Archive for Fangraphs

As many of you now know, last week we unveiled some tremendous new metrics. Available on individual player pages as well as the leaderboards, you now have access to plate discipline metrics for pitchers and pitch type statistics for hitters. The former includes information along the lines of how often a pitcher induced a swing out of the zone, in the zone, as well as his percentage of first-pitch strikes. The latter includes the percentages, and velocities, of pitches seen for hitters, as well as his percentage of first-pitch strikes seen.

I wrote a bit of an introduction to these new statistics last week, and David has written several glossary-type entries as well. This is the type of information that has piqued my interest for a long, long time, and it now adds another dimension to evaluations. For instance, did you know that Johan Santana posted an O-Swing % (percentage of pitches out of the zone that batters swung at) of 30.1 in 2005 and 2006, which decreased to 28.2% in 2007, and 26.8% this past season?

Using the new statistics, I decided to run some correlations to see if certain statistics held strong relationships to each other. First, here are the results for correlations run between the percentage of first-pitch strikes and six prominent evaluative statistics:

F-Strike %

K/9:    0.194
BB/9:  -0.719
WHIP:  -0.515
BABIP:  0.096
ERA:   -0.31
FIP:   -0.406

The results here are not that shocking, or at least they should not be. Getting ahead of the hitter is generally considered key for the pitcher. Doing so, in theory, should correlate quite strongly to any metric involving walks. As we can see, there is a very strong relationship between the percentage of first-pitch strikes and the walks per nine innings issued by pitchers. The relationship loses a bit of its strength when hits allowed are added to the equation in the form of WHIP, but the -0.719 correlation between F-Strike% and BB/9 is actually the strongest of any that I ran. Here are the results for O-Swing % and the same six evaluative metrics:

O-Swing %

K/9:     0.281
BB/9:   -0.493
WHIP:   -0.462
BABIP:   0.036
ERA:    -0.362
FIP:    -0.428

Here, the results are a bit different. Nothing is incredibly strong or on the same wavelength of strength as the FStrike-BB/9, but we have a few relationships of moderate strength. What exactly is O-Swing? It is the percentage of pitches that a pitcher threw out of the zone, that a hitter swung at. With this in mind, we might initially expect that pitchers with the highest percentages in this area would strike more batters out, walk less, and therefore be very effective in the ERA and FIP department. One thing to keep in mind, though, is the percentage of pitches that these pitchers throw in and out of the zone.

Jake Peavy and Barry Zito, for instance, were amongst the bottom in terms of percentage of pitches thrown in the zone, at around 47%. However, Peavy induced many more swings on these pitches than Zito, which is a big reason for the difference between the two, since their percentages of pitches in and out of the zone were virtually identical. When we have pitchers with different percentages in the mix, as is the case in the correlations using O-Swing, the results should not be as concrete. Overall, the strongest relationship here also involves BB/9, as the idea goes back to the Peavy/Zito example: Peavy gets swings and outs on pitches out of the zone, Zito does not. The higher the percentage is of swings out of the zone, the better the chance is that the BB/9 will be lower.

Lastly, Z-Swing%, which is still a bit curious. For instance, does a pitcher want a higher or lower percentage here? I would venture a guess that a lower percentage would be better, as the pitch is already in the zone and therefore very likely to be called a strike. A hitter failing to swing will take a called strike. It probably is not as important as FStrike or O-Swing, but here are the correlations:

Z-Swing %

K/9:   -0.067
BB/9:  -0.014
WHIP:  -0.037
BABIP: -0.150
ERA:   -0.027
FIP:    0.087

Well, I guess it really doesn’t matter for pitchers, as the percentage of swings induced on pitches in the strike zone does not share anything close to a strong relationship with any of the above six metrics. Interestingly enough, the highest correlation for Z-Swing involved BABIP, which was the lowest for F-Strike and O-Swing. The -0.150 isn’t significant by any means, though, so nothing should be taken away by that. At the very least, these results show what we would generally expect: the more first-pitch strikes, the lower the rate of walks or vice versa, and inducing swings out of the zone can result in better rate and run prevention stats.

Every so often, I will check my e-mail and find a hidden gem from our captain, David Appelman. The messages usually discuss any pertinent baseball topics we may have interest in covering, but, every now and then, inform us writers of new statistical updates at the website. This is my favorite type of e-mail, one of which I received yesterday, that almost gave me a sabergasm. See, we have some new stats on this site that are not only incredibly useful, but are incredibly interesting to peruse as well. You can find these new statistics on the individual player pages as well as the leaderboards.

To get the suspense out of the way, the statistics are: First-strike percentage for both batters and pitchers, Plate Discipline stats for pitchers, and Pitch Type stats for batters. The percentage of first strikes tells us, for pitchers, which ones get ahead 0-1 in the count most often; it also counts a ball put in play on the first pitch as a strike. For hitters, we can see which get behind 0-1 the most or least, with plate appearances ending with just one pitch intermingled as well. For instance, did you know that Corey Hart of the Brewers had a 68.9% F-Strike this year? Yeah, over two-thirds of his plate appearances began with him down in the count 0-1, or ended after just one pitch.

On the flipside, Chipper Jones had the lowest F-Strike for a hitter at just 48.3%. Albert Pujols finished at a somewhat distant second with 49.7%. From a pitching standpoint, Barry Zito threw a first-pitch strike just 51.5% of the time, with Edinson Volquez and Oliver Perez finishing close behind. Inversely, Mike Mussina led all of baseball with a 67.6% F-Strike. Close behind him were Ervin Santana, Cliff Lee, Greg Maddux, and Dan Haren, all of which exhibted exemplary control during the 2008 season.

The plate discipline stats for pitchers are not what some may think. No, it isn’t hitting stats for pitchers, explaining how often Joe Blanton swung at pitches out of the zone. Rather, these include the O-Swing, Z-Swing, etc, stats, but for hitters against pitchers. So, if you go to Joe Blanton’s page, and find the plate discipline section, you will be able to see how often hitters swung at his pitches in the zone, out of the zone, how often he threw in the zone, how often did hitters make contact on his pitches, and more along those lines.

This is an amazing addition to the site, and something I will delve into much more next week, but as an appetizer, I will say that Daniel Cabrera, by far had the lowest percentage of swings at pitches outside of the zone. Jake Peavy, however, induced the highest percentage of such swings. In fact, here’s an interesting nugget: Peavy led the league with a 32.4% O-Swing, and threw just 47.6% of his pitches in the zone. Meanwhile, Barry Zito, who had the lowest F-Strike%, threw a league-low 47.2% of his pitches in the zone, but only induced 26% swings on those pitches. Essentially, while both he and Peavy threw the same amount of pitches in and out of the strike zone, Zito could not get as many hitters to swing, which amounts to a large difference in strikeouts and walks.

The other addition is pitch type stats for hitters. Haven’t you ever wondered what percentage of pitches certain hitters see in a given year? I know I have. Countless times this year I wondered what percentage of fastballs Ryan Howard saw, given that he really cannot hit anything else. Well, with the additions here, I know now he saw 51.2% fastballs in 2008, the fourth lowest percentage in the sport. Hunter Pence, at 49.8%, actually saw the lowest percentage, with Dan Uggla, Aubrey Huff, Ryan Howard, and Geovany Soto close behind. Basically, this bottom five consists of sluggers who struggle with breaking pitches, and therefore see a wide array of such pitches.

Click the leaderboard again, to sort by descending order, and we get: Gregor Blanco (70.5%), Jason Kendall (70.0%), Chone Figgins (69.1%), Placido Polanco (68.1%), Willy Taveras (68.0%). Pretty much the opposite group, as these guys are by no means whatsoever power threats, but five hitters who rarely strike out. Moving further, we can also take a look at the average velocities these hitters faced.

Did you know that the AL East had a very high average fastball? It must have, since Kevin Youkilis led the league with a 91.8 mph heater faced, while Manny Ramirez, Alex Rodriguez, Dustin Pedroia, and Jacoby Ellsbury all found themselves in the top five. Reverse the list and we see that Chone Figgins, who saw one of the highest percentages of fastballs, led the league by seeing the slowest average fastball, at just 90.1%. I’m sure myself and my colleagues here will be using these stats much more moving forward, but hopefully this serves as a nice introduction to the new types of information now accessible.

One of the best parts of this site is the accessibility of David Appelman and his willingness to improve and/or update the site to feature more statistics and new parameters for those numbers. The newest addition to the Fangraphs statistical team is calendar year rankings. By going to the leaders page you can now sort not only by month or last 7/14/30 days, but also by the last 1, 2, or 3 calendar years.

For instance, did you know that Ryan Howard, with 145 home runs, has the most in the last three calendar years? Or that Alex Rodriguez ranks second, with 131, fourteen less than Howard?

How about the best and worst WPA counts for hitters in this same span?

BEST
1) Albert Pujols, 18.68
2) Lance Berkman, 17.49
3) David Ortiz, 17.29
4) Vladimir Guerrero, 12.57
5) Ryan Howard, 11.88

WORST
5) Jose Lopez, -3.59
4) Yuniesky Betancourt, -3.73
3) Jack Wilson, -3.86
2) Brandon Inge, -4.24
1) Ivan Rodriguez, -4.93

Hmm. Of the worst five contributors over the last three calendar, two are from the Tigers and two are from the Mariners. In terms of context-neutral wins (WPA/LI), Pujols and Berkman switch places; Berkman’s 17.34 comes in ahead of Pujols’s 16.69.

How about starting pitchers and WPA?

BEST
1) C.C. Sabathia, 9.06
2) Johan Santana, 8.92
3) Roy Halladay, 8.89
4) Brandon Webb, 8.67
5) John Smoltz, 7.91

WORST
5) Dave Bush, -1.94
4) Carlos Silva, -2.79
3) Livan Hernandez, -3.46
2) Jason Marquis, -3.64
1) Matt Morris, -7.83

Wow. Numbers 2 and 3 combine for -7.10 and Morris still comes in 7/10 of a win worse than them. In terms of WPA/LI, Johan reclaims his spot atop the throne with a 10.77, a full 1.60 wins ahead of second-place Brandon Webb’s 9.17. Santana also has the best K/BB (4.45) in this span, as well as the highest LOB% at 78.7%.

It has been reiterated recently that instead of using current seasonal statistics to evaluate players it would be much more accurate to use a rolling projection. While these calendar statistics do not necessarily weight the past any differently they do allow us to see which players have been good enough recently so as to trounce atypical poor early performance.

Amongst the several great win probability statistics kept here is one simply titled ‘clutch.’ The number measures how well players perform in previously defined clutch situations relative to how they would have performed in a context-neutral environment. It has confused some and come into question from others recently so I thought I would take this time to break it down and try to clear up any confusion or doubts.

The stat is calculated by subtracting the WPA/LI from the WPA/pLI. Now, WPA/LI is an already calculated measure freely available all throughout this site. WPA/pLI, however, would have to be manually calculated by dividing the overall WPA by the average leverage index. As an example let’s use Pat Burrell and his current numbers. Burrell has the third best clutch score in the game at 1.35. He has a WPA/LI of 2.51, a WPA of 4.08, and a pLI of 1.06.

4.08/1.06 = 3.85 and 3.85-2.51 = 1.34. The 1.34 vs. 1.35 is nothing more than a rounding discrepancy. This measures how much better Burrell performed in high leverage situations than all others. If he posted a .900 OPS in crucial plate appearances but an equal OPS in all others, he is not considered clutch. And why should he be? Sure, he posted great numbers in high LI game states but he did not raise his game at all.

This brings me to the first major point: Clutch has different definitions and to understand this statistic we need to be on the same page. No matter how important the media makes clutch performance out to be, it does not refer to performing well with the game on the line. Instead, it refers to performing well in these types of situations relative to all others. The statistic can be summed up by the question, “Does the player raise his game in important situations?” If not, he is not clutch, no matter how great his numbers are in high leverage plate appearances.

The second major point is that being clutch or not being clutch is NOT the same as being good or not being good. You do not need to raise your game in crucial situations to be a great player and those who do raise their games are not necessarily the most talented. A player with a .200 BA that hits .300 in crucial situations is, and should be, considered more clutch than someone with a .333 BA in all situations. The .333 is a better BA but it is not clutch because it did not constitute a raising of the game.

As I pointed out this morning, just 3 of the 33 NL MVP winners from 1974-2007 finished in the top ten in clutch. Barry Bonds, who won the award from 2001-2004, had clutch scores ranging from -0.49 to -1.14 from 2001-2003, and I better not hear anybody discuss those seasons not being insanely productive. His negative clutch score just means that he did not post a 1.980 OPS (exaggeration) in high leverage situations. His high leverage OPS was likely higher than everyone else’s but this statistic works to measure a player against himself since, after all, clutch refers to raising your individual game, no matter how high that game generally turns out.

I hope this clears up some confusion but I have a feeling the vast differences in definitions of this skill/phenomenon/whatever you call it will continue to generate confusion. The media has relied on clutch to the point that we are now mistaking it for good or bad performance. This is incorrect. Clutch means raising your game, not being a good player.