Analytics all but demands paying the lowest wages the market will bear, even for its analysts. Why smart teams will ignore that demand.
One of the things that really got me into baseball around 2009, when I was enjoying my first season watching literally every Phillies game I could, was reading old columns at Fire Joe Morgan. I expect that blog doesn’t need much of an introduction for this audience, but just in case: Fire Joe Morgan was a blog that ripped apart the mainstream sports media of the early-to-late 2000s, exposing it—actually, no, I was right, it doesn’t need an introduction. It was and is wonderful.
One of the major misunderstandings the blog tried to correct was sports media’s systemic inability to understand what the book Moneyball was about, and, consequently, what the general philosophy of Moneyball actually was. More often than not, commentators and journalists, particularly the titular Joe Morgan, would argue that Moneyball meant teams being cheap, privileging walks over batting average, and losing in the playoffs like the Oakland A’s. As Ken Tremendous and company would insist over at Fire Joe Morgan, though, the idea behind Billy Beane’s strategy as documented by Michael Lewis in Moneyball was the exploitation of market inefficiencies. Especially for teams that did not have the capital to spend like the New York Yankees or Los Angeles Dodgers, Beane’s philosophy—which Lewis gave the shorthand of “Moneyball”—was essentially a leveling technique, a way of attacking a hopeless mismatch by finding a completely different resource to pursue. No money? No problem!
This philosophy is very appealing in the abstract. You can imagine yourself as a kind of treasure hunter, finding players who would have never gotten a chance in previous years, and showing how they can be productive major leaguers. The bad body types, the too-patient hitters, the light hitting defensive center fielders: analytics have rehabilitated all of these kinds of players at one point or another. I would argue that the Jack Custs or the Jarrod Dysons of the world would not have had nearly the shot they have had if not for Beane’s emphasis on market inefficiency, and that’s all for the good.
But the dark side of sabermetrics also has to do with labor. On the players’ side, this is something of a necessary evil in modern baseball. With the number of analytical tools at the disposal of major-league teams currently, teams will know how risky any player is, his upside and, more importantly, his downside. So when GMs offer “team-friendly” long-term contracts to very young players, these are necessarily “player-antagonistic” as well. (With the possible exception of that overlap in each party's interest created by each party's different incentives.) The player can gamble on his own upside against his downside, but he can only ever do so knowing that the team has run the actuarial and statistical math to determine exactly where the monetary limit of their risk is. The team has run the simulations, and the player gets to bet on himself against the numerical odds or accept a contract that, while sizable, is a fraction of future potential earnings.
And that’s fine, as these things go. I’m fairly far left on the ol’ economic issues, so if I had my druthers, labor would have a lot more power over things like arbitration and pre-arbitration salaries, but that’s all for another article down the line. In terms of my issue here, inefficiency, the difficult position in which players find themselves in the negotiating room is directly related to the information that teams now have access to. It’s difficult to turn back the clock on that, and in order to argue against the proliferation of that data (outside of a stronger union or abolition of the free market), one would have to problematically argue for less knowledge. And so, players will unfortunately be more under- than over-paid until they reach free agency, which is becoming further and further away. Teams win; labor takes an admittedly survivable loss.
So, despite the curmudgeon class railing against data in defense of gut instinct and feel, data continues apace because it makes teams money. Furthermore, if you’re the owner of a team, you have to at some point start wondering how far this particular line of thinking can go. Are there market inefficiencies everywhere? Can you put together an entire working team on the cheap that can win the World Series? Are there stadium inefficiencies? Fan giveaway inefficiencies? And, most tempting, could there possibly be front office inefficiencies? Could the guys who get the most money outside of the players themselves be streamlined and made cheaper and more efficient?
Kicking off a new series focusing on baseball-related academic literature. Today: Did Moneyball really shift the market for OBP?
Caught Lookingwill take a look at articles from the academic literature relevant to baseball and statistical analysis. Mostly this means recent articles from peer–reviewed academic journals, but suggestions are welcome, especially for interesting dissertation or thesis chapters that aren’t always easy to find. Critique will come if it’s warranted, but the goal is to help share with a broader audience where the academic frontier is and to seek ways to move it forward.
This inaugural review looks at Moneyball After 10 Years: How Have Major League Baseball Salaries Adjusted? by Daniel Brown, Charles Link and Seth Rubin in the October 2015 Journal of Sports Economics.
What does it mean that the league suddenly shifted its pitch selection just as the A's suddenly shifted into a full-scale meltdown?
The Oakland Athletics completed their second-half collapse in true Oakland fashion by failing somehow to advance in the postseason against the Royals. For a season in which Beane went all-in by trading future potential for current performance, in which the A’s began the year an unstoppable, historic juggernaut, the inglorious ending has to smart.
The A’s of this year embodied one of the most beloved playoff myths, that the second half of a team’s performance predicts how that team will do in the playoffs. I say “myth” because, at least in the aggregate, there is little or no evidence in support of this idea, and so it has been debunked on numerousoccasions. And yet, there may not be a better example of that phenomenon in action than this team, which roared out to an incredible start to the season, on pace to challenge run-differential records, only to buckle in the second half, barely making the playoffs.
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Would you rather have the best GM or the best prospect for the future of your franchise? And more importantly, how would you determine the answer?
There’s this captivating scene in the movie Moneyball: Over archival footage of Dan Duquette presenting a Red Sox jersey to Johnny Damon at a press-filled event in Fenway Park, a Brad Pitt voiceover explains how the A’s will chart a new direction in player valuation and roster construction. The scene is supposed to poke fun at the Red Sox, Jonah Hill having just delivered a soliloquy about Damon’s true value and how he’ll never be worth $7.5 million. But meanwhile in Boston, a team of analysts, economists, front office folks, and consultants had determined that Damon’s value on and off the field would far exceed his salary.
The dramatized scene lays down the “new way” vs. “old way” narrative playing out over the course of the film. What it really does, though, is underscore the fact that teams value players differently.
At one position, the A's are still Moneyballing like it's 1999.
For the most part, pitch receiving operates on a level that’s easy to overlook. Over thousands of pitches, certain catchers establish an edge, and those edges add up in a way we can’t see without looking at a leaderboard. Every now and then, though, framing on a small scale comes to the fore, usually when it leads to a larger event. Brett Lawrie, let’s say, strikes out looking out a pitch that appears to be outside, hurls his batting helmet at the home plate umpire, and gets ejected from the game. Our first impulse, like Lawrie’s, is to blame the umpire who blew the call. After reviewing the video, though, we realize that the real culprit was Jose Molina, in the catcher’s box, with the catcher’s glove. The ump was a red herring, a patsy, or maybe an unwitting accomplice.
In addition to exploiting lefty-righty matchups, the A's also have a significant edge in the air.
The Oakland Athletics finished 2013 with baseball's fourth-lowest payroll, fourth-best offense, and best clubhouse chemistry. Debate hascentered on whether the latter two are related. There’s nothing objectionable about “good guy” genes—it’s a solid organizational goal to have. But chemistry alone doesn’t put runs on the board, and if a team is missing the talent, they better find the runs elsewhere. The 2002 Athletics discovered them in walk deities and college arms; once those methods pervaded front offices, the A’s slipped back into losing. Was chemistry the only undervalued commodity of their recent resurgence?
As the baseball community obtains more knowledge, roster construction strategies evolve. Previously undervalued talents like walks and defense are now accepted constructs. The A’s are Hollywood-infamous for adopting them before their competitors while prices were low. After a 74-win 2011, they cheaply signed Brandon Inge and Jonny Gomes, who Brandon McCarthy claimed bolstered the clubhouse DNA to the tune of 20 wins. But Inge and Gomes were two of several players who also bolstered a less-visible statistic: fly ball-to-ground ball ratio.
Everyone loves an underdog, and we’ve had some great stories this year. The Pirates were great while they lasted, and the Orioles... well, we project Baltimore to go 9-13 the rest of the way and miss the Wild Card entirely. But it was fun, wasn’t it?
And then there’s the Athletics. The lowly A’s, the least-valuable of the 30 MLB franchises at “only” $321 million. They of the skinflint ownership, endless stadium struggles, and ridiculously low ticket revenues. None of this is news, of course. We all saw that movie based on the book that Brad Pitt wrote about the guy from Parks & Recreation. That crafty Billy Beane always finds a way to do more with less! It’s the feelgood story of the year.
Oakland's success this year is all the more surprising considering they have departed from the small-market blueprint perfected by Tampa Bay.
The Oakland A’s and Tampa Bay Rays, two AL Wild Card contenders who looked like long shots at the All-Star break, are one game into a strangely scheduled Thursday-Saturday series. The two teams have a few things in common, in addition to both being AL Wild Card contenders who’ll be playing tonight in Tampa Bay. In fact, they might have more in common than any other two teams in baseball. This article isn’t actually about the ways in which they’re the same. It’s about one way in which they’re different. But I’m going to start with the similar stuff just to make the different thing more meaningful, which is pretty manipulative of me.
The first thing the A’s and Rays have in common is success in the second half. The A’s were the hot team in July, when they went 19-5. They’ve cooled off lately, but they’re 24-14 in the second half, and their playoff odds have risen by roughly 25 percentage points over that period. The Rays are the hot team in August. They’re 16-5 this month and 25-14 in the second half, which has raised their playoff odds by roughly 50 percentage points.
Decades before Billy Beane and Ricardo Rincon, there was Steve Boros and "computer baseball."
I spend a lot of time going through archives, and any time spent in archives inevitably leads to more time in archives, because an awful lot of things found in archives seem ironic or significant in retrospect. Like this: