Archive for Johnny Cueto

There are a multitude of ways that we can judge pitchers. Most people look at earned run average to gauge whether a pitcher has been successful, while many old school announcers will still cite a pitcher’s win-loss record. ERA is a nice, easy way of looking at how a pitcher has performed at limiting runs, but it doesn’t come close to telling the whole story. In the early 2000s, Voros McCracken created the idea of Defense Independent Pitching Stats or DIPS, which credited the pitcher only with what he could actually control. Fielding Independent Pitching was born from this theory and only took into account a pitcher’s strikeouts, walks and home runs allowed. It turns out that a pitcher’s home run rate is not terribly consistent, thus xFIP was created by Dave Studeman to normalize the home run aspect of the FIP equation by using the league home run per fly ball rate and the pitcher’s fly ball rate.

In 2015, a new metric was developed by Jonathan Judge, Harry Pavlidis and Dan Turkenkopf called Deserved Run Average or DRA. This new stat attempts to take into account every aspect that the pitcher has control over and control for everything that he does not, thus crediting the pitcher only for the runs that he actually deserves. DRA, however, is still dependent on the result of each batted ball. If the batter hits a ball deep in the gap and it rolls to the wall, the pitcher is charged with a double, but if the center fielder lays out and makes a remarkable catch, the pitcher is credited with an out. When evaluating pitchers, why should it matter whether they have a Gold Glove caliber defender behind them or not? It shouldn’t, and that’s where Expected Run Average comes in.

Expected Run Average or XRA gives pitchers credit for what they actually can control. FIP attempts to do this as well but assumes that pitchers have no control over batted balls. While the pitcher does not control how the fielders interact with the live ball, he does have an impact on the type of contact that he allows. XRA is based on a modified DIPS theory that the pitcher controls three things: whether he strikes the batter out, whether he walks the batter and the exit velocity, launch angle combination off the bat. After the ball leaves the batter’s bat, the play is out of the pitcher’s hands and should no longer have any effect on his statistics. The goal is to figure out a way to measure, independently of the defense and park, how each pitcher performs on balls in play. Since 2015, StatCast has tracked the exit velocity and launch angle of every batted ball in the majors. Each batted ball has a hit probability based on the velocity off of the bat and its trajectory. The probability for extra bases can also be determined. These batted ball probabilities have been linearly weighted for each event including strikeouts and walks to give each player’s xwOBA, which can be found on Baseball Savant. This is the perfect way to look specifically at how well a pitcher has performed on a per plate appearance basis.

Once xwOBA is found, then XRA can be calculated. The first objective is to find the pitcher’s weighted runs below average. To do this, I used the weighted runs above average formula from FanGraphs except I made it negative since fewer runs are better for pitchers.

wRBA = – ((xwOBA – League wOBA) / wOBA Scale) * TBF

For example, Max Scherzer has had a .228 xwOBA so far this season and has faced 487 batters. After finding the league wOBA and wOBA scale numbers at FanGraphs I can plug these numbers into the formula.

– ((.228 – .321) / 1.185) * 487 = 38.22

Max Scherzer has been 38.22 runs better than average so far this season, but now I need to figure out what the average pitcher would do while facing the same number of batters. To find this I need the league runs per plate appearance rate and multiply that number by the number of batters that Scherzer has faced.

League R/PA * TBF = Average Pitcher Runs
.122 * 487 = 59.41

So a league average pitcher would have been expected to surrender 59.41 runs facing the number of batters that Scherzer has so far this season. Now that we know how the average pitcher should have performed we can find the expected number of runs that Scherzer should have surrendered so far this season by subtracting his wRBA of 38.22 from the average pitcher’s runs.

Average Pitcher Runs – Weighted Runs Below Average = Expected Runs
59.41 – 38.22 = 21.19

Based on Scherzer’s xwOBA, he should have only given up 21.19 to this point in the season. If this sounds incredible it’s because this is the lowest mark of any starting pitcher though the first half of the season. Finally, XRA is found by using the RA/9 formula by multiplying the expected number of runs allowed by 9 and then dividing by innings pitched.

(9 * Expected Runs) / Innings Pitched = XRA
(9 * 21.19) / 128.33 = 1.49

Max Scherzer’s XRA of 1.49 is easily the lowest of any starter through the first half. The second best starter has been Chris Sale who has a 2.15 XRA. Of course these names are not surprising as they each started the All Star Game and are both currently the front runners for their leagues’ respective cy young award.

Here is a list of the top ten qualified pitchers:

And the bottom ten:

Full First Half XRA List

It is interesting to see that three members of the Giants rotation rank in the bottom seven in all of baseball. In fact, AT&T Park is such a pitcher-friendly park that once you park adjust these numbers, Moore, Cain and Cueto become the three worst pitchers in baseball. It’s not surprising then why the Giants are having such a disappointing season.

One measure of a good stat is whether or not it matches your perception. Therefore, while it is interesting to see Dan Straily as one of the best pitchers in baseball and Johnny Cueto as one of the worst, it is much more assuring to see Max Scherzer, Chris Sale and Clayton Kershaw as some of the very best in the sport. The numbers for relievers also reveal how dominant Kenley Jansen and Craig Kimbrel have been. This is all good evidence that XRA is doing what it is supposed to do, accurately displaying how good pitchers have actually been, independent of all other factors.

Another important characteristic of a good stat is how well it correlates from year to year. While ERA is the most simple and popular way to look at pitchers, it is not very consistent. XRA is much more consistent than ERA and FIP and also compares favorably with xFIP. However, it is not as consistent as DRA. DRA controls for so many aspects of the game that it should be expected to be the most consistent. However, being the most predictive or most consistent stat is not necessarily the goal of XRA. The real goal is to show how well the pitcher actually did, and XRA seems to do this remarkably. While not being as consistent as a stat like DRA, the level of consistency is extremely encouraging and puts it right in line with the other run estimators.

XRA is a stat that takes luck, defense, and ballpark dimensions out of the equation. When evaluating a pitcher, he shouldn’t be penalized for giving up a 350-foot pop fly for a home run in Cincinnati while being rewarded for that same pop fly being caught for an easy out in Miami. With XRA, no longer will people have to quibble about BABIP, since it is results-independent and removes all luck from consideration. A ground ball with eyes will now be treated the same whether it squirts through for a single or is tracked down for an out. Pitching ability will no longer need to be measured with an eye on the level of the defense. It takes a good offense, a good pitching staff and a good defense to make a great team, and with XRA we can finally separate all of these important factions.

It’s September 10^th, 1999, and the small flame-throwing right-hander from the Dominican Republic just struck out Scott Brosius and Darryl Strawberry. He’s about to get Chuck Knoblauch swinging (and missing) on 1-2 count for his 17^th strikeout of the night to finish the game. He does, and the fans at the old Yankee Stadium go nuts, for they’ve just seen Pedro Martinez’ finest start in the greatest pitching season of all time. The final score is 3-1, with the only Yankee run, and hit, coming off a Chili Davis home run. Pedro is 5’11’’ and 170 lb, one of the smallest pitchers in baseball. While most players tower over him off the mound, Pedro writes a different story when he’s pitching. The Yankee hitters fail to notice his height when he kicks his leg up, down, and serves a 95-mph fastball from a three-quarters delivery at their eyes.

The average male height in the U.S. is 5’10’’. You’d never know this from watching a baseball game, where the average height is about 6’2’’, with pitchers just a little taller at about 6’3’’. We all remember the success Randy Johnson had at 6’10’’, and his height was always considered an advantage. When we watched Pedro Martinez, however, commentators and baseball men viewed him as an exception to some obscure and unwritten rule: that shorter athletes can’t become successful pitchers.

Six feet, like 30 home runs or a .300 batting average, has become a number associated with a distinct meaning. If you hit 30 home runs, you’re a power hitter. Hit 29 homers, and you have some pop. If you hit .300, you’re a great hitter. Hit .299, and you just missed hitting .300. Similarly, if you’re six feet, you can pitch. If not, you’re short, but at least you might get an interesting nickname like Tim Lincecum’s (5’11”) “The Freak.”

Most Major League pitchers fall between 6’1’’ and 6’4’’. We can look at the height distribution for pitching seasons of the last 5 years and see that it’s approximately normal:

By this approximation, the chance of randomly selecting a pitcher of the last 5 years who is shorter than 5’11’’ is about 5%.

Are short pitchers really destined to fail? We’ve all been told that it’s better to be taller if you pitch. But is this true? Let’s consider short pitchers to be 5’11’’ or under and examine their effectiveness and distribution in comparison to taller pitchers, who we’ll consider to be 6 feet or taller.

The top ten best pitching seasons for shorter pitchers of the last 5 years are:

We notice that Tim Lincecum appears on this list twice and Johnny Cueto appears on it three times. All of these pitchers are 5’11’’ with the exception of Kris Medlen, who is 5’10’’. So, we see that successful pitching seasons by short pitchers don’t come completely out of the blue. Short pitchers can be successful and can dominate batters, most of whom are much taller, as Cueto did last year and in 2012.

In fact, short pitchers aren’t all that rare to come by, although they’re considerably rarer than taller pitchers. In the last 5 years, there have been 23 instances of short starting pitchers throwing at least 150 innings. In comparison, there have been 402 instances of this type for taller starting pitchers.

Shorter pitchers are generally relegated to the bullpen; there have been 95 instances in the last 5 years of full-time short relief pitchers and 968 instances of full-time taller relief pitchers.

We can see the average WAR breakdowns for all of these pools of players in the following table, along with P-Values for a two-sided t-test comparing the short relievers against the tall relievers and the short starters against the tall starters:

What the 0.0005 is telling us, here, is that we would observe these results by chance alone with probability 0.0005. Thus, there is actually a significant difference in the mean WAR for short relievers and the mean WAR for tall relievers (obviously favoring short relievers). On the other hand, the difference between the starters is not significant. Either way, we have no evidence to suggest that shorter pitchers are any less effective than taller pitchers.

Are shorter pitchers undervalued in the baseball market? If so, to what extent? We can approach this by examining the WAR value of a pitcher relative to his salary in free agency. We can do this by comparing the height groups within relievers and starters (since relievers are generally valued differently than starters).

However, we find that in the last five years, there are only 4 instances of a starter 5’11’’ or shorter pitching for a team that acquired him via free agency; and all of them are Bartolo Colon seasons from 2011-2014.

Fortunately, there are more instances of this in relievers, which is what we’ll examine. We notice the distribution of WAR and relievers’ salaries in free agency:

We see that short and tall relievers are clustered between -1 and 1 WAR and $1 million and $5 million dollars. However, we see several taller relievers past the $7.5 million mark with unremarkable WARs, which we don’t see for shorter relievers. From this, we would suspect that taller relievers are being overvalued while shorter relievers are being undervalued.

This is, in fact, the case: short relief pitchers are producing 2.33 WAR for every $10 million they earn in free agency while taller relievers are producing 1.36 WAR for every $10 million they earn. In comparing these values with a one-sided t-test, we acquire a P-Value of 0.0018, meaning these are results we would acquire by chance only .18% (a significant value) of the time. And so it goes, relievers under 6 feet are actually about 1.7 times as valuable as their taller counterparts.

Is there something inherently different about shorter pitchers that makes them less capable of pitching successfully in the big leagues? The evidence says no. In fact, it might be more worthwhile for General Managers to draft pitchers under 6 feet tall and reap the rewards.

Just because an athlete doesn’t tower over his opponents off the mound, doesn’t mean he can’t bring 55,000 dumbfounded Yankee fans to their feet on an unassuming September evening.

I was planning on covering several overvalued starting pitchers in this next article but after analyzing Reds ace Johnny Cueto, I realized I might have enough material to fill an encyclopedia. Read the rest of this entry »