With the advent of the calendar year data here on the site I have gotten a few questions regarding what constitutes “good” win probability statistics in the various time parameters. One question in particular piqued my interest: How do the context-neutral wins look across the position spectrum? The reader essentially wanted to know how, say, Brian McCann’s WPA/LI over the last two calendar years stacked up not just to all other offensive players but all other catchers. Not only would something like this help show which players’ context-neutral contributions were above- or below-average but it would allow a look at how the averages change from position to position.

Looking at the last two calendar years, with anyone amassing 450+ plate appearances (to use a qualifier but allow for mid-season callups), here are the positional averages with the top player(s) at each:

C: -0.86 WPA/LI, Russell Martin, 3.62 WPA/LI
1B: 2.17 WPA/LI, Albert Pujols, 10.77 WPA/LI and Lance Berkman, 10.65 WPA/LI
2B: -0.10 WPA/LI, Chase Utley, 8.77 WPA/LI
3B: 1.03 WPA/LI, Chipper Jones, 10.26 WPA/LI and Alex Rodriguez, 9.33 WPA/LI
SS: -0.65 WPA/LI, Hanley Ramirez, 6.75 WPA/LI
OF: 0.95 WPA/LI, Matt Holliday, 9.21 WPA/LI
SP: 1.10 WPA/LI, Roy Halladay, 6.90 WPA/LI and C.C. Sabathia, 6.06 WPA/LI

The outfielders on the leaderboards here are lumped together rather than separated by left, center, or right, so their results may shift a bit when properly divided. I also did not use relievers since there are all different types of them—closers, setup men, long relievers, etc—and I don’t much like comparing one set to another out of their element.

These overall results will change as the season goes on as well since calendar years implies a duration spanning yesterday to the same day two years ago; since we are currently in the middle of the season this is not a concrete look at the WPA/LI from concluded years, which I’ll get to sometime later this week. The scary aspect of these numbers is that, of those meeting my previously established cutoff point, any Catcher, Second Baseman, or Shortstop that has a 0.00 WPA/LI over the last two calendar years—meaning their efforts ended up cancelling each other out to the point of zero contribution—is above average. Essentially, someone at these positions contributing, on average, no context-neutral wins, is above average. For now, though, you can see that the averages supply, at the very least, the general ranges for where the benchmarks should be set.

A conundrum is loosely defined as anything that puzzles… so it makes perfect sense to use the term when describing the anomaly present in the ERA and FIPs of both Carlos Zambrano and Jeremy Bonderman. We’ve written about pitchers either outperforming their FIP or failing to live up to it plenty of times here, but, in probing the last three calendar years feature recently instituted on the leaders page, it appears that things tend to even out a bit. Except, of course, with regards to Zambrano and Bonderman.

Sixty starting pitchers qualified for inclusion over the last three calendar years and they produced the following averages:

ERA-FIP: 0.12
BABIP: .303
LOB: 71.8%
K/BB: 2.48
HR/9: 0.98

One standard deviation of the ERA-FIP is 0.28, meaning we can expect about 2/3 of the data to fall within the -0.16 to 0.40 range; additionally, 95% of the data can be expected to fall within the -0.44 to 0.68 range. Of the group of sixty pitchers, just two fell beyond the 95% confidence interval: Carlos Zambrano at -0.53 and Jeremy Bonderman at 0.83.

Now, one potential reason that someone like Zambrano consistently posts better ERAs than his FIP would suggest could deal with his BABIP: the average BABIP of this group in this span is .303 and Zambrano comes in at .273, a full thirty points lower. On the other end of the spectrum, Bonderman comes in at .325, over twenty points higher. In fact, when looking at the eighteen pitchers who fell beyond one standard deviation of the ERA-FIP mean, the nine higher than 0.40 ranged from .297-.332 in BABIP while those below -0.16 ranged between .269-.309.

I actually discovered whilst writing this post that a question regarding Zambrano outperforming his FIP was posed in the Inside the Book mailbag, to which MGL mentioned the possibility of him posting a lower than average BABIP after concluding that it is definitely possible for certain pitchers to post certain types of BABIPs. This is definitely the case. As MGL also noted in the mailbag, “FIP is a very good at eliminating the noise in BABIP, which allows us to get a better estimate of a pitcher’s run prevention skill, in the short run. In the long run, ERA, RA or ERC is MUCH better because it captures the differences in BABIP skill among pitchers, as well as the other things I mentioned above that contribute to a pitcher’s run prevention skill but are not addressed at all in FIP (like WP rate).”

So, one reason these two guys are constantly posting ERAs much better or worse than their FIP would suggest could be that they have posted above or below average BABIPs with enough regularity to show they have some type of control over it; in that regard, their ERA would be a better indicator of run prevention. Then again, they might not have control over their BABIP and this could all even out, but it would seem that this is a very likely cause at this juncture.

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.

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.

The biggest move this offseason saw Johan Santana heading to the Mets in exchange for Carlos Gomez and some more prospects. The former two-time (should be three-time) Cy Young Award winner looked to solidify a pitching rotation that seemed more than capable of making fans forget all about last year’s end of season breakdown. Coming off of a relative down year—a down year for him was still better than the up year of most others—there were some who questioned whether or not Johan would be able to regain whatever made him successful pre-2007.

One of the biggest reasons his performance suffered last year came in the form of home run balls. From 2003-2006 his HR/9 ranged from 0.85-0.97; in 2007 it jumped to 1.36 as he allowed 33 dingers. I recently took a look at his Pitch F/X data over the last year and a half to see if he had done anything differently on hits as compared to fouls or swinging strikes. The results also showed that his home run balls—or other hard hit balls—generally came from pitches not just with lesser velocity and/or movement but also very poor location: Most of his home run balls came on pitches right down the middle.

In Johan’s first 60 innings this season he surrendered 11 HR; over his last 34.2 he has surrendered just one.

First 9: 60.0 IP, 52 H, 11 HR, 15 BB, 57 K
Last 5: 34.2 IP, 36 H, 1 HR, 9 BB, 29 K

Of course it is too small of a sample to generate definitive conclusions but we can still investigate and make observations pertaining to whether or not any discrepancies in relevant Pitch F/X data exist in this split. For starters, here are the velocity and movement data for his first nine starts:

FA: 90.54 mph, 5.63 horiz/9.22 vert
SL: 84.18 mph, -0.98 horiz/4.53 vert
CH: 79.69 mph, 5.51 horiz/8.13 vert

And here is the same data in his last five starts:

FA: 92.39 mph, 6.68 horiz/9.64 vert
SL: 84.89 mph, -0.57 horiz/4.64 vert
CH: 79.94 mph, 6.48 horiz/7.54 vert

He has thrown harder and with more movement lately. One of the problems with his hard hit balls, as mentioned above, dealt with the percentage of pitches he threw down the middle. Here are his splits of pitches thrown down the middle:

First 9: 11.5%
Last 5: 11.9%

Though it appears he has thrown slightly more down the middle recently the small sample detracts from any real discrepancy. How about his accuracy? Here is his Ball/Strike/In Play breakdown for the first nine starts, followed by the last five:

K: 46.8%, 45.1%
B: 35.1%, 34.5%
X: 18.1%, 20.4%

Speaking of balls put in play, have any less fallen in for hits lately?

Outs In Play: 67.5%, 64.2%
Hits In Play: 32.5%, 35.8%

Despite sustaining a similar level of accuracy and balls put in play he has actually allowed a slightly higher percentage of those in play to fall in for hits. Looking at his WHIP in these two different spans (1.12 compared to 1.25) it seems that he was hit less in the early going though those hits were of a higher value than recently, despite the increase in hits given up lately. Lastly, has he gotten ahead of hitters any more or less lately? Here is his first-pitch strike split:

First 9: 51.2%
Last 5: 44.8%

All told, not much can truly be garnered in terms of data discrepancies but Johan has gotten ahead of hitters less as of late, has essentially sustained his patterns of accuracy, is throwing virtually the same percentage of pitches down the middle, and is allowing more hits. All of these signs would intuitively point toward similar or worse performance and yet he has thrown better lately. Perhaps his increase in velocity and movement over his last five starts has prevented hitters from getting the fat part of the bat on the ball quite as often. Definitely something to look out for as the season progresses.

For those who read the title and thought this post had something to do with food, I apologize, it does not. Instead, the spread I speak of refers to the pitch distribution in the Toronto Blue Jays starting rotation. Last month, when writing about Shaun Marcum’s hot start, some loyal readers commented that he was one of very few pitchers that threw five different pitches at least 10% of the time. Trying to verify this assertion I discovered that were only two other pitchers that fit this bill: Adam Eaton and Andy Sonnanstine.

It was recently revealed to me that Jesse Litsch joined the 5/~10% club. Catchy title, eh? I named it myself.

Now, not many starting pitchers throw even four different pitches at least 9-10% of the time and the Blue Jays have three of them: Dustin McGowan (4), Jesse Litsch (5), and Shaun Marcum (5). Group the three of them with the steady three-pitch mix of Roy Halladay and the fastball-curveball combo of A.J. Burnett and you have one extremely solid rotation.

Here are their pitch distributions, with velocity/frequency:

Roy Halladay: FA 92.7/45.9, CT 90.5/25.0, CB 78.2/23.6
A.J. Burnett: FA 94.1/66.2, CB 80.5/26.4
Dustin McGowan: FA 95.1/59.2, SL 87.6/19.3, CB 81.4/11.3, CH 86.7/10.1
Jesse Litsch: FA 88.8/17.7, SL 82.2/22.9, CT 85.0/37.5, CB 76.8/12.9, CH 80.0/9.0
Shaun Marcum: FA 86.8/39.0, SL 81.4/15.5, CT 84.5/14.0, CB 74.8/10.0, CH 80.9/21.5

Not only does this rotation mix their pitches effectively but their speeds as well; McGowan’s changeup is the same speed as Marcum’s fastball. Lastly, take a look at their stats:

Roy Halladay: 3.01 ERA, 1.01 WHIP, 5 CG, 12 BB, 72 K, 1.51 WPA
A.J. Burnett: 4.14 ERA, 1.37 WHIP, 33 BB, 71 K, 0.17 WPA
Dustin McGowan: 3.95 ERA, 1.42 WHIP, 28 BB, 55 K, 0.72 WPA
Jesse Litsch: 3.05 ERA, 1.13 WHIP, 9 BB, 33 K, 0.80 WPA
Shaun Marcum: 2.63 ERA, 0.94 WHIP, 22 BB, 67 K, 1.89 WPA

Their “worst” ERA is 4.14 and just one WHIP is over 1.40. Overall, the rotation has contributed 5.09 wins while being a steady and major factor in the success of the team. Perhaps their pitching coach has preached different spreads in order to, as a rotation, keep teams off kilter; whatever it is, though, it definitely seems to be working.

We’re about two months into the season, and it’s not a bad time to look which pitchers are allowing too many home runs. Fortunately, there’s a useful metric on FanGraphs to do just that. It’s called HR/FB and while I’m sure many of you are familiar with it, here’s a brief summary of how it works.

There’s been a number of studies done on HR/FB and for the most part, they conclude that pitchers do not have control over how many home runs they allow on outfield fly balls. Your typical starting pitcher should be expected to have a HR/FB of around 10% every year. Anything that deviates from 10% could be contributed to the park he pitches in, or to “luck”. So let’s look at who has been allowing an inordinate number of home runs this season:

Roy Oswalt (23.4%) - Oswalt leads baseball with a rather ridiculous HR/FB rate. Basically one in four of his fly balls have become home runs. I don’t care where he’s pitching, this is just Oswalt having some terrible luck. He’s never had a HR/FB above 12.9% to end the season. A couple weeks ago, Eric Seidman asked if you should trade Oswalt in your league; the answer is still no and now is another prime opportunity to go acquire him.

Brett Myers (21.4%) - Sure he plays half his games in Citizen’s Bank Park and he does have a career HR/FB of just over 15%, but 21% even for him seems quite high. He probably isn’t due for such a drastic adjustment as Oswalt, but I’d imagine it should start to trend towards his career average. He hasn’t allowed a home run in his last two starts either, so perhaps he’s well on his way to normalcy.

Carlos Villanueva (16.9%) - Currently, Villanueva leads baseball with a 2.09 home runs per 9 innings. He’s about as much as a fly ball pitcher as he is a groundball pitcher so he really shouldn’t be tied for 5th with most home runs. While Miller Park isn’t all that favorable to fly balls, he should be able to do considerably better in the home run department and decrease is ERA by more than a little come season’s end.

Johnny Cueto (16.4%) - It looks like the phenom has himself a bit of a home run problem. Since he hasn’t been around for very long, it’s a little tough to say if this is just a luck thing, or of it’s a real problem. I’d venture to say it has more to do with luck then anything else, even if he does play in a park that is prone to home runs. Unfortunately, Cueto is an extreme fly ball pitcher and isn’t expected to be particularly stingy with home runs in general.

Mike Mussina (16.4%) - We all know about Mussina’s decline in fastball velocity. John Walsh’s research suggests that mis-located fastballs of the slower variety could certainly cause an increase in home runs and it’s possible that could be happening to Mussina. I still think his HR/FB should drop as the season continues, but it’s hard for me to be enthusiastic about.

Johan Santana (15.9%) - Santana developed a home run problem last year and it seems to have continued into this year. Shea stadium is slightly worse for home runs than the Metrodome, but it really doesn’t explain such a high HR/FB. It’s hard to imagine it won’t decrease as the season goes on, but unless it drops back down to around 10% or lower, it will be difficult for him to return to sub-3 ERA levels.

Last year the Yankees struggled in the first half of the season and fought their way back into playoff contention. This year, it is no secret they are underachieving, prompting many analysts to question whether this will be the season in which the Yanks miss the postseason. In an attempt to determine what is going wrong with the team I turned to their team page and became fascinated with the numbers of Jason Giambi.

Believe it or not, Giambi is one of just three Yankees hitters with a WPA of at least 0.15; his 0.17 comes in behind just Hideki Matsui and Bobby Abreu. Additionally, he has a WPA/LI of 0.73, much higher than his WPA.

What will turn many fans off is his lowly .236 batting average. When put into perspective with the rest of his slash line—.236/.384/.516—it becomes clear that the batting average truly does not do his production justice. He has just 29 hits but 8 are doubles and 9 are home runs. Those 9 HR lead the Yankees and his 24 RBIs ranks second to Abreu.

Giambi has increased his BB% from a year ago and decreased his K% from 26 to 15. His LD/GB/FB rates were virtually identical in both 2006 and 2007, coming in at 16.4/30.2/53.4; this year he has BIP rates of 19.4/28.7/51.9. He is hitting more line drives and yet has just a .208 BABIP. We have talked here a lot about expected BABIP and how it works for hitters, so we would expect Giambi to be closer to the .314 range with this percentage of line drives. Now, this isn’t to say he will sustain 19.4% LD all season but that frequency should roughly correlate to the aforementioned BABIP.

Looking at Giambi’s numbers from 2002-2007, the only year in which his BABIP and xBABIP differed significantly was 2003; generally speaking, his BABIPs have been close to what his percentage of line drives would suggest.

What really interested me about Giambi is his shift in swing and contact percentages. He currently leads the league with the lowest percentage of swings at pitches outside the zone. Giambi has swung at just 9.9% of outside pitches, making contact on 51.7% of those swings. Last year he swung at 18.2% of the pitches outside the zone, likely contributing to his higher K%.

He has swung at 67.9% of pitches in the zone, making contact on 88.5% of them; the 88.5% puts him right in the 50th percentile. Overall, these swing and contact shifts have resulted in Giambi making contact four percent more often than a year ago. Giambi might not be the player he was five years ago, steroids or not, but his numbers seemingly absolve him from blame for the Yankees early struggles.

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.

Recently on FanGraphs, we’ve been referring to a stat called xBABIP or Expected Batting Average on Balls in Play to help justify a pitcher’s current BABIP. There’s been a few questions about what this stat means, so I thought it’d be as good a time as any to try and explain the ins and outs of this particular metric.

The initial concept of BABIP is that pitchers do not have control over what happens to balls once they are hit into the field of play.

BABIP typically fluctuates from year to year with a baseline of around .300. If a pitcher has a particularly high or low BABIP, we may say he’s been lucky or unlucky. Things are of course not quite this simple, but for the most part the rule holds true.

In enters ball in play data; we know how many line drives, fly balls, and ground balls a pitcher allows in to play. Line drives fall for hits the most often and ground balls fall for hits more often than fly balls. What types of batted balls a pitcher allows into play are going to effect a pitcher’s overall BABIP.

BABIP by Type (2007):
Fly Balls - .15
Ground Balls - .24
Line Drives - .73

Ideally, the formula is going to look something like this to find out a player’s expected BABIP:
expected BABIP = .15 * FB% + .24 * GB% + .73 * LD%

For more accuracy you could remove home runs from the batted ball percentages at a rate of 92% from fly balls and 8% from line drives. You could even account for infield fly balls and remove that from total fly balls, but the formula above will get you pretty far.

Dave Studeman a couple of years ago calculated that adding .12 to LD% was good enough for a ball park estimate of a player’s expected BABIP. This is what you’ll often see writers on FanGraphs refer to as xBABIP.

The best way to use this statistic is to attempt to validate a pitcher’s current BABIP. For instance, a pitcher might have an high line drive percentage and a high BABIP. This would give a pitcher a high xBABIP as well and you could say: “Yes, his high line drive percentage is responsible for his high BABIP.”

While this is useful for looking at past performances, the difference in xBABIP and BABIP should not be used in an attempt to evaluate future performance. This is because LD% and BABIP are somewhat independent of each other. While there is some correlation between LD% and BABIP, it isn’t enough to suggest that they will always track each other.

LD% in itself is highly variable and it would be difficult to say that a pitcher with a BABIP of .300 and a LD% of 22% (xBABIP of .340) should do considerably worse going forward because you really don’t know what his LD% is going to be the rest of the season. His xBABIP of .340 was his expected BABIP and will not be his expected BABIP in the future. Typically a pitcher’s expected BABIP in the future will be around the original baseline of .300.