The Hardball Times -- John Beamer http://www.hardballtimes.com/main Baseball. Insight. Daily. en studes@hardballtimes.com Copyright 2013 2013-06-19T08:32:15+00:00 Five questions: Atlanta Braves http://www.hardballtimes.com/main/article/five-questions-atlanta-braves8/ http://www.hardballtimes.com/main/article/five-questions-atlanta-braves8/#When:07:23:15
There is only way to find out—and that is by taking a journey of prognostication and speculation with The Hardball Times’ five questions.

Chipper retiring—is it a big deal?


Although the Braves’ dynasty officially ended in 2007, when their famous run of consecutive playoff appearances came to an end, it is only now that Chipper Jones has retired that Braves’ fans can call time on what has been an extraordinary 20 years of baseball in Atlanta.

With the bat and glove, Chipper has been a 2-3 fWAR player for the last three years (despite playing in only 100 games a season), so his production is not to be sniffed at. With the acquisition of Chris Johnson from the D-backs, it looks like the Braves’ plan is to platoon him with Juan Francisco.

In Phoenix, Johnson added some punch to the lineup batting .281 with 15 long-ballers—and was good for 1.5 fWAR. Francisco didn’t do too badly last year, hitting nine homers and clearing 0.8fWAR while filling in for Chipper.

In total the Braves were 3.5 fWAR at third base last year and looking at the 2013 projections a Johnson/Francisco platoon will get you to 2-2.5 WAR, so the Braves will still be some short. However, taking cost per WAR into account, it isn’t a bad trade-off given that the Tomahawks have added the Upton Bros. into the lineup.

Perhaps the bigger issue is whether losing a clubhouse leader will have any effect on the Braves’ psyche. The quantum of this is impossible to say, other than to say that role models (of which Chipper clearly was one) help. No doubt if the Braves have a bad season or a run of losses the media may point to Chipper’s absence. Reality is that it probably won’t matter.

Super Upton Bros.—will it work?


Before the offseason not many would have picked Frank Wren to add one, let along both of the Upton brothers to the lineup.

The Braves' offense in 2012 was anemic, so something had to be to done. As a team the Braves hit a pedestrian .247/.311/.389 versus a league average of .255/.319/.405 … and remember that Atlanta won 94 games!

Dan Uggla misfired again (relative to his mega-contract) and Brian McCann battled injury most of the season and toiled to a disappointing .698 OPS.

B.J. Upton has averaged between 3-5 fWAR over the last three years and the projections for his age 28 season are unsurprisingly the same. Justin, on the other hand, is expected to garner between 4-5 fWAR, but that assumes he bounces back strongly from a disappointing 2012, when he was partly distracted by a tiff with the D-backs' front office about his future.

In total then we should expect the Upton brothers to add 8-10 wins to the lineup. Taking into account the loss of Michael Bourn (3-4 fWAR expected in 2013) and Martin Prado (another 3-4 fWAR) and throwing in the 2.5 fWAR deficit identified in the first question, then best case is that the Braves offense is equal to its 2012 production.

In addition to production, having the Upton brothers around the clubhouse is likely to be good from a marketing standpoint. However, suggestions that the brothers will "feed off each other" are likely wide of the mark.

Craig Kimbrel—better than Mariano Rivera?


I for one am all for the Braves offering to lock up Kimbrel’s early free agent years, but that very much goes against the ball club’s ethos, which is that relief pitchers are commodities that are easily replaced. Not so Kimbrel.

This will be his third season in the bigs and already he has established himself as an elite closer. To wit: his FIP last year was an unearthly 0.78—2011 was 1.52. Let’s put that in a bit of context: Mariano Rivera’s lowest FIP season is 1.88 in his second season as a pro (1996). His next lowest was 2.15 in 2005. Jonathan Papelbon? He recorded 1.53 with the Red Sox in 2011.

Although it is too early to call him that, Kimbrel is on a trajectory that could see him being lauded as one of the greatest closers of all time. Oliver projects an FIP of 1.59, Bill James forecasts 1.38. Who knows where he’ll end up, but the prognosticators are suggesting that Kimbrel’s 2013 will be better than any Mariano Rivera season in history (okay, as far as FIP is concerned). Superlatives will never do justice to how good this kid is.

Braves rotation—will it be better than 2012?


Last season was a hallowed year for Braves starting pitching. Brandon Beachy was hurling like he was a future Cy Young winner until he got arm ache and ended up with season-ending Tommy John surgery. As Beachy was under the scalpel, Kris Medlen came back from Tommy John surgery to win all 10 games he started as a Brave in the regular season, finishing the year with a 1.57 ERA after 138 innings pitched.

Tim Hudson did his typical job of eating innings and posting a respectable 3.78 FIP, confirming his status as the workhorse of the rotation. Approaching the trade deadline the Braves picked up Paul Maholm from the Cubs. He delivered a ~4 FIP, which is very consistent with his past performance. Mike Minor struggled initially but bounced back well to be a key member of the starting clan—his ERA was above 6 at the All-Star break but ended at 4.1.

And all that is before you get into the young turks—specifically Julio Teheran, who is still projected as a No. 1 or No. 2 starter when he establishes himself in the bigs.

Based on history, Maholm and Hudson should eat around 180-200 innings apiece for a decent return. How well the 2013 rotation will pitch depends on three factors: how well Medlen picks up from his 2012 performance, whether Minor can continue on the trajectory he was following in the second half of 2012, and Beachy’s ability to pick up where he left off before TJ surgery.

Looking at the big picture, as long as two of these three conditions turn out true, the Braves should be able to replicate their 2012 level. Medlen is likely to regress some and Minor shouldn’t be as bad as he was in the first half of last year. With luck Beachy should add a little oomph to the rotation when he returns in the middle of the year. Then as for Teheran? Count him as downside protection. Putting it together it will likely mean that the 2013 Braves will pitch similarly to their 2012 brethen.

Net net—can the Bravos win the NL East?


In short, yes. Taking a quick look at various projection systems, the consensus view is that Atlanta should win around 88-90 games, right in the playoff sweet spot. That is a couple of games behind the Nationals but well ahead of the rest of the NL East.

There is little doubt that the Nationals are the pick of the East. The rotation is probably the best in baseball, and certainly the best in the NL. And they aren’t too shy with the bats. However, that’s not to say the Braves can’t win the division. One standard deviation in performance is about seven wins, so by the laws of statistics don’t count out the Atlanta.

When the Upton brothers were signed, there was some speculation that the NL East could have two 100-win teams. That is unlikely given how often the Braves and Nationals will play each other. But I’m willing to wager that whoever has the better head-to-head record will take the division. The other team is a shoo-in for a Wild Card.

Two years ago, that would have guaranteed postseason ball, but these days it gives you the right to play a one-off game against the other Wild Card winner. Given the Braves’ atrocious history in playoff games this millennium, I’ll shoot for Wild Card and then exit in the one-off game. Anything less will be a disappointing, anything more very welcome. Sit back and enjoy!

Click here to learn about THT's download subscriptions.]]> John Beamer 2013-03-13T07:23:15+00:00 Five questions: Atlanta Braves http://www.hardballtimes.com/main/article/five-questions-atlanta-braves7/ http://www.hardballtimes.com/main/article/five-questions-atlanta-braves7/#When:09:02:15 Bryce Harper into their lineup.

Onward with our five questions then.

Will Jason Heyward recover?


Stats never lie, and J-Hey’s 2011 had all the hallmarks of a classic sophomore slump. After a respectable .376 wOBA in 2010, he fell to a mark of .314 in 2011. The issue was both contact and power as he struggled with a dud shoulder for much of the year. As the slump set in, he tried to force contact at the plate and ended up striking out too often. His Achilles' heel was pitches outside the zone, where he had a 28.3 percent swing rate compared to 23.3 percent in 2010.

Wind the clock back a couple of years and Heyward had superstar potential. In fact, going into 2010 he was Baseball America’s top prospect. That year, he showed enough potential in the bigs to suggest he has what it takes to be a regular All-Star.

The good news is he's still very young. Heyward turned 22 in August, and that makes him younger that many of the top hitting prospects expected to get playing time this year like Brett Jackson and Devin Mesoraco. The other factor that hurt J-Hey last year was a precipitous drop in BABIP from .335 to .260, and that should rebound somewhat. Factor in an xBABIP of .315, and 2011 suddenly is a .365 wOBA-type season.

As a result, his projections going into 2012 are good. Oliver pegs him at .274/.367/.475 with 20 homers. My view is that line feels a little conservative. Given the Braves’ lack of power in general, Heyward needs to deliver if the Braves are going to play deep into October.

Can the 2012 bullpen replicate the success of last year?


In 2011, the Braves bullpen was lights out—well, at least until the last couple of weeks of the season. Even with that little slip, the Eric O'Flaherty-Jonny Venters-Craig Kimbrel combination was among the best shutdown end-game in the bigs. Can the young trio maintain its form into 2012?

One issue that potentially plagued Kimbrel & Co. was overuse. Kimbrel hurled 77 innings while Venters notched up 88—that’s a lot for two hard-throwing young relievers. The projections for 2012 are strong. Venters is expected to have a FIP in the high twos, while the expectation is that Kimbrel will be substantially better. On paper, that makes for a lights-out bullpen again in 2013.

Perhaps the biggest question is mental. Time and again we’ve seen young relievers have a couple of bad high-leverage outings that then go on to affect the rest of their careers. Joey Devine anyone? Of course, comparing Devine to Kimbrel may be akin to comparing Kraft cheese slices with a fine French Brie.

Kimbrel has a powerful fastball/slider combination and over 97 career innings, he has struck out 167 batters. The biggest change is his command as he has slashed his walks-per-nine innings from 5.7 in Triple-A to 3.7 last year in the majors. The key to a successful 2012 is ensuring his command stays strong.

Can Tommy Hanson remain healthy?


One reason the Braves fell away in 2011 is because Hanson spent quite a bit of time on the DL hampered by a sore shoulder. The reports for 2012 so far are positive, but reality won’t hit until Hanson has thrown in anger, which won’t be before Opening Day. Also, it is one thing to be healthy at the start of the season, but the big question is durability. A baseball season is 162 games, so if the Braves are to contend, Hanson will need to pitch deep into September.

Pitcher health is always difficult to predict, but Hanson has had a history of (minor) ailments. In 2010, he struggled with back pain as a result of a deviated facet joint. The Braves' belief is that the back injury contributed to the shoulder problem as he adjusted his mechanics slightly. In the offseason, Hanson has worked on a comprehensive strengthening program that he hopes will fix both issues.

However, if history is a guide, the odds are that Hanson won’t be able to stay healthy all 2012. However, despite not adding anyone this offseason, the one advantage the Tomahawks have in 2012 over 2011 is a much deeper pitcher bench as some of the elite pitchers in the minor leagues become big-league ready. Braves uber-prospects like Julio Teheran, Arodys Vizcaino and Randall Delgado are all waiting in the wings if any of the starters end up on the DL.

Can Chipper Jones still be productive?


At 39 years old, Chipper is certainly in the twilight of his career. Indeed, this is likely his penultimate season in the majors as his contract is up at the end of 2013. A few days ago, a rather unflattering photo of Chipper doing his best Prince Fielder impression was flying around the Internet, casting doubt on his ability to keep it together. Jones responded that he’s in the better shape than he was this time last year, having dropped 10 pounds—now weighing a svelte 225.

The expectation is that Jones will be fortunate to play in 120 games this year; over the last two seasons, he’s averaged 110 games and about 450 plate appearances. Over the same time period, he has averaged a .350 wOBA, implying there is little doubt he’ll be productive. However, it is worrying that he is one the better hitters in the lineup, since he's not expected to be there full-time.

Given Chipper’s career history, it is little surprise that the projection systems converge around a consistent forecast, with the expectation is that he’ll continue to deliver a .350 wOBA in 2012 and around 14 home runs. Arguably, the bigger issue is health.

Since 2005, Jones has only really had one injury-free season (in 2009 when he racked up close to 600 plate appearances). In a way, he’s been fortunate that he has only suffered niggles, but those add up and wear down the body. Chipper’s projection doesn’t feel to have much upside, but it could have a lot of downside. Let’s hope the Braves don’t need to rely on his bat too much.

What will be the final pecking order in the NL East?


As mentioned at the top of the article, the NL East will be among the toughest divisions in baseball this year. The Phillies still have a star-studded rotation and will contend, the Marlins have upgraded significantly, and the Nationals have Stephen Strasburg back full time and Bryce Harper potentially about to enter the fray.

A quick look at Vegaswatch gives us some over/unders. Vegas has the Phillies at 96 wins (incidentally, the best in baseball), the Braves at 86, the Marlins at 83, the Nationals on the line at 81 and the Mets at 75. Vegas also predicts the first NL Wild Card winner will log 87 wins, so Atlanta is a genuine contender, especially with a second Wild Card coming to fruition this year.

We need to be careful with win projections, and particularly Vegas lines, which have a tendency to reflect betting patterns. The standard deviation of team wins is around eight, which is large enough to make drawing conclusions a fool’s game. That issue aside, the Braves are a young and improving team, and they should have learned a lot from last year’s meltdown.

Dropping Derek Lowe while moving a few of the young arms into the rotation will improve the pitching, and there is no reason to expect the pen will be anything other than the "blue screen of death" for opponents. The big question is batting and, frankly, I’m not sure it can be much worse than last year. The Phillies likely will remain out of reach, but I’d bet my second house that the Braves make October ball.

Click here to learn about THT's download subscriptions.]]> John Beamer 2012-03-05T09:02:15+00:00 Five questions: Atlanta Braves http://www.hardballtimes.com/main/article/five-questions-atlanta-braves6/ http://www.hardballtimes.com/main/article/five-questions-atlanta-braves6/#When:09:02:15
How things change. The Phillies have won the NL East four times on the spin and, with the acquisition of Cliff Lee, appear ready to cement their position as the new perennial champions. Can the Braves possibly challenge them? Read on and find out.


Will Bobby's departure have an impact?


Of course it will.

The man led Braves for over 20 years, so when an individual of his standing steps down, the team will be affected in some manner. The question is, how much?

First, let's examine the evidence. The accepted wisdom is that managers are generally responsible for two to three wins/losses per year, max. The general sabermetric view is that the majority of managers rely too much on tradition (rather than game theory or understanding optimal probabilities) when managing (read: tinkering with lineup, platoon plays, or reliever use) and, as a result, actually cost their teams games.

There is little doubt that Bobby Cox fits into this latter category. He used to make some ridiculous player decisions—wasn't it obvious Brooks Conrad should have been pulled before making his third error in the last fall's NLDS that cost the Braves Game Three?

However, playing the percentages effectively is only one attribute of a successful manager. The other is leadership and man management. And that was Cox's long suit. At every juncture, through every low point, the players respected him deeply. That engendered loyalty and, thus, he was often able to get more out of certain players that others would have.

With Cox moving upstairs, former Braves third base coach Fredi Gonzalez steps into the hot seat. Despite being fired by the Marlins last year, Gonzalez had a good record with the Fish. He steered them to winning seasons in the 2008 and 2009 campaigns despite having to work with the lowest budget in baseball.

That bodes well in a sense—the Braves aren't the big spenders they used to be, and Gonzalez isn't a doofus. However, the reasons for his firing still remain a mystery, with some suggestions that he lost the clubhouse following Hanley Ramirez's famous lollygagging incident last year.

Winning the players over is key to his success. Gonzalez starts in a good position, as he knows the senior players and has an affinity to the Braves. Cox won't be more of a phone call away either, so the new guy will be okay.


Will Jason Heyward suffer from a sophomore slump?


The famed sophomore slump is very real and is more commonly known as regression to the mean. The theory is straightforward: player A (a rookie) plays way above average in his first season so is more likely to get playing time the next. However, as he was lucky in the first season, he reverts to type and appears to have a sophomore slump.

Heyward's line is 2010 was .277/.393/.456, which was a little way off what many projections thought. In other words, he didn't overperform (if you believe the forecasts for a minute). His 2011 projections also show a stark improvement. Marcel the monkey has him at .286/.391/.476, mostly because of age adjustment—the kid is only 21.

Another factor that affected his 2010 season was injury. By the end of May, Heyward was averaging over .300 but hurt his thumb and suffered at the plate for most of June before taking a little time off to recuperate. He came back strongly, but his hand never fully healed, and that had an effect in the last third of the season. He should be fully healed know and, after a year in the bigs, better attuned to the demands of a long season.

Let me stick my neck out for a minute and hope it doesn't get chopped off: Heyward underperformed in 2010; that has hurt his 2011 projections, and regression to the mean works both ways, you know...when you underperform one year, the expectation is you bounce back the next. If Heyward were a traded security, I'd be taking a long position.


Dan Uggla...is he worth $62 million?


In the great scheme of things, trading for Uggla and re-upping his contract is a tidy piece of business by the Braves. Sure, stretching to a fifth year may end up costly, but he wouldn't likely have signed without it. Let's take a closer look.

Over the last three years, Uggla has averaged an fWAR of around 4.0 (indeed fangraphs pegs his 2011 fWAR at 3.8, as projected by the fans. Assuming that the going rate for a free agent is $4 million a year, then 4.0 fWAR is worth $16 million a year (versus an average annual contract value of $12M).

The problem is that Uggla is no spring chicken. In fact, he's a 31 years old, which means by all aging models he's expected to get worse over time. Not only that, but Uggla isn't the world's finest gloveman, and by the end of his contract he could well be shunted to a different position.

So if we factor in aging and assume Uggla will peak at 3.8 WAR in 2011 and lose, say, 0.5 WAR in each subsequent year, then over five years he projects to contribute 14 WAR, which (by no coincidence) when multiplied by $4.5 million per win gives you $63 million over the duration of his five-year contract. So say what you like about the deal, but unless you're predicting a serious injury, it doesn't look as though the Braves overpaid.

Perhaps the more important factor is that Uggla is likely to mash 30 long balls a year. His home run numbers are amazingly consistent, and last year the one thing the Braves lacked was consistency in the power department. Uggla will be a good foil to Heyward and Chipper, if Jones returns successfully. So, yes, he's worth $62 million. Just!


Can Derek Lowe maintain his September 2010 form?


Last year the big story in Atlanta, other than Heyward's impending debut, was whether the Braves would be unable to unload Lowe instead of Javier Vazquez. In 2009, after Lowe signed a cushy 4-year, $60 million deal, he posted a woeful 4.67 ERA for a WAR of precisely zero. Contrast that to Vazquez, who logged an ERA of 2.87, and you can see why the Braves were chomping at the bit to let Lowe go.

Despite trying, there we no takers for Lowe, while Vazquez was snapped up by the Yankees. We all know what happened next. Vazquez had his worst season in 12 years, going 10-10 with Yankees with a 5.32 ERA. He was hurt and ineffective.

Lowe stayed with the Braves and pitched more or less as expected. Going into September his ERA was 4.53, which was pretty much bang on league average. He was also on course to throw 190 innings, which has some value (although not $15 million a year worth of value).

However, then something strange happened. In September he suddenly started to appear dominant. He won all five stats, conceding only four earned runs and dropping his season ERA all the way down to 4.00. His form continued in the postseason where, despite losing both games, he pitched well. So, has Lowe made an important adjustment to his mechanics or is this a classic case of small sample size at work?

Let's take a look at what we can see using Joe Lefkovitz's awesome PITCHf/x tool. Below is a plot of pitch movement by type (as classified by MLBAM) for the two spells in question:

image

Lowe claims to have rediscovered his slider in September. Can we see it in the data? In both charts sliders are red dots. In September he definitely threw more sliders (the left panel has six times the number of pitches than the right, and going through the numbers he was throwing about twice the number of sliders in September). Also, the sliders seem more tightly clustered, which could imply that he was locating the pitch better.

Whether fluke or mechanical adjustment, the question is, can he maintain the pitch's effectiveness? It depends on exactly what adjustment Lowe has made. If it is a mechanical adjustment that results in a more effective pitch (which is hard to tell from the charts above), he is likely to maintain a small advantage.

However, game theory suggests that if he keeps throwing the pitch, hitters will likely make adjustments. Pretty much all the projection systems expect an ERA in the low fours. And that is likely where he'll end up—unfortunately, his September 2010 form won't hold.


Do the Tomahawks have enough to topple the Phillies?


Yikes. The Phillies added Cliff Lee in the offseason and probably have the best rotation in baseball since the Braves in the late 90s. The good news for the rest of the NL East is that the uber rotation will probably only be together for a maximum of two years, as both Cole Hamels' and Roy Oswalt's contracts come up at the end of 2012.

However, the situation isn't terrible for the Braves. Lee will contribute only slightly more than the Phillies will lose from the departure of Jayson Werth. Although the Phillies won the NL East last year, until the Braves faded in September it was a reasonably close race. Expect the same this year.

The Braves have a good, young rotation. Tommy Hanson and Jair Jurrjens should bounce back from down years and, as discussed above, Lowe will hurl 180+ innings at fourth-starter level, which is all the Braves need from him. And with Kris Medlen likely to miss most of the 2011 recovering from Tommy John surgery, don't be too surprised to see Julio Teheran called up. Teheran is the top prospect in the Braves system and arguably the best pitching prospect in the game.

The loss of Billy Wagner in the bullpen is a blow but Craig Kimbrel has the stuff to close and, provided he has the mental game, will do well. Uggla adds runs to the lineup, and Heyward will likely improve on his 2010 numbers.

I don't think the Phillies will win 97 games again next season. The Braves had the sniff of a 90-92 win team given how they played last year. My guess is that won't be quite enough to take the divison—the Phillies will likely do that—but it should be enough to secure the wild card and another heartbreaking loss in the NLDS.

Click here to learn about THT's download subscriptions.]]> John Beamer 2011-03-17T09:02:15+00:00 Five questions: Atlanta Braves http://www.hardballtimes.com/main/article/five-questions-atlanta-braves5/ http://www.hardballtimes.com/main/article/five-questions-atlanta-braves5/#When:10:30:15 Jason Heyward mash 75 homers? Can Billy Wagner strike out every hitter he faces? These questions and more are answered below.

To mix things up a bit I've teamed up with Alex Remington, who writes about the Braves at Chop-n-Change and also blogs at Fangraphs and at Yahoo! Sports Big League Stew. Hang on—here we go.

1. What production can we expect from Jason Heyward in 2010?


Alex: As I wrote elsewhere, I think that Heyward's superior control of the strike zone will lead him to exceed the expectations of most of the offseason projection systems, nearly all of which expect him to hit fewer than 20 homers with an OPS right around .800. I'm expecting a tick better on all those counts. Most importantly, though, he stabilizes the Braves' right field situation, where he'll be far, far, far better than the .262/.305/.409 that Jeff Francoeur produced from 2007 to 2009, and isn't likely to be a downgrade defensively.

Of course, he isn't guaranteed for immediate success. Even Matt Wieters, who once stole second, third and the shortstop's hat on the same pitch, had an adjustment period last year before catching fire in the last month of the season. While Heyward's floor is probably higher than Francoeur's baseline, it may not necessarily be that much higher. So he'll be a welcome improvement for the Braves, and he'll likely post Nick Johnson-like P/PA numbers, but he won't be an offensive catalyst just yet.

John: There's no doubt Heyward is good—you've just got to look at his spring training stats. His line is a massive .347/.467/.490 and some of his home runs were true monsters. The most impressive thing is his ability to get on base. Make no mistake: This kid has a 20:20 batting eye, and unlike Francoeur, whose place Heyward will inherit as face of the franchise, he'll walk enough to get to Everest and back (aside: for goodness sake, Jason, don't do any Delta ads). That should mean at the very least he'll survive in the bigs.

He also tops most prospect lists, which is encouraging but by no means guarantees success. Remember who the last Brave was to be the No. 1 rated prospect in all of baseball? Yes ... Andy Marte. His career line of .216/.272/.352/ is anything but magical and is a stark reminder of how quickly baseball talent turns from boom to bust. However, let's not kid ourselves. Heyward's minor league career has already been on a different planet than Marte's.

Given the weakness that the Braves have in the outfield, it wouldn't be too surprising to see Heyward top the pile come the end of the year. One thing is certain: It is unlikely the Braves will challenge for the division without a productive Heyward.

2. Is Wagner going to dominate the ninth?


Alex: Mariano Rivera's contemporaneous career has obscured just how good he's been, but Wagner has been one of the most consistent and excellent relievers of the modern closer era—as Mac Thomason has written,
"There aren’t many relievers, ever, who have been better than Wagner."

Wagner has had exactly one bad year in the major leagues: 2000, when he posted a 6.18 ERA in 27.2 innings before going on the DL and eventually undergoing elbow surgery. That is the only time in his major league career that he had an ERA over 3.00 or a K/9 under 10.

It was also, prior to 2009, the only time he'd ever pitched fewer than 47 innings in a season. Then, of course, he blew out his elbow again, but came off Tommy John surgery just in time to throw upper-90s heat in September to secure a one-year payday. When he's been healthy, he's never been anything short of excellent. If he's healthy, there's little reason to doubt that he can be excellent again, especially considering the year that the 41-year-old Trevor Hoffman just had in Milwaukee.

If he's healthy. After all, he's a 38-year-old lefty coming off his second elbow surgery. He's a physical freak—an undersized righty who can somehow throw 100 mph with his left hand—but even physical freaks can't stretch their tendons forever. Tommy John surgery is viewed as a relatively safe procedure, but it's not foolproof. Wagner had a nice stretch run in 2009 and has pitched well in the spring of 2010, but his arm will be monitored closely. If he goes down, the Braves will rely on their setup duo, fellow Tommy John returnee Peter Moylan and the 40-year-old Takashi Saito. If the Braves can get 150 innings out of that trio, the late innings will be well in hand. If not, they could be in trouble.

John: Certainty. That is what a great closer gives you. When did the Braves last have certainty in the ninth? In 2004, when John Smoltz was at the back end of the rotation. Since then the Braves have wheeled out sub-standard closers: Dan Kolb, Kyle Farnsworth, Mike Gonzalez, Rafael Soriano ... there's no certainty there. At least Soriano and Gonzalez can pitch ... day to day you can't be sure if they'll be healthy.

Certainty is what the Braves hope to get from Wagner. Sure he has been far from healthy over the last couple of years. But last year, after Tommy John surgery, he did well at Fenway Park. His heat was in the mid- to high-90s and he looked like his old self. I'm optimistic that Wagner will work out well for the Braves.

Let's go through the facts. First, Wagner is a seriously good pitcher. His career ERA is 2.39; his K/BB is 3.9 and his K/9 is 12. Second, despite being on the DL he is STILL a seriously good hurler. As Alex mentioned, the only time that Wagner has had an ERA over 3.00 was in 2000, and that was when we was hurt. You want me to prognosticate? He'll keep off the DL, put in a sub-3 ERA and get at least 30 saves. You heard it here!

3. How good is the rotation?


Alex: The Braves' 2009 Opening Day starter, Derek Lowe, was so unimpressive last year after signing a four-year, $60 million contract that the Braves spent most of the offseason fruitlessly trying to offload him. When it became clear that no one wanted him any more than the Braves did, they turned around and traded their best pitcher of 2009, Javier Vazquez, for Melky Cabrera and a flame-throwing teenager.

After all that, Lowe's still a Brave, and he's not bad for the back of the rotation, where he and Kenshin Kawakami are still likely to deliver an ERA somewhere south of 4.50. Anything better than that will be gravy.

That's because, as usual, the Braves' top three starters are terrific. Tommy Hanson, Jair Jurrjens and Tim Hudson are all No. 1/No. 2 starters, and all look set to excel in 2010. Like Wagner, Hudson came back from Tommy John surgery last year, and like Wagner, he pitched quite well in the final months of the year. Jurrjens is likely to come back to earth a bit after 2010, when he had an ERA about a run less than the year before despite virtually identical components. But his 2008 was plenty good. And Hanson was simply dominant during the final months of the season, looking every bit like the ace he was predicted to be. He and Jurrjens might be the best under-24 one-two punch in baseball.

John: I like the Braves' rotation. Sure I'd rather have seen Lowe disappear rather than Vasquez, but that was never going to happen when Lowe still has three years left on his $60 million contract. Anyway, our old friend, regression to the mean, should help Braves fans get past their initial disappointment.

Also, for the first time in many years, the Braves have a stable rotation that should hold together for a few years. Kawakami is only one year into his contract, Hudson re-upped in the offseason, and Jurrjens and Hanson, the young whippersnappers, are still pre-arb. This crew could hang together for three more years (although if Lowe has a good year he'll be offloaded). The last time the Braves had a stable rotation, with a solid top three ... yup, Smoltz, Glavine, Maddux.

Now I'm not suggesting that the current trio is a patch on what may have been the best 1-2-3 in baseball. Nowhere near. But a look at the projections shows Jurrjens on a 3.80 ERA, Hudson with a 3.90 ERA and Hanson at a 3.50 ERA. On paper it sounds great—but then again most rotations do. One of these guys is bound to hit the DL at some stage and that is when the Braves will struggle. Who's the reserve starter at Turner Field? Jo-Jo Reyes? .... Please, no.

4. Can Chipper Jones and Troy Glaus stay on the field?


Alex: The pitching probably won't be the problem for the Braves. It's the offense. No regular slugged over .500 or hit 25 homers for the Braves in 2009, and there's a very good chance no one will in 2010 either, considering that Heyward won't turn 21 until August. The team is severely underpowered, and the No. 3 and No. 4 hitters are aging sluggers with injury problems.

Glaus and Jones were two of the best third basemen of the last decade, but they're both past their prime, and Glaus is trying to learn how to wear a first baseman's mitt, too. Glaus has played 149 games in three of the last five years, a total that Jones hasn't reached since 2003, but the fact is: Over the past seven years, Glaus is averaging 104 games played, and Chipper's averaging 131. (Over that same period, Jones is slugging .535, and Glaus is slugging .498.) When they've played, they've hit. They've just been off the field a lot.

If the Braves can get 1,000 combined at-bats out of their corner infielders, they'll have a decent middle of the order. The rest of the lineup is filled with pesky hitters with double-digit home run power: center fielder Nate McLouth, shortstop Yunel Escobar, second baseman Martin Prado, catcher Brian McCann (the Braves' best hitter in 2009), right fielder Heyward, and the left field platoon of Matt Diaz and Cabrera. None of them are automatic outs, but none of them is all that frightening, either. Without production from Jones and Glaus, the Braves will be playing a lot of 4-3 ballgames.

John: Who the hell is asking these questions ... another health-related conundrum. Given Alex has done such a good job with his answer and I'm bored of talking about the DL I'll answer a different question, "Can Chipper Jones regain batting title form?".

Chipper's demise has been predicted for years. Remember in 2004 when he went .248/.362/.485? Pundits were ready to pen an obituary to what even was at that point a stellar career. Since then Jones has accelerated: 2005 was .296/.412/.556 and then his average didn't drop below .300 until 2009, when he hit a disappointing .264/.388/.430. Can he bounce back once again?

Unfortunately, the odds are more against him than last time. He's 38 years old and at this point in their careers many hitters see a sharp drop-off in performance. And it wasn't like he was especially unhealthy in 2009. Sure he had some knocks and niggles, but he started 143 games—the most since 2003.

However, his batting eye is as sharp as a fox's, as evidenced by his ability to get on base at a .388 clip. That has a lot of value even if his arms can't deliver the power they used to. My admiration for Jones as a player has grown this season as he has confounded the baseball community with his approach at the plate.

The many projections figure that Jones should hit for a .290/.400/.470 clip. I don't know about you, but I'll take that line right now. My head tells me he'll be closer to where he was last season, but that's still better than the rest of the Braves infield.

5. The biggie... can Bobby win again?


Alex: The 2010 Braves look a lot like the teams that Cox took to the playoffs like clockwork: a deep, dominating starting rotation and a pesky but underpowered offense. They certainly look like one of the best four teams in the league, but while Cox certainly can escort this team to October, these Braves don't immediately look dominant enough to win it all. The Phillies return a terrific team with Roy Halladay at the fore, and the Marlins, Rockies, Cardinals, Dodgers and Giants all finished with better records than the Braves in 2009. Some of those wins may have been fluky—I'm looking at you, Ryan Franklin—but the Braves will have a lot of teams to leapfrog.

The Braves don't necessarily have more question marks than any other team—after all, just about every team has to caveat its chances against the likelihood of injury. The Braves had the third-best run differential in the NL last year, and they've had the best run differential in the Grapefruit League this spring. They're virtually guaranteed to score a lot more runs than they give up. But they won't bludgeon anyone to death—they'll win with pitching and singles. That may well be enough in the regular season, but it likely won't once October rolls around.

John: I could write an essay on this, but I'll keep it short. The Braves were unlucky last year. They score a lot more runs than they gave up and should have ended up with a better record. The rotation should be an improvement despite the loss of Vasquez. Lowe will improve a bit, Hudson will replace Vasquez, and Hanson and Jurrjens are a year wiser. Although the bullpen is a weak spot, a healthy Wagner will covert a lot of close games if he is given the chance. And Heyward has the discipline and power to be a rock star in the outfield.

If that happens, that I fancy their chances. If one of those factors goes off, then no way. The problem is that the NL East is probably the strongest division on the Senior Circuit. The Phillies have reached the last two World Series and, if anything, have upgraded with Doc Halladay over the offseason. The Mets have the talent but underperform and the Marlins are starting to up their payroll and still have a very talented bunch of ballplayers.

It's Bobby Cox's last season in charge. You know he wants it—that is why Heyward is starting. Here is hoping that the Tomahawks can give one of the greatest managers in the game the send-off he deserves. Go Braves!

Click here to learn about THT's download subscriptions.]]> John Beamer 2010-04-02T10:30:15+00:00 One sport that has the greatest-ever debate sewn up ... http://www.hardballtimes.com/main/blog_article/one-sport-that-has-the-greatest-ever-debate-sewn-up-/ http://www.hardballtimes.com/main/blog_article/one-sport-that-has-the-greatest-ever-debate-sewn-up-/#When:01:30:15
Click here to learn about THT's download subscriptions.]]> John Beamer 2009-08-17T01:30:15+00:00 The Rangers win the Teixeira trade http://www.hardballtimes.com/main/blog_article/the-rangers-win-the-teixeira-trade/ http://www.hardballtimes.com/main/blog_article/the-rangers-win-the-teixeira-trade/#When:20:32:15
Click here to learn about THT's download subscriptions.]]> John Beamer 2009-08-16T20:32:15+00:00 Will the Red Sox win the AL East? http://www.hardballtimes.com/main/blog_article/will-the-red-sox-win-the-al-east/ http://www.hardballtimes.com/main/blog_article/will-the-red-sox-win-the-al-east/#When:03:06:15
Click here to learn about THT's download subscriptions.]]> John Beamer 2009-08-15T03:06:15+00:00 A-Rod in the clutch http://www.hardballtimes.com/main/article/a-rod-in-the-clutch/ http://www.hardballtimes.com/main/article/a-rod-in-the-clutch/#When:05:01:15 Fangraphs. Proprietor David Appelman has done a tremendous job over the last three years transforming the site from a cutesy WPA-based portal to a full-fledged statistical powershop.

Now he has live WPA for every game, play-by-play data for each player, MGL's UZR, plate discipline stats, minor league stats, projections (courtesy of CHONE, Bill James, Oliver, ZiPS and Marcel) and a bushel of bloggers led by USSMariner's inestimable Dave Cameron.

Enough bluster and brown-nosing—I want to use Fangraphs to analyze how Alex Rodriguez performed in the clutch last year. There is continuous noise that the Yankees slugger can hit for toffee when it counts. Fangraphs will tell us if that is true.

Measuring clutch


There are plenty of ways to measure clutch. The most oft-used definition is close and late—for instance, beyond the seventh inning in a one-run ballgame with a runner in scoring position. While that feels intuitively correct it is ultimately a subjective measure. In this day and age we deserve a more quantitative measure of clutch.

Fortunately it exists. It is called Leverage Index, or LI for short.

What is LI and how does it work?

In simple terms, it is the maximum change in win expectancy from a given at-bat divided by the average change in win expectancy over all situations (0.364). Take the highest LI possible which is 10.9—bottom of ninth, down by one, two outs, bases loaded. Before the event, win expectancy is 22 percent, and the maximum swing in LI is when the batter mashes one over the fences for a walk-off homer—a swing of 0.88. Divide that by the average swing in win expectancy (0.36) and we get 10.9.

For the inquisitive among you, Tom Tango wrote a great three-part series on LI for THT which you can find here, here and here.

My view is that Leverage Index IS the most accurate measure of clutchiness. It is also continuous. In other words, an LI of 1.8 is more clutch than an LI of 1.6 and so we can measure the difference in clutchiness between these two situations. Some people disagree with this view and set a boundary on high clutch situations by defining it as LI > 3. This accounts for 5 percent of PA, which feels about right—I can live with that but it's still subjective. I've got an article in the pipe on using LI as a continuous measure of clutch.

A-Rod play-by-play


Below is a long list of every batting play for A-Rod in 2008 (from Fangraphs). Along with the date and pitcher faced we have Leverage Index, Run Expectancy, Win Expectancy, Win Probability Average and RE24 (run expectancy above average). I've omitted the play-by-play description of each event but you can work out whether A-Rod made an out by the direction of WPA or RE24 in the table. The table is ordered by descending LI. (Press page down about six times to get to the end.)
Date       Pitcher       LI         RE         WE         WPA        RE24
Aug-15     J  Soria      6.76       0.46       17.80%     0.102      0.34
Aug-30     B  Ryan       6.01       1.55       53.20%     -0.383     -1.21
03-Sep     S  Shields    4.46       1.62       77.50%     0.141      1
Aug-16     J  Peralta    4.28       0.95       71.40%     -0.072     -0.43
12-Sep     J  Papelbon   4.03       0.47       24.70%     -0.107     -0.47
04-Sep     M  Delcarmen  3.88       0.8        17.70%     -0.102     -0.8
Aug-15     L  Nunez      3.81       1.23       51.10%     0.032      0.02
May-27     G  Sherrill   3.71       0.36       45.50%     0.013      0.13
09-Sep     J  Soria      3.56       0.54       21.30%     0.135      0.39
Jun-30     C  Wilson     3.56       0.54       21.30%     -0.093     -0.25
01-Sep     F  Rodriguez  3.56       0.54       21.30%     -0.093     -0.25
02-Sep     J  Accardo    3.55       1.55       19.20%     -0.083     -0.6
Apr-28     A  Laffey     3.47       2.39       47.20%     0.13       1
07-Sep     Y  Yabuta     3.46       0.46       59.10%     -0.091     -0.46
Jul-29     G  Sherrill   3.46       2.04       22.60%     -0.086     -0.59
06-Sep     R  Mahay      3.35       0.56       32.30%     -0.082     -0.31
Aug-22     J  Walker     3.32       1          35.90%     0.23       1.1
12-Sep     J  Blanton    3.32       1.4        48.00%     0.126      0.85
12-Sep     J  Litsch     3.32       0.73       41.70%     0.193      1.68
May-31     D  Reyes      3.27       0.69       55.80%     -0.163     -0.58
Apr-26     J  Lewis      3.19       0.95       55.90%     -0.074     -0.5
Sep-20     J  Miller     3.18       1.55       82.10%     0.006      -0.33
Jul-19     J  Blevins    3.16       0.71       70.40%     0.01       0.24
Jul-19     L  DiNardo    3.16       0.71       70.40%     0.01       0.24
May-22     J  Johnson    3.16       0.93       71.80%     -0.078     -0.38
Aug-26     J  Masterson  3.08       1.62       18.00%     -0.142     -1.62
01-Sep     N  Blackburn  3.06       1.59       40.10%     -0.084     -0.81
May-31     D  Reyes      3.05       0.53       50.00%     0.058      0.16
10-Sep     P  Maholm     2.92       0.45       48.70%     -0.076     -0.45
02-Sep     J  Nathan     2.88       0.51       16.50%     0.115      0.39
02-Sep     D  Reyes      2.83       1.51       64.50%     0.064      0.35
May-27     M  Albers     2.83       2.45       82.70%     -0.36      -1.96
Aug-28     J  Lester     2.83       1.23       36.70%     -0.099     -0.7
Aug-13     J  Crain      2.79       0.51       13.80%     -0.078     -0.51
Aug-19     B  Ryan       2.78       0.44       14.90%     -0.068     -0.21
May-25     J  Putz       2.73       1.17       28.30%     0.092      0.38
Jun-29     B  Wagner     2.66       1.12       16.20%     -0.067     -0.44
05-Sep     S  Downs      2.64       0.71       41.20%     -0.066     -0.33
Aug-16     Z  Greinke    2.63       2.39       64.40%     -0.084     -0.77
10-Sep     J  Arredondo  2.58       0.69       54.30%     -0.126     -0.58
Jun-28     J  Santana    2.47       1.49       49.40%     0.095      0.85
02-Sep     W  Madrigal   2.47       1.17       50.20%     0.055      0.38
05-Sep     E  Guardado   2.4        0.68       10.80%     -0.108     -0.68
Aug-26     T  Wakefield  2.39       0.95       24.80%     -0.056     -0.5
Aug-16     R  Ramirez    2.38       0.56       61.10%     0.062      0.4
08-Sep     J  Garland    2.38       0.9        39.90%     -0.122     -0.79
12-Sep     J  Nathan     2.34       0.23       42.60%     -0.066     -0.23
09-Sep     L  Hochevar   2.32       1.17       32.90%     0.262      1.37
Aug-20     D  Purcey     2.31       2.26       67.10%     -0.068     -0.76
May-31     B  Bonser     2.31       0.61       40.00%     0.187      1.62
08-Sep     J  Weaver     2.31       0.61       40.00%     0.017      0.17
06-Sep     H  Okajima    2.3        0.54       64.50%     -0.06      -0.25
Sep-16     G  Floyd      2.29       0.46       28.80%     0.045      0.34
03-Sep     D  McGowan    2.28       2.04       64.00%     -0.087     -0.59
04-Sep     F  Francisco  2.18       0.69       29.50%     -0.061     -0.31
Jun-19     J  Banks      2.14       0.97       65.40%     0.084      0.59
Jun-21     D  Thompson   2.1        0.95       59.70%     -0.049     -0.5
05-Sep     J  Putz       2.09       0.68       9.40%      -0.057     -0.32
Sep-13     J  Howell     2.09       0.71       62.50%     -0.06      -0.37
03-Sep     J  Lackey     2.07       1.89       22.20%     0.064      0.5
Jul-27     J  Lester     2.03       2.41       18.90%     -0.053     -0.77
02-Sep     L  Mendoza    2.03       0.52       33.40%     0.03       0.28
Jul-30     D  Sarfate    2          0.95       49.10%     0.192      2.02
03-Sep     J  Arredondo  2          0.56       59.20%     0.055      0.4
10-Sep     D  Eveland    1.97       1.12       57.00%     0.025      0.17
Jul-20     J  Duchscherer1.97       1.23       62.80%     0.023      0.11
Jul-29     D  Cabrera    1.97       0.46       43.30%     0.036      0.34
Sep-14     E  Jackson    1.92       2.39       70.30%     0.155      2.15
May-30     G  Perkins    1.92       0.9        54.70%     0.157      1.24
10-Sep     D  Moseley    1.91       0.92       53.70%     -0.044     -0.48
Jun-25     Z  Duke       1.91       0.93       53.70%     0.058      0.66
03-Sep     D  McGowan    1.91       0.93       48.10%     -0.045     -0.38
May-22     B  Burres     1.91       0.93       48.10%     -0.045     -0.38
Apr-15     E  Jackson    1.89       0.88       42.10%     0.135      1.1
02-Sep     L  Hernandez  1.89       0.95       56.50%     -0.08      -0.58
Aug-17     B  Bannister  1.89       0.95       31.20%     0.215      2.33
Apr-14     G  Glover     1.87       0.5        74.90%     -0.052     -0.5
Aug-16     R  Tejeda     1.86       0.29       58.50%     -0.047     -0.17
04-Sep     J  Beckett    1.86       0.95       58.50%     0.177      2.02
09-Sep     E  Jackson    1.86       0.95       58.50%     -0.037     -0.43
01-Sep     E  Santana    1.86       0.95       58.50%     -0.043     -0.5
Aug-28     J  Lester     1.86       0.95       58.50%     -0.043     -0.5
Sep-16     G  Floyd      1.86       0.95       58.50%     -0.043     -0.5
Jul-25     J  Beckett    1.86       0.97       53.60%     -0.044     -0.5
Apr-13     D  Matsuzaka  1.86       0.97       53.60%     -0.086     -0.97
10-Sep     J  Saunders   1.86       0.37       48.40%     -0.051     -0.37
Apr-15     J  Howell     1.85       0.77       83.20%     -0.047     -0.77
01-Sep     E  Santana    1.85       0.34       55.30%     0.012      0.12
01-Sep     J  Verlander  1.83       2.46       64.80%     0.111      1.5
Sep-19     R  Liz        1.83       0.46       54.80%     -0.048     -0.46
Jul-19     B  Ziegler    1.82       0.25       55.20%     -0.052     -0.25
12-Sep     M  Guerrier   1.82       0.27       44.00%     0.378      1
May-31     D  Reyes      1.82       0.27       44.00%     0.06       0.26
03-Sep     J  Lester     1.8        1.23       42.30%     -0.066     -0.7
12-Sep     B  Bonser     1.79       0.44       63.10%     -0.046     -0.44
03-Sep     R  Halladay   1.78       0.56       42.50%     -0.044     -0.31
08-Sep     J  Garland    1.78       1.2        56.40%     0.066      0.73
Apr-18     D  Cabrera    1.75       0.6        50.10%     0.023      0.15
May-26     G  Olson      1.75       0.6        50.10%     0.023      0.15
11-Sep     J  Duchscherer1.74       0.46       62.60%     -0.046     -0.46
02-Sep     M  Garza      1.74       0.94       43.10%     0.028      0.24
05-Sep     J  Masterson  1.73       0.95       70.00%     0.051      0.67
Apr-28     A  Laffey     1.72       1.23       56.20%     -0.063     -0.7
Apr-15     E  Jackson    1.72       0.94       55.80%     0.045      0.32
06-Sep     K  Davies     1.72       1.23       61.20%     -0.112     -1.23
May-28     J  Johnson    1.7        0.4        71.10%     -0.049     -0.4
Jun-27     M  Pelfrey    1.7        1.23       74.50%     -0.054     -0.6
Jun-22     J  Cueto      1.69       0.93       60.30%     -0.04      -0.38
01-Sep     E  Santana    1.69       0.93       60.30%     -0.04      -0.38
01-Sep     R  Halladay   1.69       0.93       60.30%     -0.09      -0.82
06-Sep     R  Corcoran   1.66       0.94       75.70%     0.014      0.24
05-Sep     B  Ryan       1.64       0.25       4.90%      0.002      0.09
08-Sep     B  Bannister  1.64       0.87       54.00%     -0.038     -0.36
Aug-13     K  Slowey     1.64       0.53       22.40%     -0.04      -0.3
11-Sep     G  Perkins    1.64       0.53       22.40%     -0.07      -0.53
07-Sep     J  Hammel     1.64       0.52       66.60%     -0.047     -0.52
07-Sep     R  Feierabend 1.64       0.32       33.50%     0.015      0.11
05-Sep     D  McGowan    1.63       1.45       20.50%     -0.025     -0.07
06-Sep     R  Rowland-Smi1.62       0.43       60.90%     0.028      0.33
08-Sep     R  Mahay      1.61       1.23       84.30%     0.085      0.89
Jun-20     E  Volquez    1.6        0.93       48.70%     0.025      0.24
Apr-27     C  Sabathia   1.59       0.97       71.00%     -0.069     -0.59
May-31     B  Bonser     1.59       0.33       47.80%     -0.045     -0.33
Aug-16     Z  Greinke    1.58       0.54       22.90%     0.099      0.63
06-Sep     T  Wakefield  1.58       0.54       22.90%     0.068      0.39
Apr-25     R  Betancourt 1.57       0.54       8.00%      -0.041     -0.25
01-Sep     J  Rupe       1.56       0.54       40.40%     -0.041     -0.25
May-27     D  Sarfate    1.56       0.59       50.00%     -0.042     -0.27
Jul-25     J  Beckett    1.55       0.95       68.90%     0.029      0.24
02-Sep     L  Hernandez  1.54       0.51       50.00%     0.108      0.63
Aug-22     R  Liz        1.54       1.62       66.60%     -0.091     -1.22
01-Sep     R  Halladay   1.52       0.54       59.10%     0.056      0.39
Sep-20     B  Burres     1.52       0.54       59.10%     0.056      0.39
01-Sep     B  Bass       1.51       0.53       25.10%     -0.036     -0.3
03-Sep     J  Lackey     1.51       1.89       18.70%     -0.047     -0.66
12-Sep     N  Blackburn  1.51       0.53       37.90%     -0.036     -0.3
Sep-16     G  Floyd      1.51       0.38       54.20%     -0.042     -0.38
Apr-26     J  Sowers     1.5        0.93       23.10%     0.061      0.62
01-Sep     J  Rupe       1.49       0.25       54.30%     -0.043     -0.25
Jun-27     P  Martinez   1.49       1.12       57.40%     0.035      0.37
May-25     J  Washburn   1.48       0.25       35.60%     0.017      0.09
Sep-19     L  Cormier    1.48       0.46       77.70%     -0.039     -0.46
Jun-27     M  Pelfrey    1.47       0.97       38.10%     0.063      0.59
Jun-30     S  Feldman    1.47       0.93       58.90%     0.025      0.24
10-Sep     J  Bale       1.46       1.18       81.00%     0.019      0.05
Sep-28     D  Matsuzaka  1.46       0.73       52.00%     0.023      0.24
Aug-15     G  Meche      1.46       0.71       46.20%     -0.042     -0.37
05-Sep     M  Harrison   1.45       1.11       26.30%     -0.082     -0.96
Jun-29     O  Perez      1.45       0.68       28.70%     -0.04      -0.36
Sep-17     C  Richard    1.43       0.29       32.30%     0.054      0.27
Apr-20     S  Trachsel   1.43       0.76       51.90%     0.11       1
Aug-24     D  Cabrera    1.43       1.27       76.40%     0.022      0.4
09-Sep     J  Howell     1.43       0.29       56.40%     -0.036     -0.17
09-Sep     E  Santana    1.42       1.86       79.30%     0.125      1.65
Sep-13     M  Garza      1.42       1.45       63.10%     -0.002     -0.11
Jun-19     J  Banks      1.42       1.45       63.10%     -0.074     -0.82
Sep-20     B  Burres     1.42       0.56       56.40%     0.041      0.4
Aug-26     T  Wakefield  1.42       0.56       44.40%     -0.063     -0.56
Apr-18     D  Cabrera    1.41       0.6        50.10%     -0.025     -0.24
Jul-19     S  Gallagher  1.4        0.63       74.90%     -0.042     -0.63
10-Sep     J  Arredondo  1.4        0.27       45.30%     0.09       0.42
07-Sep     S  Feldman    1.4        1.11       63.70%     -0.083     -0.96
Jul-29     D  Cabrera    1.4        0.54       26.80%     0.135      1
04-Sep     A  Sonnanstine1.4        0.54       26.80%     -0.036     -0.25
09-Sep     E  Jackson    1.4        0.38       69.40%     -0.039     -0.38
12-Sep     J  Beckett    1.38       0.56       19.60%     0.059      0.4
Aug-24     R  Cherry     1.36       0.76       74.60%     0.016      0.25
10-Sep     J  Saunders   1.36       1.86       70.70%     0.082      1.14
May-31     J  Rincon     1.35       0.92       86.00%     -0.025     -0.42
Sep-13     M  Garza      1.35       0.54       42.20%     -0.035     -0.25
Sep-17     C  Richard    1.35       0.54       42.20%     -0.035     -0.25
03-Sep     R  Halladay   1.34       0.38       43.70%     0.013      0.14
Jul-20     J  Duchscherer1.32       0.54       57.80%     -0.034     -0.25
10-Sep     C  Gaudin     1.32       0.81       79.60%     -0.03      -0.34
Aug-29     A  Burnett    1.3        0.93       76.90%     -0.071     -0.82
Aug-22     R  Liz        1.3        0.6        50.10%     -0.033     -0.34
Jul-19     S  Gallagher  1.3        0.95       80.60%     -0.025     -0.43
Jun-14     W  Rodriguez  1.29       0.33       36.90%     0.013      0.12
01-Sep     K  Millwood   1.29       0.38       53.60%     -0.036     -0.38
Sep-20     B  Burres     1.29       0.38       53.60%     -0.036     -0.38
10-Sep     P  Maholm     1.29       0.33       48.10%     -0.037     -0.33
Jun-13     S  Chacon     1.28       0.37       48.70%     0.085      0.87
Jun-15     R  Oswalt     1.28       0.33       48.10%     0.011      0.12
Jun-28     J  Santana    1.28       0.52       66.40%     0.02       0.16
10-Sep     D  Eveland    1.28       0.3        64.00%     -0.035     -0.3
05-Sep     D  McGowan    1.28       1.23       80.70%     -0.047     -0.7
Jun-20     F  Cordero    1.27       0.29       4.90%      -0.033     -0.17
09-Sep     J  Lackey     1.27       0.51       81.60%     -0.036     -0.51
07-Sep     B  Bannister  1.27       0.38       34.30%     0.014      0.14
Jul-26     T  Wakefield  1.26       0.39       48.60%     -0.036     -0.39
Apr-16     J  Tavarez    1.26       0.54       29.70%     -0.033     -0.25
Apr-19     B  Burres     1.24       0.36       37.50%     -0.037     -0.36
Jun-20     E  Volquez    1.24       0.34       42.70%     0.004      0.04
Aug-19     A  Burnett    1.24       1.05       69.90%     -0.042     -0.42
10-Sep     P  Maholm     1.24       0.52       22.10%     -0.032     -0.24
Apr-19     B  Burres     1.23       0.4        48.50%     0.013      0.15
Aug-19     A  Burnett    1.23       0.48       67.90%     -0.028     -0.27
Jul-18     G  Smith      1.22       0.56       45.40%     0.037      0.4
08-Sep     R  Ramirez    1.21       0.51       10.40%     -0.029     -0.29
May-30     G  Perkins    1.2        0.69       51.80%     -0.034     -0.36
03-Sep     E  Jackson    1.2        0.68       63.30%     0.098      1
Sep-19     R  Liz        1.2        0.34       68.90%     0.008      0.12
05-Sep     J  Masterson  1.2        0.34       68.90%     0.005      0.04
Jun-28     D  Sanchez    1.19       0.52       74.90%     0.02       0.16
05-Sep     M  Harrison   1.19       0.68       33.20%     0.012      0.15
Aug-27     P  Byrd       1.18       0.54       43.40%     0.078      0.63
May-30     G  Perkins    1.17       1.44       28.30%     0.045      0.46
12-Sep     J  Blanton    1.16       0.3        48.60%     0.01       0.11
05-Sep     D  McGowan    1.16       0.97       68.30%     0.018      0.14
11-Sep     R  Halladay   1.16       0.3        48.60%     -0.032     -0.3
Aug-21     R  Halladay   1.16       0.3        48.60%     -0.032     -0.3
Aug-29     A  Burnett    1.16       0.71       70.10%     0.044      0.51
May-25     J  Washburn   1.16       0.29       36.30%     0.044      0.27
Jun-30     S  Feldman    1.16       0.29       36.30%     -0.029     -0.17
Jul-30     D  Sarfate    1.16       0.38       63.30%     0.068      0.73
04-Sep     A  Sonnanstine1.15       0.34       43.30%     -0.033     -0.34
Aug-31     R  Halladay   1.15       0.34       17.70%     -0.034     -0.34
Apr-13     D  Matsuzaka  1.15       0.35       27.60%     -0.034     -0.35
10-Sep     P  Maholm     1.15       0.54       50.20%     -0.028     -0.3
Jul-19     S  Gallagher  1.15       0.56       55.10%     -0.028     -0.31
Sep-26     D  Pauley     1.14       1.19       66.40%     -0.041     -0.46
10-Sep     J  Saunders   1.14       0.51       37.70%     0.046      0.39
May-25     J  Washburn   1.14       0.54       31.80%     -0.03      -0.25
01-Sep     K  Millwood   1.14       0.54       31.80%     -0.03      -0.25
03-Sep     S  Downs      1.13       0.95       88.30%     -0.026     -0.5
Jun-19     J  Hampson    1.13       0.56       73.80%     0.017      0.15
Jul-22     D  Reyes      1.13       0.56       73.80%     0.017      0.15
Jul-19     A  Embree     1.13       0.56       73.80%     -0.017     -0.22
Jun-22     G  Majewski   1.13       0.56       73.80%     -0.028     -0.31
May-28     J  Guthrie    1.12       0.59       38.80%     -0.03      -0.27
06-Sep     J  Shields    1.12       0.46       80.20%     0.018      0.34
03-Sep     E  Jackson    1.12       0.32       48.40%     -0.032     -0.32
Jun-25     Z  Duke       1.12       0.96       75.00%     -0.048     -0.59
Apr-20     S  Trachsel   1.12       1          78.80%     -0.027     -0.39
06-Sep     T  Hunter     1.12       0.4        57.00%     -0.035     -0.4
02-Sep     L  Hernandez  1.11       0.33       48.40%     0.174      1.78
Aug-30     J  Parrish    1.11       0.34       53.20%     0.089      0.91
Aug-15     G  Meche      1.11       0.34       53.20%     0.01       0.12
May-21     G  Olson      1.11       0.34       53.20%     -0.032     -0.34
09-Sep     L  Hochevar   1.11       0.34       53.20%     -0.032     -0.34
Sep-15     M  Buehrle    1.11       0.34       53.20%     -0.032     -0.34
Jun-24     T  Gorzelanny 1.11       0.33       48.30%     -0.032     -0.33
Aug-19     A  Burnett    1.11       0.63       62.40%     -0.03      -0.33
Jun-21     D  Thompson   1.11       0.25       53.20%     -0.032     -0.25
Aug-15     G  Meche      1.11       0.25       25.90%     -0.032     -0.25
Aug-27     P  Byrd       1.11       0.25       25.90%     -0.032     -0.25
Aug-22     D  Sarfate    1.1        0.27       46.20%     -0.033     -0.27
03-Sep     J  Lackey     1.1        0.34       33.90%     -0.032     -0.34
Sep-26     D  Pauley     1.09       0.35       48.20%     0.085      0.91
Jul-27     J  Lester     1.09       0.35       48.20%     -0.032     -0.35
Sep-13     M  Garza      1.08       0.56       66.80%     0.032      0.4
Apr-25     P  Byrd       1.08       0.54       50.00%     -0.028     -0.25
Apr-27     C  Sabathia   1.08       0.54       50.00%     -0.028     -0.25
06-Sep     W  Madrigal   1.07       1.35       83.50%     -0.08      -1.35
Apr-20     S  Trachsel   1.07       0.36       48.10%     -0.032     -0.36
May-26     G  Olson      1.07       0.36       48.10%     -0.032     -0.36
04-Sep     J  Beckett    1.07       0.54       56.20%     0.042      0.39
Aug-28     J  Lester     1.07       0.54       56.20%     -0.028     -0.25
Aug-30     B  Tallet     1.06       1.55       86.40%     -0.033     -0.6
Aug-28     J  Masterson  1.06       0.11       52.80%     -0.028     -0.11
12-Sep     N  Blackburn  1.05       0.53       59.90%     -0.025     -0.3
11-Sep     G  Perkins    1.04       0.51       27.00%     0.044      0.39
Jul-13     A  Burnett    1.04       1.05       14.60%     -0.03      -0.42
May-28     J  Guthrie    1.04       0.59       64.50%     -0.028     -0.27
Aug-23     J  Guthrie    1.04       0.59       64.50%     -0.028     -0.27
Jun-18     J  Peavy      1.04       0.71       65.90%     -0.03      -0.37
05-Sep     B  Morrow     1.03       0.23       34.10%     -0.029     -0.23
Jun-17     R  Wolf       1.01       0.38       75.20%     -0.028     -0.38
Apr-18     D  Cabrera    1.01       0.32       46.40%     0.037      0.28
May-26     G  Olson      1.01       0.32       46.40%     0.037      0.28
Aug-24     D  Sarfate    1.01       0.32       46.40%     0.037      0.28
07-Sep     J  Wright     1.01       1.08       86.10%     -0.048     -0.64
Jul-25     J  Beckett    1.01       0.56       65.10%     0.039      0.4
May-30     B  Bass       1.01       1.44       80.10%     0.025      0.43
01-Sep     F  Dolsi      1          0.84       90.60%     -0.027     -0.84
01-Sep     S  Downs      1          0.38       85.70%     0.005      0.14
Sep-15     M  Buehrle    0.99       0.29       54.30%     -0.025     -0.17
Jun-20     E  Volquez    0.99       0.54       44.70%     0.04       0.39
Sep-21     C  Waters     0.99       0.54       44.70%     0.04       0.39
May-24     C  Silva      0.99       0.54       55.70%     -0.026     -0.25
02-Sep     A  Burnett    0.99       0.54       44.70%     -0.026     -0.25
Apr-17     J  Beckett    0.99       0.54       44.70%     -0.026     -0.25
Aug-16     Z  Greinke    0.99       0.54       44.70%     -0.026     -0.25
May-27     B  Burres     0.99       0.59       50.00%     -0.027     -0.27
09-Sep     J  Lackey     0.98       0.27       46.60%     0.169      1
Apr-13     D  Aardsma    0.98       0.56       11.60%     -0.026     -0.26
Jul-21     N  Blackburn  0.98       0.52       81.60%     0.054      0.93
08-Sep     B  Bannister  0.97       0.49       39.30%     -0.024     -0.23
02-Sep     J  Weaver     0.97       0.54       34.80%     -0.025     -0.25
Sep-19     R  Liz        0.97       0.54       34.80%     -0.025     -0.25
10-Sep     F  Rodriguez  0.97       0.27       3.60%      -0.025     -0.17
May-31     B  Bonser     0.97       0.51       39.60%     -0.025     -0.24
06-Sep     R  Rowland-Smi0.96       0.49       66.40%     -0.024     -0.23
07-Sep     B  Tomko      0.95       0.56       7.80%      0.11       1.73
Jul-23     G  Perkins    0.94       1.55       89.10%     0.078      1.92
09-Sep     E  Santana    0.93       0.51       50.00%     0.037      0.39
Sep-23     J  Litsch     0.93       0.44       50.00%     -0.023     -0.21
09-Sep     Z  Greinke    0.93       0.49       50.00%     -0.023     -0.23
05-Sep     B  Morrow     0.93       0.49       50.00%     -0.023     -0.23
Apr-15     E  Jackson    0.93       0.5        50.00%     -0.024     -0.23
02-Sep     M  Garza      0.93       0.5        50.00%     -0.024     -0.23
09-Sep     J  Lackey     0.93       0.51       50.00%     -0.024     -0.24
11-Sep     C  Buchholz   0.93       0.56       50.00%     -0.024     -0.26
12-Sep     J  Beckett    0.93       0.56       50.00%     -0.024     -0.26
Sep-28     D  Matsuzaka  0.93       0.56       50.00%     -0.024     -0.26
May-28     J  Guthrie    0.93       0.59       50.00%     -0.025     -0.27
06-Sep     T  Hunter     0.93       0.69       50.00%     -0.026     -0.31
Jun-29     O  Perez      0.92       0.5        10.40%     -0.024     -0.24
Jun-17     R  Wolf       0.92       0.54       55.30%     0.102      1
06-Sep     T  Wakefield  0.92       0.54       55.30%     0.102      1
08-Sep     J  Weaver     0.92       0.51       30.20%     0.095      1
Jun-21     D  Thompson   0.92       0.54       55.30%     0.061      0.63
Jul-22     K  Slowey     0.92       0.54       55.30%     0.061      0.63
May-24     C  Silva      0.92       0.54       55.30%     0.036      0.39
May-25     J  Washburn   0.92       0.54       55.30%     0.036      0.39
Jun-30     S  Feldman    0.92       0.54       55.30%     0.036      0.39
02-Sep     L  Mendoza    0.92       0.54       55.30%     0.036      0.39
05-Sep     M  Harrison   0.92       0.69       33.30%     0.035      0.42
03-Sep     D  McGowan    0.92       0.54       55.30%     -0.024     -0.25
06-Sep     J  Shields    0.92       0.54       55.30%     -0.024     -0.25
May-22     B  Burres     0.92       0.54       55.30%     -0.024     -0.25
05-Sep     J  Masterson  0.92       0.54       55.30%     -0.024     -0.25
08-Sep     S  Kazmir     0.92       0.54       55.30%     -0.024     -0.25
Jul-23     G  Perkins    0.92       0.54       55.30%     -0.024     -0.25
Jul-31     J  Garland    0.92       0.54       55.30%     -0.024     -0.25
Aug-29     A  Burnett    0.92       0.54       55.30%     -0.024     -0.25
Sep-17     C  Richard    0.92       0.54       55.30%     -0.024     -0.25
Jul-26     T  Wakefield  0.92       0.25       36.20%     0.042      0.39
Sep-21     C  Waters     0.92       0.93       83.10%     -0.022     -0.38
Jul-25     J  Beckett    0.91       0.25       46.90%     0.029      0.28
May-26     G  Olson      0.91       0.27       46.80%     0.021      0.22
08-Sep     B  Bannister  0.91       0.26       32.50%     -0.022     -0.16
09-Sep     E  Santana    0.9        0.51       62.30%     -0.023     -0.24
Aug-13     K  Slowey     0.9        0.51       62.40%     -0.023     -0.24
Jun-24     T  Gorzelanny 0.89       0.52       18.70%     -0.023     -0.24
07-Sep     J  Peralta    0.88       0.29       53.80%     -0.022     -0.17
Apr-25     P  Byrd       0.87       0.29       19.50%     -0.022     -0.17
Sep-23     J  Litsch     0.87       0.44       63.60%     -0.021     -0.21
04-Sep     J  Beckett    0.87       0.25       16.90%     -0.025     -0.25
10-Sep     D  Eveland    0.87       0.47       73.30%     0.026      0.37
02-Sep     A  Burnett    0.87       0.93       6.30%      0.072      1.61
Jun-19     J  Banks      0.86       0.29       40.30%     -0.022     -0.17
07-Sep     J  Hammel     0.86       1.47       82.90%     0.033      0.47
11-Sep     R  Halladay   0.85       0.44       9.00%      -0.021     -0.21
04-Sep     J  Litsch     0.84       0.54       68.30%     -0.022     -0.25
Aug-30     J  Parrish    0.84       0.56       76.20%     -0.021     -0.31
04-Sep     V  Padilla    0.83       0.3        56.10%     -0.026     -0.3
08-Sep     Z  Greinke    0.83       0.56       73.00%     0.012      0.15
Apr-17     J  Beckett    0.83       0.34       11.10%     -0.024     -0.34
07-Sep     S  Feldman    0.82       0.69       74.00%     -0.023     -0.31
Jun-27     M  Pelfrey    0.82       0.25       42.40%     0.027      0.28
Jul-29     D  Cabrera    0.82       0.25       42.40%     0.027      0.28
05-Sep     J  Masterson  0.82       0.25       67.80%     0.011      0.09
Jun-14     W  Rodriguez  0.81       0.52       22.40%     0.08       1
Jun-30     S  Feldman    0.81       0.29       28.90%     0.114      1
12-Sep     J  Crain      0.81       0.53       81.70%     -0.036     -0.53
Aug-27     P  Byrd       0.8        0.25       33.10%     0.102      1.09
10-Sep     D  Moseley    0.8        0.23       58.60%     -0.023     -0.23
May-31     B  Bonser     0.8        0.23       58.70%     0.01       0.09
11-Sep     J  Duchscherer0.8        0.21       47.60%     -0.022     -0.21
12-Sep     J  Blanton    0.8        0.23       33.50%     0.032      0.24
Aug-31     R  Halladay   0.8        0.54       15.20%     0.072      1
07-Sep     B  Bannister  0.8        0.54       15.20%     0.034      0.39
Jun-24     T  Yates      0.8        0.69       6.70%      -0.022     -0.36
04-Sep     S  Kazmir     0.79       0.23       47.50%     0.01       0.09
Jul-21     N  Blackburn  0.79       0.25       52.30%     0.178      1.87
01-Sep     R  Halladay   0.79       0.25       52.30%     0.106      1.09
05-Sep     E  Jackson    0.79       0.25       52.30%     0.106      1.09
Aug-23     J  Guthrie    0.79       0.27       47.20%     0.103      1.14
11-Sep     G  Perkins    0.79       0.23       47.40%     0.026      0.27
01-Sep     N  Blackburn  0.79       0.23       47.40%     0.019      0.21
Apr-27     C  Sabathia   0.79       0.25       47.30%     0.019      0.21
Aug-24     D  Cabrera    0.79       0.27       47.20%     0.019      0.22
Jun-27     P  Martinez   0.79       0.23       47.40%     -0.022     -0.23
Jun-28     J  Santana    0.79       0.23       47.40%     -0.022     -0.23
Jun-15     R  Oswalt     0.79       0.24       47.40%     -0.023     -0.24
Apr-25     P  Byrd       0.79       0.25       47.30%     -0.023     -0.25
Apr-26     J  Sowers     0.79       0.25       47.30%     -0.023     -0.25
04-Sep     J  Litsch     0.79       0.25       52.30%     -0.023     -0.25
Jun-22     J  Cueto      0.79       0.25       52.30%     -0.023     -0.25
Apr-18     D  Cabrera    0.79       0.27       47.20%     -0.023     -0.27
May-27     B  Burres     0.79       0.27       47.20%     -0.023     -0.27
Apr-28     A  Laffey     0.79       0.29       47.20%     -0.02      -0.17
06-Sep     J  Shields    0.79       0.29       53.40%     0.049      0.43
06-Sep     K  Davies     0.79       0.29       53.40%     0.03       0.27
Jun-20     E  Volquez    0.79       0.29       53.40%     -0.02      -0.17
04-Sep     V  Padilla    0.79       0.3        46.90%     0.018      0.23
Jun-18     B  Corey      0.79       0.56       80.90%     0.012      0.15
Aug-30     B  League     0.79       0.54       78.50%     -0.02      -0.25
05-Sep     E  Jackson    0.78       0.29       41.40%     -0.02      -0.17
Aug-22     F  Cabrera    0.77       0.27       79.20%     0.029      0.4
Apr-17     J  Beckett    0.77       0.56       10.10%     0.027      0.4
Jul-28     J  Guthrie    0.76       0.54       18.90%     -0.02      -0.25
Jul-13     A  Burnett    0.76       0.48       7.00%      0.029      0.37
Jul-25     M  Delcarmen  0.75       0.3        70.80%     -0.019     -0.18
Jun-20     E  Volquez    0.75       0.29       10.50%     0.033      0.27
06-Sep     W  Madrigal   0.73       1.34       86.90%     -0.008     -0.25
May-31     C  Breslow    0.73       0.27       66.80%     -0.018     -0.17
Jul-26     T  Wakefield  0.73       0.3        60.50%     0.027      0.28
03-Sep     C  Bradford   0.73       1.19       90.70%     0.026      0.73
03-Sep     R  Halladay   0.73       0.29       53.10%     0.028      0.27
Jul-18     G  Smith      0.73       0.29       53.10%     0.028      0.27
Aug-31     R  Halladay   0.73       0.25       24.90%     -0.021     -0.25
Apr-19     B  Burres     0.72       0.59       9.70%      -0.019     -0.27
08-Sep     J  Garland    0.72       0.27       47.50%     -0.018     -0.17
Sep-28     J  Lopez      0.72       0.35       81.50%     0.009      0.19
Jun-28     J  Santana    0.72       0.26       63.70%     0.027      0.26
Aug-30     J  Parrish    0.71       0.25       62.10%     0.008      0.09
05-Sep     T  Miller     0.71       0.93       4.00%      0.032      0.62
Aug-17     B  Bannister  0.7        1.89       88.10%     0.016      0.5
01-Sep     J  Verlander  0.7        1.52       86.20%     0.022      0.5
11-Sep     R  Halladay   0.69       0.23       22.60%     0.03       0.24
May-22     B  Burres     0.69       0.11       51.80%     -0.018     -0.11
Jun-28     D  Sanchez    0.68       0.26       72.50%     0.024      0.26
Jun-13     S  Chacon     0.68       0.28       59.80%     0.025      0.27
Aug-24     D  Cabrera    0.67       0.27       65.90%     -0.02      -0.27
11-Sep     C  Buchholz   0.67       0.12       43.90%     0.019      0.13
Jun-27     P  Martinez   0.67       1.85       90.50%     -0.029     -0.66
Jul-29     D  Cabrera    0.67       1.17       4.50%      0.027      0.38
Jun-13     S  Chacon     0.66       0.11       44.10%     -0.017     -0.11
Sep-16     M  Thornton   0.66       0.54       4.80%      -0.017     -0.25
Aug-20     D  Purcey     0.65       0.44       73.10%     -0.016     -0.21
Jul-22     K  Slowey     0.65       0.29       71.50%     0.023      0.27
07-Sep     R  Feierabend 0.65       0.23       69.70%     -0.018     -0.23
Apr-13     D  Matsuzaka  0.64       0.3        15.00%     -0.017     -0.18
Apr-26     J  Sowers     0.64       0.29       11.90%     0.028      0.27
Jul-28     J  Guthrie    0.64       0.25       12.80%     -0.019     -0.25
Jul-22     K  Slowey     0.63       0.29       66.10%     -0.016     -0.17
02-Sep     L  Hernandez  0.63       0.27       59.00%     0.024      0.26
08-Sep     S  Kazmir     0.62       0.54       78.20%     -0.016     -0.25
05-Sep     B  Ryan       0.61       0.11       1.60%      0.032      0.13
Jun-14     W  Wright     0.61       0.24       79.40%     0.019      0.28
Jun-18     J  Peavy      0.6        0.29       64.50%     0.099      1
Apr-18     R  Bierd      0.6        1.01       3.30%      -0.014     -0.52
06-Sep     K  Davies     0.6        0.11       20.70%     0.032      0.23
Sep-15     M  Buehrle    0.6        0.25       74.10%     -0.017     -0.25
10-Sep     H  Nomo       0.6        0.36       89.60%     0.004      0.14
Aug-23     L  Cormier    0.59       0.59       84.60%     -0.016     -0.27
08-Sep     Z  Greinke    0.59       0.11       51.60%     0.017      0.13
05-Sep     B  Morrow     0.58       0.26       7.80%      -0.015     -0.16
02-Sep     L  Mendoza    0.58       0.25       78.90%     0.013      0.21
Jun-14     G  Geary      0.57       0.37       92.00%     0.003      0.14
Jun-19     H  Bell       0.57       0.54       86.30%     -0.015     -0.25
Apr-16     C  Buchholz   0.55       0.29       62.40%     0.088      1
Sep-18     J  Vazquez    0.55       0.29       62.40%     0.021      0.27
Apr-27     M  Kobayashi  0.55       0.29       81.00%     -0.014     -0.17
12-Sep     J  Litsch     0.54       0.23       58.70%     -0.013     -0.14
05-Sep     E  Jackson    0.54       0.29       10.80%     -0.014     -0.17
Jul-27     J  Lester     0.53       0.56       8.80%      0.033      0.63
09-Sep     L  Hochevar   0.53       0.11       51.40%     -0.014     -0.11
Jul-23     G  Perkins    0.53       0.11       51.40%     -0.014     -0.11
Jun-14     W  Rodriguez  0.53       0.28       73.10%     -0.013     -0.17
Jun-18     C  Meredith   0.52       1.17       91.40%     0.034      0.76
Sep-21     L  Cormier    0.52       1.17       91.40%     -0.003     -0.2
11-Sep     C  Buchholz   0.52       0.12       45.10%     -0.014     -0.12
12-Sep     J  Beckett    0.52       0.12       45.10%     -0.014     -0.12
May-30     M  Guerrier   0.51       0.27       81.80%     0.033      0.42
09-Sep     J  Lackey     0.51       0.11       45.30%     0.015      0.13
09-Sep     Z  Greinke    0.51       0.1        45.40%     0.028      0.22
Apr-15     G  Glover     0.51       1.49       95.30%     -0.016     -0.58
May-23     E  Bedard     0.51       1.89       93.20%     -0.015     -0.44
07-Sep     M  Batista    0.51       0.1        7.20%      -0.013     -0.1
Sep-13     C  Bradford   0.5        0.46       93.40%     -0.013     -0.46
10-Sep     K  Jepsen     0.5        0.11       16.70%     -0.013     -0.11
04-Sep     S  Kazmir     0.5        0.5        3.50%      -0.014     -0.5
08-Sep     Z  Greinke    0.5        0.11       39.40%     0.015      0.13
Jun-13     D  Brocail    0.49       0.11       70.00%     -0.013     -0.11
Apr-14     A  Sonnanstine0.49       0.23       78.40%     0.011      0.21
09-Sep     Z  Greinke    0.48       0.1        16.20%     -0.013     -0.1
Jul-26     J  Masterson  0.48       1.47       93.80%     0.022      0.77
Aug-23     J  Guthrie    0.48       0.13       35.10%     0.014      0.14
Jul-18     G  Smith      0.48       0.38       90.40%     0.033      0.87
07-Sep     S  Dohmann    0.48       0.95       94.70%     -0.011     -0.5
May-21     G  Olson      0.47       0.54       83.50%     0.055      1
Sep-18     J  Vazquez    0.47       0.29       74.40%     -0.012     -0.17
03-Sep     G  Balfour    0.47       0.52       89.80%     -0.016     -0.42
08-Sep     Z  Greinke    0.46       0.29       71.30%     0.017      0.27
07-Sep     J  Hammel     0.46       0.29       71.30%     -0.012     -0.17
10-Sep     J  Bale       0.46       0.1        45.70%     -0.012     -0.1
Apr-13     M  Delcarmen  0.45       0.3        1.60%      -0.012     -0.18
Jun-18     B  Corey      0.45       0.29       79.20%     0.016      0.27
05-Sep     J  Masterson  0.44       0.11       66.60%     0.012      0.13
05-Sep     M  Harrison   0.44       0.15       12.50%     -0.012     -0.15
04-Sep     V  Padilla    0.44       0.15       67.30%     -0.013     -0.15
Jul-20     A  Embree     0.44       0.29       84.80%     0.015      0.27
11-Sep     J  Lopez      0.43       0.3        83.80%     0.015      0.28
Sep-23     J  Litsch     0.43       0.23       78.50%     -0.01      -0.14
Apr-15     J  Howell     0.43       0.5        86.70%     -0.011     -0.23
May-24     C  Silva      0.42       0.11       64.50%     -0.011     -0.11
06-Sep     T  Wakefield  0.42       0.11       64.50%     -0.011     -0.11
07-Sep     S  Feldman    0.41       0.15       54.00%     -0.012     -0.15
Sep-23     B  Tallet     0.41       0.21       86.80%     -0.011     -0.21
05-Sep     C  Wilson     0.41       1.11       2.40%      0.018      0.63
Jul-20     J  Duchscherer0.41       0.11       51.10%     0.012      0.13
May-23     E  Bedard     0.41       0.11       51.10%     -0.011     -0.11
Apr-16     D  Aardsma    0.41       0.29       82.70%     0.015      0.27
08-Sep     G  Balfour    0.41       0.29       82.70%     -0.01      -0.17
09-Sep     E  Jackson    0.4        0.11       63.00%     0.012      0.13
Sep-21     C  Waters     0.4        0.11       63.00%     0.012      0.13
Apr-16     C  Buchholz   0.4        0.11       63.00%     -0.011     -0.11
May-23     E  Bedard     0.4        0.11       63.00%     -0.011     -0.11
Aug-13     K  Slowey     0.4        0.11       46.20%     0.022      0.22
Sep-26     D  Aardsma    0.4        0.95       92.20%     -0.01      -0.38
02-Sep     A  Burnett    0.4        0.11       18.10%     -0.011     -0.11
Jun-29     O  Perez      0.4        0.1        46.20%     -0.01      -0.1
04-Sep     S  Kazmir     0.4        0.1        46.20%     0.012      0.13
10-Sep     J  Bale       0.4        0.1        46.30%     -0.01      -0.1
Jul-31     J  Garland    0.4        0.29       8.40%      0.024      0.43
03-Sep     J  Lester     0.4        0.29       8.40%      -0.01      -0.17
04-Sep     A  Sonnanstine0.39       0.11       20.30%     0.013      0.13
Jul-30     B  Burres     0.39       1.17       93.00%     -0.015     -0.46
Jul-13     A  Burnett    0.39       0.09       46.40%     -0.01      -0.09
Aug-31     S  Downs      0.39       0.29       3.10%      0.019      0.27
Sep-18     H  Ramirez    0.38       0.52       94.30%     0.023      1
Apr-14     A  Sonnanstine0.37       0.1        56.40%     0.096      1
06-Sep     R  Rowland-Smi0.37       0.1        56.50%     -0.009     -0.1
07-Sep     R  Feierabend 0.37       0.1        56.50%     -0.009     -0.1
Aug-26     T  Wakefield  0.37       0.11       61.00%     -0.01      -0.11
Jun-15     R  Oswalt     0.37       0.28       82.10%     -0.009     -0.17
Sep-28     J  Papelbon   0.35       0.64       96.30%     0.02       0.89
04-Sep     S  Camp       0.35       0.93       94.70%     -0.003     -0.22
Apr-16     M  Timlin     0.35       0.56       93.70%     0.024      0.89
03-Sep     G  Balfour    0.35       0.5        89.70%     0.013      0.39
Jul-30     D  Sarfate    0.35       0.29       82.30%     -0.009     -0.17
08-Sep     J  Weaver     0.35       0.11       12.40%     -0.009     -0.11
05-Sep     J  Lopez      0.34       0.29       87.20%     0.012      0.27
Jul-26     C  Hansen     0.33       0.53       95.90%     0.003      0.28
02-Sep     J  Weaver     0.33       0.54       90.80%     0.041      1
02-Sep     J  Weaver     0.32       0.11       73.20%     0.009      0.13
Jun-25     Z  Duke       0.32       0.52       89.60%     -0.008     -0.24
01-Sep     A  Lopez      0.31       0.13       78.70%     0.008      0.14
Sep-15     M  MacDougal  0.3        1.17       95.50%     -0.013     -0.46
Sep-28     H  Okajima    0.3        0.56       92.10%     -0.008     -0.26
May-27     B  Burres     0.3        0.13       76.80%     0.071      1
Jul-13     A  Burnett    0.3        0.44       1.20%      -0.007     -0.21
12-Sep     B  Tallet     0.3        0.44       89.50%     0.041      1
01-Sep     J  Crain      0.29       0.27       2.30%      -0.007     -0.17
08-Sep     J  Arredondo  0.28       0.51       1.80%      -0.007     -0.24
03-Sep     J  Lester     0.28       0.54       2.20%      -0.007     -0.25
06-Sep     R  Messenger  0.27       0.5        96.60%     -0.008     -0.5
02-Sep     M  Garza      0.26       0.5        92.10%     0.01       0.39
06-Sep     J  Howell     0.26       0.54       93.60%     -0.007     -0.25
Jun-24     T  Gorzelanny 0.25       0.11       7.50%      -0.006     -0.11
Sep-14     E  Jackson    0.24       0.11       79.50%     -0.006     -0.11
01-Sep     A  Lopez      0.24       2.46       97.20%     0.009      1
12-Sep     A  Brown      0.21       0.44       94.10%     -0.005     -0.21
04-Sep     J  Litsch     0.2        0.11       86.20%     0.011      0.23
12-Sep     B  Wolfe      0.2        0.81       96.80%     -0.004     -0.34
03-Sep     T  Percival   0.2        0.32       96.50%     0.028      1.78
04-Sep     S  Kazmir     0.19       0.1        6.90%      0.007      0.13
Jun-21     J  Burton     0.19       0.54       1.10%      -0.005     -0.25
Jul-18     G  Smith      0.18       0.25       93.70%     0.002      0.09
Sep-14     D  Price      0.18       0.29       92.10%     -0.005     -0.17
Jun-15     W  Wright     0.18       0.45       97.20%     0.023      2.66
11-Sep     J  Nathan     0.17       0.27       0.60%      -0.004     -0.17
May-24     C  Silva      0.17       0.11       89.30%     -0.005     -0.11
Aug-26     J  Papelbon   0.17       0.25       0.60%      -0.006     -0.25
May-20     D  Cabrera    0.17       0.54       2.50%      0.008      0.39
Apr-17     J  Beckett    0.17       0.11       2.10%      -0.005     -0.11
11-Sep     K  Calero     0.17       1.9        0.60%      -0.004     -0.57
09-Sep     Y  Yabuta     0.16       0.26       0.50%      0.009      0.26
11-Sep     D  Aardsma    0.16       0.25       95.70%     -0.005     -0.25
03-Sep     B  Tallet     0.16       0.25       1.40%      -0.005     -0.25
Aug-20     B  Tallet     0.16       0.44       94.90%     -0.004     -0.21
Aug-17     J  Newman     0.14       0.38       97.10%     0.009      0.62
04-Sep     J  Hammel     0.14       0.1        0.40%      0.008      1
Apr-14     S  Dohmann    0.14       0.23       94.90%     0.003      0.21
02-Sep     J  Hammel     0.13       0.5        96.60%     0.018      1
02-Sep     J  Wright     0.12       0.52       98.50%     0.013      2.59
Sep-14     D  Price      0.12       0.54       97.10%     0.009      0.63
Jun-22     M  Lincoln    0.12       0.54       97.10%     0.004      0.39
Jun-27     S  Schoeneweis0.12       0.32       97.30%     0.001      0.12
Jun-27     P  Feliciano  0.12       0.29       1.40%      0.012      1
Aug-20     J  Carlson    0.12       0.23       94.80%     -0.003     -0.14
09-Sep     J  Bulger     0.11       0.23       96.50%     0.003      0.21
Aug-21     R  Halladay   0.11       0.23       1.80%      -0.003     -0.14
Apr-14     S  Dohmann    0.1        0.5        97.00%     0.004      0.39
Jun-17     R  Wolf       0.09       0.34       97.90%     -0.003     -0.34
01-Sep     C  Fossum     0.09       0.61       98.10%     -0.002     -0.28
May-21     D  Sarfate    0.08       0.29       96.30%     0.005      0.43
Jul-21     B  Bonser     0.08       0.29       96.30%     -0.002     -0.17
Jul-28     R  Bierd      0.08       0.25       0.70%      -0.003     -0.25
Jul-27     M  Delcarmen  0.08       0.56       0.50%      0.004      0.4
Jul-18     D  Braden     0.08       0.29       96.90%     0.015      1
May-21     L  Cormier    0.08       0.71       98.60%     0.007      1
May-20     D  Cabrera    0.07       0.54       0.80%      -0.002     -0.25
10-Sep     H  Nomo       0.07       0.1        96.20%     0.021      1
Sep-17     S  Linebrink  0.07       0.11       96.50%     0.019      1
Jul-21     C  Breslow    0.07       1.17       98.90%     0.001      0.38
Jul-22     B  Bass       0.07       1.17       98.90%     0          -0.2
Jul-31     J  Garland    0.07       0.29       0.80%      -0.002     -0.17
08-Sep     J  Hammel     0.07       1.17       99.10%     -0.003     -0.46
04-Sep     C  Bradford   0.06       0.26       0.40%      -0.002     -0.16
Jul-28     J  Guthrie    0.06       0.46       0.30%      0.002      0.34
May-21     L  Cormier    0.06       0.95       99.30%     0.001      0.27
03-Sep     J  Frasor     0.06       0.52       0.20%      -0.002     -0.52
Sep-18     L  Broadway   0.06       0.63       99.30%     -0.002     -0.63
12-Sep     J  Frasor     0.05       0.21       98.50%     -0.001     -0.21
Aug-17     J  Newman     0.04       0.46       99.30%     0.001      0.34
11-Sep     R  Halladay   0.04       0.3        0.10%      -0.001     -0.3
02-Sep     D  O'Day      0.04       0.56       99.30%     -0.001     -0.31
Apr-19     J  Johnson    0.04       0.13       0.40%      0.002      0.14
May-26     C  Bradford   0.04       0.13       0.40%      -0.001     -0.13
11-Sep     J  Duchscherer0.03       0.09       0.60%      -0.001     -0.09
May-20     D  Cabrera    0.03       0.25       0.20%      0.004      1.87
May-24     B  Morrow     0.03       0.63       99.70%     0          0.17
Jul-23     J  Nathan     0.03       0.54       99.50%     -0.001     -0.25
Jun-17     C  Guevara    0.02       0.71       99.70%     0.001      0.26
Jun-25     T  Beam       0.02       0.11       99.10%     0          0.13
May-23     R  Dickey     0.02       0.52       99.80%     0          0.93
Jul-31     J  Arredondo  0.02       0.25       0.10%      -0.001     -0.25
Jun-27     J  Smith      0.01       0.5        99.80%     0          -0.5
Jul-30     F  Cabrera    0.01       0.54       99.80%     0.001      1
Jun-17     C  Guevara    0.01       0.29       99.60%     0.001      0.43
Aug-21     R  Halladay   0.01       0.44       0.10%      0.001      0.37
Jul-31     D  Oliver     0.01       0.29       0.10%      0          -0.17
Jul-18     L  DiNardo    0.01       0.25       99.70%     0          -0.25
Aug-17     J  Fulchino   0          0.34       99.90%     0          0.91
Jun-15     O  Villarreal 0          0.28       100.00%    0          0.27
Jun-24     F  Osoria     0          0.11       0.00%      0          -0.11
Jun-17     C  Guevara    0          0.46       100.00%    0          -0.46
Aug-27     M  Timlin     0          0.11       0.00%      0          -0.11
03-Sep     J  Lester     0          0.11       0.00%      0          -0.11
08-Sep     K  Jepsen     0          0.11       0.00%      0          -0.11

So how did he do?

Not as badly as you may think. A-Rod's highest LI plate appearance was on Aug. 15 against the intrepid Royals. It was the bottom of the ninth, two out, two on (first and second) with the Yankees behind 4-3. Yup—that checks the "clutch" box! The boy came through with an infield grounder up the middle that got him to first and moved both runners over. Job done, although New York went on to lose the game.

The second highest LI appearance was less successful. In a similar situation against the Blue Jays but with no outs A-Rod grounded into a double play. The Yankees lost that one too.

The next highest, against the Angels, was slightly better. LI was 4.46 and A-Rod reached, albeit on error. If we continue to go through play by play we'll be here all day. We need a more systematic approach. Check the table below:
LI         Number   Success    WPA
>3         28       0.464      -0.547
1.5-3      110      0.427      -0.195
1-1.5      138      0.391      -0.642
<1         348      0.44       1.871

By segmenting A-Rod's plate appearances into strata we can try to isolate how well he did in high LI (i.e., clutch) situations. We use an LI of 3 as a cut-off—this represents the top 5 percent of clutch PAs. In the table above, number is the number of instances of that LI situation occurring. Success is the number of times WE went up as a result of A-Rod's endeavours. It proxies to OBP but doesn't equal OBP as the list contains instances of A-Rod stealing or being picked-off. WPA is the sum of WPA in the stratum in question. A positive number is good.

What does all this mean?

Let's look at the numbers. In the highest LI stratum, "success" is above average (0.427)—in fact it is the highest of any strata. However, cumulative WPA is a disappointing -0.547. Compare that to the lowest LI bracket where WPA is 1.871. A word of caution: Be careful comparing the WPA data. First, the WPA is driven by leverage. In other words, high LI situations result in the biggest swings in win probability. Similarly the higher number of "trials" for lower LI events will drive cumulative WPA. (Note: Li x WPA is sometimes used as a metric to adjust for this issue.)

Superficially A-Rod did better in the clutch than he did in non-clutch situations, although WPA does not reflect that. However, the margin is small&mash;one walk-off HR could account for the WPA swing in the ">3" bracket. How else can we judge A-Rod's 2008 performance?

One way we can determine how good he was in the clutch in 2008 is to compare his performance to other years. Fangraphs gives LI and WPA data back to 2002. If we produce the same table for the years 2002-2007 we get the following:
LI         Number   Success    WPA
>3         128      0.422      1.004
1.5-3      684      0.447      7.596
1.5-1      949      0.447      5.713
<1         2666     0.405      10.228

Although we are ignoring aging, park etc., we at least can compare the 2002-2007 data to the 2008 data. First, "success" for the 2002-2007 period is 0.420 vs. 0.427 for 2008. However, in highest LI situations A-Rod performed better in 2008 than he did in previous years when judged by 'success'.

Finally it is also worth noting that that the lowest "success" is for the lowest LI situations.

Is this evidence that A-Rod performs well in the clutch or had a good 2008 in clutch situations? Not really. The simple truth is that clutch ability is impossible to predict and even harder to measure. However, based on this cursory look, Mr. Rodriguez doesn't do too badly in high LI situations. Give the dude a break.

Click here to learn about THT's download subscriptions.]]> John Beamer 2009-04-17T05:01:15+00:00 Five questions: Atlanta Braves http://www.hardballtimes.com/main/article/five-questions-atlanta-braves4/ http://www.hardballtimes.com/main/article/five-questions-atlanta-braves4/#When:05:01:15

1. Will it?


In all honesty, probably not.

In my book (well, the THT Season Preview) the Braves project as an 84-win team this time around;a large dollop of luck is required if they are to challenge the Phillies and Mets for the division. That gives the team a 20 percent chance of winning the division and a further 10 percent shot of making it via the wild card.

However, a look around the interweb suggests that, if anything, those THT numbers are a little conservative. CHONE, which was probably the best performing projection system of all last year, rates the Braves as an 86-win team—even with the Phillies and a game behind the Mets. PECOTA, interestingly, has the Braves as an 87-win team, which is tied with the Phillies but lags the Mets at 92 wins.

Prognostication is a fraught science and must be taken with a pinch of salt. At the start of last year many thought the division was a three-horse race, but the Braves lagged, finishing with only 72 wins, 12 games behind the Marlins. However, with the Mets significantly upgrading by adding two of the best in baseball to their bullpen, closing games shouldn't be a problem.

The only question is whether they can get into position to close games, with Daniel Murphy, Fernando Tatis and Ryan Church patrolling the outfield corners and Luis Castillo taking charge of the middle infield. There is hope.

2. Will the rotation work as advertised?


If the Braves are to challenge, the rotation needs to come good. Wren ripped apart last year's excuse for a pitching staff and rebuilt from the ground up. Please welcome: Derek Lowe, Jair Jurrjens, Tom Glavine, Javier Vasquez and Kenshin Kawakami as your starting five.

Perhaps the most impressive aspect on paper is its depth—Atlanta hasn't had a staff this good for half a decade. Let's start with the old. Jurrjens, who was acquired from the Tigers, was one of the bright spots for the Braves last year. He notched 13 wins and a 3.68 ERA in 188 innings. He's the real deal, too, with at least four pitchers and a fastball that clocks 94 on the gun.

Glavine is the other returnee from last year, although his 2008 was truncated by injury—he hurled only 63 innings. At 43, Glavine must be close to retirement, so let's hope he can sign off in style. If healthy there is no reason why he can't register a league average ERA.

Look up Derek Lowe in Merriam-Webster:

Derek Lowe: noun, workhorse.

Lowe, the sinker ball specialist, has averaged 15 wins a year over the last seven seasons and the last time his ERA was over 3.90 was in 2004, when he was pitching at Fenway. At the very least he should give the Braves innings, which they desperately need from a front-line starter. The only question mark is that his strong recent years were recorded in Dodger Stadium—yes, a pitcher's park.

Vasquez is another workhorse, albeit one with a slight limp. While he has pitched more than 200 innings in each of the last four years, he has had a sub 4.00 ERA only once, in 2007. Throughout his career he has been spotted as a breakout candidate, but the man is 31, for goodness sake. Knowing the Braves' luck, what are the odds that neither Vasquez and Lowe hurls 200 innings? It doesn't bear thinking about.

Kenshin Kawakami is the biggest unknown. So .... um .... what do we know about him? Well, he's Japanese (ed: c'mon, you can do better than that) ... oh, okay, ... and in Japan he has a 112-72 record with a 3.30 career ERA. It is always difficult to translate Nippon success to the majors, but Kenshin figures for a league average starter in the bigs. The only issue I suspect is that it may take him a while to settle in—let's hope there are some decent translators in the dugout.

3. Will Frenchy look better than a Double-A hitter?


With each passing season, this question gets easier to answer. Jeff Francoeur was nothing short of horrific last year, recording .239/.294/.359—and he is a corner outfielder from where you'd expect some pretty decent production! We can discount small sample size. Despite being relegated to the minors, the kid (he's no longer a kid, really) played in an eye-popping 155 games.

Walks and strikes remain a big problem—he swings and misses too much and entering his fifth year of big-league baseball many feel this is his last chance to rescue his career. Here's a quote from Frenchy in late 2006:

"I learned I've got to give up something ... if I'm covering out over the plate and he throws it inside, I've got to spit on it (take the pitch). You can still be a free swinger, but you need to be more selective. You just learn as you go. You learn to realize that's not a good pitch to swing at … I want to keep learning and get to the point where the team can totally depend on me, like they do Chipper (Jones) and Andruw (Jones)."


And this is what Bobby Cox said shortly after:

"(Francoeur) can sit on pitches now. He's sharp kid. He is still working. He is not going to walk an awful lot, which is fine with me where he is hitting in the lineup. And I think if you took his aggression at the plate away from him, I don't think he'd be as good."


Oh, ****.

4. Can Chipper win the batting title again?


yes I said yes I will Yes (to quote perhaps the famous ending of any English language novel).

After missing out on the last day of the 2007 season to Colorado's Matt Holliday, Chipper Jones stormed to the batting title last year with a career best .364 average. That belies a season of two halves. For the first half, Chipper was going like a jet and batted over .400 until mid-June. In the second half, he tailed off but still managed to hit over .300. Let's not forget that all this was at the tender age of 36! Impressive.

Given the providence of luck, a repeat is unlikely. But based on recent form expect Chipper to hit over .300—he's rapidly building a Hall of Fame resume.

5. Will the bullpen finally come good?


Although the Mets have loaded their bullpen with arms, the best and most cost-effective strategy is usually to fill the pen with middling pitchers bookended by a couple of relief aces for those high-leverage innings. The Braves have tried this strategy for the last couple of years but failed miserably

How do the Braves of 2009 fit with this model?

On paper, not badly. However, on paper, the Braves' pen last year wasn't bad and then Rafael Soriano goes and gets hurt pitching only 14 innings. Atlanta's other putative relief ace, Mike Gonzalez, has pitched fewer than 50 innings in two years. Ouch. Peter Moylan, another potential bright light, tossed fewer than six innings in 2008. Two out of those three need to remain healthy for the Braves to lock down close games.

Behind these three Jorge Campillo, Blaine Boyer, Manny Acosta and Jeff Bennett are usable relievers. Once more it's not a bad relief corps ... on paper at least, anyway.

Click here to learn about THT's download subscriptions.]]> John Beamer 2009-03-20T05:01:15+00:00 Statistical Shenanigans (part 2) http://www.hardballtimes.com/main/article/more-statistical-shenanigans/ http://www.hardballtimes.com/main/article/more-statistical-shenanigans/#When:05:01:15
There was a surprisingly positive reaction to my column a few weeks ago about the perils of correlation and regression—it appears that at least some THT readers are closet statheads. In this installment I take a look at how one can think about and interpret baseball analysis, specifically regression studies. This statistical tool is commonly used in sports analysis. Some of the lessons we discuss today also apply to non-regression techniques.

There are five things we’ll look:

  • First, how to determine that regression is the right tool for the job
  • Second, how to detect bias in sample data
  • Third, how to think through the regression equation to work out whether the dependent variables make sense
  • Fourth, how to interpret the results, in particular the coefficients
  • And fifth, what some watch-outs are when drawing conclusions

To help ease the pain we'll use plenty of examples to illustrate the relevant points. Let’s get cracking.

1. The right tool


Irrespective of whether you are poring over a regression, an important question is whether the analyst chosen the best tool for the job.

Regression is a much overused tool that often favored by academics—especially economists—but not always the best approach. Regression analysis is easy and convenient: you have a bushel of data and are unsure of the interdependencies—a natural tendency is to chuck it into a regression and see what falls out. This approach usually causes odd results.

The first question to ponder is whether a regression analysis is required. A perfect illustration is the use of regression to calculate linear weights (LWTS). The LWTS equation we want looks something like this: 
LWTS = a + b*1B + c*2B + d*3B +e*HR

The a,b,c,d coefficients represent the value of the various hitting events. Running this equation for 2002 game data gives the following coefficients: 
a = -4.5 (this is average runs per game)
1b= 0.47
2b= 0.81
3b= 1.09
HR = 1.43

Number of game in regression = 2,426

Superficially, the events appear weighted correctly. But they are not—both doubles and triples (which are relatively less common) are overvalued—this is a foible of the regression. If you do this for different years you’ll see the less frequent events jump around in value quite a lot, which is nonsensical. The valuation for triples is especially nonsensical. In some years it is close to a home run in value while in others it is closer to a double. The triple's rarity creates statistical noise in the equation, resulting in an error.

How do we know whether to use regression or a different tool?

The answer lies in having a deep understanding in whatever it is you’re trying to model. For instance, scoring in baseball depends on how many men are on base, which base these men are on, and how many outs are left. We can represent each base/out combination as a different state. If you have some mathematical background you’ll realize this could be modelled as a Markov process. On the other hand, thinking about baseball as “getting on base” and “moving runners over” will lead you to BaseRuns.

One tip is to identify how linked the outcome is to the dependent variables. In our LWTS example it is clear that hitting events are closely tied to offensive contribution, which speaks to a more fundamental (non-regression) model. Were we trying to link two things with no obvious link—say, payroll to wins—regression is probably better suited.

When a causal link is less obvious, regression probably will be a better right too. For instance, some of the work that Colin Wyers has been doing on wins and MRP. Regression should always be a second or third answer, never the first.

2. Bias in the Data


Biased data is one factor that will invariably lead to wonky conclusions—this affects all types of analyses, not just regressions. Selection bias happens a lot more frequently than you might imagine. Every time a study imposes an at-bat cut-off (or any arbitrary cut-off), bias is an issue.

Consider Jim Albert’s age-curve analysis. Jim’s study tries to work out age curves for ball players and tease out how it changes by decade of birth. Jim concludes that overall average peak age is 28.4 but for some decades, for example hitters born in the 1960s, it crept up to 30. That is substantially different from the oft quoted and generally accepted age peak of 27. What’s going on?

A 4.0 GPA isn’t required to work out that we have selection bias!

If you look at the criteria that Jim uses to select hitters he has a 5,000 career PA cut-off. This means that only very good hitters qualify for the study. In fact, we’d expect that the best hitters, those with 5,000 PA, probably have longer careers because either they peak later or don’t have a particularly steep drop-off.

Tellingly, the number of players included in the study rarely rises above 100 per decade. Given that thousands of players register at-bats every season, players with at least 5,000 plate appearances are the elite of the elite. Jim’s study is not finding the age curve of your average hitter, but rather finding the age curve of a select group of uber-batters.

Players who are less good may peak earlier, or may have a steeper decline phase that dramatically alters the shape of the age curve. To account for this effect, a different study needs to be run. In fact, a rigorous analysis would cut the data by position, handedness, and talent (based on regressed OPS) to work out age curves for different player types.

This example is typical of many analyses. Remember that any criteria used to choose a sample, either before or after the data have been captured, has potential to introduce bias. If this happens the insight and conclusions must be caveated appropriately.

3. Making sense of the variables


An analysis using the wrong variables is about as useful as a credit card, sans credit. There are no shortcuts except common sense and logic. Here’s a slightly facetious example that proves that team OPS and team ERA have nothing to do with wins. Don’t believe me? Run the following regression (go on, do it): 
Wins = a*winning% + b*teamOPS +c*teamERA

It will spit out:
Wins = 162*winning% + 0 *teamOPS + 0*teamERA

As promised teamOPS and teamERA are completely unrelated to wins!

The problem, as I’m sure you can see, is that winning% absorbs the effect of OPS and ERA—in other words, the model suffers from what statisticians pithily call multicorrlinearity. It is an obvious problem, but would still exist (more subtly) if we swapped winning% for runs scored and runs allowed. Someone who knew nothing about baseball would infer that OPS and ERA aren’t important.

This isn't the only problem—the equation is tautological (wins=game*win%). Sometimes it is almost impossible to get around this issue.

A couple of years ago David Gassko penned an article about why teams outperform Pythagorean records, which contained this subtle flaw (sorry David). He concludes that one of the main factors is performance of the bullpen in close games—he uses a regression to calculate the effect, which he terms leverage.

However, he uses saves as an independent variable. Of course, teams that rack up more saves will win close games and outperform their pythag—by definition a save equals a win in a close game. Using a variable dependent on winning in close games to determine win difference from Pythag isn't correct. A better approach would have been to use a metric like relative bullpen ERA, which isn't polluted by the save stat. David, Guy and I had a good discussion on Ballhype about this.

Unfortunately there is no recipe to wheedle out unsuitable variables, but rigor and thought go a long way. I find it useful to apply three tests.

  • First: Look at the independent variables and ask whether they are correlated with each other, or indeed if they are closely and obviously linked to the dependent variable. If they are, omit some, or run a correlation and see.
  • Second: Systematically go through each variable and work out what effect it is testing. Try to build a counter argument to see how robust the hypothetical effect is—identify the weak spots in the approach and address them in your commentary.
  • Third: Take a blank sheet of paper and ask what over variables you’d possibly include. Refer back to the model to see if anything is obviously missing. Apply 1) and 2) above to make sure they will add new information to the model. If so include them; if not don't.

Let’s try this approach by looking at a study that attempts to regress the independent variables below on attendance.
1) Games Behind (sum of GB for both teams)
2) Whether the game is a weekend game
3) Whether the game is a night game
4) Population of home city
5) Unemployment rate of home city
6) Per Capita Income of home city
7) Distance between the teams' two cities

Okay. Let’s apply the first test which is to work out whether any of the independent variables are correlated. Here are a few potential snares:

  • Sunday games tend to be day games, so there is a chance that variables 2) and 3) capture part of the same effect
  • The population of a city is likely to be related to per capita income (money attracts money), which means 4) and 6) could be ambiguous
  • Ditto for unemployment—a richer city will likely have lower unemployment, which throws 5) and 6) together

These points feel relatively minor so we’ll live with them for the time being—if I had to tinker I’d probably drop unemployment rate. Now let’s take apply the second test and look at the rationale for including each variable:

  • I’m not sure the sum of GB for both teams drives attendance. If a contending team is one game out of lead but their opponents are 30 then I’d bet that attendance would still be healthy. At the very least this needs to be separated into two variables.
  • Fans are more likely to flock to the yard when they aren’t working, i.e. at weekend or in the evening. Variables 2) and 3) make sense to me.
  • Cities that have more people or that are richer are likely to attract more fans—this accounts for 4), 5) and 6).
  • Distance between the two teams could be a bit of a red herring. Would Braves fans travel to Chicago but not San Francisco to watch their team play? Probably not—this doesn’t feel like a real effect for me

The final test is to think through whether there any are other variables missing (this is really is the realm of brainstorming). Here are a couple of ideas:

  • Ballpark age is likely to be a factor, especially in recent times
  • Ballpark amenities: Concessions, parking, proximity to public transport
  • Depth of fan base (there are Yankees fans everywhere)

Some of these are difficult to measure but the point is that by following a reasonably rigorous approach it is easy to pinpoint the strengths and weaknesses of a model. It’s amazing how far a little thought goes.

Arriving at the best regression takes time. The discerning analyst should try multiple combinations of different variables to see how coefficients and significance changes across different models. This will give a sense of what variables should be included and which omitted.

4. Results and interpretation


Exhaustion might be setting in, but our most important work is still ahead. Correctly interpreting the results is the trickiest part of any analysis, regression or otherwise. There are two steps: First, understand the structure of the regression equation to allow correct interpretation, and second, develop the insight from the results.

Understand the equation

If the regression equation is complicated (e.g. is a logarithm or a logit) then some mathematical gymnastics may be required to translate the coefficients into something meaningful.

Let’s take a look at a simple example. Suppose you saw the following correlation (more details here):
ln(player salary) = 3.681*OBP + 2.175*SLG + …..

The 3.681 coefficient is meaningless because the dependent variable is a logarithm. Say we want to find out the impact of a 0.100 OBP increase we have to take the exponent of 0.368, which gives 1.44. This means at extra .100 points of OBP increases salary by 44%. It is always helpful to read the notes to the equation so you know how the authors have presented their results.

Once you have a feel for what the coefficient means you can interpret the equation and start to test some of the underlying assumptions. For instance, take David Gassko's DIPS 3.0 equation:
DIPS ERA = (-0.041*IF+0.05*GB+0.251*OF+0.224*LD+0.316*BB-0.12*SO+0.43*HBP)/IP*9

The GB coefficient, for instance, says that for every extra GB a pitcher gives up DIPS ERA increases by 0.05/IP*9. Think about that for a second. The assumption is that all hurlers give up ground balls in a similar way. If hurler A only gave up hard ground balls that found the gap while hurler B gave up soft worm burners that always lollygagged to the shortstop then we expect a difference in the GB contribution to ERA. However, although some pitchers can induce softer grounders than others, the effect is probably small and can be ignored.

Develop the insight

The next step is to develop the insight. This is largely predicated on deep understanding of what you are analysing—there is no substitute for expertise. A skeptical eye is critical, so always look for alternative explanations.

A good example is the Jim Albert paper we discussed earlier. If you cast you mind back you’ll remember that Jim concludes that peak age has increased in recent decades. The obvious explanation is better health, fitness and nutrition.

A good analyst won’t be satisfied with that. Are there any other reasons? Yes—absolutely. Jim defines peak age using linear weights per plate appearance. We know that over the last 15-20 years run scoring has increased. Could this be responsible for part of the age effect we are seeing?

If run scoring has become easier then we’d expect LWTS/PA to drift upwards over time—this would shift the age curve. Given the spate of power hitting since the 1990s this is a likely explanation. This explanation is equally as plausible as better nutrition.

Another example is a ludicrous paper on home field advantage (HFA) in baseball by a couple of professors at Georgia Southern University.

They model the propensity to win at home by looking a bunch of variables, including runs, runs squared (why?), one-run games, two-run games, and roster size (25 vs 40 man). The main conclusion is that HFA is more prevalent in close games (one or two run games) than in games where three or more runs are scored.

I’m sure you’ll agree that this conclusion seems a bit suspicious. Why should HFA evaporate in blowouts? It doesn’t make sense. Do the authors proffer any explanations? No. When there is no rational explanation for the result then that is a signal that something with the data or the study isn’t right.

A deeper look at the study reveals that the authors forgot about the impact of the bottom of the 9th and extra innings. This means that home teams are more likely to win by one or two runs than they are by three or more as, Tom Tango discovers.

There are no hard and fast rules on interpreting studies, and it is not uncommon for people to interpret the same results differently. It is often helpful to think through under what conditions the model would throw up exceptions. For instance, PrOPS, which is a regression model, underrates speedsters (they are more likely to turn a ground ball into a base) and overrates sluggards.

Check whether the results pass the sniff test and proceed to think up a bunch of possible counter arguments. Then use your baseball intuition and knowledge to pass judgement. If the answer is ambiguous, then either the wrong question was asked or more work is required. Issues normally lie hidden in the methodology or data selection so revisit those parts of the analysis for ideas of what may be wrong. Most important of all remain deeply dissatisfied.

5. Other watchouts


There are a couple of other tips and tricks worth knowing that we haven’t covered, namely statistical significance and effect size, and standardized coefficients.

Statistical significance and effect size

People bandy around correlation and regression as statistically significant without really understanding what it means. Significance is the confidence that we have in the results—and is calculated from standard errors. The higher the sample size, the lower the standard error so if we have a lot of data points it isn’t too difficult to demonstrate significance.

However, significance is irrelevant if the effect is small. Consider, say, a hypothetical study trying to work out a link between HFA and team OPS. Such a study may yield an equation like this, which has all coefficients significant at the 1 percent level:
Team_OPS = 0.001*Home_Team + …

Great. But check out the Home_Team coefficient. The equation says that we expect that HFA accounts only for one additional point of OPS (remember this is all fabricated). It may be significant but the effect is negligible to the point that it is not worth worrying about.

Sometimes effect size and significance work in the opposite direction too. An analyst can detect a strong effect that, because of data inadequacy, doesn’t appear significant. This is not an excuse to blindly disregard the effects fail our significance test—with different data or sample the effect may become valid. The trick is to keep an open mind and try to understand what is possible.

J.C. Bradbury’s DIPS study analyses which pitching components have the biggest impact on ERA. He does regressions for each year and concludes that, in general, BABIP is rarely statistically significant True, but a glace at table 6 shows that although BABIP coefficient is always negative and in many cases would be significant at the 10-20 percent level. This indicates that BABIP does loosely influence ERA, and indeed further DIPS studies have borne that out.

Standardized coefficients

Also in some analyses it is difficult to compare the relative impact of two variables. Imagine if you wanted ran a regression to see the impact of height and weight on the amount of food someone eats. From the equation that is spat out it is difficult to assess the relative impact of the two dependent variables because they are calculated in completely different units. Standardized coefficients adjust for this. A standarized coefficient will tell you that how many standard deviations the independent variable will change with a one standard deviation change in the dependent variable. It normalizes for both units and variance.

For instance, consider an equation that appeared in the THT 2007 annual that attempts to work out whether hitters or pitchers have more say on whether a pitch outcome is a groundball or not:
Match-up GB% = 0.67*hitters GB% + 0.33 *pitchers GB%

The author goes on to conclude that this means that hitters have a bigger influence on outcomes than pitchers do. However, this isn't necessarily true. First, it isn't anchored to the mean, as match-up GB% is a scale from 0-100, where 0 is scaled to the pitcher's GB% and 100 to the hitter's GB%. This means that every single batter pitcher match-up is using a different scale (based on their relative GB%). The right way to do this would have been to anchor the result around the mean—imagine a match-up between a .300 hitter (above average) and a .300 pitcher (below average) ... our expected result is not .300. The correct representation is +0.03 for the hitter and -0.03 for the pitcher.

Also the higher coefficient for hitters could simply reflect (indeed, likely reflects) greater variance among hitters than pitchers. The hitter coefficient tells us that a 1 percent GB-rate increase for hitters adds 0.67 to match-up GB%—however, that does not imply that hitters have more "influence" on GB outcomes—just more variance. Imagine if we take a 100 such match-ups all on a scale between 0 and 100 (these marks are not anchored). If the hitter GB% has more variance, then we'd expect the results to be closer to 100 more often than not—this is what the regression found. The fact that we refer to groundball pitchers rather than groundball hitters is a big clue. Use of a beta coefficient, which adjusts for variance would correct this.

One final point is to be careful about what conclusions you draw. The old adage that correlation is not causality is very true. Doing this stuff it isn’t too difficult to look like a complete ass. That is something I try to avoid … and you should too!

In summary

Phew. That was tough work, right? Anyway, I hope you’ve learned a thing a two about regression and will cast an even more eagle eye over any baseball analysis you see in the future, regression or otherwise.

Click here to learn about THT's download subscriptions.]]> John Beamer 2009-03-19T05:01:15+00:00