Tag: Sabermetrics

Theo Epstein: Boston Red Sox GM Routinely Commits Sabermetric Heresy

Theo Epstein is the general manager of the Boston Red Sox.  He is also a practitioner of sabermetrics. 

At least that’s what we’re lead to believe.

After all, Bill James has been a senior advisor for the Red Sox since 2002.

You see, in the Church of Sabermetrics, Bill James is the deity. Many of his followers believe that he was begotten, not made, and that he will reign eternal in the halls of Strat-O-Matic Valhalla.

Their “good book” is divided into an old testament, Baseball Abstract, and a new testament, The Bill James Handbook.  When in groups, they speak in a tongue that is incomprehensible to most heathens.

Pythagorean Winning Percentage

Win Shares

Runs Created

Range Factor

What looks like pure gibberish to the non-believer is sacred text to the SABR-phile.

Fortunately, for those individuals unable to grasp the majesty of it all, along came Michael Lewis, a modern-day Martin Luther.  He translated the complexities of James-speak into a language most people could easily understand. 

Lewis’ book, Moneyball, made the abstract tangible.  The masses were no longer in the dark. 

Moneyball taught us that a walk was as good as a hit and that objective values can be used to find a player’s true worth. 

Chubby guys that could work a pitcher are now sexy.  No longer does it matter if a guy “looks” like a ballplayer—he needs the numbers.  To quote Billy Beane in Moneyball: “We’re not selling jeans here.”

What is being sold is a philosophy—one that favors empirical data over gut reaction.  Some owners and general managers buy into it with fervor.  Epstein purports to be one of those people.

The fact is, he waivers from James’ philosophy so frequently that, in my opinion, he is a SINO (Sabermetrician In Name Only).

In many ways, Epstein is a bit of a paradox: he does a good job drafting and building talent for the future based off very little except college and high school production and scouting reports.  Evidence of his success: Dustin Pedroia, Clay Buchholz, Jonathan Papelbon, Jacoby Ellsbury and Daniel Bard.

Yet, when a player has a body of work in front of him that includes significant major league experience, Epstein appears to deviate tremendously from sabermetric philosophy by allowing emotion to cloud his vision and, in the process, overvaluing that player. 

And it’s not just a few isolated sins; he has a litany of offenses that require much penance. 

The list is long, yet undistinguished:  Matt Clement, Edgar Renteria, Wily Mo Pena, J.D. Drew, Coco Crisp, Julio Lugo, Daisuke Matsuzaka, Eric Gagne and John Lackey, just to name a few.  Add Josh Beckett’s contract extension to that list, too.

Now, I’m not saying that all of these players lacked value at the time.  Some did; others did not.  But all of the players were grossly overvalued by Epstein.

And that’s the crux of the matter.  James’ philosophy looks for the true value in a player.  Implied in that philosophy is to not pay above market value for a player.  Epstein has violated this principle repeatedly.

Take Carl Crawford this year. He is a good ballplayer, but is he really worth a $142 million contract? 

I don’t think so.

But Epstein signed Crawford as a way to renew interest in the Red Sox after a lackluster performance on the field and in the ratings last year.  In sabermetric terms, the move made no sense—Crawford’s added value was not on par with the size of the contract. 

However, when you have the funds, sense sometimes takes a back seat to politics.  Epstein might not be selling jeans, but he is trying to sell tickets.

What this tells me is that Epstein is human.  And as such, when put in a situation where he has more cash to throw around than his competitors, he will play loose with the sabermetric ideal and give in to temptation.  In other words, he has a larger margin of error that forgives lapses in judgment.

I don’t fault him for this; it’s his prerogative. 

Indeed, sabermetrics is for the have-nots with no money (and to help members of the BBWAA make Hall of Fame decisions).  But for the landed gentry, sabermetrics is a convenient little way for them to pretend to be hip, in much the same way that a wealthy person might have a “Stop Global Warming” bumper sticker on his Cadillac SUV.

As for Theo Epstein, he should quit committing heresy against sabermetrics and renounce his faith. Perhaps, he can be like Henry VIII who separated the Church of England from the Vatican. 

Once excommunicated from the Church of Sabermetrics as being the anti-Beane, Epstein can base his church on this simple philosophy: I have money; you don’t.  Deal with it.

It may be harsh, but at least it speaks the truth. And as the saying goes, the truth will set you free.  

So, set yourself free, Theo.  Set yourself free.

Do I get an Amen?

Read more MLB news on BleacherReport.com

The Value of Sabermetrics: An Author’s Perspective

Co-author Alan Hirsch was kind enough to answer and respond to questions and criticisms of his new book, The Beauty of Short Hops: How Chance and Circumstance Confound the Moneyball Approach to Baseball.


Q: Billy Beane didn’t/doesn’t watch A’s games because, in your words, “He can’t bear seeing the damn players muck up what should be a perfectly predictable contest.” 

Don’t all GM’s wish their moves would work out as planned and wish the game was predictable in some sense?  And is the wish and goal of GM’s and sabermetrics in general actually to make things perfectly predictable or to just gain as much insight as possible into who players are and what they are capable of? 


AH:  Yes, GMs are in the business of winning, and when they hire sabermetricians they try to improve their teams’ chances via statistical study. There’s obviously no problem with that.  We were on the Bill James bandwagon early, and we hope the teams we root for find edges wherever they can.  

It’s the excesses we argue against, and the failure to recognize limitations.

Here’s Moneyball’s description of Billy Beane’s perspective: ‘The game can be reduced to a social science…It is simply a matter of figuring out the odds, and exploiting the laws of probability’ because ‘baseball players follow strikingly predictable patterns.’  As for other GMs, I can’t speak for them but I know that many of them watch the games!

Q: I think one of the strong points of this book is your critique of Moneyball. But don’t the problems of Moneyball have more to do with oversimplifying the A’s and their use of sabermetrics into a narrative of stats versus scouts or sabermetrics versus tradition (for lack of a better word)? 


AH: It’s true that the flaws of Moneyball don’t necessarily carry over to sabermetrics.  At the end of the chapter on Moneyball we specifically note that the errors we identified by Michael Lewis and Beane could be their own and thus it would be unfair to judge sabermetrics accordingly. Then we turn to a chapter which discusses sabermetrics more broadly and directly.  


Q: Also, it seems Michael Lewis suffers from a lack of perspective on how sabermetrics influenced the game. It was published in 2003, when sabermetrics was shedding the label of being a dirty word amongst baseball insiders. Shouldn’t Moneyball be viewed differently than sabermetrics?  

AH: It’s true that a lot has happened in sabermetrics since 2003. 

That’s why we have a chapter called “The Third Wave,” devoted to post-Moneyball developments. But your question also raises the relationship between Moneyball and sabermetrics more broadly. Moneyball revolves around Beane’s success with a small budget, principally due to insights allegedly gleaned from sabermetrics. 

If Michael Lewis had just written a book that looked at how some low-budget team succeeded, without introducing a new paradigm for success, it wouldn’t have had nearly the impact it did. 

But if sabermetrics is central to Moneyball, how about the converse—is Moneyball important to sabermetrics? As an historical matter, yes (Moneyball publicized and accelerated the sabermetric revolution), but as an analytic matter, you’re right—you can’t judge sabermetrics by Moneyball. We don’t.  


Q: You focus a lot of attention on Jeremy Brown, the slow catcher drafted in the 2002 “Moneyball” draft, and how he was a prominent figure in Moneyball. But you conveniently fail to note that the A’s took Nick Swisher, Joe Blanton and Mark Teahen in that draft.  Again, I think this points out the failures of Moneyball focusing too much on narrative more so than it points out the failures of sabermetrics. 

AH: We focus on Jeremy Brown rather than Swisher and company because Lewis does, and he does because Brown illustrates his central point: Beane won with little money in large part because sabermetrics enabled him to identify undervalued players. 

The issue is not whether Beane won on a limited budget, which is indisputable, but how he did so. In that regard, Jeremy Brown took on symbolic significance. Beane craved him because of a new paradigm of how to recognize undervalued talent (which was not the case with Swisher, who was widely recognized as a top prospect).

Thus Brown is not just one player whom one general manager misevaluated. In fact, Beane didn’t evaluate him at all—he thought Brown’s college statistics were all he needed to know. 

Brown and Brant Colamarino (another player Beane craved based on statistics despite his non-athleticism) are pretty good examples of one way in which statistics can be over-valued—in this case at the expense of old-fashioned scouting. 

Q: You say, at the highest level Bill James’s doctrine comes down to idea that baseball decision-makers can’t know what they’re doing without numbers.  How can one objectively break down everything that has happened in major league baseball, in a meaningful way, without measuring everything that happened (i.e., without numbers or statistics)? 


AH: We were praising James. 

That’s the part of the book where we talk about how he rescued baseball from a tradition of ignorance. We’re all for objective data. It’s true that elsewhere in the book we give many examples of data that are useless and things that simply cannot be quantified. 

For the record, I am pretty sure that James would agree with a good deal of what’s in the book. He’s publicly expressed misgivings about sabermetrics that track closely some of our criticisms.


SP: You are critical of Voros McCraken’s ideas of Defense-Independent Pitching Statistics (DIPS), the idea that besides strikeouts, walks and home runs, pitchers basically have little or no control over what else happens.

You point out that Sandy Koufax, for example, had a much lower BABiP (Batting Average on Balls in Play) than most other pitchers, therefore McCracken’s theory doesn’t hold water.  His theory was built around evaluating pitchers without looking at hits allowed or statistics that are heavily influenced by hits allowed (ERA, WHIP). 

Yes, certain pitchers are better at preventing hits but that will almost always show up in home runs allowed and strikeouts. 

AH:  It’s not just Koufax, of course. 

We offer substantial evidence to refute the suggestion that pitchers have no control over outcomes except home runs, walks, and strikeouts. It’s no surprise that Mariano Rivera has a low BABiP—all those broken bats tend to produce weakly hit balls. I disagree with your suggestion that BABiP can be dissociated from the other metrics of DIPS; they are all of a piece.  

We discuss all this in the Moneyball chapter.  Michael Lewis argues that, thanks to his attention to McCracken’s idea, Beane was able to identify undervalued pitchers — guys whose ERAs were high solely due to a randomly high BABIP, while their more reliable numbers suggested their true quality. 

In fact, when followed long enough, BABIP is not random—one of the ways pitchers can succeed is by inducing weakly hit balls. As for your suggestion that this skill almost perfectly tracks pitchers’ ability with respect to strikeouts and avoiding home runs, look at Dave Stieb and Catfish Hunter—not big strikeout pitchers and gave up plenty of home runs, but succeeded in large part because of low BABiP.


Q: You bring up the fact that Roger Maris had no intentional walks in 1961 hitting in front of Mickey Mantle, and conclude that one can’t quantify value with precision because of variables like Mantle helping Maris to get better pitches or increasing his opportunities to hit with runners on base and not walk. 

But maybe we can’t quantify the value of these players in terms of overall influence on the team but can’t we quantify the value of these players in terms of their results?  Doesn’t a distinction need to be pointed out there?

Statistically Ben Zobrist was one of the most valuable players in the game in 2008, but that doesn’t mean his value was representative of his skills rather than factors outside his control. 


AH: You can certainly limit yourself to Mantle’s and Maris’ statistics, but precisely the point we were making is just how many variables go into a player’s value that one can’t even begin to quantify.  If you were ranking these two in 1961, how do you factor in what Mantle did for Maris by batting behind him? 

Bill James has said that there’s no evidence suggesting that a player can help the batter in front of him. Mantle and Maris are an apparent counterexample, as we show. But we also show that the extent to which Mantle helped Maris can’t be quantified. I don’t just mean it can’t be quantified with precision. I mean that any effort to begin to estimate it runs into several problems that apply to many sabermetric projects and that have not been acknowledged. 


Q: What about the fact that Maris, by some measures, was actually as good or better in 1960 than in 1961? 

In 1960 Mantle mostly hit in front of Maris, not behind him. And Maris only had four intentional walks in 1960 hitting mostly in front of the rather mediocre Bill Skowron.  Should we question the impact of Skowron on Maris’ performance in 1960, the season in which he was probably more deserving of the MVP award?   

AH: First, I’d take issue with the suggestion that Maris was as good in 1960 as in 1961. His slugging percentage and OPS were significantly better in ‘61, and he hit 22 more homes runs.

In terms of the intentional walks, keep in mind that ‘60 was his breakout season—he was quite ordinary until then. In ‘61, he was the reigning MVP and quickly established himself as a truly feared slugger.  

So if your implication with the Skowron stat is that the zero intentional walks in ’61 wasn’t because of Mantle, I’d respectfully disagree. It’s staggering that, in the midst of a record-breaking home run season, Maris received zero intentional walks. 

But the 1960/61 inquiry is a diversion from out main point. We provide significant data suggesting that Mantle’s presence in ‘61 helped Maris, but we fully acknowledge, indeed emphasize, that the extent of the benefit cannot be quantified.  Moreover, we explain why additional data (from 1960 or 1962 or any other year) won’t help much, if at all.  

This is one of several examples we cite in which potentially important aspects of a player’s contribution simply can’t be measured.


Q: Regarding Ricky Henderson’s baserunning, you point out that many sabermetricians discount what he did to disrupt opposing pitchers and help his teammates at the plate.  You point out that several hitters—Dwayne Murphy, Don Mattingly and Dave Winfield—had their best seasons with Henderson batting in front of them. 

This simply isn’t true. 

Mattingly was as good in 1984 without Henderson as he was in 1985 with him. And Mattingly’s best season was 1986, Henderson’s worst or second-worst. 

Winfield’s best season was clearly 1979, without Henderson. Murphy hit behind Henderson from age 24 to age 29. Is it really saying anything that his best season was one in which he hit behind Henderson? What about the other five, rather mediocre seasons behind Henderson? 

AH: I think if you look at the data comprehensively (and don’t forget Edgardo Alfonzo, who may be the clearest example), you will find that overall players batting behind Henderson seemed to prosper. 

But let’s put this in context. 

For a long time, sabermetricians argued that stolen bases were attempted too often because the negative effect of a caught stealing was insufficiently considered. They were probably right, but their analyses neglected the fact that the threat of a steal might unnerve a pitcher and produce better results for the next few batters. 

Then a prominent sabermetrician wrote an article which did consider this dynamic but nevertheless concluded that Henderson (because he was caught so often) was barely more valuable on the bases than guys who never steal. 

The problem is that, in considering a player like Henderson’s effect on subsequent batters, he ignored several variables. This wasn’t just a failure that can be corrected by the next study.  Rather, there are simply too many variables to consider, and no way to do a prospective study even if you somehow cataloged them all.  More data is not always the answer. 

Sometimes you’re just spinning your wheels and not getting any closer to the truth. I doubt that we’re closer today than we were 20 years ago to quantifying Mantle’s value to Maris or Henderson’s impact on a game. 

You gave a good example why (and we actually made this very point).  Dwayne Murphy had his best years playing with Henderson, but there’s no way of knowing how much of that was for reasons unrelated to Henderson. 


Q: You discuss Babe Ruth’s stolen base attempt in Game 7 of the 1926 World Series.  You write that Ruth’s odds of a successful attempt in that situation were probably anywhere from 20 percent to 80 percent.  And if he’d have stolen successfully, then only the batter at the plate (Bob Meusel) needed to get a hit to tie the game. 

But with Ruth on first, the Yankees needed hits from both Meusel and on-deck hitter Lou Gehrig to tie the game. So Ruth attempting a steal may have given the Yankees a better shot at winning. 

You seem to be guilty of that which you criticize sabermetricians, you fail to take the specific situation. Meusel led the American League in homers in 1925 and posted a  respectable slugging percentage in 1926. Plus, if by chance Meusel got on base (he had posted a .361 on-base percentage to that point in his career), then Gehrig comes up with the tying run at least on second.    


AH: Meusel’s power is actually one of the many variables we consider. 

There are additional variables we don’t consider. Part of our point is that you couldn’t possibly know them all. We argue that numerical analysis simply cannot help Ruth decide whether to steal, which is just one example of the larger point: Sabermetrics generally does not provide much help with respect to in-game decision-making such as whether to steal or bunt. The conclusion to the contrary rests on over-extrapolation from base rate data. 

Look at this way.  If Ruth is a 55 percent successful stealer and sabermetricians find that you need to be successful roughly 75 percent of the time to make a stolen base attempt worthwhile, isn’t it obvious that Ruth should not have attempted the steal?

Actually, no.  

Both the 55 percent and 75 percent figures are highly variable depending on the specifics of the situation—score, inning, pitcher, catcher, and any number of other things…Ruth’s likelihood of stealing the base in that very specific situation was a virtual guess. 

And while sabermetricians can tell us that, on balance, you need to succeed 75 percent of the time to justify the steal, you don’t face “on balance” situations. The percentage needed to justify a steal when Grover Cleveland Alexander is throwing the way he is in a one-run game in the ninth inning—good luck figuring that out. Even if you could, by the time you did the inning would be long over. 


Q: The way I understand it, 75 percent is kind of an estimated break-even point over the course of a season. Obviously you can’t know if a guy is going to be successful that often except through trial and error. If a guy has the speed and baserunning skills, he should utilize it until it’s proven he shouldn’t. 

But I would argue, and I think most sabermetricians would argue, that a guy shouldn’t steal in any specific situation unless he’s almost certain he’s going to succeed, especially when the hitter at the plate has a decent shot for an extra-base hit and an out would end the game. 

Even 80 percent certainty of success wouldn’t have been good enough in that situation. The odds are still probably against the Yankees even if Ruth steals the base and the batter in that situation has a pretty good shot at a game-tying extra-base hit, whether Ruth is on first or on second. 


AH: I disagree that one should always be “almost certain of success” before stealing.

That really depends on inning, score, pitcher, batter, and more.  You’re down one run in the ninth inning, two outs, a singles hitter at bat against a dominant closer—you should be willing to gamble quite a bit. 

What percentage is needed to justify an attempt in that situation? It can’t be known, just as you can’t know the likelihood that the runner will be successful: his overall success rate may be a poor predicter in the specific situation.  T

That’s why when you quibble over the particulars of the Ruth example (e.g., whether Meusel’s OBP of .361 tips the balance), you seem to me to miss our main point. When Babe is standing on first base deciding whether to steal, he has to take into account whether or not the pitcher is holding him on tightly, short-striding or not, throwing fast balls or breaking balls, and a host of other situation-specific variables which he can’t think about because he doesn’t even know.  The decision whether to steal necessarily rests on old-fashioned judgment and intuition.

Q: You seem to argue that from a sabermetric and statistical perspective, Pete Rose doesn’t appear to have Hall of Fame credentials because his career on-base percentage and slugging percentage were both too low and that the primary reason he’s considered a solid candidate statistically is because he played for so long and racked up impressive counting statistics. Context matters and sabermetric stats that attempt to adjust for context suggest that Rose is indeed at least a decent Hall of Fame candidate. Plus, longevity matters to some degree. 


AH: This was the chapter in which we praised sabermetrics’ major contributions but also argued that some people overrate those contributions. We used Rose as a case in point of a player who, with the benefit of sabermetrics, we realize was overrated. 

Just compare his OPS to many players who no one thinks worthy of Hall of Fame consideration. But we also point out that Rose had spectacular intangibles, and these must be taken into account when evaluating a player. Rose is hard to rate right—considerably overrated if you don’t crunch the numbers, considerably underrated if all you do is crunch them. 


Q: You argue that Rose is a Hall of Famer largely because of intangibles. If intangibles are a primary reason why a solid player like Rose should be in the Hall of Fame, why not put someone like Brett Butler in the Hall?  I don’t see how taking intangibles into account makes Rose a Hall of Famer when, as you claim, he doesn’t have the statistics, but intangibles do not make someone like Brett Butler a Hall of Famer. 


AH: Judgments about who belongs in the Hall of Fame are extremely subjective but I’m not sure what we said that you disagree with.  

Both statistics and intangibles are relevant to assessing whether a player belongs in the Hall. Rose clearly belongs (putting aside gambling, an issue we don’t touch) and Butler obviously doesn’t. Rose has better statistics than Butler, and may have better intangibles too. 


Q: You write, “When data trumps all else, you end up…underrating Rickey Henderson and Mickey Mantle.” 

I don’t know many sabermetricians who underrate Henderson and Mantle.  In some respects, sabermetricians argue that Henderson and Mantle were underrated and belong in a tier right at or just below elite-level Hall of Famers like Ruth, Williams and Mays. 

AH: You’re right that by emphasizing OBP, sabermetricians enhanced appreciation of both Mantle and Henderson. 

In context, we were making a specific point about the value of Mantle batting behind Maris and the value of Henderson in unnerving and tiring pitchers. I think we make a strong case in the book that these non-measurable contributions (and, of course, similar contributions by other players) have been underrated by sabermetricians.  


Q: You spend a great deal of time on whether hitters own certain pitchers. I’ve read sabermetricians who argue that while we can’t say for certain whether hitters own particular pitchers, we may be able to determine whether hitters may own certain pitches. 

For all intents and purposes, this may be a minor distinction but a distinction nonetheless. And I think sabermetrics is closer to your view on this subject than you realize. 


AH: This was in the context of whether Joe Torre should have played Enrique Wilson against Pedro Martinez in the 2003 ALCS when Wilson seemed to own Pedro, but based on a very small sample size. 

We talked about the way such decisions were traditionally approached, and contrasted that with how we think sabermetricians would have approached it based on an interesting article by James Click. 

And we proposed a “third way” which synthesized aspects of the traditional approach and Click’s perspective. If you’re saying sabermetricians would actually embrace our analysis, my answer is: I hope so.  We’re not looking to pick fights for the sake of it.  There are any number of places in the book where we express agreement with   sabermetricians.   


Q: You bring up the Minnesota Twins as an example of a very successful anti-sabermetric team in the “Moneyball” era.  I would argue, in a broad sense, the Twins are in fact a “Moneyball” team, although I agree they are not really a sabermetric team. 

Again, I think this points out the flaws of the narrative within Moneyball of stats versus scouts.  Sabermetrics is more about meaningful evidence (mostly statistics) versus seemingly intuitive guessing or meaningless statistics. The Twins and other quality organizations, like the Phillies, fall into neither of these categories.  And most serious sabermetricians will tell you that it’s better to look at no stats than the wrong stats.

AH: We quote Twins manager Ron Gardenhire and their assistant general manager Rob Anthony about their contempt for sabermetrics. Rob Neyer says they show an “utter lack of sophistication regarding statistical analysis.”  In any event, we can agree that they’re doing something right. 

Q: You make the claim that one reason sabermetrics is misguided is because there is not a narrow path or formula for team success. 

I would argue there is.

The formula is outscoring your opponents through good offense, good pitching/defense or both.  There is strong correlation with some statistics and team success. A team doesn’t necessarily have to use sabermetrics to outscore opponents, but I think you have to admit sabermetrics made a significant contribution into which player attributes were overrated and which were underrated. 

AH: Yes, the formula for winning is to outscore your opponent! 

We point out that there’s enormous variety in the construction of successful teams (regular season and postseason alike). 

Teams win with great offense or great defense or both, and offense built around power or small ball or both—every permutation. When you say we “have to admit” sabermetrics has made a contribution to baseball understanding, I’ll go further: we not only “have to” admit it but we do so without reluctance. 


Q: It seems you misinterpret Dayn Perry and Nate Silver’s study on postseason success. 

I don’t think any sabermetrician would argue that luck isn’t a huge factor in winning over the course of 5-7 games. The study was about factors that may influence postseason success, not coming up with a definitive formula for guaranteeing postseason success. 

AH: In a way I hope you’re right, because I’m a fan of Nate Silver—particularly his political analysis. If you convince me that FRAA and WXRL are in fact meaningful statistics, and weren’t used by them tendentiously, I’ll admit the error. But we may want to have that conversation in private lest we put most of your readers to sleep. 

Q: Regarding Ultimate Zone Rating (UZR), I think the section of the book on it speaks to a misunderstanding of sabermetrics as claiming to be final and complete. 

UZR measures something and attempts to adjust those measurements for context.  No one claims that it’s flawless. But neither is watching every play a defender makes. Does that mean we should discount watching games?  In the same way, we shouldn’t discount UZR.  Both ways of analyzing fielders is useful.


AH: I readily agree that just watching defense will not always yield reliable assessments. That point extends to everything. 

We mention a scene in Ball Four when Bouton starts reciting statistics to let his manager know how well he’s been pitching. Joe Schultz says, “Aw ****, I don’t want to see any statistics.  I know what’s going on out there just by watching the games.” 

We do not endorse Schultz! Rather, I agree with what you said earlier—the key distinction is between statistics that are meaningful and those that are not.

With respect to those that are, there’s a question of how meaningful. In the book, we try to show why UZR is not very meaningful. Is it possible that better fielding statistics will be developed that don’t share some of UZR’s flaws? I’m skeptical (so, apparently, is Bill James)—this may be a case where more and more data simply do not help overcome inherent limitations in the measurement.

Q: You write, “We are, needless to say, not opponents of data. To the contrary, as should be clear, we’re prone to traffic in numbers ourselves. But one needs to do so with a healthy dose of skepticism and awareness of limitations. One senses sabermetrics careening almost randomly from one pole to another.  Baserunning and defense are overvalued, then undervalued.” 

But, in a broad sense, that was pretty much the whole point of Moneyball. Players’ market values often careen almost randomly from one pole to another. I think sabermetricians are more aware of its limitations. No one only uses sabermetrics or statistics and most on the scouting side do not avoid statistics. The “holy war” is overplayed, and Moneyball certainly didn’t help to put this “struggle” into the appropriate perspective. 


AH: Sabermetricians are not monolithic. But do many of them overstate the extent to which baseball decisions can be quantified?  I think we make pretty good case that they do. It’s hard to discuss this in generalities, but we give examples throughout the book.

Q: I’ve never known a sabermatrician write or say, “a walk is as good as a hit.”  You make the claim that it was actually Little League that taught us what sabermetricians claim to have taught us. 

But I think most sabermatricians would take a player who posts a high on-base percentage via hardly any walks, especially if that means more extra-base and home run power. How often a player gets on base or how many bases he gains at one time is more important than how a player gets on base. Sabermetricians understand this as well as anyone. 


AH: To be fair, what you’re talking about was in the chapter that discusses sabermetricians’ contributions…Some of their insights did not emerge ex nihilo, and in that context we note that the value of the base on balls was apparent to many observers long before sabermetrics made OBP a point of emphasis.  \

But credit where credit is due and we give credit where appropriate to lots of folks, including Michael Lewis, Billy Beane and (very much so) Bill James. Contrary to what many of our critics (those who have not read the book) assume, and as I think you can attest, Short Hops isn’t a Joe Morgan-like screed against sabermetrics.


Q: You make the common mistake of equating on-base percentage with walks. But it’s  about baserunners and avoiding outs. I don’t know any sabermatrician who is more concerned with how a player arrives at a high on-base percentage than if a player arrives at a high on-base percentage. 

Most sabermetricians would agree with you that Kevin Youkilis was more valuable in 2008-2010 when he walked less but posted a higher cumulative on-base percentage and slugging percentage than in his previous seasons when he walked more. 

In other words, most sabermetricians have always understood that walks and even on-base percentage aren’t the be-all, end-all. 


AH: The Youkilis example was in the specific context of an irony in Moneyball. 

Lewis emphasizes Beane’s annoyance with players who are impatient at the plate, over-valuing power and under-valuing walks. The A’s front office worshipped Youkilis (“The Greek God of walks,” though he isn’t in fact Greek), and we point out that Youkilis became a superstar only when he changed his approach at the plate in the direction that Beane generally opposed.   

Q: You fail to address the fact that team on-base percentage has a very strong correlation with runs scored. I know, correlation isn’t causation, but it’s not just correlation; it’s also reasoning. The more baserunners, the more likely a team is to score runs. But slugging also matters. I’ll get to slugging later.   


AH: Actually, we’re very clear that the emphasis on OBP was a major contribution by sabermetricians. That’s because it correlates with runs scored—that’s what counts.     

Q: When Jack Cust finally got a chance to play regularly, with Billy Beane’s A’s, he slugged .457 during his time with the A’s and Giambi slugged .445 with the A’s.  These are not outstanding slugging percentages, but hardly Eddie Yost and Eddie Stanky, especially when you consider Oakland is not really a home run park. 

In your “cheers” for sabermetrics, you absolutely ignore the second key batting statistic that sabermetrics helped bring to the forefront as the sister stat to on-base percentage: slugging percentage. No sabermetrican prefers players who are like the Eddies and are likely to post higher on-base percentages than slugging percentages.


AH: It’s not true that we ignore SP.

We write, ‘Of course OBP isn’t everything. To many sabermetricians, OPS (the sum of OBP and SP) is the best gauge of offensive production.’

We agree with all sabermetricians that some combination of OBP and SP captures performance better than either statistic alone, while either alone is more revealing than batting average. 

The discussion of the “Eddies” that you refer to involves an effort to answer this question: if Beane values players who are underrated because of high OBP, why hasn’t he acquired more of them? 

Cust and Giambi are examples of such players.  (The fact that they also hit home runs–more power to them, ha-ha.).  The question is why Beane hasn’t found more players like them and continued to get such great bang for his buck.  I think we offer some good explanations (which, for the record, do not denigrate Beane).  

Q: Another aspect of sabermetrics you fail to address in your “cheers” section is sabermetrics’ attention to context, especially position scarcity and park effects. 

The reason the Eddies were as valuable as they were was because they were middle-infielders.  It’s always been harder to find a middle infielder who can actually play even adequate defense in the majors everyday, yet still post respectable on-base and/or slugging percentages. 

A huge contribution of sabermetrics, I would argue at least as important a contribution as bringing on-base percentage and slugging percentage to the forefront, is its attempt to bring context to the world of statistics.

AH: Perhaps we should have talked about that more. We do note that, long before Bill James came along, there were plenty of statistics thrown around. James’ search was for meaningful statistics and to a large extent he succeeded. But later generations of sabermetrics have also produced a plethora of less helpful statistics, as James himself has acknowledged and lamented. 

Q: The last 50-or-so pages of the book deal with the strange occurrences during 2009 Red Sox games, unique qualities of some individual players and other things that help point out that the beauty of the game is its majesty, mystery and colorfulness. 

You imply that sabermetricians (with the possible exception of Bill James) do not appreciate the game’s “majesty and mystery” because sabermetrics reduces the game to numbers. 

Just because someone pours themselves into sabermetrics or has an appreciation for sabermetrics, does not mean they find the mystery and majesty of the game disturbing.  In fact, I would argue the game’s qualities that make it somewhat measurable and somewhat predictable, that makes its strange occurrences even more enthralling. 


AH: We emphasize James’ fascination with all sorts of extra-statistical aspects of baseball, and we certainly don’t say he’s the only one. On the other hand, we give plenty of examples of sabermetricians missing the forest for the trees. 


Q: Some devote themselves to sabermetrics, but that doesn’t mean those people reduce the game to pure numbers and statistics or view players as robots or a series of zeros and ones. 

Bill James defines sabermetrics as a search for objective knowledge about baseball.  Just because some baseball analysts prefer that search for objective knowledge about baseball, does not mean they are closed off to the subjective, the mysterious, the majestic aspects of the game. 


AH: Here’s a quote from the book: “While I still believed that numbers could reveal things about the game that were invisible to the naked eye, my own eyes had glazed over as the combination of fantasy baseball and mathematical arcana conspired to squeeze the life from the game I loved.”  That’s not us talking. That’s John Thorn, a leading sabermetrician. 



Q: With all due respect to you and Mr. Thorn, I see no reason why in-depth statistical analysis and sabermetrics would squeeze the life out of the game unless one is reaching for something that will squeeze the life out of the game. 

I’m not a sabermetrician but I’m very sympathetic to sabermetrics and try my best to learn and understand as many sabermetric concepts as possible. Perhaps it’s because I’m not really a sabermetrican that I don’t understand the joylessness of those sad sabermetricians who are merely watching the game of zombie or robot baseball. 

The predictable and the statistical have taught me a great deal about the game and give me more appreciation of its majesty and mystery, not less.  

I appreciate the unpredictable as much now as I ever have, largely because I have a better understanding, through statistics and sabermetrics, of what the numbers say is supposed to yet doesn’t happen.  The fact that the meaningful numbers usually get things right makes the unexpected events in baseball seem even more miraculous.  

I would argue that sabermetrics in a certain sense is an anti-statistical movement in that it opens the door to the organic parts of the game.  Sabermetrics make baseball statistics into a language and not just cold and limited symbols on the backs of baseball cards. 

AH:If you see no merit in Thorn’s reflection, and disagree that sabermetrics has been taken to excess, I doubt I can convince you.  But you gave Short Hops a thoughtful read and clearly take seriously the issues we raised. That’s all we can ask.


From the Short Hops website: “Alan Hirsch, a visiting professor at Williams College,  is the author of numerous books and articles. His articles on sports  and other subjects have been published in the Los Angeles Times,  Washington Post, Washington Times, and Newsday, among many other  publications. He contributes a regular sports column to Frumforum.”

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Statistic of the Week: WHIP

This is a feature I’d like to start here on the blog. I not only want to use this blog for commentary, but also as a place where sports fans can learn.

Part of that learning will be history lessons—telling stories about events that shaped sports and the people that made them what they are today. But it also involves knowing the terminology, and some of the most complicated of those are statistics.

This section will help you understand what someone means when they throw out some seemingly random acronym.

WHIP stands for Walks + Hits per Inning Pitched. It is a simple sabermetric statistic that is used to show how effective a pitcher is at keeping the opposing team off the basepaths. It is measured by adding the number of hits and walks and then dividing that total by the number of innings pitched.

Naturally, the lower this number is, the better the pitcher should be at keeping the bases empty—and after all, if the other team can’t reach base, they can’t score runs.

The statistic was probably invented by the man who is credited with inventing fantasy baseball: Dan Okrent. Okrent, who designed the game as a way to have fun with friends, created the statistic by using the Strat-O-Matic baseball game and a newspaper. The statistic was originally called IPRAT (Innings Pitched Ratio) and was later renamed WHIP.

Okrent developed the statistic in 1980, and it didn’t take long for the stat to become integrated as a useful tool for fans and baseball professionals alike.

The stat, however, is not without its flaws. In the Wall Street Journal article that credits Okrent for creating the statistic, the Director of Baseball Operations for the Tampa Bay Rays, Dan Feinstein, notes the team ignores the statistic when evaluating players. He gives the following explanation in the article for the organization’s decision:

“Once a ball is hit, the pitcher has no control over the outcome of the play, with the exception of the home run,” Mr. Feinstein explains. “There are too many factors that determine whether or not that ball will be a hit, including ballpark size and dimension, positioning of the defense and ability of his defenders.”

That said, WHIP is one of the more widely accepted sabermetrics in baseball. While there will never be one single, flawless statistic in sports, in context, there are many useful pieces of data. It is up to us, as humans, to properly apply each statistic properly.

I’ll wrap up this post with a list of the leaders in this statistic. Please note that for single-season data, a minimum of one inning pitched per game is required. For career data, a minimum of 1,000 innings pitched is required.

Lowest Single-Season WHIP: 0.7373, Pedro Martinez (2000)
Lowest Career WHIP: 0.9678, Addie Joss (1902-1910)
Lowest Career WHIP (Active): 1.0035, Mariano Rivera (1995-present)

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Moneyball: The Art of Losing With Style in MLB

Moneyball is a baseball film starring Brad Pitt and Oscar winner Philip Seymour Hoffman, and it’s set to open sometime in 2011.

Hoffman will perform as former big league manager Art Howe, and Pitt — one of the most famous people in the universe — will be playing Billy Beane, the “mastermind” general manager of the Oakland A’s.

Can you imagine that? Beane has been so successful in Oakland that a movie is being made about his innovations and triumphs as the A’s leading man. Not only is the film being made, but Beane’s character was given to one of the most recognizable faces in the business — a sex symbol, nonetheless.

And who can blame Hollywood for wanting a piece of this action? Beane has achieved so much during his time in Oakland…wait a second…

Has a Beane-led A’s team ever won anything?

This is Beane’s 13th season as GM of the Athletics, and his club has won the World Series zero times during his reign. Wait, it gets better.

In the previous 12 seasons, the A’s have won zero American League championships.

During that time period, they’ve only appeared in the ALCS once (2006). Beane’s Athletics performed well in that series against the Detroit Tigers…if “well” means getting swept. The Tigers made quick work of the light-hitting boys from Oakland.

Simply put, these results don’t make any sense. They don’t make any sense because Michael Lewis’ Moneyball: The Art of Winning an Unfair Game is likely the most popular baseball book in publishing history. It may not only be the most popular baseball book of all time, it is arguably the most popular book of all sports.

Lewis’ detailed work elevated Beane to a stratosphere never before occupied by a general manager. As far as media coverage and attention, GM’s are often secondary to the skippers that patrol the dugouts of their respective teams.

Thanks to Lewis and Moneyball, things are quite different in Oakland. Beane is the star. The managers (Howe, Ken Macha, and Bob Geren) are puppets manipulated by the front office’s many strings and hindrances. 

The question is: does Beane deserve the stature he has achieved?

Many consider him the best general manager in the game; is he worthy of that distinction?

Well, at the very least, I can’t argue with his ability to evaluate starting pitching. It started with the extremely impressive trio of RHP Tim Hudson (an all-star again this year), LHP Barry Zito (having a bit of a bounce-back season), and LHP Mark Mulder.

Then there was RHP Rich Harden, an incredible but oft-injured talent. RHP Justin Duchscherer has been an all-star, and Beane’s trade for RHP Dan Haren came at exactly the right time in his career.

Today the A’s have a slew of capable young arms, including sinkerballer Trevor Cahill, flame-throwing lefty Gio Gonzalez, workhorse Dallas Braden (of the Perfect Game fame), electric closer Andrew Bailey, and potential long-term ace LHP Brett Anderson.

But the 2010 Oakland Athletics are a mere .500 ballclub. This infusion of impressive arms isn’t leading them to playoff-type success. And why, you ask?

Because Billy Beane teams don’t hit. Not since the steroid star power of 1B Jason Giambi and then-SS Miguel Tejada have the A’s had a lineup for opposing pitchers to fear. Their leading regulars this season are OF Ryan Sweeney (.294 BA) and limited-pop 1B Daric Barton (.279).

Although for Beane, it’s not about batting average; it’s about OBP and OPS. Unfortunately, Oakland’s on-base experts are 25th in the bigs in runs scored. What good is a razor-sharp understanding of the strikezone if you can’t drive in runners in scoring position?

Not much good at all, of course.

While we’re on the topic of offense, I can’t ignore the fact that Beane traded OF Carlos Gonzalez (aka “Cargo”).

Cargo, now an immensely popular member of the Colorado Rockies, is currently leading the National League in batting average at .326. In addition to that impressive average, he has 29 HR, 90 RBI, 20 SB, 86 R, and a .955 OPS.

With those outstanding numbers in mind, Cargo is locked in a nip-and-tuck MVP battle with Reds’ 1B Joey Votto. Both candidates have the statistics to warrant an MVP award, but Cargo is the better all-around player.

If the Rockies find a way into the postseason, in my opinion, Cargo should take home the hardware.

Can you imagine that? Beane, the “mastermind” at the helm of an offensively-starved franchise, traded an all-world talent when he was just 23 years old. Even worse, he traded Cargo for a one-year rental in LF Matt Holliday, who was shipped to the St. Louis Cardinals as soon as the wheels fell off the A’s 2009 season. 

Go figure.

And yet, in the end, I know Billy Beane is a talented executive. I completely understand the financial deficiencies of the Oakland A’s franchise. I know that Beane has drafted and developed some excellent major league ballplayers.

But…the best general manager in professional baseball? Really?

Hollywood, a full-length movie, and Brad Pitt? Really?

I’m sorry folks, but I’m not buyin’ it…

Unless Billy Beane is sellin’ it. I’d probably rip him off in a deal.


(John Frascella is the author of “Theo-logy: How a Boy Wonder Led the Red Sox to the Promised Land,” the first and only book centered on Boston ‘s popular GM Theo Epstein. Check it out on Amazon.com or Barnes and Noble online. Follow John on Twitter @RedSoxAuthor.)

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When in Doubt, Grab a Book!

I know this article is a little different and contradicts bloggers in this internet age, but bear with me!

This past weekend I was reading the latest Sports Illustrated Issue—the one with Dwyane Wade, Chris Bosh, and their newest pick-up LeBron James.

I will admit it that I have always been a sucker for a good sports read. Whether it be, SI, ESPN The Magazine, or since I discovered this whole new world we call the business of baseball, books focused of the economics of the game.

In SI’s newest article, a two-page spread featuring Lady Gaga in some sort of bubble bikini or something is on the left, while the right side is full-page writeup, that more or less states a fact that maybe lost on all of us in this generation of  up to the millisecond information.

One worry of the internet is how it could destroy the magazine, or book industry, turning a genuine form of print into a dinosaur.  Contrary to popular belief, “readership is increasing, and adults between 18 and 34 are among the most dedicated readers.”

While everything is done online these days, so is the ordering of subscriptions,  books etc…

Seemingly, the internet age, once thought to eliminate print, is actually helping its growth.

Online searching, increases viewership, thus driving subscribers to new sites.

Simple right…

So, while we are on the topic of subscriptions, reading, and online purchases, I thought I would introduce a book, that some may have read, debated or  have discussed for years, “The Numbers Game” by Alan Schwarz—foreword by Peter Gammons of ESPN.

Anyone interested in the history of statistics for the game of baseball, this is the book for you.

Baseball is a sport so entrenched by numbers that the mention of .406, *61, 190, .367, 755, 56, or even *73, brings along a story or a tall tale within seconds of its mention.

Yet, where did the obsession come from?

Did it gain momentum with Bill James and his abstracts?

Was it brought to light by Allan Roth? Or has it been a fixture in our minds since Henry Chadwick gave it a life of its own?

Either way, it is a, I wont’ say gut wrenching thriller! Instead ,a unique adventure for anyone that loves baseball and the numbers that go with it.

If anyone out there has read it, let me know what you think, I would love to hear your thoughts.

This article can also be found on The GM’s Perspective

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Life Is Not Grand-erson: First Half Woes Continue

The biggest signing of the 2009 Winter Meetings was of course a deal that involved the New York Yankees.

A three-way trade between the New York Yankees, Detroit Tigers, and the Arizona Diamondbacks was the most intriguing, a deal that included the following players;

  • CF Curtis Granderson to the New York Yankees
  • SP Edwin Jackson and SP Ian Kennedy to the Arizona Diamondbacks
  • SP Max Scherzer, RP Phil Coke, CF Austin Jackson, and RP Daniel Schlereth to the Detroit Tigers

The main components of that deal were Granderson, who was nothing short of a star center fielder of the Detroit Tigers, and Austin Jackson, the prized prospect of the New York Yankees.

Nearing the midway point of the 2010 season, the Detroit Tigers have snuck one past the Yanks, so to speak.

Jackson is enjoying a decent rookie season, while only costing the Tigers a base salary of $400,000.  He leads the club in stolen bases (13), ranking fourth in batting average (.306), and coming fifth in OBP (.353).

Granderson, on the other hand, is struggling on the field and costing the Yankees a pretty penny in the bank account—he is owed $5.5 million next year, $8.25 million in 2011 and $10 million in 2012, and his contract includes a $13 million club option for 2013 with a $2 million buyout , (this courtesy of ESPN.com ).

After 56 games, Granderson is hitting .232, with a .309 OBP, while slugging an anemic .412. His OPS of .721 ranks him 46 out of 85 AL outfielders.

More specifically, that ranks him between Juan Rivera of the Los Angeles Angels and Scott Podsednik of the Kansas City Royals.

Granderson has always been known as one of the good guys in the game as stated by Jim Leyland, Granderson’s former manager.

“I think, in my opinion, Curtis Granderson is one of the things that’s all good about baseball in today’s baseball world. He is one heck of a player. He has a great face. He’s very bright. He’s very articulate. He’s everything that’s good about baseball,” Leyland said. “He’s the total package.”

Unfortunately, there is a disturbing trend in Granderson’s ability over the past two seasons that might cost him more than a contract, but playing time in the “BIGS”.

His ability to hit left-handed pitching is becoming a serious problem.

In ’07, he batted .160 against lefties.  In ’08 there was some significant improvement batting .259.  That appeared to be an aberration, as ’09 saw  that number drop back down .183, and presently sees him hovering at .197.

The 2007 and 2008 seasons are where Granderson really started to gain credibility throughout the league.

Playing tremendous and consistent defense is natural for an athlete who has a career .993 fielding percentage committing only 14 errors in almost 1900 total chances.

As mentioned, in ’07 and ’08, Granderson average 22 plus homers a year with 70 RBI, he was on base nearly 37% of the time and had a single season career high slugging percentage in ’07 of .552.

With all this information, you would think his numbers would get better. However, at this point in his career, those numbers have taking a drastic nosedive.

A career .279 hitter prior to 2009, Granderson saw his average drop to .249, although he hit career highs in homers (30) and RBI (71).

The downturn in average has continued this year.

When scouring FanGraphs , two pieces of information stuck out to me that could explain Granderson’s struggles of late.

First, his “O-Swing Percentage” (percentage of pitches a batter swings at outside of the strike zone) is at a career high—23.6 percent.  Meaning, patience at the plate is wearing thin, more or less based on the struggles and pressure placed upon him by the fans and the media.

Second, Granderson’s fly ball percentage has been at the highest they have ever been—49.3 percent in ’09 and 45 percent in ’10, five percent higher than in ’08.

Whether or not Granderson has become homer happy, or that his mechanics need a tweak, we may not know.  The numbers do tell us that something is wrong in his swing or pitch selection is possibly the major factor.

One thing is for sure, playing in New York is a lot different from playing in Detroit.  Every little thing is scrutinized—every out dissected, every boo heard just that much more.

Being a part of a major trade gives Granderson some leeway, as more time will be given to work through this funk. But, once the second half rolls around and the numbers are not moving in the right direction, knowing the Yankees and their history, a change could happen sooner than later.

This article can also be found on The GM’s Perspective

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The Complexity of Articles Is Pushing Fans From Sabermetrics

Newer stats are moving to the mainstream—there is no question about it. The general fans are starting to understand more and more as the years pass. OPS (which is still VERY basic) is starting to become a generally recognized statistic, and we are starting to see better metrics pushed to the masses.

The more that the common sports sites push these statistics, the more the mainstream will begin to understand and accept them.

It is a slow process—which is quite aggravating given the simplicity of what we are trying to get across. One statistic simply tells a much fuller story than another. It’s not too difficult of a concept, and if explained appropriately, people will understand.

This is the case with nearly every “old school” statistic compared to the contemporary ones. Batting average, RBI, fielding percentage, etc… They all tell a much smaller portion of the story than the contemporary numbers.

The problem, I am beginning to realize, isn’t the difficulty of understanding the statistics, but the difficulty in the read itself. People reading about sports don’t want to take time to read long-winded articles with numbers, explanations, and comparisons spread throughout them.

They want their information, and they want it quickly.

It isn’t that the general sports fan is avoiding new numbers for the sake of staying “old school,” but that the people trying to explain the numbers themselves are writing these articles to impress others in the business and have other agendas more important than educating the masses. The more business like the article is and the more in-depth the analysis in the piece is, the more they feel they have accomplished.

Unfortunately, this isn’t the case. Some people stick around and read the articles and learn, and others just skim through them and miss the entire point of the article. It is hard to put blame on the writer for fully explaining himself, but they simply aren’t pushing their product the correct way, in my opinion.

These same people who have alternate agendas are the ones who get upset at those who speak of the sport with flawed opinions.

Well, if you want to educate the masses, speak to the masses. Speaking to yourself and to the rest of the saber-driven community only further segregates the “new” and “old school.”

The reason for the ignorance we see from the “old school” is the smug nature of the “new school.”

I don’t wish to point out certain sites or certain writers to get my point across; they are spread throughout the web.

Not all saber-driven blogs are operated in this nature, and again, I enjoy reading them because I care to take the time out of my day and want to learn as much as I can. However, the masses don’t.

With the attention span of the average American shrinking by the second, the quicker the point gets across the better. The saber community needs to realize that in order to get the masses on your side, you must appeal to them. The appeal is a quick, informative read. Even though it may be difficult to get an entire case study posted in one article, posting a link to that study in a smaller article may be a wiser way to go.

I know, this article is long-winded itself and it seems as if I’m contradicting myself. But in this case, I’m aiming this towards the saber community and they, obviously, don’t have a problem with reading a thousand words and understanding certain opinions.

Paul Lebowitz  (@PRINCE_OF_NY  on twitter) wrote an article today pointing out how pompous some of the “stat zombies,” as he calls them, can be. The knowledge they have is useful, but their means of operation is becoming a problem. Altering the opinions of the general consensus is not easy to do, and rather than doing it in a condescending manner, a more genuine and simplistic approach would be more affective.


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