Archive for clutch

With a classic World Series — the most exciting in a long time, if not the best-played or best-managed — now over, it is time to hand out individual awards for the 2011 regular season. Sure, some people are anticipating the Cy Young, MVP, and Rookie of the Year announcements, but I bet true baseball fans really pumped for stuff like today’s award, which attempts to measure how much a hitter has contributed to his team’s wins beyond what traditional linear weights indicates. Who is 2011’s King of Little Things?

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When the “rebuilding” San Diego Padres started 2010 well, most thought they wouldn’t stick. However, with with less than fifty games to go, the Padres are still in first place in the National League West. Predictably, various explanations have been given for this, and talk of how they are “staying within themselves” and being “consistent” is cropping up, as in this recent entry by Buster Olney (Insider) quoting a scout to the effect that the Padres don’t have a very good offense outside of Adrian Gonzalez, but are winning more due to their willingness to move guys over and play their “roles” in an intelligent way to maximize their plate appearances.

It is probably true that the Padres are outplaying their “true talent” to an extent, but teams and individuals overperform and underperform their true talent all the time. What is more interesting is the implication that the Padres are getting “more bang for their buck” offensively by doing the “little things” that just help a team win. My interest is not in taking Olney or the scout he quoted to task. Rather, I want to see if the numbers bear out the idea that the Padres are getting more wins out of their offense than they “should” because of their execution, because of the “little things.”

The “little things” are often brought up in reference to teams who outperform their run differential, e.g., some recent Angels teams. The first thing to note about the Padres, however, is that they are not outplaying their Pythagorean expectation: they are actually two wins under what their run differential suggests. So one could argue on that basis alone that the Padres are being “inefficent” in their wins.

But that does not specifically address whether their offense has generated more wins than they “should.” This implies that the Padres have a poor offense. At first glance, one would say “yes,” as the Padres’ team wOBA of .311 (43 linear weights runs [a.k.a. wRAA] below average) is the among the worst in baseball. However, that needs to be understood in context. The Padres have one of the most hitter-unfriendly home parks in the major leagues. In addition, runs above/below average is baselined against all of the MLB, and includes pitchers hitting. To get a better picture, let’s use the park-adjusted linear weights runs from the team value pages and compare to the rest of the NL. In this light, we see that the Padres’ offense is actually four runs above average, and the only team in the NL West above average. So the Padres’ offense has been one of the better in the NL, and the picture of a team miraculously scraping out runs with inferior hitters is already a bit distorting.

Still, even if the Padres offense has been good, is it doing things to deliver more wins than than traditional linear weights measures?

One way of trying to quantify this is to measure their traditional “context-free” linear weights (wRAA, Batting Runs, etc.) against the difference in run expectancy based on base-out state, as I discuss for individuals here. In short, we can subtract a team’s traditional linear weights (“Batting”) from their RE24 to see how much run value is added by hitting “to the context.” Doing this for the Padres (35.84 RE24 – 4.2 Batting) gives a “situational” added value of about 36 runs, which is obviously good.

However, if we’re going to emphasize “context” when discussing a situational hitting, shouldn’t we go all the way, and include not just base/out state, but inning and overall game situation? This is what WPA/LI does. For more detailed explanation of the following, click here, but a brief example can illuminate the difference. Take the following situation: tie game, bottom of the ninth inning, bases loaded, two outs. In this situation, wRAA and RE24 consider a walk and a home run to have very different linear weights values, whereas for WPA/LI it has the same, since it adjusts linear weights to game-state contexts. So if we subtract traditional linear weights (converted to a wins scale) from that, we see how many contextual wins they’ve added beyond the average value of events. And when we do this for the 2010 Padres, we get -0.79 wins. In other words, their offense has actually helped their team win fewer games than one would expect by just looking at the events out of context.

The 2010 Padres are a good team. Their pitching (particularly in relief) has been very good, although that praise should be tempered for the same reasons that we should realize that their offense has actually been better than one might think: the park. They also have been excellent in the field. Those are the reasons that should be given for their success this season. I don’t know whether or not the “little things” stat used above represents a repeatable skill, but whatever the case may be, it is not true that the Padres are getting more wins for less offense.

In the course of a discussion at The Book Blog about the Angels’ (of late) recent outperformance of (some) projections, I was reminded of a related yet quite different issue I’d thought about looking into a while back (and then promptly forgot about). The Angels are one of the teams in baseball that are praised for “playing the right way” and “doing the little things.” Whatever people mean by that, one thing we can say is that recently, the Angels have consistently outperformed their Pythagorean Win Expectation. Looking (somewhat arbitrarily) at the last three seasons in which the Angels have won the American League West and comparing their actual record with what we’d expect given their run differential based on PythagenPat.

2007: Actual 94-68, Expected 90-72, difference +4
2008: Actual 100-62, Expected 88-74, difference +12
2009: Actual 97-65, Expected 93-69, difference +4

I should say right now that this post is not saying that I am not claiming either a) that the Angels “just got lucky” and weren’t as good as their record, or b) that they have some “intangible” ability (perhaps from their manager) that has enabled them to outperform their run differential the last three seasons. Both of those are copouts, at least at this point. For now, I’m only going to look at this issue with reference to their offense.

One might say that they’ve been “good in the clutch.” And that is, in fact, true. FanGraphs’ clutch score, which measures whether players outperform not only their peers, but themselves in high leverage situations, has the following win values for the Angels’ hitter from 2007-2009:

2007: 5.19
2008: 7.34
2009: 3.22

These numbers are impressive, but they sort of beg the question. Unlike relievers, hitters don’t “earn” their high leverage playing time — unless you think most of those scores were put up by Angels pinch-hitters picked for their “clutchness.” This seems to say what we already knew — the Angels won more game than their runs scored indicate that they “should have”. Undoubtedly, there are “clutch hits,” but this doesn’t tell us how they did it — just that they did.

One thing that “right way” teams are praised for is situational hitting. FanGraphs has a stat for that: RE24. While FanGraphs’ primary “runs created above average” stat, wRAA, uses the average change in run expectancy given an event irrespective of the base/out situation, RE24 does incorporate base/out state. For wRAA, a home run is a home run whether the bases are empty with none out or loaded with 2 out, while RE24 takes into account the different base/out run expectation. As I discuss here, if we subtract the average linear weight runs (wRAA) from the RE24, we can see how much better the Angels performed in terms of “situational hitting.”

2007: wRAA +7, RE24 30.5, situational +23.5
2008: wRAA -18, RE24 18.7, situational +36.7
2009: wRAA 88, RE24 92.8, situational +4.8

Impressive. However, it actually doesn’t tell us what we want to know. This tells us that we would expect the Angels to have scored more runs than traditional linear weights (wRAA) would suggest, but the Pythagorean expectation is already using their actual runs scored. We want to know why they outperformed their run differential (for now, from the offensive perspective) — not why they scored more than their linear weights suggest, but why they won more than their actual runs suggest.

Enter WPA/LI. While RE24 takes base/out context into account, WPA/LI goes one step further, by taking base/out/inning into account. You can follow the link to read up, but basically, it’s “unleveraged” Win Probability. It sounds like Clutch, but it’s actually WPA without the Clutch/Leverage element. To use an example to differentiate WPA/LI: with two outs in the bottom of the ninth with the bases loaded, for WPA/LI a walk and a home run have the same linear weight, whereas those events would be different for both wRAA and RE24, since they don’t take game state into account. So, if any stat could take into account a player or team adjusting their play to a situtation, this would be it. As I did in my earlier Little Things post for individuals, we can do for teams: convert wRAA to wins (I crudely divide by 10), then subtract that from WPA/LI to get the situational wins above average linear weights.

2007: wWAA +0.7, WPA/LI -1.32, -2.02 Little Things
2008: wWAA -1.8, WPA/LI -1.21, +0.59 Little Things
2009: wWAA +8.8, WPA/LI +6.37, -2.43 Little Things

Now that is just bizarre. With RE24, we saw that the Angels the last three seasons have been very good at maximizing their situational hitting in certain base/out states. But “Little Things” shows the exact opposite in 2007 and 2009. They’re about “even” in 2008, although far short of what RE24 shows, and they’re 2 wins below their traditional linear weights in 2007 and 2009. It’s not just that the Angels’ hittesr aren’t living up to their reputation (according to this measure) of “doing the little things,” it’s the contrast between RE24 and WPA/LI based “little things” that is striking. It’s as if the Angels do a great job of hitting with runners in scoring position when they’re playing in blowouts, but make terrible situational plays (relative to the average run expectancy) in close games. And then if you look at their hitter’s “Clutch” scores from those years… It’s really hard to know what the big picture is.

This post has no conclusion other than to note that the title is ironic. It would be foolhardy to dismiss this all as luck. The Angels have been a very good team no matter how you slice it. And just because we don’t understand “how they do it” at the moment doesn’t mean we can never know. But at the moment, I’m simply struck by the oddity.

Most of us are still recovering from this week’s Big Awards Euphoria, especially from Monday’s announcement of the 2009 Carter-Batista Award winner (I recommend reading that post before this one), which found that Ryan Ludwick was the 2009 player whose RBI total most exaggerated his offensive contribution.

Personally, I feel that the RBI/wRC system is the best way for figuring out how much RBI totals reflect true offensive contribution. But I also understand that some prefer a more “contextual” approach. As I did at greater length in an earlier series, let’s revisit the same ground using one of FanGraphs’ more context-sensitive stats — RE24 (Cf. Part Two of my Driveline Series) — to discover an “Alternate Universe” winner.

RE24 might appeal to those who believe situational hitting is a repeatable skill (I’m currently agnostic on this). The basic difference between RE24 and traditional linear weights (e.g. wRAA) is that it takes base/out state into account. For traditional linear weights, a double with two men on and two outs “counts” the same as a double with none on and no outs. RE24 recognizes that in those situations, the run expectancy both before and after the plate appearance are different. To quote myself:

There are 24 base-out states (hence the “24” in “RE24”): eight different combinations of baserunners (e.g., runner on first, bases empty, runners on second and third, etc.) multiplied by the three out states in which hitter might have that situation (no outs, 1 out, 2 outs). RE24 measure the difference in Run Expectancy from the beginning of the play until the next play.

For our purposes, the application is obvious — RE24 might identify players who were particularly good in situations with high run expectancy, and thus “earned” their RBI more than wRAA lets on.

To convert RE24 to an “absolute” measure like wRC, subtract the wRAA from wRC and add RE24. I call this “24RC“. Divide RBI by 24RC to get the comparison of real (situational) production to RBI. [Note that it’s not quite apples-to-apples, RE24 is park-adjusted, and the RBI are not, although it’s not a big problem.] The players are ranked by RBI/24RC. I’ve also included a number that sort of isolates situational contribution by subtracting wRAA from RE24. I dubbed it “Sitch.” Clever, huh?

Here are the 2009 Alternate Universe Carter-Batista Award leaders (among qualified hitters with at least 90 RBI).

5. David Ortiz, 1.134 RBI/24RC. .340 wOBA, 99 RBI, 6.40 Sitch
4. Alex Rodriguez, 1.138 RBI/24RC. .405 wOBA, 100 RBI, -10.03 Sitch
3. Michael Cuddyer, 1.141 RBI/24RC. .370 wOBA, 94 RBI, -17.48 Sitch
2. Cody Ross, 1.188 RBI/24RC. .342 wOBA, 90 RBI, -3.02 Sitch
1. Jose Lopez, 1.202 RBI/24RC. .325 wOBA, 96 RBI, 3.72 Sitch

Congratulations, Mr. Jose Lopez! You may have been just outdone by Mr. Ludwick on Monday, but here in the alternate universe, You’re the Man. Maybe in that alternate universe you’re on Shaq Vs., too. Kate Hudson works wonders, I wonder what B-list actress Big Papi is dating? Michael Cuddyer is showing that it’s not his Sitch (or defense) that got him resigned, but those awesome RBI. And what can I say about Cody Ross? Seriously, what can I say?

2009 “Trailers”

47. Adrian Gonzalez, .772 RBI/24RC. .402 wOBA, 5.29 Sitch
48. Joe Mauer, .751 RBI/24RC. .438 wOBA, 0.32 Sitch
49. Chase Utley, .727 RBI/24RC. .402 wOBA, 4.14 Sitch

Someone recently asked me what it would take for Chase Utley to win the NL MVP. I said to wait a couple years for Pujols to reach free agency and come home to Kansas City. I guess I didn’t realize how terrible Chase is at maximizing his RBI opportunities.

2007-2009 Leaders and Trailers (qualifed, 250 RBI minimum):

1. Jeff Francoeur, 1.30 RBI/24RC. .313 wOBA, 252 RBI, -17.62 Sitch
2. Bengie Molina, 1.28 RBI/24RC. .317 wOBA, 256 RBI, 23.29 Sitch
3. Robinson Cano, 1.28 RBI/24RC. .346 wOBA, 254 RBI, -53.47 Sitch
4. Garrett Atkins, 1.19 RBI/24RC. .339 wOBA, 258 RBI, -6.31 Sitch
5. Mike Lowell, 1.18 RBI/24RC. .359 wOBA, 268 RBI, -4.79 Sitch
6. Ryan Howard, 1.16 RBI/24RC. .385 wOBA, 423 RBI, 22.80 Sitch
…
43. Lance Berkman, 0.80 RBI/24RC. .397 wOBA, 288 RBI, 25.32 Sitch
44. Albert Pujols, 0.80 RBI/24RC. .440 wOBA, 354 RBI, 15.22 Sitch
45. Hanley Ramirez, 0.72 RBI/24RC. .409 wOBA, 254 RBI, -27.34 Sitch

Note how much the Sitch scores fluctuate on both ends of the rankings and draw your own conclusions. Any list with Frenchy and Bengie on one end and Pujols and Han-Ram on the other speaks for itself. Other than noting Cano’s Sitch issues (!), I’ll leave it to you all to fill in the blanks. Perhaps this spreadsheet with complete rankings will help.

As the Official-Baseball-Awards-Are-Awarded-Amid-The-Bitter-Protests-and-Feigned-Indifference-from-the-Internet season winds down, it’s also time for websites and individual bloggers to hand out their own made up awards. I have already crowned the King of the Little Things for 2009, so it’s time to move on to the Carter-Batista Award for 2009. What’s that? If an award is named after Joe Carter and Tony Batista, you might surmise that it has to do with players whose offensive value is exaggerated by their RBI totals.

Readers of this blog don’t need a lecture on why RBI are a bad measure of offensive performance, value, and skill. Like much of my work, this is an excuse to play with a “toy” or “junk stat” to get a point across. Earlier this year, I did a three-part series (1, 2, 3) where I go into much greater detail on the methodology, etc. Here, I’ll just give you the bare-bones.

The idea, inspired by Jonah Keri, is that by dividing a players RBI total by a better counting stat, we can get an idea of how much a players RBI total “overrates” his offense. My earlier version had a more complex construction, but interactions with Tango and terpsfan convinced me that the best way to go about it was to simply use unadjusted “absolute” runs created, like wRC (wOBA Runs Created). The idea stays the same: the higher a player’s RBI/wRC, the more RBI totals “overrate” his contribution, and the more he enters Carter-Batista territory.

[In case you’re wondering I didn’t park-adjust: I did initially, but realized that the RBI are a just as much a product of the environment as wRC, so dividing an unadjusted RBI by an adjusted wRC would be problematic. As usual, simpler turned our to be better.]

Who is this season’s winner? The pool is qualified hitters with at least 90 RBI. Here are the top five candidates:

5. Jorge Cantu, 1.18 RBI/wRC. .343 wOBA (.289/.345/.443), 100 RBI
4. Brandon Phillips, 1.20 RBI/wRC. .337 wOBA (.276/.329/.447), 98 RBI
3. David Ortiz, 1.22 RBI/wRC. .340 wOBA (.238/.332/.462), 99 RBI
2. Jose Lopez, 1.26 RBI/wRC. .325 wOBA (.272/.303/.463), 96 RBI

This stat should not be taken to mean that these guys are bad players or even bad hitters. It just says something about their RBI totals in relation to their true offensive contribution. Brandon Phillips isn’t a great hitter, but he’s a good player because of his 2B defense. Jose Lopez managed to contribute at an above average level this season because of decent defense and durability. We shouldn’t look down on him just because he hit behind Ichiro and his .386 OBP. Sure, Big Papi had a down year with the bat, but his other contributions are incalculable. Literally.

And now, your 2009 Carter-Batista award winner:

1. Ryan Ludwick, 1.26 RBI/wRC. .336 wOBA (.265/.329/.447), 97 RBI

Wow! Ludwick already won the prestigious Average-est Player of 2009 Award. This is entering Michael-Jackson-at-the-1984-Grammys territory. I’m not sure how he did it. Are there any high-OBP guys hitting ahead of Ludwick?

It’s illlustrative to look at the “trailers,” as well. In the last two spots:

47. Joe Mauer, .753 RBI/wRC. .438 wOBA (.365/.444/.587), 96 RBI
48. Chase Utley (naturally), .751 RBI/wRC. .402 wOBA (.282/.397/.508), 93 RBI

Finally, the 2007-2009 leaders and trailers (minimum 250 RBI)

1. Bengie Molina, 1.45 RBI/wRC. .317 wOBA (.278/.302/.440), 256 RBI
2. Ryan Howard, 1.24 RBI/wRC. .385 wOBA (.266/.363/.565), 423 RBI
3. Jeff Francoeur, 1.19 RBI/wRC. 313. wOBA (.271/.314/.409), 252 RBI
…
43. Albert Pujols, .827 RBI/wRC. .440 wOBA (.337/.444/.626), 354 RBI
44. Chase Utley, .821 RBI/wRC. .404 wOBA (.301/.395/.536), 300 RBI
45. Hanley Ramirez, .664 RBI/wRC. .409 wOBA (.325/.398/.549), 254 RBI

Much more could be written, but you all can take it from here draw your own conclusions. Check out the extended list of rankings on this Google spreadsheet.

I’ll be back Tuesday or Wednesday with a follow-up on situational hitting.

Much of my work this week has focused on the ‘Clutch’ statistic kept here, attempting to shed light or help the confusion surrounding its meaning and usage to dissipate. A great discussion took place in the comments section at my post ‘All About Clutch’ wherein it was suggested that the best hitters in the league will struggle to post high clutch scores because, essentially, they would be so high up the performance chart that there would be no higher ground to which their games could be raised. The inverse would then be true for poorer hitters; since their games were so low much more room exists for game-raising performance.

The major confusion stemmed from the fact that a player with a .333 BA in situations with a high leverage index could be less clutch than one with a .225 BA in the same situations. The way the clutch statistic works is that it measures a player against himself, comparing production to what that production would be in a context-neutral environment. Clearly, I would rather have the .333 guy up to bat in a crucial situation and, because of that, heads begin to spin when it is realized that the .225 guy could have a higher clutch score because in all others he hit .200; the .333 guy posted the same BA in all situations, therefore failing to raise his game.

With this in mind I decided to do a little digging in order to see if this generally holds true. I took the qualifying major league players from 2000-2007, first found the average WPA/LI, and then calculated the average clutch score for those with above average WPA/LI as well as the average clutch score for those with below average WPA/LI. Keep in mind that, in the results below, BA refers to the average clutch for below average WPA/LI with AA meaning the same for above average:

2000: 1.15 WPA/LI, -0.10 BA, 0.07 AA
2001: 1.39 WPA/LI, 0.05 BA, -0.10 AA
2002: 1.38 WPA/LI, -0.02 BA, -0.19 AA
2003: 1.15 WPA/LI, 0.03 BA, -0.32 AA
2004: 1.20 WPA/LI, -0.06 BA, -0.25 AA
2005: 1.15 WPA/LI, 0.01 BA, -0.27 AA
2006: 1.07 WPA/LI, 0.22 BA, -0.13 AA
2007: 0.98 WPA/LI, 0.03 BA, -0.14 AA

As you can see, other than in 2000 and 2007, the average clutch score for those with below average WPA/LI was much better than their above average colleagues. Not to say that their clutch scores were earth-shatteringly spectacular, but, rather just much higher and more indicative of game-raising performance. Deciding to go a little deeper, I looked at the top and bottom 10% in each year to see if the results differed:

2000: 0.06 BA, -0.25 AA
2001: 0.03 BA, -0.54 AA
2002: 0.05 BA, -0.87 AA
2003: 0.02 BA, -0.39 AA
2004: -0.20 BA, -0.11 AA
2005: -0.01 BA, -0.46 AA
2006: 0.16 BA, 0.21 AA
2007: 0.34 BA, -0.27 AA

Here we get very similar results; those in the bottom 10% of WPA/LI generally post much higher clutch scores than those at the top. 2004 and 2006 are the exceptions to this “rule” but even they do not differ too heavily; they actually come within ten points of each other whereas every other year is vastly different in the average clutch scores.

Based on these results it would seem that, yes, the players with below average performance are more likely to post higher clutch scores because they have more room to work with, so to speak. I would still rather take, with much confidence, those in the top 10% of WPA/LI in crucial situations, even though the clutch statistic, in its current state, will debit their performance for having nowhere to go really but down.

Now, to clarify the above paragraph, after some tests, there is no correlation between WPA/LI and Clutch, meaning that it is not a concrete rule that all good players will post lower clutch scores and vice versa. From these results, though, it does seem that those with a higher WPA/LI have more opportunity to post lower clutch scores.

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.

The end of each season brings with it a few certainties: eight teams make the playoffs, one team wins the world series, and we are likely to argue or debate about which player’s performance merits the Most Valuable Player award. Some years house less debates than others but the award’s definition is so ambiguous that there are usually a few players that meet the loose “criteria.” By definition, the MVP award was spawned from the idea back in 1922 to honor the player “who is of greatest all-round service to his club and credit to the sport during each season; to recognize and reward uncommon skill and ability when exercised by a player for the best interests of his team, and to perpetuate his memory.”

Now, in 21st century language, this translates to the player who was most valuable to his team; the player who, if removed from his team, would hinder the success of the team the most; the player the team cannot live without. From a statistical standpoint this would seem to refer to which player contributed the most wins to his team. Luckily, we have a statistic for that here, known none other as WPA.

I decided to look at the win probability statistics for all years currently on Fangraphs (1974-2007) in order to see if the definition of MVP has held true, as well as see the average total and rank for a few of these statistics. The stats in question are WPA, WPA/LI, and Clutch. WPA/LI refers to context-neutral wins and so the different game states comprising plate appearances are not taken into account. Clutch, which I will discuss a bit more in-depth later tonight, measures a player’s performance in high leverage situations against his performance in all others.

Using just the National League for now, I recorded the WPA, WPA/LI, and Clutch, as well as the league ranks, for all MVPs from 1974-2007. The only exceptions were Chipper Jones in 1999, since we don’t currently have that year recorded, and Willie Stargell’s co-award in 1979; according to the league leaders page he didn’t even qualify that year. After calculating the average scores and ranks, here are the results:

WPA: 6.10, Rank: 3.88
WPA/LI: 6.11, Rank: 3.48
Clutch: -0.15, Rank: 19.69

A few things initially stand out. First, the average WPA and WPA/LI are virtually identical. Second, the average rank for MVPs in these categories is between 3rd and 4th. Lastly, the average clutch score is negative.

Of the 33 NL MVPs recorded, 14 finished #1 in WPA; 15 were #1 in WPA/LI; and nobody finished #1 in clutch. In fact, just 3 of the 33 finished in the top ten, the highest being Steve Garvey’s second place rank in 1974 (the other two were Kirk Gibson as #8 in 1988 and Bonds as #6 in 2004). So, despite the hoopla surrounding clutch ability prevalent in today’s mainstream media, it has not necessarily translated into MVP success.

Now, of the 17 players who won the award while posting negative clutch scores, 13 finished 1st-4th in WPA while finishing 1st or 2nd in WPA/LI. The only negative clutch scores that did not were the following players, with their WPA and WPA/LI ranks in parenthesis:

1987: Andre Dawson (19,11)
1991: Terry Pendleton (9,7)
2000: Jeff Kent (7,7)
2005: Albert Pujols (5,2)

Of those with positive clutch scores, 7 of 16 finished 5th or lower in WPA, 6 of 16 finished 5th or lower in WPA/LI, and just 3/16 were in the top ten in clutch.

The highest WPA in this span belongs to (guess who?) Barry Bonds, with a 12.63 in 2004. In fact, from 2001-2004, Bonds averaged 10.79 wins contributed. All four of those seasons ranked in the top four, with Ryan Howard’s 8.10 in 2006 being the only other above eight wins. The lowest two WPA scores came with Dawson’s 1987 season (2.84) and Jimmy Rollins last year with a 2.69. The highest WPA/LI totals were Barry Bonds 2001-2004 and fifth place happened to be Bonds in 1993. Again, the lowest belonged to Jimmy Rollins.

It appears that clutch has not factored into NL MVP voting since at least 1974 and that those with great all around numbers/win contributions have been more than capable of winning the award while seeing a decline in their performance during high leverage situations. I tried to see if anyone this year matched up with the average ranks but the results were not too strong. Lance Berkman is currently 1st in WPA, 1st in WPA/LI, and 15th in clutch, which was the closest. When we get closer to the end of the season it should be interesting to see which players come closest to these averages, if not exceeding them.

When I posted my article on Kosuke Fukudome yesterday, loyal reader VegasWatch pointed out that the Cubs outfielder’s opening day home run likely contributed the bulk of his 0.52 clutch score. Therefore, after being given the label of “clutch” the net sum of all of Kosuke’s clutchiness would not add up to much.

The formula for clutch, as defined in the glossary here, is:

Clutch = WPA/pLI – WPA/LI

For further clarification, pLI refers to the average leverage index of all game events for a given player while WPA/LI refers to context neutral wins; in other words, what the player produced regardless of the situation he entered into. This formula calculates the performance level of a player in crucial situations relative to his standard production. If a player has a .330 batting average in high leverage situations but hits .330 everywhere else, he is not considered clutch. This is not to say he lacks talent, but rather he just produces at a high level in all situations and isn’t necessarily stepping his game up in crucial plate appearances.

The Kosuke example made me wonder which other players were greatly benefiting from a big play. Looking at the top eight clutch scores before the stats updated last night, I tracked the biggest individual play for each of the eight and compared the clutch score of that singular play to the net sum of their other plays. This way we can see which player’s clutch labels are truly derived from one big play as opposed to those who have been a bit more consistent in stepping up. Here are the eight, with their overall clutch score and the three required components of their biggest play – note that the pLI refers to the season average, not the game average:

Pat Burrell (1.33): 0.899 WPA, 3.56 LI, 1.09 pLI
Melvin Mora (1.30): 0.418 WPA, 5.14 LI, 1.04 pLI
Freddy Sanchez (1.27): 0.363 WPA, 4.65 LI, 1.03 pLI
Skip Schumaker (0.93): 0.287 WPA, 4.29 LI, 1.04 pLI
Jeremy Hermida (0.86): 0.294 WPA, 2.61 LI, 0.94 pLI
Bobby Abreu (0.84): 0.512 WPA, 5.44 LI, 0.92 pLI
Manny Ramirez (0.81): 0.482 WPA, 2.38 LI, 0.95 pLI
Joe Mauer (0.80): 0.364 WPA, 4.35 LI, 1.07 pLI

With these figures, here is the breakdown of the big play clutch vs. the clutch in all other plate appearances:

Pat Burrell: 0.57 big play, 0.76 other
Melvin Mora: 0.32 big play, 0.98 other
Freddy Sanchez: 0.27 big play, 1.00 other
Skip Schumaker: 0.21 big play, 0.72 other
Jeremy Hermida: 0.20 big play, 0.66 other
Bobby Abreu: 0.46 big play, 0.38 other
Manny Ramirez: 0.30 big play, 0.51 other
Joe Mauer: 0.26 big play, 0.54 other

Pat Burrell had the most clutch “big play” when he hit a walkoff two-run home run against the Giants on May 2nd. However, according to these numbers, Abreu actually benefited the most from his play; he is the only one whose big play exceeded the net sum of all other clutch plays.

On the flipside, Freddy Sanchez and Melvin Mora have been very consistent in raising their performance level in high leverage situations. When talking about a player’s clutchiness, though, it really only takes one or two big plays to cement the label. We could remove the one big play and look at all other performances but since one play can change a fan’s perception of clutchiness that just would not be fair; regardless of whether or not the clutch benefits from a huge play or a group of smaller plays added together, the bottom line is that these players have helped their team win games by stepping up in crucial situations.

Without a doubt, Alex Rodriguez is one of the greatest baseball players of all time. He’s a hall of fame talent with a tremendous career behind him despite only being 32 years old. He’s got a shot at the all time home run record, is the highest paid player in the game, and plays on the biggest stage in baseball every night. However, despite all his ability and his impressive career performances, he’s also become the mainstream poster boy for an “unclutch” player. His disastrous performances in the 2005 and 2006 playoffs have been well documented, and he’s heard about his failures in the clutch for years, even if they weren’t always justified.

Well, if you take a look at the Win Probability leaderboard and sort by clutch performance, you’ll notice a familiar name currently posting the worst performance in high leverage situations of any hitter in baseball. Yep, there he is, again sitting atop a list that he’d rather never hear mentioned again. And, while it’s early, the ten plate appearances he’s had in situations where the LI has been greater than 1.50 show that he’s lived up to the reputation during the first two weeks of the season.

From his play log:

April 12th, Top 8, 2 out, 1st and 2nd, up 4-3: Alex Rodriguez struck out swinging
April 2nd, Bottom 9, 2 out, 1st and 2nd, down 5-2: Alex Rodriguez struck out swinging
April 3rd, Bottom 6, 0 out, 2nd and 3rd, down 2-1: Alex Rodriguez struck out swinging
April 14th, Top 8, 2 out, 1st and 3rd, down 8-7: Alex Rodriguez reached on an FC
April 3rd, Bottom 4, 0 out, 1st, down 1-0: Alex Rodriguez flew out to second base
April 13th, Top 1, 1 out, 1st and 2nd, 0-0: Alex Rodriguez grounded into a double play
April 1st, Bottom 4, 1 out, 1st, 1-1: Alex Rodriguez grounded into a double play
April 7th, Bottom 3, 2 out, 1st and 3rd, up 2-1: Alex Rodriguez reached on an FC
April 8th, Top 3, 0 out, 1st, 2-2: Alex Rodriguez struck out looking
April 1st, Bottom 7, 0 out, no one, 2-2: Alex Rodriguez singled to right field

It’s only ten plate appearances, but it’s ten fairly miserable plate appearances. Four strikeouts, two double plays, a couple of fielders choices, and a lone single. He made 11 outs in these 10 trips to the plate and lowered his team’s chance of winning by a combined 58.4%. So far, this season, Alex Rodriguez has been a problem when he had a chance to help his team the most. This fits right into the narrative that has been told about him for years.

However, I absolutely have to note that this is not a continuation of any real trend. Thanks to the addition of leverage splits on his Baseball Reference player card, we can see that A-Rod has actually performed better in high leverage situations over his career than he has in low or medium leverage situations. Over 1508 plate appearances with an LI of 1.50 or greater, Rodriguez has hit .307/.393/.590, marginally better than his career line of .306/.389/.578. That includes being a monster in high leverage situations last year, posting a .349/.439/.706 mark over 132 plate appearances.

No one should draw any conclusions from the first 10 high leverage at-bats of Alex’s Rodriguez 2008 season, especially in light of his career performances. I had to chuckle, however, when I checked out the clutch ratings this morning and saw a familiar name sitting at the bottom. I’m guessing this will be a moniker he’s going to have to fight his entire career.