The Orioles crush the ball when they make contact. Through 76 games, according to data via ESPN Stats and Information, they average a .361/.358/.628 line and a .427 wOBA when they put the ball into play, easily the best in the majors. Out of 169 qualified batters, while the Rockies have the top two players on the list (Story and CarGo), Chris Davis ranks third, Mark Trumbo ranks seventh and Manny ranks eighth. It’s fair to say that these three hitters are the heart of the Orioles’ offense. Then, while Wieters, Kim, Jones and Alvarez are all having strong years, it is arguable that Schoop has been the fourth best offensive piece.
Schoop is no exception to this rule. Respectably for a second baseman, Schoop ranks 35th in production on balls put into play. However, he is taking a surprising route to success. Chris Davis ranks 1st, Mark Trumbo ranks 8th and Manny Machado ranks 18th when putting pitches in the strike zone into play. Schoop has a .435 wOBA when putting pitches in the strike zone into play, good for only 73rd. Schoop has a .506 wOBA, good for second in the majors, when putting pitches out of the strike zone into play. In contrast, Manny Machado ranks 9th, Trumbo ranks 25th, and Davis ranks 142nd.
Schoop’s success against pitches out of the strike zone put into play is sparked in large part due to a high BABIP. He has a .315 BABIP when putting pitches in the strike zone into play, while having an absurd .442 BABIP against pitches out of the strike zone. Despite the fact that Schoop has more production hitting pitches out of the strike zone than in the strike zone, he has a 6.3% chance of hitting a home run when hitting a pitch in the strike zone compared to a 4.4% chance of hitting a home run when the pitch is out of the strike zone. Likewise, he has an 8.5% chance of hitting a double when hitting a pitch in the strike zone and an 5.2% chance when the pitch is out of the strike zone. It seems that he has had extreme success hitting singles, but only singles, against pitches out of the strike zone as he has a 33.3% chance of hitting a single on a pitch out of the strike zone compared to a 21% chance when the pitch is in the strike zone.
He isn’t getting better contact against pitches out of the strike zone. He hits 22% Line Drives, 36% Fly Balls and 41% Grounders when hitting pitches in the strike zone and 13% Line Drives, 20% Fly Balls and 62% Grounders when hitting pitches out of the strike zone. He’s just having a lot of success hitting ground balls when putting pitches out of the strike zone into play. He has a .289 wOBA when hitting a grounder on a pitch in the strike zone and a .396 wOBA (.429/.429/.464) when hitting a grounder on a pitch out of the strike zone. He’s only 22 for 72 with 3 doubles (4.2% double rate) on pitches in the strike zone that are grounders, but is 12 for 28 with 1 double (3.5% double rate) on pitches out of the strike zone that are grounders. According to this data set, he’s only hit 15 fly balls and line drives combined on pitches thrown out of the strike zone, and therefore it’s too small of a sample to derive anything meaningful.
Schoop’s .396 wOBA against grounders hit on pitches thrown out of the strike zone is second in the majors and is more than double the major league average of .185. Not only this, but a .396 wOBA would rank second in production for all ground balls hit regardless of if they're in the strike zone or not. It would only trail Ozuna’s .407 mark while being 166 points above the major league average of .230. Unsurprisingly, this has led him to being fifth in wOBA on ground balls in MLB this year. However, his .289 wOBA against grounders hit on pitches in the strike zone ranks 36th and is only 40 points above the major league average of .247. It’s fascinating how he’s doing excellently hitting ground balls when a pitch is out of the strike zone, but is merely above average when hitting grounders when a pitch is in the strike zone.
Schoop needs to be successful when putting the ball into play because, as fellow author Elie wrote, he doesn’t walk. Schoop swings at over 60% of all pitches, which is the most of any batter in the majors. As a result, he only has a 30% called ball rate. It’s hard to walk when he doesn’t let pitchers throw him balls and therefore he only has a 3.7% walk rate and just a .119 wOBA (162nd out of 169) in plate appearances that result in a pitch being put into play. His offense comes from his ability to hammer the ball.
Furthermore, he needs to continue hammering pitches outside of the strike zone. If his ability to do that drops to just average, then his wOBA on pitches put into play will drop from .449 to .408 and his rank will drop from 35th to 69th. That means his overall wOBA will drop from .365 to .334 and his overall offensive rank will drop from 58th to 99th. In other words, his offensive performance will go from the 66th percentile to the 40th percentile so he’ll go from being above average to below average. Below average performance is acceptable from a second baseman, but it will certainly have an impact on the offense.
Then again, Schoop had significant success last season when putting pitches out of the strike zone into play. Indeed, last year he had a .358 wOBA when putting pitches out of the strike zone into play as ground balls. Maybe he is able to turn pitches in those areas into hard grounders that are more likely to become singles.
Looking at Statcast data from 2015-2016, he’s 23 for 42 with 2 reached on error and 3 doubles when he hits a ground ball over 100 miles per hour good for a .515 wOBA. He’s 10 for 25 with a reached on error and a double when he hits a ground ball between 95 and 100 good for a .371 wOBA. He’s 2 for 11 when he hits a grounder between 90 and 95 good for a .162 wOBA. He’s 9 for 32 with no errors and 1 double when he hits a ground ball between 80 and 90 good for a .262 wOBA. He’s 8 for 50 with a sacrifice bunt and two reached on errors when he hits a grounder below 80 miles per hour good for a .142 wOBA. It’s clear that there’s a correlation between Schoop hitting a ground ball hard and getting a hit. The Baseball Savant and the ESPN Stats and Information strike zones and pitch classification techniques are completely different and therefore I am unable to compare them to each other.
In general, batters do well when hitting ground balls harder than 100 mph. So far in 2016, they’re 2178 for 4990 good for a .436 batting average and a .471 slugging percentage. That's in the ballpark of 2,000 singles and 178 extra base hits. A batter that can do that will put up roughly a .400 wOBA. If Schoop can continue hammering groundballs at over 100 mph, then he may be able to keep up this performance. But then again, why is he only able to hammer groundballs when they’re not in the strike zone? Plus he’s putting up a .515 wOBA compared to the average of .400. Is that luck or skill? I tend to think luck, but it could be a skill.
Schoop’s success this year has largely been due to getting singles when hitting groundballs on pitches thrown out of the strike zone. There is a decent year-to-year correlation when it comes to hitters’ production on pitches hit inside of the strike zone or for hitters’ production when they don’t put the ball into play but only a small one for pitches hit outside of the strike zone. This suggests that in general, hitters’ production on pitches outside of the strike zone is largely based on luck.
I tend to think that Schoop has been lucky this season and his offensive performance will soon regress to the mean. But he does have significant power and the tools to be an elite hitter. What happens if he can turn more of those 100 mph groundballs into 100 mph fastballs and line drives as he gets older? He might just have enough power to overcome his poor plate discipline.
Showing posts with label ESPN Stats and Information. Show all posts
Showing posts with label ESPN Stats and Information. Show all posts
01 July 2016
19 February 2016
Breaking Down wOBA
Weighted On-Base Average (wOBA) is considered one of the best stats to quantify batting performance. Per Fangraphs, wOBA combines all the different aspects of hitting into one metric, weighting each of them in proportion to their actual run value. But there are a number of questions about wOBA. Is it more important to have a strong BB/K rate or to produce when making contact? Is production when hitting fly balls more important than hitting ground balls and if so by how much? In order to answer these questions and more, I used 2013-2015 data from ESPN Stats and Information and Pitch FX to see what correlates best with overall wOBA.
The first test I ran used data from ESPN Stats and Information. I measured players wOBA based on contact against pitches thrown in the strike zone, wOBA on contact against pitches not in the strike zone and wOBA based on at bats that ended in either a hit by pitch, strikeout or unintentional walk. Then, I determined a given player’s percentage rank for each of these three categories from 2013-2015 as well as his rank for the likelihood of each of these events occurring.
I found that the average wOBA for pitches put into play in the strike zone was .385, for pitches out of the strike zone was .290 and for pitches not put into play was .204. I expected pitches hit in the strike zone to be the most productive type, but I was surprised to see that contact made against pitches thrown outside of the strike zone was more productive than at-bats not resulting in contact.
This indicates that batters are stuck in a game theory situation since they want to make contact as often as possible. Swinging aggressively may result in higher contact rates but also more strikeouts, fewer walks and potentially less productive contact. This means they need to decide whether to swing at a bad pitch early in the count. Ideally, batters would never walk or strikeout because they have their best results when making contact. On average, they should be willing to give up roughly 11 walks in order to prevent 9 strikeouts. For comparison, a player like Adam Jones roughly strikes out 5 times for each time he earns an unintentional walk.
A stepwise regression analysis suggests that wOBA for pitches hit inside the strike zone percentile rank is the variable with the strongest relationship to actual wOBA percentile rank. The next most influential variable was wOBA on pitches which weren’t put into play. On average, 85% of all at bats end in one of the two above scenarios. There is a weaker relationship between actual wOBA rank and the percentage of balls in each of the three categories, suggesting that this has some relevance but not a huge amount. In general, the percent of pitches resulted in strikes or balls put into play had a positive impact on total wOBA (obviously, putting strikes into play is better than putting balls into play) while failing to put a ball into play had a negative impact.
The R^2 for this analysis was .9225 indicating that these wOBA categories accurately describe overall wOBA. This is expected but important to verify.
There is a high year-to-year correlation between how often a batter puts a pitch in the strike zone (.746) or out of the strike zone into play (.725) or alternatively fails to put the ball into play (.836). There is some year-to-year correlation between a batter’s ability to produce when putting pitches in the strike zone into play (.544) or when not putting the ball into play at all (.639), but only minimal when putting pitches not in the strike zone into play (.169). This suggests that players largely have the same outline from year-to-year but their production is considerably more variable. It also suggests that production on pitcher-friendly pitches is considerably more variable than production on hitter-friendly pitches. It also suggests that while these results have some predictive value, they’re more helpful for illustrating actual results.
The second test I ran used PITCHf/x data from 2013-2015 and studied players that faced at least 1000 pitches in a given season. I then used zone information to determine whether a pitch was a clear strike, a clear ball or unclear defined as being within 1 inch of the strike zone in any direction. Then I determined a players’ overall wOBA percentile rank as well as his wOBA rank for pitches that are strikes, balls and unclear as well as the likelihood percentile rank of him putting a ball, strike or unclear pitch into play.
Unsurprisingly, batters were most successful against pitches in the strike zone with a .411 wOBA, worse against pitches that were questionable with a .342 wOBA and worst against pitches that were clearly balls with a .284 wOBA. It’s pretty clear that batters are most successful when swinging at strikes.
Roughly two-thirds of balls put into play were strikes. As a result, it should not be surprising that a regression analysis indicated that how a player does against strikes has the largest impact on his wOBA by a significant margin. Performance against pitches that are unclear or not in the strike zone are significant variables but with minimal impact. The model’s R^2 was .96 suggesting that these components do accurately describe what occurred.
As with the ESPN data, a players’ year-to-year profile stays reasonably static. There’s a strong correlation of about .73 between a players current “In Play Percentile Ranks” and his rankings the following year for pitches in the strike zone or pitches not in the strike zone. There is a smaller correlation of .516 between a players’ current “wOBA against strikes” percentile rank and his rank in the following year. All in all, it shows that we can be reasonably certain that a player who swings at good or bad pitches will continue to do so in the future, but that this dataset is better used to describe what happened rather than to predict what will happen. This chart can be found below:
The third dataset that I looked at was also PitchF/x data from 2013-2015 based on players that faced at least 1000 pitches in a year. For this dataset, I determined their wOBA percentile rank based on batted ball type (fly ball, ground ball, line drive and pop up).
As one might suspect, batters were most productive hitting line drives with a .732 wOBA, flyballs ranked second with a .354 wOBA, grounders were third with a .251 wOBA and pop-ups were fourth with a .022 wOBA.
A regression analysis suggested that fly balls were more predictive of future wOBA than line drives. Hitting a larger percent of fly balls and line drives resulted in a higher wOBA while hitting grounders and pop ups resulted in a lower wOBA. An R^2 of .91 suggests that these categories accurately describe what occurred.
I found a moderate year-to-year correlation for the likelihood of a player being ranked at a given percentile for his profile type. I also found a reasonable year-to-year correlation for a player hitting fly balls, line drives and pop-ups but only a minimal one for a player hitting ground balls. A chart summarizing this data can be found here:
All in all, these datasets largely show things that are obvious. They show that hitters do better against pitches in the strike zone than against pitches that aren’t. They show that hitters are more productive when hitting line drives than when hitting ground balls. But they also show a few things that aren’t obvious. They illustrate how strikeouts, walks and hit by pitches relate to balls put into play. And they also can illustrate a player’s strengths and weaknesses.
Being able to visualize the data like this can lead to some surprising findings. Hopefully I’ll get a chance to show you some.
The first test I ran used data from ESPN Stats and Information. I measured players wOBA based on contact against pitches thrown in the strike zone, wOBA on contact against pitches not in the strike zone and wOBA based on at bats that ended in either a hit by pitch, strikeout or unintentional walk. Then, I determined a given player’s percentage rank for each of these three categories from 2013-2015 as well as his rank for the likelihood of each of these events occurring.
I found that the average wOBA for pitches put into play in the strike zone was .385, for pitches out of the strike zone was .290 and for pitches not put into play was .204. I expected pitches hit in the strike zone to be the most productive type, but I was surprised to see that contact made against pitches thrown outside of the strike zone was more productive than at-bats not resulting in contact.
This indicates that batters are stuck in a game theory situation since they want to make contact as often as possible. Swinging aggressively may result in higher contact rates but also more strikeouts, fewer walks and potentially less productive contact. This means they need to decide whether to swing at a bad pitch early in the count. Ideally, batters would never walk or strikeout because they have their best results when making contact. On average, they should be willing to give up roughly 11 walks in order to prevent 9 strikeouts. For comparison, a player like Adam Jones roughly strikes out 5 times for each time he earns an unintentional walk.
A stepwise regression analysis suggests that wOBA for pitches hit inside the strike zone percentile rank is the variable with the strongest relationship to actual wOBA percentile rank. The next most influential variable was wOBA on pitches which weren’t put into play. On average, 85% of all at bats end in one of the two above scenarios. There is a weaker relationship between actual wOBA rank and the percentage of balls in each of the three categories, suggesting that this has some relevance but not a huge amount. In general, the percent of pitches resulted in strikes or balls put into play had a positive impact on total wOBA (obviously, putting strikes into play is better than putting balls into play) while failing to put a ball into play had a negative impact.
The R^2 for this analysis was .9225 indicating that these wOBA categories accurately describe overall wOBA. This is expected but important to verify.
There is a high year-to-year correlation between how often a batter puts a pitch in the strike zone (.746) or out of the strike zone into play (.725) or alternatively fails to put the ball into play (.836). There is some year-to-year correlation between a batter’s ability to produce when putting pitches in the strike zone into play (.544) or when not putting the ball into play at all (.639), but only minimal when putting pitches not in the strike zone into play (.169). This suggests that players largely have the same outline from year-to-year but their production is considerably more variable. It also suggests that production on pitcher-friendly pitches is considerably more variable than production on hitter-friendly pitches. It also suggests that while these results have some predictive value, they’re more helpful for illustrating actual results.
The second test I ran used PITCHf/x data from 2013-2015 and studied players that faced at least 1000 pitches in a given season. I then used zone information to determine whether a pitch was a clear strike, a clear ball or unclear defined as being within 1 inch of the strike zone in any direction. Then I determined a players’ overall wOBA percentile rank as well as his wOBA rank for pitches that are strikes, balls and unclear as well as the likelihood percentile rank of him putting a ball, strike or unclear pitch into play.
Unsurprisingly, batters were most successful against pitches in the strike zone with a .411 wOBA, worse against pitches that were questionable with a .342 wOBA and worst against pitches that were clearly balls with a .284 wOBA. It’s pretty clear that batters are most successful when swinging at strikes.
Roughly two-thirds of balls put into play were strikes. As a result, it should not be surprising that a regression analysis indicated that how a player does against strikes has the largest impact on his wOBA by a significant margin. Performance against pitches that are unclear or not in the strike zone are significant variables but with minimal impact. The model’s R^2 was .96 suggesting that these components do accurately describe what occurred.
As with the ESPN data, a players’ year-to-year profile stays reasonably static. There’s a strong correlation of about .73 between a players current “In Play Percentile Ranks” and his rankings the following year for pitches in the strike zone or pitches not in the strike zone. There is a smaller correlation of .516 between a players’ current “wOBA against strikes” percentile rank and his rank in the following year. All in all, it shows that we can be reasonably certain that a player who swings at good or bad pitches will continue to do so in the future, but that this dataset is better used to describe what happened rather than to predict what will happen. This chart can be found below:
The third dataset that I looked at was also PitchF/x data from 2013-2015 based on players that faced at least 1000 pitches in a year. For this dataset, I determined their wOBA percentile rank based on batted ball type (fly ball, ground ball, line drive and pop up).
As one might suspect, batters were most productive hitting line drives with a .732 wOBA, flyballs ranked second with a .354 wOBA, grounders were third with a .251 wOBA and pop-ups were fourth with a .022 wOBA.
A regression analysis suggested that fly balls were more predictive of future wOBA than line drives. Hitting a larger percent of fly balls and line drives resulted in a higher wOBA while hitting grounders and pop ups resulted in a lower wOBA. An R^2 of .91 suggests that these categories accurately describe what occurred.
I found a moderate year-to-year correlation for the likelihood of a player being ranked at a given percentile for his profile type. I also found a reasonable year-to-year correlation for a player hitting fly balls, line drives and pop-ups but only a minimal one for a player hitting ground balls. A chart summarizing this data can be found here:
All in all, these datasets largely show things that are obvious. They show that hitters do better against pitches in the strike zone than against pitches that aren’t. They show that hitters are more productive when hitting line drives than when hitting ground balls. But they also show a few things that aren’t obvious. They illustrate how strikeouts, walks and hit by pitches relate to balls put into play. And they also can illustrate a player’s strengths and weaknesses.
Being able to visualize the data like this can lead to some surprising findings. Hopefully I’ll get a chance to show you some.
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