Differentiating between Luck and Skill Part III

In my last two posts on luck in pitching, I first defined a regression model and then used individual pitchers to determine if different types of pitchers were lucky or unlucky. In this post, I am going to use all of the pitchers in the data set (those pitchers that had enough innings to qualify for the ERA title from 2002-2010), separate them into different types of pitchers, and determine whether those types were lucky or not.

The first step is to determine what a ground ball, fly ball, and strikeout pitcher looks like. To determine this, I found the 90th percentile in the data set for GB%, FB%, and K/9. I then set this number as the lowest possible value a pitcher could have in a certain year to be qualified as a type of pitcher. For example, the 90th percentile for strikeouts per nine innings was 8.63. Only 10% of the time did a pitcher strike out at least this many hitters. So, any time a pitcher struck out more than that in a season, he was qualified as a "strikeout pitcher". I also did this for ground balls (90th percentile = 52.8%) and fly balls (90th percentile = 43.3%), and found that there were 78 qualifying ground ball pitcher seasons, 75 qualifying fly ball pitcher seasons, and 77 qualifying strikeout pitcher seasons. The graph below summarizes the results.

In any season, there has been between 4 and 15 total pitchers that qualify as one of the types. The rest of the pitchers were set as "No Type", and will be ignored for the study. It is interesting to see how the number of groundball, flyball, and strikeout pitchers fluctuate over the past decade. One would think that they would remain relatively constant, as it is mostly the same pitchers pitching year to year.

One reason I can think for the fluctuation is the offensive environment for each year. If there are more runs scored, than there is better hitting, which means worse pitching, which should lead to less pitchers classified as types (this is because I classified based on the 90th percentile of all years, so if there is better pitching, there should be less pitchers that are better than the 90th percentile in that specific year). This is especially true with strikeout pitchers. When we add runs per game on to the graph, we get the following:

We see the the inclusion of R/G slightly explains the fluctuations, as generally the higher the R/G, the lower the amount of pitchers qualified, especially strikeout pitchers. The other fluctuations I believe are due to luck, and determining the answer to that question is not the purpose of this post.

The first group of pitchers I am going to look at are groundball pitchers. The graph below shows both the predicted and actual ERAs by year for ground ball pitchers. Although the points are not paired, which makes the graph less meaningful, we can still use it to figure out some important things.

We can see the range of ERAs for groundball pitchers, as well as any outliers, which would show luck. Most years, the average ERA tends to be slightly below 4.00, but in some years (2003) it is much lower, and in some (2004) it is much higher. Overall, the actual ERA for groundball pitchers was 3.62 and the predicted ERA was 3.63, showing just the slight bit of luck. The model seems to predict fairly well the ERA of a groundball pitcher, but we cannot yet make any significant conclusions about the luck of a groundball pitcher.

Since the mean ERAs don't tell us much about the luck of a groundball pitcher, another measure might. If we look at each individual season (78 in total), we find that there were 34 cases where the actual ERA < predicted ERA, showing that the pitcher was lucky. The other 44 cases showed the pitcher to be unlucky (actual ERA > predicted ERA), meaning that 43.6% of the time, a groundball pitcher will experience a lower ERA than expected. This agrees with the previous conclusion in my last post, showing that Derek Lowe, representing groundball pitchers, was unlucky.

The next group of pitchers I am going to look at are flyball pitchers. The graph below is the same as the one above for flyball pitchers, and although it does look very similar, there are some slight differences.

The average ERA for a flyball pitcher looks to be slightly higher than a groundball pitcher. Also, there seems to be less outliers for flyball pitchers, showing that the regression is probably slightly more accurate for flyball pitchers (probably because FB% was included in the regression but not GB%). The actual ERA for flyball pitchers was 4.08, and the predicted ERA was 4.10, again showing the slightest bit of luck for flyball pitchers.

Again, since the mean actual and predicted ERAs don't tell us much as they are so similar, we turn to individual cases. For fly ball pitchers, 54.7% of the time (41 out of 75) a pitcher had an actual ERA < predicted ERA, showing that he was lucky. This seems to agree with the statement made above from comparing the mean actual and predicted ERAs, and we can conclude that flyball pitchers are most likely lucky, or at least luckier than groundball pitchers. 

The final group of pitchers to look at are strikeout pitchers. The graph below again shows ERAs by year for strikeout pitchers. The most interesting thing to note is how there is often an actual ERA outlier, showing that strikeout pitchers more often have their predicted ERA further from their actual ERA.

The mean actual ERA for strikeout pitchers was 3.32, while the predicted ERA was 3.28, showing that strikeout pitchers were actually slightly unlucky. There is slightly bigger gap in the predicted ERA, showing what I mentioned above about actual ERA outliers. For strikeout pitchers, 42.9% of the time (33 out of 77) the pitcher had an actual ERA < predicted ERA, showing that the pitcher was lucky. So just like groundball pitchers, strikeout pitchers tended to be more unlucky than lucky.

Now that we have looked at all of the different types of pitchers, we can see which pitchers tend to be lucky and which tend to be unlucky. The luckiest pitchers by far seem to be flyball pitchers. More often than not, their actual ERAs were lower than their predicted ERAs. Groundball and strikeout pitchers were more unlucky, as they tended to have higher actual ERAs.

This is by no means significant proof that different types of pitchers tend to have different luck, but it does show that based on the regression I ran, flyball pitchers are slightly luckier than any others. However, they did also have by far the highest ERA, showing that while it may be good to be lucky, it is much better to be skilled.

Differentiating between Pitching Luck and Skill Part II

In my first post on differentiating luck and skill for pitchers, I defined a regression model for determining a pitcher's skill. In this post, I want to look at different individuals pitchers as examples of certain types of pitchers to see if some are luckier than others, or if some are more skilled than they seem.

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.

Differentiating between Pitching Luck and Skill Part I

A few weeks ago, I did a couple of posts on differentiating between luck and skill for hitters. I want to now look at it from the other side: how to differentiate between luck and skill for pitchers. This time, instead of using batting average like I did for hitters, I am going to use ERA. Batting averages against pitchers have shown to be wildly inconsistent, and as such, a better dependent variable would be to look at the runs that a pitcher gives up, because it is much more consistent and definitive over time. I don't want to simply look at counting variables such as strikeouts, walks, and home runs, but look at batted ball statistics and detailed pitching statistics.

Just like last time, I want to introduce a bunch of variables and figure out which of them are important using Mallow's Cp and p-values. I tried running a stepwise regression on all of the variables together, but there were too many variables, so I ran two separate regressions and combined the results.

The first stepwise regression I ran is with counting and batted ball stats. The regression predicting ERA includes K/9, BB/9, HR/9, WHIP, GB/FB, LD%, GB%, FB%, and HR/FB. When we run the stepwise regression, we find that the best regression predicting ERA is ERA = K/9 + BB/9 + HR/9 + WHIP + GB/FB + FB%.

The second stepwise regression involves the "plate discipline" variables. These variables deal with things such as how often a batter swings and makes contact or the percentage of pitches in the strike zone. I collected 9 of these variables from Fangraphs, divided into three categories. Swinging includes O-Swing %, the percentage of pitches a batter swings at outside of the strike zone, Z-Swing %, the percentage of pitches a batter swings at inside of the strike zone, and Swing %, which is the total percentage of pitches swung at. Contact includes O-Contact %, the percentage of pitches a batter makes contact with when swinging at pitches outside the strike zone, Z-Contact %, the percentage of pitches a batter makes contact with when swinging at pitches inside the strike zone, and Contact %, which is the total percentage of contact made when swinging at all pitches. Finally, accuracy includes Zone %, the percentage of pitches inside of the strike zone, F-Strike %, which is the first strike percentage, and SwStr %, which is the percentage of strikes that were swung at and missed.

When I ran a stepwise regression with all of these variables, the best regression output is ERA = Z-Swing % + Swing % + Z-Contact % + First Strike % + SwStr %.

Now that I have run two separate stepwise regressions, I can combine the results and run one more stepwise regression to make sure the model is the best it can be. When I do that, I find that Swing % no longer becomes needed. Another change I need to make concerns confounding variables. Since the variable WHIP includes walks in it's calculations, I can't have both WHIP and BB/9 in the regression. Since WHIP includes hits, which could be important in predicting ERA, I must remove BB/9 from the regression.

The regression now looks like this: ERA = K/9 + HR/9 + WHIP + GB/FB + FB% + Z-Swing % + Z-Contact % + First Strike % + SwStr %. When I run a linear regression on this model, I find that the p-value for GB/FB rate is an astronomically high 0.864, so it is clearly not as important as I first thought. K/9 also has a very high p-value of 0.594, so that can also be taken out. We are left with seven variables that should, with relatively high confidence, predict a pitcher's ERA. The final regression model is as follows: ERA = WHIP + HR/9 + FB% + Z-Swing % + Z-Contact % + First Strike % + SwStr %. The output table from R is below.

Coefficients:
                      Estimate   Std. Error   t value   Pr(>|t|)   
(Intercept)     -6.89402   0.92749     -7.433    2.83e-13 ***
WHIP            3.82090    0.12118     31.532    < 2e-16 ***
HRper9         0.95049    0.05343     17.791    < 2e-16 ***
Fbperc           0.49971    0.24764     2.018      0.043950 * 
Zswingperc   0.95022    0.42860     2.217      0.026914 * 
Zcontactperc 3.55909    0.80202     4.438      1.04e-05 ***
Fstrikeperc    1.32946    0.39701     3.349      0.000851 ***
Swstrperc      2.44227    1.26266     1.934      0.053452 .
R-Squared = 0.8071

The R-squared value for the regression is actually quite good, showing that over 80% of the variation in ERA can be explained by the seven independent pitching variables.

An increase of one in WHIP is associated with an increase of 3.821 runs in ERA. Again, this one makes a lot of logical sense. Giving up one more baserunner per earning should really hurt your ERA. Since ERA is based on 9 innings, we can see that the one extra baserunner per inning would increase the runs allowed per inning by 0.425 runs. This number makes a lot of sense both intuitively and through statistics. Looking at the expected runs matrix from The Book, we can see the effect of one extra baserunner per inning. If you subtract the expected runs for a certain base/out situation by the base/out situation with one less runner, and sum all of the possibilities, we can get a good estimate of the effect of extra baserunners. For example, the expected runs for no out and no runners is 0.555, and the expected runs for no outs and a runner on first is 0.953. The difference between those is 0.398. If we calculate all of the differences, we find that the expected increase in runs per inning is 0.4129 with one more baserunner per inning. Now this is not rigorous math, but a simple way of showing that the coefficient for ERA makes a lot of sense.

An increase of one HR/9 is associated with an increase of 0.9505 runs in ERA. Clearly, giving up more home runs is the fastest way to increase your ERA. However, this value does seem somewhat low. In January, I found that the true value of a home run hit in 2010 was worth 1.406 runs. So giving up a home run should right away be worth about 1.41 runs, which means your ERA should increase by about 1.41. Unearned runs are playing a part in decreasing that value, but shouldn't decrease it by close to half a run.

A one-percentage point increase in a pitcher’s fly ball percentage is associated with an increase of 0.5 runs in ERA. As I found in my post on hitting luck vs. skill, a higher fly ball percentage leads to a lower batting average for hitters, which seems to contradict this result. However, fly balls are associated with a much higher slugging percentage than ground balls, and the chance of a fly ball becoming a home run is a great risk to ERA. As an example, Javier Vazquez had a great season in 2009, with a 2.87 ERA and a FB% of only 34.8%. When he moved to the Yankees in 2010, his fly ball rate jumped to 47% and his ERA blew up to 5.32. So far in 2011, he has a 48.1% FB% and an ERA of 5.23, pretty much in line with his 2010 stats.  Although FB% is clearly not the only reason why his ERA jumped, it certainly contributed, especially because his HR/FB rate jumped from 10.1% in 2009 to 14.0% last year.

A one-percentage point increase in a pitcher's swing percentage in the strike zone is associated with an increase of 0.95 runs in ERA.  If pitchers are inducing more swings and misses, then this should be a good thing, but it is possible that hitters could simply be hacking more often because the pitches look much better to hit. Pitchers that are truly successful will be able to get outs by pitching to corners and making the batter only swing at a good "pitcher's pitch". A pitcher constantly painting corners will make the batter take more pitches as he looks for better pitches to hit, before being forced to swing with two strikes.

A one-percentage point increase in a pitcher's contact percentage in the strike zone is associated with an increase of 3.559 runs in ERA. Obviously, if hitters are hitting a higher percentage of pitches, then they are most likely seeing the ball better and hitting it more squarely. This would definitely lead to a higher ERA. Although the coefficient may seem very high, contact percentages are pretty consistent, so a big jump is rare and would lead to a much higher ERA.

A one-percentage point increase in a pitcher's first strike percentage is associated with an increase of 1.33 runs in ERA. This is really the first debatable result in the regression. One would think that throwing more first pitch strikes would lead to a lower ERA, but that is not the case. One plausible explanation can be found in this table. That shows the hitting splits for all of MLB in 2010 on different counts. The slash stats for hitters on the first pitch of an at-bat is a robust .334/.340/.534. That is well above league average, so if a hitter hits a first pitch they are going to have more success overall. Throwing more first pitch strikes leads to more hittable pitches and thus a higher ERA. However, throwing less first pitch strikes leads to pitchers getting behind in the count, and when that happens hitters hit .302/.473/.498. Although BA and SLG are lower, the OBP is much higher (mostly due to walks), and it is the statistic that is most important in creating runs. This coefficient needs to be looked at more in-depth, but right now the regression believes that more first pitch strikes leads to a higher ERA, so we are going to take that as a given.

A one-percentage point increase in a pitcher's swinging strike percentage is associated with an increase of 2.442 runs in ERA. This is an almost identical explanation to the coefficient for swing percentage in the strike zone. The more strikes a batter swings at means there are less strikes that they are simply taking. Strikes that aren't swung at have no negative consequences (other than maybe stolen bases) because the ball has no chance of being put in play, so pitchers should want the swinging strike percentage to be lower, because it will lead to a lower ERA. However, having a lower swinging strike percentage does not necessary lead to a lower ERA. A pitcher must have great control in order to take advantage of a hitter.

In my next post, I will explore different pitchers' luck and skill, just like I did for hitters. Now that the model has been defined, it will again show a pitcher's predicted ERA, and the fluctuations from career ERA to predicted ERA will show the improvements the pitcher has made that season and will be defined as skill. The difference between predicted ERA and actual ERA will show the pitcher's luck. It will be interesting to look at certain examples of pitchers who are lucky or not, and whether they fit a certain stereotype. Maybe ground ball pitchers have, on the whole, a lower ERA than their counterpart fly ball pitchers. I will show examples of certain pitchers, and we will be able to figure out whether they are truly a good pitcher, or have simply gotten lucky.

Improving a team's Pitching

I have already written two posts on the best way of improving a team, and improving a team's hitting. In this post, I want to do much of the same as the hitting post, but this time on pitching statistics. I am again going to run a linear regression model to determine which statistics are best correlated with pitching performance, which will show us which statistics can be best used to improve pitching.

In this model, instead of trying to estimate runs scored, I am going to use ERA as the dependent variable. Using runs against is a possibility, but since we are estimating the effect of statistics on pitching, and not pitching and defense, using runs against would include the effect of defense, so it is not an appropriate DV in this scenario. We again need to be careful in our selection of independent variables as to avoid collinearity.

Pitching statistics are almost opposite of hitting statistics. Good hitters are generally grouped into two categories: those that can get on base, and those that can hit for power. Good pitchers are those who do not allow very many baserunners and do not allow many home runs. We can measure these qualifications by using the two statistics Walks and hits per innings pitched (WHIP), which measures the average number of baserunners a pitcher allows per inning, and home runs allowed, which will not encompass all extra base hits, but should give us a good feel for pitchers who do and do not allow many home runs that will hopefully be a decent predictor for all extra base hits. Finally, I am also going to include strikeouts as a predictor, because pitchers with high strikeouts rates are valuable, and maybe a pitcher with more strikeouts will allow less runs because he has to rely less on his defense. When we run the regression, we get the following:

                  Estimate        Std. Error     t value    Pr(>|t|)
Intercept    3.2986958     0.4207396    7.840      6.45e-14 ***
WHIP        0.4431320     0.2483891    1.784      0.0753 . 
SO            -0.0014423    0.0001842     -7.829    6.97e-14 ***
HR            0.0117201     0.0008172     14.342   < 2e-16 ***
R² = 0.551

As you can see from the R² value, this regression explains a lot less variability than the hitting regression. However, if we replace WHIP by the number of hits and walks given up, we get a lot better regression:

                  Estimate        Std. Error     t value    Pr(>|t|)  
Intercept    -3.410e+00   2.855e-01    -11.942    <2e-16 ***
Hits           3.815e-03      1.547e-04    24.661     <2e-16 ***
BB            2.463e-03      1.414e-04    17.424     <2e-16 ***
SO            -4.711e-05     1.063e-04    -0.443      0.658  
HR            5.298e-03      4.567e-04    11.601     <2e-16 ***
R² = 0.8906

Now, the R² value is almost as high as the hitting regressions. All of the variables are significant except for strikeout, so when we take it out of the regression we get the following:

                  Estimate        Std. Error    t value    Pr(>|t|)  
Intercept    -3.5115171   0.1703878   -20.61     <2e-16 ***
Hits           0.0038502     0.0001321    29.14     <2e-16 ***
BB            0.0024598     0.0001410    17.45     <2e-16 ***
HR            0.0053015     0.0004561    11.62     <2e-16 ***
R² = 0.8905

We can see how insignificant strikeouts were in the regression, because when we remove it the R² value decreases by only 0.0001 (0.01%). We can now determine which variables impact pitching the most. One more hit given up is associated with a 0.00395 increase in ERA, one more walk given up is associated with a 0.00246 increase in ERA, and one more home run given up is associated with a 0.00530 increase in ERA. Since there are vastly different numbers of hits, walks, and home runs given up, we must also look at the mean of each to determine which will most affect ERA. The mean number of hits given up by a team in a single season is 1469.9, the mean walks is 540.2, and the mean home runs is 172.0. If we multiple the means by the coefficients, we get that, on average, hits will increase team ERA by 5.66, walks will increase ERA by 1.33, and home runs will increase ERA by 0.91. Obviously, we are only looking at statistics that will negatively impact (increase) ERA, so the numbers will look very high, as we are not inputting statistics such as outs or double plays that will positively impact (lower) ERA.

So from the results we can easily see that hits are the statistic that most impacts a team's ERA. So the obviously solution for a team would be to give up less hits, but how? One way would be to acquire pitchers with greater command, possibly leading to those pitchers being able to "nibble" more, making hitters swing at worse pitches. This would probably increase walks, and we already saw that walks also are bad for ERA. A better solution would be to acquire pitchers with a low batting average against and also a low batting average on balls in play (BABIP - although it has been shown that BABIP fluctuates year to year and may not be consistent for any pitchers). Pitchers also want to give up less home runs, but if they can reduce the number of hits against them this should in turn reduce the number of home runs against them.

Fact of the Week VII: 2010 - Year of the Pitcher?

As has already been reported in many, many places, 2010 has been known as the year of the pitcher. (You can view just some examples from ESPN, Fanhouse, and Time.) Although 1968 is known as THE year of the pitcher, because strikes zones were expanded and the mound was raised, 2010 has become the year of the return of dominant pitching.

It started early on, when Ubaldo Jimenez threw the first no-hitter on April 17. Then Dallas Braden and Doc Halladay threw perfect games within three weeks of each other in May. Edwin Jackson threw a no-hitter in June, and finally Matt Garza threw yet another no-hitter in July. In all, there have been 5 no-hitters and 23 one-hitters so far in 2010. This is actually the record for most no or one-hitters in a season, passing 1988 which had 26 no or one-hitters. So we can see that 2010 has been a year filled with dominant pitching performances.

However, another amazing thing about this season is how often there have been games where both teams have pitched extremely well. This shows up in the amount of 1-0 games we have seen this year. which has already happened 59 times this season, which is tied for the 6th most in a single season, and the most in any season since 1976.

So we can see that there have been a very high number of extraordinary pitching performances this year. We could compare different stats such as ERA or WHIP to see how 2010 stacks up compared to different years in terms of overall pitching performance, but that is not what I wanted to figure out. I just wanted this post to show that 2010 has in fact been the year of the return of dominant pitching.

Fact of the Week IV: Strikeouts

Tonight was Brandon Morrow's last start of 2010, as the Jays are capping his innings around 150 for the year to make sure his arm is in great shape for next year. Although Morrow had a rough start to the year (his ERA ballooned to 6.69 at one point), he has pitched extremely well since June and has had a remarkable season, ending with a 10-7 record and a 4.49 ERA.

However, the thing that Brandon Morrow does best is strike batters out. He struck out 17 Rays in one game, and ended the year with 178 Ks. That puts him 11th all-time for Blue Jays pitchers for strikeouts in a season (tonight's stats are not included). You may notice that on that list, he also has fewer innings pitched than most pitchers, so we can look at his SO/9 IP, which is the average number of strikeouts a pitcher records per 9 innings (a complete game).

Morrow ended the year with 178 Ks in 146.1 IP, which means that his SO/9 IP was an astounding 10.95. This ranks as the second highest season SO/9 IP ever recorded by a Blue Jays' pitcher with at least 100 IP (Duane Ward had a SO/9 IP of 11.07 in 1991). If we look purely at starting pitchers, Morrow is only the third pitcher (after Roger Clemens and A.J. Burnett) to post a SO/9 IP of greater than 9 (meaning at least one strikeout per inning), and has the highest SO/9 IP of any Blue Jays starter ever.

So we can see that although Morrow didn't have the greatest season ever, it was certainly a step in the right direction. He is well set up for next year (as this previous post details) with a fresh arm, confidence gained from this year, and the ability to strike out almost any hitter in the American League on any given night.

RA Dickey's One-Hitter

Just wanted to do a quick post on RA Dickey's one-hitter against the Phillies tonight. What made the game so special was that the only Phillies hitter to get a hit off of Dickey was the opposing pitcher, Cole Hamels. It made me wonder, when was the last time, and how rare is it for a pitcher threw a one-hit, complete game shutout, allowing only a single hit to the opposing pitcher and less than two baserunners?

This table will help us figure it out, but then we need to cross-reference those games with games on a list like this (but for every year, that only includes games in 2010). The second table is all games in which the 9 hitter for an NL team (presumably the pitcher) got at least one hit, but also did not score or drive in any runs (thus eliminating the shutout). So what we are doing in cross-referencing all one-hitter in the NL since 1920 with all games in which an NL pitcher got a hit.

You actually have to go back quite a ways to find the last such occurrence, which was a game on August 18, 2003 between the Rockies and Mets. That day, Steve Trachsel of the Mets (interesting that both today and the last time was by a Mets pitcher) gave up only one hit and two baserunners (the other was on an error), and the only hit was a double by opposing pitcher Chin-hui Tsao of the Rockies.

It would take a lot of research to find out every occurrence of a one-hitter with only the pitcher getting a hit, but it is definitely a rare case. Going back to 1985, there have only been three occurrences, the two above as well as this game on September 21, 1986 (in which Padres pitcher Jimmy Jones threw a perfect game except for one hit he gave up to opposing Astros pitcher Bob Knepper). So tonight's gem by RA Dickey really was a special event.

UPDATE: I have found two more games that had only one hit by the opposing pitcher, one on May 1, 2006 (which would be the most recent one), and one on June 8, 1992. The first game had a hit and three walks, and the second one had a hit and four walks, which is why they did not turn up in my previous searches. Still, only five occurrences in twenty-five years is very rare, so we will probably be waiting awhile for the next one.

Best, Worst Performances vs the Jays

As a follow-up to my post yesterday, where I analyzed the best and worst performances by Jays' players, today I am going to analyze the best and worst performances so far this year by players against the Blue Jays. Again, I am going to use WPA as a measuring stick, as it is the most useful game-to-game statistical tool.

The best hitting against the Jays so far was Mark Teahen of the White Sox on April 12. Teahen went 3 for 5, with a single, triple, and home run, and 3 RBIs. His leadoff homerun in the top of the 9th inning tied the game at 7, and raised the probability of the White Sox winning by 33%. Then, in the 11th inning, Mark Kotsay led off with a single, and Teahen drove him in with a triple, again raising the probability of the White Sox winning by 33%. Overall, he had a WPA of 0.761, on the day, easily the highest WPA by a Jays' opponent this season.

The best pitching performances against the Jays so far can be found here. Ervin Santana's complete game win on April 18 (boy, was that a bad week for the Jays!) has been the best performance so far. In the Angels' 3-1 win, Santana only allowed 4 hits, and the only run was an Adam Lind solo home run with 2 outs in the bottom of the ninth inning. Santana never faced more than 4 hitters in an inning, and before the home run had retired 17 Jays in a row (a Lind single in the 4th was the last hit). He steadily gained WPA over the course of the game, and since the Angels were already up 3-0 and only needed one more out to win when Lind hit the home run, his WPA barely dropped. He ended up with a WPA of 0.675, pretty easily the top pitching performance against the Jays this year. Surprisingly, the performance only merited a Game Score of 81, and is only the 4th best pitching performance (measured by Game Score) this year by an Angels' pitcher.

Now that we have taken a look at the best performances (well, the worst from the Jays perspective), we can take a look at the worst performances so far this year against the Jays. This shows the worst hitting performances, and Mike Sweeney's 0 for 5 performance on May 19 leads the way. He was already 0 for 3 with a strikeout when he came to the plate in the bottom of the 7th in a 3-2 game (Jays leading) with runners on second and third and 2 out, and when he popped out to second the Jays probability of winning increased by 13%. Then, in the bottom of the 9th, with the Jays still leading 3-2, there were runners on 1st and 2nd and two outs when Sweeney flew out to deep left, increasing the Jays probability of winning by 17% (to 100%). Those two outs were key in his WPA of -0.381 for the game. His aLI was 2.790 for the game, which means that because he made key outs at key points in the game, his WPA took a tumble.

Finally, the worst pitching performance against the Jays so far this year belongs to Bobby Jenks on May 9. Jenks entered the game in the top of the 9th inning with the White Sox winning 7-5, and did not record an out while giving up 4 hits and 4 runs (3 earned). The inning went like this: ground-rule double, single, home run, single, and that was it for Jenks as Scott Linebrink replaced him. The 3-run home run by Fred Lewis single-handedly increased the Jays winning probability by 51%, from 31% to 82%. Overall, Jenks had a WPA of -0.767, and the Jays ended up winning the game 9-7. Interestingly, Jonathon Papelbon's disaster of a ninth inning yesterday finished second on the list of worst pitching performances with a WPA of -0.739. I will be doing a post later this week about last night's game and blown saves.

So there are your best and worst performances by a Jays' opponent so far in 2010. The best performances seemed to occur mostly in April, while the worst performances happened in May (which probably have a correlation to the Jays 12-12 record in April and 19-10 record in May). Surprisingly, none of the games discussed yesterday were repeats today, but some of them were lower down on the list of good and bad performances.

Best, Worst Jays Performances so far in 2010

I thought it would be interesting to do a quick study on the best and worst batting and pitching performances so far this year. A quick way to measure the value of a performance is WPA, which is Win Probability Added*. It sums the value of each play made by a certain player in a game. For a quick example, say that Vernon Wells comes up in an inning with the Jays chance of winning at 45% (e.g. down by one in the middle innings). If Wells hits a home run, and the chance of winning increases to 50%, then the value of the home run is 5%. If you add up (or subtract) the total changes in chances of winning the games, you will get WPA. It is a little confusing at first, but it is an extremely useful (if not the most useful) statistic for measuring a player's contribution to winning individual games or his value over a season or career.

*Quick note: I am using the WPA from Baseball-Reference, which may differ from that of Bill James or FanGraphs as each site has a slightly different WPA formula, but all numbers end up very close

The first thing we are going to look at is the best performances for Jays hitters. This shows the the most valuable game so far this year by a Jays hitter was Adam Lind against the Cleveland Indians on May 5. The Jays won the game 5-4, as Lind hit a 2-run home run with 2 outs in the 9th inning and the Jays down by 1 run. For the game, he went 2 for 4 with a walk, a run, and 2 RBIs. FanGraphs shows that the home run had a WPA value of .724, as the probability of the Jays winning the game went from 8% to 80.5%. That game by Adam Lind, and especially the home run, is the most valuable hitting performance by any Jays player so far this year.

The next thing to look at is the best performance by a Jays pitcher. Any guesses on what the result might be? Yep, it was Brandon Morrow's gem on Sunday against the Rays that had the highest WPA, which corresponds to what I described here this week. One of the biggest reasons why it was so high was because of aLI, or the Average Leverage Index. aLI measures the average pressure that a player will face in each situation, with 1 being average pressure, under 1 being low pressure, and above 1 being high pressure. (An interesting sidenote is that the top 12 best pitching performances are from starters, while we will see that 6 of the 7 worst pitching performances are from relievers. This has a lot to do with aLI, as the best performances are usually when pitchers pitch 8 or 9 shutout innings in a close ball game, while the worst performances are usually when relievers enter a close game and get lit up.) Morrow had an aLI of 1.454, which was also higher than any of the other performances on the list. A big reason why it was so high was because the Jays were only winning 1-0 throughout the entire game, so Morrow could not take any innings off because of a big lead, but had to pitch hard the entire game.

Now, the flip side. We have looked at the best performances of this year so far, now we need to look at the worst. First up, the worst hitting performances so far. Alex Gonzalez's June 23 game against St. Louis comes in as the worst performance so far with a WPA of -0.376. Gonzalez went 0 for 3 with a walk, and 2 double plays grounded into. Both double plays came after leadoff singles by Vernon Wells (in the 4th and 9th innings), and since the Jays ended up with a 1-0 loss, any run would have helped a lot. The 9th inning double play was the play with the largest change in WPA for the Jays, as the Jays went from having a 34.2% chance of winning to only a 4.9% chance of winning (WPA of -0.294). That play, along with his hitless night, gives him the top spot in the worst hitting performance of this year.

Finally, we are going to look at the worst pitching performance of this year. I don't know about you, but when I think about bad pitching so far this year, one name pops into my head: Kevin Gregg. Gregg has gotten a lot of saves (25 so far), but has also blown a couple (4 so far, resulting in an 86% save percentage). But to me, it always seems as if he pitches well under the least pressure, that is, if he comes into the 9th with a 3-run lead against the Orioles, he will always get the save, but if he comes into the 9th with a 1-run lead on the Yankees, he will blow it. And when we take a look at the leaderboard, Gregg's name is all over it. He actually holds the top 3 spots for worst pitching performances, but the one that takes the cake is his performance against Seattle on May 20. He came in with a 3-1 lead, and then: single, single, walk, walk, sac fly, single, game over. Gregg's line: 0.1 IP, 3 H, 2 BB, 3 ER, BS, and L. He threw 25 pitches, only 12 for strikes, and his WPA was an astoundingly bad -0.905, as the Jays had over a 90% chance of winning when he entered, and ended up losing.

There are your best and worst performances so far in 2010. An interesting note is that all performances came in 1-run games, with the games winning 5-4 and 1-0, and losing 1-0 and 4-3 in the respective order of the performances. This again has to do with aLI. The higher the aLI, the higher the probability that a single play (or a couple plays in a row) can swing the entire outcome of the game. And although we measured everything in terms of WPA, I believe that the results we have come up with should pass the "gut-check"; that is, if you were to eyeball some of the best and worst performances so far this year, many of these performances would be at the very top (or very bottom).

We will check back on this post at the end of the year, to find out if the results will still be the best and worst performances after 162 games. Also, tomorrow I will be working on a post similar to this one, for the best and worst performances by opposing players when they are facing the Jays. I would not be surprised if many of the same games we looked at today will be involved tomorrow.

Young Guns and Strikes

I was reading an interesting piece by Tom Verducci of SI the other day (you can read it here), and it got me wondering: how well is the rotation set up for now, but more importantly, for the future?

The article points out that the key to winning in the AL East is having pitchers that can throw lots of strikes, and also generate lots of strikeouts. We are going to look at statistics for pitchers 25 and under, which classify as young, either still prospects that are trying to break into the major leagues or pitchers that have just established themselves in the past few years. We will look at both the number of strikes and strikeouts individually, but I think the easiest place to start would be with wins.

It has been proven that wins are a very flawed statistic, but if your young pitchers are getting more wins in comparison to other years, it should be a good sign that you have some good young pitchers that are able to pitch and win at the major league level. This table shows the numbers of wins each year by pitchers on the Blue Jays that are 25 or younger (turning 25 before June 30 of the season). As the table shows, the 2009 Blue Jays had 23 total wins from pitchers under 25, mostly from Ricky Romero, Brett Cecil, and Marc Rzepczynski. That total ranked 9th all time for the Blue Jays, which shows that there was some promise there (as a comparison, 2008 had 25 and under Jays' pitchers winning only 13 games). You can see the 2009 totals for all major league teams here, which shows that the Jays ranked 14th. However, this year there has been a big step-up. There have already (barely 2/3 of the way through the season) been 26 occasions where a pitcher 25 and under has gotten the win this year. Again, the familiar names are Romero, Cecil, and now Brandon Morrow. They are currently in 2nd place for all major league teams for 2010. If the pitchers continue at this pace for the rest of the season, they will shatter the record of 34 set in 1982, and get to around 39 or 40. This shows that, in terms of wins, the rotation is set up well for both next year and many years to come.

Now that we have seen what wins have told us about the future, we can look at some less flawed statistics, such as strikes and strikeouts. Since wins are dependent on the pitcher as well as both the offense (scoring runs) and the defense (making plays for the pitcher), strikes are based purely on the pitcher's performance. The first point mentioned in the article is that pitchers in the AL East need to get a lot of strikeouts. If we look at the strikeouts per nine innings for pitchers 25 and under for the Jays, we can see that the past few years have been very good. Last year, there were 5 pitchers (who started at least 60% of their games) 25 or under who struck out at least 6 batters per nine innings. If we look at all teams in the major leagues, we can see that the Jays were second only to the Marlins, who had 6 pitchers that fit. So far this year, the Jays have 3 pitchers (Cecil, Morrow, and Romero) 25 and under that are averaging at least 6 strikeouts per 9 innings. Again, if we look at all teams, the Jays are tied for first with the Braves, Reds, Dodgers, and A's. So in terms of strikeouts, their young pitchers are performing better than ever, which shows that the future is bright for their rotation.

Finally, we are going to look at the other point mentioned in the article: throwing lots of strikes. Although it may seem counter intuitive to look at both strikeouts and % of strikes, there are cases (52 % strikes, 8.5 SO per 9 IP) where a pitcher may get a lot of strikeouts but not throw a high percentage of strikes. If we look at the % of strikes thrown by pitchers 25 and under, we can get an idea of how good they can be versus the AL East. This table shows the number of pitchers 25 and under each season for the Jays that threw strikes at least 60% of the time. Unfortunately, this data is only available from 2000 onwards, but we can still see that the last two years have the most pitchers 25 and under throwing at least 60% strikes (5 and 4, respectively). Again, if we want to view all teams in the major leagues, we can look at 2009 here and 2010 here. The Jays rank in the top 6 in both years, so we can see that their young pitchers are not only striking out hitters, but also throwing a lot of strikes.

It is always interesting to read something and then use statistical analysis to either back up what you just read, or completely falsify it. In this case, we can see that the article is completely correct in stating that the Jays have the pitching to win in the AL East based upon throwing strikes and getting strikeouts. It is excited to know that the rotation is set up very well for the next couple of years, and if the team continues to hit like this year in the future, the team should do very well.

Brandon Morrow and Other Blue Jays Pitching Gems

Well, now is as good of time as any to write my first post. On Sunday, I went to my first Blue Jays game this season, and happened to witness one of the greatest pitching performances by a Blue Jays pitcher ever. What a game to see! As you probably already know, Brandon Morrow pitched a complete game, one-hit shutout with 17 (17!) strikeouts, and gave up the only hit with two outs in the ninth inning.

So the question is: where does this rank in terms of the greatest games ever pitched by a Blue Jay? Certainly, the best game ever pitched was probably on September 2, 1990, when Dave Stieb no-hit the Cleveland Indians for still the only no-hitter in Blue Jays history.

Here is a list of all games in which a Blue Jays pitcher pitched at least 9 innings and gave up 1 or no hits.

There have been a total of 15 games in which a Blue Jays pitcher threw at a complete game with only one hit. It is interesting to note that the only time a run was scored against in these games was the September 27, 1998 game, where Roy Halladay had a no-hitter through 8 innings before giving up a home run to Bobby Higginson in the 9th (in his rookie season nonetheless!). But Morrow did not simply throw a complete game one-hit shutout. He also struck out seventeen batters. Here is another list of games in which a Blue Jays pitcher has thrown a one-hitter with at least 10 strikeouts.

As you can see, only three times has a pitcher for the Jays gotten at least 10 strikeouts and only given up one hit. And they have all happened this year! Morrow on Sunday, Brett Cecil in May (8 IP), and Ricky Romero in April (also 8 IP). Pretty amazing.

So this game was the first time a Blue Jays pitcher has thrown a complete game one-hitter (also a shutout) with at least 10 strikeouts. And again, he struck out 17 batters, which is the second highest total in Blue Jays history, only behind Roger Clemens' 18 on August 25, 1998 (he's also the first Blue Jays pitcher besides Roger Clemens to get at least 15 K's).

I think the conclusion here is that this was probably either the best or second-best game ever pitched by a Jays' pitcher (up for grabs with the no-hitter). If you want to judge a game by Bill James' Game Score, it ranks as the highest game score ever recorded by a Blue Jays pitcher. It was an amazing performance, and we all hope that Morrow can continue pitching this well the rest of this season and his career!