To review: a pitcher's career ERA is the defined baseline, or average skill of the pitcher. His predicted ERA is taken from the regression model and used to define his skill for that particular year as the difference from career ERA. His actual ERA is observed yearly, and any difference from predicted ERA is due to luck. One thing that is noticeable is that the predicted values are much closer to the actual values for these graphs as opposed to the hitter's AVGs. This is because the R-squared of the pitching regression is .807, while in the hitting regression it was only .344, so much more of the variability is explained by the independent variables.
The first pitcher I want to look at is Roy Halladay. Halladay has been remarkably consistent over the past few years, so it will be interesting to see if the model follows his ERA or jumps around each year. As we can see in the graph below, there has been an interesting split to Halladay's career. Until 2008, his actual ERA was always higher than his predicted ERA, showing that he was unlucky. However, from 2008 through today, his actual ERA has been lower than his predicted ERA, showing that he has been lucky.
There are a couple of possible reasons behind the change in "luck" for Halladay. Since 2008, he has had a much lower WHIP than before, has induced more fly balls, thrown many more first pitch strikes, and has had a much higher swinging strike percentage. The lower WHIP played a huge role in him decreasing his ERA almost a full run (3.71-2.78) from 2007 to 2008, and also would result in the model predicting a much lower ERA.
The other factors are much more interesting, and can explain the difference in luck much better. A higher FB% should result in more home runs and a higher ERA, but Halladay offset this by having a much lower HR/FB ratio. He was allowing many more fly balls but only slightly more home runs. And since fly balls result in lower AVG and OBP, Halladay was basically mitigating the bad result of more fly balls (home runs), and simply using them to his advantage.
The other two differences, throwing more first pitch strikes and a higher swinging strike percentage, go hand-in-hand in explaining the luck factor. Because he increased both variables, the model predicted that his ERA would increase, at least from these variables. In fact, he had a much lower ERA, and this is probably due in large part to the more strikes he threw and the swings and misses he generated. Getting ahead of more hitters probably led to a lower WHIP, which would decrease his ERA. This really goes back to the previous post and the controversial regression model. I believe that in Halladay's case, more strikes led to a lower ERA and not a higher ERA as the regression model predicted. This would perfectly explain his luck.
Now that I have discussed some factors of luck and skill, I want to look at different types of pitchers, using examples of pitchers to try and acknowledge type. I am going to do this by looking at GB%, FB%, and K%, but I am also going to look at the experience of each pitcher. I want pitchers who have pitched in MLB for a decent amount of time so their regressions are more stable.
The first type of pitcher I want to look at is a ground ball pitcher. I am going to use Derek Lowe as an example, as in 2010 he had the third highest ground ball rate in MLB at 58.8%. He has had a long career, and his reputation as a ground ball pitcher has only grown with time as he has relied more and more on his sinker as his career has progressed.
Lowe's ERA has fluctuated a lot since 2002. In five seasons he has had a predicted ERA below his career ERA of 3.87, and in five seasons he has had a predicted ERA above his career ERA. In nine of ten seasons his actual ERA has been higher than his predicted ERA, showing that he has been unlucky. Only in 2005 has he shown to have any kind of luck, when his predicted ERA was 3.96 and his actual ERA was 3.61. A much lower WHIP (1.61 in 2004, 1.25 in 2005) led to a lower predicted ERA, but he also had career highs in first pitch strike %, HR/9, and HR/FB in 2005. All of these were predicted to increase his ERA, but Lowe actually managed to post an ERA almost two runs lower while allowing more home runs. So these factors led to his predicted ERA only decreasing a small amount compared to his actual ERA decrease.
2005 seems like an outlier, and that's why the model shows that he was much luckier in 2005 than any other year. The important take away is that Lowe seems to be overall an unlucky pitcher, at least by the regression's standpoint. This is an interesting point, because the regression says that the higher the FB%, the higher the ERA, so a pitcher with a low FB% should have a lower ERA. But this is not the case. I am very curious now as to what the result of a fly ball pitcher will be.
I am going to look at Ted Lilly as an example of a fly ball pitcher. He posted by far the highest FB% of any pitcher in 2010 at 52.6%. He has always been a fly ball pitcher, but has become more so as his career has progressed.
Lilly has an interesting chart: he seems to fluctuate between being lucky and unlucky from 2003-2007, and since then he has performed exactly as predicted. Between 2008 and 2010, the model predicted his ERA to be within 3 points of his actual ERA every season (including dead on in 2009), and this year it is only off by about 16 points so far. Lilly may not be the best example of a fly ball pitcher as his FB% has fluctuated over the years. A follow up study on all fly ball pitchers, and not just Lilly, will be required to determine if they are lucky or not, because Lilly seems to have performed just as predicted (although that may be the case for all fly ball pitchers).
The final type of pitcher I want to look at is strikeout pitchers. I am going to use Justin Verlander as an example, even though he only finished 11th in baseball in K/9 in 2010 with 8.79 K/9. All of the pitchers above him were younger and had less experience, which may show how pitchers change over time. Young pitchers may be able to get by with mostly a fastball, but once they age and their fastball loses some speed they have to rely on other pitches and craftiness to get hitters out.
Verlander's actual ERA and predicted ERA seem to mirror each other in the graph, except that his actual ERA is always slightly higher (except for 2006) than his predicted ERA, showing that he is unlucky. The worst year for luck (2008), Verlander had a predicted ERA of 4.15 but an actual ERA of 4.84. Much of the difference in skill for 2008 seems to be due to a career high WHIP, but the difference in luck is less clear. One reason, which I haven't talked about yet, may be Verlander's left on base % (LOB%). This variable measures the number of runners a pitcher leaves stranded out of the total amount of runners on base (so 1 - LOB% would be the percentage of runners who score). In 2008, Verlander had a LOB% of only 65.4%, which is by far the lowest percentage in his career (his next lowest percentage is 72.0%). Although his increase in WHIP showed that he was allowing more runners, thus more runs (which was predicted by the model), he was also allowing more of those baserunners to score, which would not be predicted by the model. Thus the model would show him to be unlucky.
It will be interesting to see if this conclusion holds up for all strikeout pitchers. They have higher swinging strike % and should have higher first pitch strike %, but also probably have lower strike zone swing % and strike zone contact %. These factors oppose one another, and it will be interesting to see whether they cancel out, or if one effect dominates another and the pitchers are shown to be either lucky or unlucky.
In this post, I used individual pitchers to represent different types of pitchers. This was not an especially effective method, but it was good to explain some of the reasons behind the difference in luck and skill for certain pitchers. In my next post, I am going to actually separate pitchers into different types based on GB%, FB%, and K% to see if there is any differences by type. This will allow the differences to be much more clear instead of simply seeing differences due to individual pitcher types. This post concluded that ground ball and strikeout pitchers are shown to be unlucky, while no conclusion can be made for fly ball pitchers. It remains to be seen if those conclusions will hold up in the next post.
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