Position Players on the Hill

Since the Laws of Voros McCracken were published way back in the early 21st century, baseball statistical analysts have sought to firmly establish whether or not pitchers have any control over their batting average on balls in play. However, one confounding variable has always been selection bias, whether the pitchers who make it to the major leagues have some special control over their BABIPs and that the players who have control are not in the majors because they can’t control this variable. As a result, the assertations of statistical analyses don’t have a set “control group” through which to analyze their players. If we had a reliable control group of players to test this BABIP theory against, we would have a clearer picture of whether or not these players can control their BABIP.

Therefore, I thought it would be interesting to see the results of position players who take the mound. Certainly, they fit the criteria that we want in a control group. For one, they must certainly be worse (though, it’s possible that they are better than minor league pitchers) than minor league pitchers. Second, they pass the “scout selection bias” that goes along with pitchers who make the major leagues, that they were not selected by scouts or player analysis experts to play in the majors. Though, it should be noted that many of these pitchers do have some sort of pitching experience, and should have enough athletic ability to post good velocities. In addition, they are selected by their managers as competent pitchers. Either way, it is a reasonable assumption that these pitchers are far worse than their major league counterparts and that they do not fall under the “attrition” bias, that their poor performance will shut them out of the league, as happens with many players with poor debuts.

Alas, let’s get on to the results. The sample was taken from all player seasons in the last 15 years, where pitchers threw fewer than 10 innings and played as a position player for more  than 50 games. The table is compiled at the end of the page and was derived from statistics at the Baseball Databank. The total sample comprised 54 innings.

I’ll leave the results here then let you guys talk it over.

First, the BABIP. I still think that I may have totaled the number of balls in play wrong, so I’d love for someone else to check it for me. However, the total BABIP for the sample was .269. This was especially intriguing given that it was actually lower than the standard .300.  I was hoping to see a number in the upper .300s, which would mean that there would be a spectrum of BABIPs that could include the results of lesser pitchers. It’s still possible that there is such a spectrum. However, this study did not lend evidence to this effect.

Second, was the relative skill of the pitchers. Don’t fear, just because the BABIP didn’t pan out as expected doesn’t mean that the rest of the numbers didn’t as well. First, the pitchers compiled a total 7.33 ERA, with a 7.66 BB/9 rate and 4.0 K/9 rate. These results were a little surprising, as I expected the ERA to be much higher than 7.33, at some place in the teens. In addition, I thought that the K rate would be much lower, as I didn’t think that MLB hitters struck out against position players at such a frequency. Maybe it isn’t so embarrassing to be retired via the K by a non-pitcher, or maybe players should just be embarrassed every time they are K’d by Carlos Silva.

Without fly ball data, I was unable to assess the HR/FB rates. However, they were not all that high, as 9 home runs were registered in 178 balls in play. However, without fly ball data, it is difficult to say the effect. However, if we guess and say that 37.07 percent of BIP were fly balls (for a total of 66 fly balls), this means that 9/ 66+9 balls left the yard, or  12 percent of fly balls – just 1-2 percent worse than the league average for MLB pitchers. Strange.

With such a small sample size, it is yard to pull any concrete results from the data. However, it does seem to lend evidence against the notion that there is a BABIP and HR/FB selection bias against major league pitchers.

Beyond that, I’ll let you readers discuss.

Here are the sums of the data:

BIP: 178        H on BIP: 48      BABIP .26966

HBP: 6           H: 57                     IPouts: 162

BFP: 263      HR: 9                     BB: 46

SO: 24           IBB: 0                  ER: 44

IP: 54          K/9: 4.0               ERA: 7.3333

BB/9: 7.666

And, one last note, I removed Rick Ankiel from the results, as he was formerly an accomplished pitcher, but still crept into the query.

playerID playerID G HBP H IPouts BFP HR BB SO IBB ER G_batting AB yearID
alexama02 alexama02 1 0 1 2 7 1 4 0 0 5 54 149 1997
bellde01 bellde01 1 0 3 3 10 0 3 0 0 4 158 627 1996
benjami01 benjami01 1 0 0 3 3 0 0 0 0 0 35 103 1996
bogarti01 bogarti01 2 0 2 6 9 1 1 1 0 1 97 241 1997
boggswa01 boggswa01 1 0 0 3 4 0 1 1 0 0 132 501 1996
bonilbo01 bonilbo01 1 0 3 3 6 1 1 0 0 2 159 595 1996
burkeja02 burkeja02 1 0 1 3 4 0 0 0 0 1 57 120 2004
burrose01 burrose01 1 0 4 3 7 1 0 0 0 3 63 192 2002
cangejo01 cangejo01 1 0 1 6 7 0 0 0 0 0 108 262 1996
cansejo01 cansejo01 1 0 2 3 8 0 3 0 0 3 96 360 1996
cirilje01 cirilje01 1 0 0 3 5 0 2 1 0 0 158 566 1996
davisch01 davisch01 1 1 0 6 7 0 0 0 0 0 145 530 1996
durritr01 durritr01 1 0 0 1 1 0 0 0 0 0 43 122 1999
espinal01 espinal01 1 0 0 2 2 0 0 0 0 0 59 112 1996
finlest01 finlest01 1 1 0 3 4 0 1 0 0 0 161 655 1996
francma01 francma01 2 0 3 4 10 1 3 2 0 2 112 163 1997
gaettga01 gaettga01 1 1 1 1 3 0 0 0 0 0 141 522 1996
giovaed01 giovaed01 1 0 1 4 7 0 2 0 0 0 92 139 1998
gonzawi01 gonzawi01 1 0 0 3 4 0 1 0 0 0 95 284 2000
gracema01 gracema01 1 0 1 3 4 1 0 0 0 1 142 547 1996
haltesh01 haltesh01 1 0 1 3 3 0 0 0 0 0 74 123 1997
harrile01 harrile01 1 0 0 3 3 0 0 1 0 0 125 302 1996
howarda02 howarda02 1 0 2 6 12 0 5 0 0 1 143 420 1996
jacksda03 jacksda03 1 0 3 6 10 0 2 0 0 2 49 130 1997
jimenda01 jimenda01 1 0 0 4 4 0 0 0 0 0 86 308 2001
lakerti01 lakerti01 1 0 1 3 5 0 1 1 0 0 52 162 2003
loretma01 loretma01 1 0 1 3 5 0 1 2 0 0 73 154 1996
mabryjo01 mabryjo01 1 0 3 2 6 0 1 0 0 2 151 543 1996
martida01 martida01 1 0 2 1 5 0 2 0 0 2 146 440 1996
maynebr01 maynebr01 1 0 1 3 5 0 1 0 0 0 85 256 1997
mccarda01 mccarda01 3 0 2 11 14 0 1 4 0 1 91 175 1996
menecfr01 menecfr01 1 0 6 3 8 1 0 0 0 4 66 145 2000
milesaa01 milesaa01 2 1 3 6 9 1 0 0 0 2 134 522 2004
nunezab01 nunezab01 1 0 0 1 1 0 0 0 0 0 90 259 1999
ojedaau01 ojedaau01 1 0 0 3 3 0 0 0 0 0 78 144 2001
oneilpa01 oneilpa01 1 0 2 6 11 1 4 2 0 3 150 546 1996
osikke01 osikke01 1 1 2 3 8 0 2 1 0 4 48 140 1996
penato02 penato02 1 0 0 3 3 0 0 1 0 0 152 509 2007
perezto03 perezto03 1 0 0 1 2 0 0 0 0 0 91 295 1996
relafde01 relafde01 1 0 0 3 3 0 0 1 0 0 142 494 1998
seitzke01 seitzke01 1 0 0 1 1 0 0 1 0 0 132 490 1996
sheldsc01 sheldsc01 1 0 0 1 1 0 0 1 0 0 58 124 2000
spiezsc01 spiezsc01 1 0 0 3 4 0 1 0 0 0 147 538 1997
venturo01 venturo01 1 0 1 3 4 0 0 0 0 0 158 586 1996
wallati01 wallati01 1 0 1 3 3 0 0 0 0 0 57 190 1996
whitema01 whitema01 1 1 1 3 7 0 2 3 0 1 40 140 1996
wilsojo03 wilsojo03 1 0 1 3 5 0 1 0 0 0 90 263 2007
woodja02 woodja02 1 0 0 3 3 0 0 0 0 0 98 117 2007
zeileto01 zeileto01 1 0 1 3 3 0 0 1 0 0 29 117 1996

The Current Criteria For Defining Batted Balls

With all the emphasis placed on BABIP in the statistical forums, we really could use a better method of classifying batted balls than line drives, groundballs, fly balls, and pop-ups. I guess that’s why Hit F/X is about to take the stat world by storm. For now, we have to deal with what we have.

In order to get a good sense of what we are dealing with, we should see how well these batted ball descriptions correlate with BABIP. Therefore, I took a sample of all qualified 2008 starting pitchers and made a regression equation to compare batted balls to BABIP. The results were not particularly encouraging.

Here’s the equation:

Pitcher BABIP = 1.90 – 1.11 LD% – 1.67 FB% – 1.75 GB% – 0.144 IFFB%

The R-Squared of this equation was 0.352. Unfortunately, this is a moderate to weak correlation. In other correlations, such as trying to find the relationship between break and curve ball success or count versus BABIP, we may be happy with this result. However, with the importance placed on batted ball data, especially when analyzing pitchers, this shows that the current classifications are inadequate.

Another important factor to remember is defense. Every defense influences the pitchers that throw in front of it. Therefore, we should test this equation while accounting for defense, to see if we can bring the correlation anywhere closer to a linear trend.

 Here is the regression equation:

Pitcher BABIP = 1.06 + 0.616 Team BABIP – 0.51 LD% – 1.01 FB% – 1.09 GB%
                – 0.102 IFFB%

R-Squared: .418

Again, there is only a moderate correlation, as even factoring defense into the equation raised the linear trend only marginally.

As we are on the eve of the availability of Hit F/X data, hopefully these points will become moot. Until then, be sure to take batted ball tendencies of pitchers with a grain of salt when making inferences on BABIP.

Breaking Down a Pitcher’s Stuff: Fastball Velocity

Sorry about the last entry, everyone. I made a mistake in my query, which skewed the results.

Anyway, here are the actual results for fastball velocity, particularly swing and miss percentage and foul balls.

 

The sample includes every fastball that was swung at during the 2008 season, broken down into velocities of 85 mph and up. This yielded a sample of 38108 events. There were a number of interesting trends, particularly the correlation coefficients of fastball velocity relating to swing&miss percentage, and foul ball percentage.

To me, the foul ball percentage was the most interesting, but I’ll let you decide.

Below is a description of the data, with velocity in the first column, followed by the percentage of all swings at each velocity according to: Swing&Miss Percentage, the Foul Percentage, then Foul Tip Percentage, then In Play Percentage, followed lastly by the number of total events at each velocity.

 

Table Capture.JPGPart 1: Velocity versus Swing and Miss Percentage

This one was no big surprise. In essence, the higher your velocity is, the more swings and misses you get.
Velocity SwingMiss.JPG

The correlation coefficient for this data set was 0.89. Therefore, there is a strong linear relationship between the velocity thrown and the percentage of swings and misses at the pitch. One thing to notice, however, is that this graph is not completely linear. At the velocity gets above 95, especially with the point at 98 (which, granted, has a small sample size), the graph becomes non-linear, with what looks like an exponential relationship. Therefore, it gets exceedingly harder to make contact with a pitch that is going that additional mile per hour.

As a result, this also causes a lower value of the correlation coefficient, even though the graph has a clear upward trend. Remember, a correlation coefficient is a measure of linear relation. Therefore, when the graph is exponential, the linear relation will be less.

Still, no surprises, as this was expected.

Part II: Foul Ball Rates

This graph was particularly surprising. Maybe because I never have really given it much thought, but I didn’t think I would find such an interesting trend. Here’s the graph:
Capture foul ball rates.JPG

For the rate of foul balls per swing, there is a clear upward, linear trend until 95 mph, where the graph falls at a pretty steep rate.

Foul balls are one of the last unexplored realms of baseball statistical analysis. Hopefully Hit F/X will be able to give us some useful data, but until then, I’ll be waiting. Also, why do we only measure foul balls when they are caught by a fielder? Otherwise, they wouldn’t even be counted as a ball in play. There’s a lot we can learn about the batter-pitcher interaction by foul balls, but there is very little information out there. It would be a great leap forward if there were some good studies on foul ball data.

But, back to the graph. There isn’t a strong linear trend on the graph because of its parabolic shape. However, the correlation coefficient between 85 and 95 mph is .97949, which is an incredibly strong correlation. 

This is a very important point when analyzing the success of soft-tossing pitchers. For pitchers who throw at low velocities, it is important to note that by getting fewer fouls, they are essentially giving away free strikes. These batted balls become balls in play, while for pitchers at higher velocities, the batter now has one additional strike on them, with a great chance for a strikeout. Besides the low swing and miss totals, these low-velocity pitchers have fewer strikes in their favor.

As to why there is a sharp downward trend in the data after 95 mph, I’m not totally sure as to why, though I do have a hypothesis. One, is to think of the graph not in terms of foul or non-foul, but in terms of being late on a pitch. While some of these fouls are going to be pulled, the fact that it is dictated by velocity means that the ones affected by velocity are those that the batter is late on. Therefore, as the velocity goes up, the batter will be late on the pitch to a greater degree. As a result, when the batter gets beyond 95 mph, they are no longer late and fouling off the pitch, but they are late for a swing and miss. This probably has something to do with the exponential increase in swing and misses for high velocities.

This may not change the end result of the at-bat too much, as a strike is still a strike whether its a whiff or a foul; though, higher velocities will have more 2 strike swing and misses (for a K), while lower velocities have longer 2-strike at-bats, due to the at-bat staying alive. The lower velocities will probably have more foul-outs as a result, however. 

Part 3: Ball In Play Rate

This last graph shows the rate of balls in play per swing at each velocity. Again, the data is about where we’d expect it, as its harder to put a ball in play at a higher velocity. This speaks volumes as to why low-velocity pitchers struggle in the majors: if the batters can put your stuff in play more often, there are more chances for hits, and fewer for free outs (strikeouts). This one follows common logic: the faster the velocity, the fewer balls in play per swing.
Capture InPlay.JPG

The graph follows a very consistent linear trend from 85 to 97 mph, then drops suddenly at 98+ mph. It is difficult to say why there is a sudden drop, as it could be due to small sample size or due to the fact that they’re just so hard to make contact with at those speeds. It may very well be a mix of both, though the fact that even 97 mph is within the linear trend makes me believe there is a significant sample size component to this issue.

From 85 mph to 97 mph, the correlation coefficient is -0.977, which is another very, very strong correlation. The fact that there is a correlation is not surprising, though the strength of the correlation is quite shocking. I didn’t expect there to be such a substantial correlation.

 

This study brings about some very interesting trends, as the strength of these correlations are very strong. In particular, the relationship between velocity and foul balls (which is probably causal, velocity causing foul ball percentage for the reason explained) is particularly interesting, especially because the issue is rarely discussed. I think this could give us some insight as to the relationship between velocity and pop-up rate, as pop-ups are generally thought to be the result of being late on a pitch, particularly on inside pitches, where its hard to get the bat head to the ball on time.

In the end, the data seem to back up the reasons why it is so hard to succeed in the MLB without fastball velocity: low-velocity means fewer Ks, more balls in play. I’ll do more research on this, and I hope to post more next time.

 

Thanks to TheHardballTimes.com for their contributions to this article.

Mike Silver recently completed his requirements for the Sport Management Major at THE University of Massachusetts-Amherst, where he is a brother of Theta Chapter of Theta Chi Fraternity, the best house in the country. He is a huge Red Sox and Bruins fan, and longs for the days of the REAL Boston Garden, Cam Neely, and the ultimate Dirt Dog Trot Nixon. Aside from StatSpeak, you can find Mike at TheHardballTimes.com and FireBrandAL.com. If you have any questions, you can reach him at mjasilver@gmail.com. Have a good night readers, and know that Mike hopes to hear from you soon. If you quote Mike in an article, please let him know. He’d love to hear it.

Breaking Down a Pitcher's Stuff: Fastball Velocity

Sorry about the last entry, everyone. I made a mistake in my query, which skewed the results.

Anyway, here are the actual results for fastball velocity, particularly swing and miss percentage and foul balls.

 

The sample includes every fastball that was swung at during the 2008 season, broken down into velocities of 85 mph and up. This yielded a sample of 38108 events. There were a number of interesting trends, particularly the correlation coefficients of fastball velocity relating to swing&miss percentage, and foul ball percentage.

To me, the foul ball percentage was the most interesting, but I’ll let you decide.

Below is a description of the data, with velocity in the first column, followed by the percentage of all swings at each velocity according to: Swing&Miss Percentage, the Foul Percentage, then Foul Tip Percentage, then In Play Percentage, followed lastly by the number of total events at each velocity.

 

Table Capture.JPGPart 1: Velocity versus Swing and Miss Percentage

This one was no big surprise. In essence, the higher your velocity is, the more swings and misses you get.
Velocity SwingMiss.JPG

The correlation coefficient for this data set was 0.89. Therefore, there is a strong linear relationship between the velocity thrown and the percentage of swings and misses at the pitch. One thing to notice, however, is that this graph is not completely linear. At the velocity gets above 95, especially with the point at 98 (which, granted, has a small sample size), the graph becomes non-linear, with what looks like an exponential relationship. Therefore, it gets exceedingly harder to make contact with a pitch that is going that additional mile per hour.

As a result, this also causes a lower value of the correlation coefficient, even though the graph has a clear upward trend. Remember, a correlation coefficient is a measure of linear relation. Therefore, when the graph is exponential, the linear relation will be less.

Still, no surprises, as this was expected.

Part II: Foul Ball Rates

This graph was particularly surprising. Maybe because I never have really given it much thought, but I didn’t think I would find such an interesting trend. Here’s the graph:
Capture foul ball rates.JPG

For the rate of foul balls per swing, there is a clear upward, linear trend until 95 mph, where the graph falls at a pretty steep rate.

Foul balls are one of the last unexplored realms of baseball statistical analysis. Hopefully Hit F/X will be able to give us some useful data, but until then, I’ll be waiting. Also, why do we only measure foul balls when they are caught by a fielder? Otherwise, they wouldn’t even be counted as a ball in play. There’s a lot we can learn about the batter-pitcher interaction by foul balls, but there is very little information out there. It would be a great leap forward if there were some good studies on foul ball data.

But, back to the graph. There isn’t a strong linear trend on the graph because of its parabolic shape. However, the correlation coefficient between 85 and 95 mph is .97949, which is an incredibly strong correlation. 

This is a very important point when analyzing the success of soft-tossing pitchers. For pitchers who throw at low velocities, it is important to note that by getting fewer fouls, they are essentially giving away free strikes. These batted balls become balls in play, while for pitchers at higher velocities, the batter now has one additional strike on them, with a great chance for a strikeout. Besides the low swing and miss totals, these low-velocity pitchers have fewer strikes in their favor.

As to why there is a sharp downward trend in the data after 95 mph, I’m not totally sure as to why, though I do have a hypothesis. One, is to think of the graph not in terms of foul or non-foul, but in terms of being late on a pitch. While some of these fouls are going to be pulled, the fact that it is dictated by velocity means that the ones affected by velocity are those that the batter is late on. Therefore, as the velocity goes up, the batter will be late on the pitch to a greater degree. As a result, when the batter gets beyond 95 mph, they are no longer late and fouling off the pitch, but they are late for a swing and miss. This probably has something to do with the exponential increase in swing and misses for high velocities.

This may not change the end result of the at-bat too much, as a strike is still a strike whether its a whiff or a foul; though, higher velocities will have more 2 strike swing and misses (for a K), while lower velocities have longer 2-strike at-bats, due to the at-bat staying alive. The lower velocities will probably have more foul-outs as a result, however. 

Part 3: Ball In Play Rate

This last graph shows the rate of balls in play per swing at each velocity. Again, the data is about where we’d expect it, as its harder to put a ball in play at a higher velocity. This speaks volumes as to why low-velocity pitchers struggle in the majors: if the batters can put your stuff in play more often, there are more chances for hits, and fewer for free outs (strikeouts). This one follows common logic: the faster the velocity, the fewer balls in play per swing.
Capture InPlay.JPG

The graph follows a very consistent linear trend from 85 to 97 mph, then drops suddenly at 98+ mph. It is difficult to say why there is a sudden drop, as it could be due to small sample size or due to the fact that they’re just so hard to make contact with at those speeds. It may very well be a mix of both, though the fact that even 97 mph is within the linear trend makes me believe there is a significant sample size component to this issue.

From 85 mph to 97 mph, the correlation coefficient is -0.977, which is another very, very strong correlation. The fact that there is a correlation is not surprising, though the strength of the correlation is quite shocking. I didn’t expect there to be such a substantial correlation.

 

This study brings about some very interesting trends, as the strength of these correlations are very strong. In particular, the relationship between velocity and foul balls (which is probably causal, velocity causing foul ball percentage for the reason explained) is particularly interesting, especially because the issue is rarely discussed. I think this could give us some insight as to the relationship between velocity and pop-up rate, as pop-ups are generally thought to be the result of being late on a pitch, particularly on inside pitches, where its hard to get the bat head to the ball on time.

In the end, the data seem to back up the reasons why it is so hard to succeed in the MLB without fastball velocity: low-velocity means fewer Ks, more balls in play. I’ll do more research on this, and I hope to post more next time.

 

Thanks to TheHardballTimes.com for their contributions to this article.

Mike Silver recently completed his requirements for the Sport Management Major at THE University of Massachusetts-Amherst, where he is a brother of Theta Chapter of Theta Chi Fraternity, the best house in the country. He is a huge Red Sox and Bruins fan, and longs for the days of the REAL Boston Garden, Cam Neely, and the ultimate Dirt Dog Trot Nixon. Aside from StatSpeak, you can find Mike at TheHardballTimes.com and FireBrandAL.com. If you have any questions, you can reach him at mjasilver@gmail.com. Have a good night readers, and know that Mike hopes to hear from you soon. If you quote Mike in an article, please let him know. He’d love to hear it.

Breaking Down Pitcher's Stuff

Sorry if you had read the entry “Breaking Down Pitcher’s Stuff”. There was an error in the data that I have since corrected. I will be posting the corrected results shortly. Sorry about that.

Breaking Down Pitcher’s Stuff

Sorry if you had read the entry “Breaking Down Pitcher’s Stuff”. There was an error in the data that I have since corrected. I will be posting the corrected results shortly. Sorry about that.

Plate Discipline Correlations for Pitchers.

I decided to go back to researching pitcher strikeout correlations today, which has been one of my favorite topics of research lately. Now that I have MINITAB, I can generate this stuff a little easier. The interface is nice as well. So, here are a few graphs and correlations. Again, same rules as before: qualified pitchers from 2008.

Today we’ll look at some strong correlations and the regression equation I produced for projecting strikeouts.

First, here’s the graph for actual strikeouts versus the regression equation. It’s R-squared is .84.
Regr Eq.JPG

It was generated using Fangraphs.com’s plate discipline statistics. There are a few interesting points. The two I liked most were the highest expected strikeouts (C.C. Sabathia: .2659 exp. K percentage, versus .2455 actual K%) and the highest actual strikeouts (Tim Lincecum, .2399 expected K%, .2858 actual K%). Lincecum’s outlandish strikeout totals make him an easy pick for an outlier or a player with substantial error.

However, I have a suspicion that the regression line for this problem would fit better as a non-linear relation.  

Here are some other correlations that were pulled out of the data. Each correlation is the R value between the given variable and actual strikeout percentage.

Contact Percentage: -0.869

This one took the cake… and not surprisingly, either. If you miss bats, you will get lots of strikeouts. No surprise here.

Swing Percentage: 0.177

This one was a little surprising. I expected the correlation to be much stronger. If you swing more, you will make contact in more at-bats. The correlation is still there, but it is very weak. I would like to investigate this one a little more.

Zone Percentage: 0.065

To me, this one was shocking. I expected there to be at least some meaningful correlation between zone percentage and strikeout percentage. However, if seems that, for the range of zone percentage thrown among MLB pitchers, there is no correlation. Of course, a pitcher who never throws strikes will never strike anyone out, but, for the range that MLB players throw strikes, it makes no difference. If you want to avoid BBs, pound the zone. If you want strikeotus, I guess it doesn’t matter much.

O-Swing: 0.323

This was another surprising development. I expected this to be much higher: if you can get a batter to chase pitches, he’ll miss more often. The logic is certainly true, but, again, for the range of values among MLB pitchers, there is a weak correlation. Don’t get me wrong, it does matter, just not as much as I had expected.

ZSwing %: -0.052

Another surprise. This may have further implications to the ability of pitchers to get called strikes. The amount that a hitter swings in the zone matters very little and is negligible. Wouldn’t it seem that a hitter who swung a lot in the zone would make contact more and strike out less? Guess not. Funny how things are sometimes.

 

That’s all for now. Next time, we’ll continue this study of plate discipline statistics

 

Thanks to Fangraphs.com for their contributions to this article.

Mike Silver recently completed his requirements for the Sport Management Major at THE University of Massachusetts-Amherst, where he is a brother of Theta Chapter of Theta Chi Fraternity, the best house in the country. He is a huge Red Sox and Bruins fan, and longs for the days of the REAL Boston Garden, Cam Neely, and the ultimate Dirt Dog Trot Nixon. If you have any questions, you can reach him at mjasilver@gmail.com. Have a good night readers, and know that Mike hopes to hear from you soon. If you quote Mike in an article, please let him know. He’d love to hear it.

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