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It’s unbelievable how much you don’t know
about the game you’ve been playing all your life.
—Mickey Mantle

The first act lays out the problem: the Oakland A’s, run by General Manager Billy Beane (Brad Pitt) is a low-budget team that can’t possibly compete against the deep pockets that other teams like the New York Yankees possess. As a result, whenever the A’s recruit and train star performers, these assets are soon lost to the big ball clubs. As Beane notes, “There are rich teams and there are poor teams. Then there’s fifty feet of crap. And then there’s us.” That’s right: baseball’s not a level playing field.

The film’s inciting incident occurs when Beane meets Peter Brand (Jonah Hill), a character based on real-life Harvard economics graduate Paul DePodesta who is committed to “finding value [in players] that other people don’t see.” Flying in the face of conventional wisdom, Brand analyzes players in a new way—through sabermetrics, defined by David Grabiner as “the search for objective knowledge about baseball.”

You guys are talking the same old nonsense.
We’ve got to think differently.
—Billy Beane, "Moneyball"

Brand challenges the “medieval” practice of valuing players by batting average (the percentage of at-bats that result in hits) when he declares that since baseball games are won by runs, players should be evaluated instead by the number of runs they create. The sabermetric formula for runs can be expressed as follows:

             (HITS + WALKS) (TOTAL BASES)
RUNS = --------------------------------------------------
                     AT-BATS + WALKS

By adhering to a strict regimen of quantitative analysis, Beane finds the hidden value his team needs for a price his club can afford, and thus—by parting out overlooked players from other teams—Beane builds a dream team for the Oakland A’s. As Brand confides to Beane:

I believe there is a championship team that we can afford
because everyone else undervalues them, like an island of misfit toys.
—Billy Beane, "Moneyball"

As you may have guessed, the case "Moneyball" makes for the game is the essential case for the scientific method, characterized by gathering empirical and measurable evidence in order to test (and possibly modify) hypotheses. Baseball thus entered its age of enlightenment in 1999 when Billy Beane—ever the scout—hired DePodesta as his assistant. Yet as with any revolution, it did not arrive unopposed. "Moneyball’s" Grady Fuson (the Oakland A’s scouting director) insists that:

Baseball’s not just numbers. You can’t approach
baseball with a bean-counting statistical approach.
—Grady Fuson, "Moneyball"

This reminds me of my first quantitative analysis course as an undergrad. Each day we were given a single-question high-stakes quiz. We had about a minute to answer the question and turn it in. Here’s an example:

Approximately how many new number two pencils does it take to reach end-to-end from the earth to the moon?

(a) 2 X107
(b) 2 X108
(c) 2 X109
(d) 2 X1010

Ok, let’s see. I recall that the average distance to the moon is about a quarter-of-a-million miles. A mile is 5,280 feet. I figure a pencil is about 7 or 8 inches long. So here’s the math:

250,000 X 5280 = 1,970,149,253 = (1.97 x 109)
     0.67 feet

After turning our papers in, we always went over the quiz in class. “How many of you thought (a) was the correct answer?” asked the teaching assistant. I alone raised my hand.

“Uh, this feels too low.”

“You feel?” he demanded. “You feel?!”

“Uh, yes. I feel it is low by a few orders of …” I replied, squirming on an oaken butt-buffed chair. But I never made it to the end of the sentence. Booming, the teaching assistant intoned:

This is quantitative analysis. There is no room for feeling!

Thirty years later I recall this incident as if it was yesterday. Acing the quiz was cold comfort for being so publicly ridiculed. Worse, I felt in my gut that this pronouncement was deeply misguided. Whereas "Moneyball’s" Brand considered pure qualitative decision-making to be “medieval,” the young teaching assistant in my story considered empirical facts beyond the scope of intuition.

Quantitative analysis can be compared to placing a subject under a microscope. Attributes unseen from a traditional point of view often stand out upon closer examination. Trends normally washed out against a bright background of data can move into sharp focus. Hypotheses can be developed and tested. Predictive analysis—part fact, part gut, can be undertaken.

In an environment marked by increased accountability in higher ed, the value of data-driven decision-making has never been greater. George Siemens of the Technology Enhanced Knowledge Research Institute notes that:

Education is, today at least, a black box.
Unfortunately, we don't really know how our inputs influence
or produce outputs. We don't know, precisely, which academic

practices need to be curbed and which need to be encouraged.
We are essentially swatting flies with a sledgehammer
and doing a fair amount of peripheral damage.

As with baseball, appropriately using the data normally collected by academic institutions may lead to surprising advances across an array of organizational priorities including business intelligence, scheduling, and perhaps most fundamentally, student learning. EDUCAUSE identifies three premises for the use of learner analytics in higher education:

  1. Analytics is a proven approach to predicting student learning performance that has been successfully applied to a variety of courses and programs.
  2. Monitoring and/or predicting student performance enables targeted interventions that are more efficient and effective for students and the institution than just-in-case, across-the-board student support programs.
  3. Performance information and predictions enable students, faculty, and advisors to improve student success.

Although perhaps among the educational avant-garde, they are not alone. The opportunity to build deep analytics into learning management systems such as Moodle has never been more timely. According to the 2011 Horizon Report:

Learning analytics refers to the interpretation
of a wide range of data produced by and gathered on behalf of students
in order to assess academic progress, predict future performance,
and spot potential issues. At its heart, learning analytics is about
analyzing a wealth of information about students in a way
that would allow schools to take action.

Recently I gave a talk to a series of groups on campus. It comprised data derived by our Office of Institutional Research and Planning from our student information system and from the Chancellor’s Office Data Mart. Each time I presented, someone from the audience insisted that my information was flawed, or worse, that I was lying outright. Such is the power of empirical information to challenge traditional assumptions. When I saw "Moneyball" the following week, I was moved by the scene in which an admiring owner says of Beane that “the first man through the door always gets bloodied.” In retrospect, I should have seen the movie first. Then I could have replied to my detractors with this prescient line from the film:

We’re not bean counters. We’re just paying attention.

Google, Facebook, and—to name a few—have developed game-changing services by leveraging the power of consumer analytics. With higher education accountability pressures mounting in lockstep with an increasingly uncertain domestic economy, the intelligent, appropriate use of data to improve institutional efficiency and increase student success should be a priority for a nation that values its latent human capitol.

Names such as Gates, Brin, Jobs, and even Zuckerberg have become synonymous with the new American ingenuity. What college student today will become the next captain of 21st century industry, approaching old challenges in new ways while providing jobs and entire spinoff economies? And how can we—as educators—help our students identify and reach their goals? “Edumetrics” may hold the key. As "Moneyball’s" Billy Beane says:

We are card-counters at the blackjack table …
We’re going to turn the odds on the casino.