | 201 | .284 | .039 | .334 | .035 | .493 | .049 | .314 | .053 | .346 | .043 |
Steven Souza | TB | 47 | 191 | 175 | .227 | .036 | .293 | .032 | .401 | .056 | .322 | .056 | .297 | .042 |
Billy Burns | OAK | 47 | 200 | 188 | .227 | .034 | .271 | .031 | .275 | .055 | .263 | .030 | .238 | .039 |
Higher differences indicate a player has over performed their expected stat.
Josh Harrison has benefited from some smoke and mirrors. Over the past two seasons Josh Harrison has matured from the 25th man to a valuable utility infielder for the Pirates. So valuable, in fact, that the Pirates were willing to bet on him going into the 2016 season by trading second basemen Neil Walker to the Mets for lefty starting pitcher Jon Niese, effectively promoting Josh Harrison to the starting second basemen role. So far this season Josh Harrison has provided solid defense, good foot speed and base running, and a pretty solid bat. However, there might be more to his offensive performance than his .329/.363/.435 stat line is telling us.
Starting with the more basic Statcast numbers, his average exit velocity and launch angle are effectively the same as last season, hovering around 86 mph and 11 degrees respectively. His value hit rate, my version of the hard hit stat using that statcast data, also hasn’t changed much, going from 4.1% in 2015 to 3.8% in 2016. However, his xOBA and xBABIP have both dropped substantially. In 2016 he was rocking a .328 xBABIP and .300 xOBA. It is important to note that neither of these stats incorporate speed, and Harrison is a pretty good runner. However, in the 2016 season, his BABIP was only .336, very close to his xBABIP, and his wOBA was .313. So, judging by that, his speed adds perhaps 10-15 points to his x stats. However, his xOBA this year is .285, and his xBABIP is .293. Adding a generous 15 points to these figures to account for speed would give you .300 xOBA and .308 xBABIP. Nowhere close to his .346 wOBA and .367 BABIP he has produced in games.
So what’s going on here? I put a more granular breakdown of some of his balls in play in the table below. This is just a small sample from this season, but it should illustrate the point pretty well.
Date | Result | xOBA | xBABIP | xBACON | 1B% | 2B% | 3B% | HR% | Out% |
04/22/16 | Double | .500 | .543 | .543 | 50.9% | 1.8% | 1.8% | 0.0% | 45.6% |
05/30/16 | Double | .161 | .148 | .155 | 11.3% | 2.9% | 0.6% | 0.8% | 84.4% |
04/16/16 | Triple | .281 | .083 | .159 | 0.0% | 2.2% | 6.5% | 8.7% | 82.6% |
05/19/16 | Single | .100 | .113 | .113 | 11.1% | 0.0% | 0.0% | 0.0% | 88.9% |
05/23/16 | Single | .172 | .164 | .174 | 14.1% | 2.0% | 0.1% | 1.1% | 82.8% |
05/24/16 | Single | .163 | .170 | .175 | 15.7% | 0.9% | 0.1% | 0.5% | 82.8% |
05/31/16 | Single | .184 | .192 | .197 | 18.4% | 0.9% | 0.1% | 0.6% | 80.0% |
04/22/16 | Single | .203 | .209 | .209 | 17.2% | 2.3% | 1.1% | 0.0% | 79.3% |
05/07/16 | Double | .332 | .124 | .215 | 2.4% | 9.8% | 0.00% | 9.8% | 78.0% |
05/17/16 | Single | .198 | .225 | .225 | 22.1% | 0.0% | 0.0% | 0.0% | 77.9% |
05/07/16 | Single | .218 | .228 | .228 | 18.2% | 4.5% | 0.0% | 0.0% | 77.3% |
04/27/16 | Single | .226 | .256 | .256 | 23.1% | 0.0% | 0.0% | 0.0% | 76.9% |
05/31/16 | Single | .233 | .231 | .241 | 20.8% | 2.6% | 0.0% | 1.1% | 75.6% |
04/11/16 | Single | .222 | .253 | .253 | 25.0% | 0.0% | 0.0% | 0.0% | 75.0% |
05/30/16 | Single | .239 | .251 | .257 | 24.2% | 0.8% | 0.3% | 0.6% | 74.0% |
Notice how one of his base hits only had 2.9% chance of being a double, and another with a 1.8% chance, and yet both landed for doubles. In fact, many of his doubles are between 9 and 16% likelihood. One of his triples had an 82.6% chance of being an out. One of his singles had an 88.9% chance of being an out. Two more had an 82.8% chance. All in all, 13 of his base hits each individually had a 75% chance of being an out. It appears Harrison might have benefited from a fair deal of good luck so far this season. Luck that, one would think, likely wouldn’t last. If he continues to make this same sort of contact for the remainder of the season, you should expect his production to steadily drop.
Expected: .256/.295/.357
To date: .328/.360/.431
Career: .288/.322/.419
Jay Bruce is for sale! The Reds have erected a rather large “For Sale” sign pointed at Jay Bruce and his 13 million dollar 2017 option. It has neon flashing lights and everything. After a lot of trade talk surrounding his name last July, he ended up sticking with the Reds to finish last season. Which was more of a curse than a blessing, considering his massive slump going down the stretch. Bruce is primarily an offensive threat and a defensive liability in right field. This season he has gotten off to a pretty good start, registering a slash line of .271/.321/.559 with .368 wOBA and .294 BABIP. However, there is some room for concern with Bruce.
As with Josh Harrison, Jay Bruce has maintained a relatively stable Value Hit rate, going from 9.4% in 2015 to 9% in 2016. Unlike Harrison, Bruce has not been able to keep a stable exit velocity or launch angle. He has lost 1.3 degrees off his vertical launch angle, dropping from 12.7 to 11.4 degrees. Worse yet, he has fallen from 90.4 to 88.2 mph average exit velocity.
xOBA | xBABIP | xBACON | 1B% | 2B% | 3B% | HR% | Out% | ||
05/18/16 | Home Run | .250 | .036 | .140 | 1.9% | 1.9% | 0.0% | 9.3% | 87.04% |
05/31/16 | Triple | .157 | .147 | .152 | 9.6% | 3.4% | 0.5% | 0.4% | 86.13% |
04/16/16 | Home Run | .177 | .157 | .162 | 8.4% | 7.0% | 0.5% | 0.5% | 83.51% |
05/16/16 | Single | .154 | .175 | .175 | 17.1% | 0.0% | 0.0% | 0.0% | 82.86% |
05/05/16 | Home Run | .162 | .165 | .171 | 15.7% | 0.9% | 0.1% | 0.5% | 82.73% |
04/20/16 | Home Run | .322 | .082 | .180 | 0.0% | 2.2% | 6.5% | 8.7% | 82.61% |
04/18/16 | Double | .347 | .152 | .220 | 0.0% | 10.0% | 6.0% | 6.0% | 78.00% |
05/24/16 | Double | .220 | .233 | .233 | 20.0% | 4.0% | 0.0% | 0.0% | 76.00% |
04/17/16 | Single | .220 | .250 | .250 | 25.0% | 0.0% | 0.0% | 0.0% | 75.00% |
05/30/16 | Double | .364 | .276 | .276 | 0.0% | 20.8% | 4.2% | 0.0% | 75.00% |
Here is a table of ten of Bruce’s more unlikely hits so far this season. Two those batted balls pop off the screen: two homers that had less than 1% chance of clearing the fence. He also landed on third base with a triple on a ball that only had a 0.5% chance of being three bases. Those three hits alone represent a large shift in his stats, if they had landed for singles instead, his slugging percentage would drop 46 points. If they were outs, his slugging would drop 63 points.
Jay Bruce’s batted ball quality is very similar to what he produced last season, which was below league average and probably the second worst season of his career. This year his batted ball quality has actually been a slight tick lower than last season. His in game production has benefited greatly from registering a bunch of low probability hits, and, probably, boosted by a very hitter friendly home ball park as well. Since 2012, when he peaked in value as a player, he has slowly descended in value, to the point where his offense may no longer make up for his very poor defense. Anyone looking to buy him should be wary, he could regress pretty badly, especially if he leaves Great American Ball Park. Unless something changes, you should expect him to put up roughly the same numbers as last season.
Expected: .241/.293/.440
To date: .271/.321/.559
Career: .287/.319/.466
Daniel Murphy has gone insane, in a good way. Murphy has been ridiculously good. He has produced the second best batted balls in MLB this season, only behind David Ortiz. His Exit Velocity is up nearly 1 mph, his Value Hit rate has gone up to 12.5%, his xBABIP is up 75 points, xOBA is up 44 points. All of these numbers are building on the best year of his career last season, the one where he went on to hit home runs off of Greinke, Kershaw, Arrieta, and Lester all in the same post season. Daniel Murphy has stepped it up to another level. Yes, his game stats are way too high given his batted ball quality, but the correction being made is pushing him from the second best production in MLB to the tenth best. That’s nothing to sneeze at. I mean, look at these donut charts for his batted balls (2015) (2016). Batted ball value does up both clockwise and with color. The purple in the 1 o’clock area is the least valuable, and the red in the 12 o’clock is the most valuable. Look at how, between 2015 and 2016 he has shrunk the low value areas by a tremendous amount. He has almost as many high value hits as low value ones.
Not many people ever saw Murphy becoming this good for any stretch of time, and yet he has done so for almost a calendar year. It was around July of last season that things really turned on for him, when he stepped closer to the plate and started to pull the ball with authority. He can pull the ball, he kept his contact ability, and he still knows how to slap a ball to any part of the infield when he’s down in the count. I’m not sure white hot does him justice.
Expected: .340/.380/.578
To date: .394/.427/.636
Career: .294/.336/.436
Jacoby Ellsbury is proving harder isn’t always better. Jacoby Ellsbury has the same average launch angle this year as he did last year at around 10.2 degrees, and has even gained a tick on his average exit velocity, going from 86.8 to 87.8 mph. As a result his Value Hit rate as slightly increased as well, so that should mean he is making better contact this year, right? As it turns out, not so much.
His uptick in exit velocity appears to be coming in two launch windows, greater than 30 degrees and less than -10 degrees. Hitting the ball greater than 30 degrees can be great, but only at very high exit velocities, 100 mph or higher. The higher, the better. However, Ellsbury is, in the case of both of these windows, hitting the ball in the 90-100 mph range, which is significantly less valuable, holding an average xOBA value of .268 and .209 respectively in these two launch windows. So, while his exit velocity has gone up, it has done so in a very bad way. He is hitting a lot of ever so slightly faster balls on bad angles, as opposed to hitting a a few very hard balls on valuable angles. Not only is he hitting more balls on these weaker angles than he did last year, but he has also been hitting a lot fewer balls on the higher value trajectories as well. That isn’t a great combination, and it explains why his xOBA has fallen to a pretty dismal .269, with a .276 xBABIP. He does have some speed left, so he might be able to outrun these numbers a little, although his xBABIP and xOBA matched his BABIP and wOBA almost perfectly last season, so perhaps his speed doesn’t play up. Interactive 2015 chart. Interactive 2016 chart.
Expected: .234/.302/.315
To date: .281/.344/.415
Career: .288/.343/.425
Jackie Bradley Jr. is looking at things from a different angle. Two weeks ago, Alex Chamberlain wrote about Bradley’s apparent lack of plate discipline, high BABIP, and several other issues. With two more weeks of the season under the belt, Bradley Jr. is now one of the players with the greatest differentials between wOBA and xOBA.
Starting with the more simple stats, his exit velocity has gone up 1.5 to 90.9 mph and his value hit rate has gone from 6.7% to 8.4%. Those are both pretty great starting points, however, his average launch angle has also dropped from 10 degrees to 6.3 degrees. That means a lot fewer fly balls and a lot more ground balls and line drives. The way he is creating these ground balls is pretty interesting, though. It appears he lowered his launch angle not by hitting more balls on the ground, but instead by hitting fewer balls into the air. Which is a subtle difference, but can create a pretty big swing in stats, especially BABIP. Last season he hit 12.1% of his balls with a launch angle greater than 30 degrees and 9.1% with a launch angle between 5 and 30 degrees. This year he is hitting 9.3% above 30 degrees, and 16.4% between 5 and 30. He is also hitting fewer balls lower than 5 degrees. He has kinda honed in on hitting those balls on a much more narrow range of launch angles, and his BABIP and xBABIP have gone up as a result. Interactive 2015 chart. Interactive 2016 chart.
His xBABIP this year is .314 and xOBA .366. He is certainly the type of player who has the raw speed to outplay these stats by a wide margin, though. Especially since he is hitting so many balls on or near the ground. Bradley Jr. has totally revamped his batted ball distribution, and it is leading to some pretty interesting results. I’m curious how long he can keep this up, and whether it might be permanent or a small sample size issue.
Expected: .288/.371/.472
To date: .332/.409/.601
Career: .237/.314/.400
As you can probably tell by the donut charts and the subtle mentioning of bacon, I’m trying to make you hungry. Just kidding. I’ve been working on ways to visually represent this sort of data. Personally, I am a very visual person, and I bet a lot of you are as well. These donut things are just a little experiment I ran with this week. I have a google doc set up with all of the data that you can play with, if you’re so inclined. Just pick a player’s name and it will draw the chart for you. Pitcher or batter. There are three different types of chart, each with a 2015 and 2016 version. You can find that google doc here.
Finally, I’ve made a few changes to my main stats doc this past week. I’ve added Innings Pitched for the pitchers, although it comes with a bug that I may or may not ever get around to fixing. Long story short, I don’t have the base running data, so the base running outs (such as pick offs) are not included. This means many of the pitchers have 4,5,6 missing outs. I’m also not correcting for strike outs where the runner reaches base due to the uncaught third strike rule, so a few pitchers have too many outs for that reason. Beyond the innings pitched thing, I’ve changed xRA to xERA, and rewrote the code that selects switch hitters correctly. As always, you can find that main stats doc here.
Andrew Perpetua is the creator of CitiFieldHR.com and xStats.org, and plays around with Statcast data for fun. Follow him on Twitter @AndrewPerpetua.
Great work again Andrew. You stepped it up with those donut charts. Seeing the combo of crack quantitative analysis with awesome clusters and visuals releases all types of endorphins and dopamine in my brain.