The Moneyball Story Revisited

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From the course by Northwestern University

Leadership Through Marketing

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Northwestern University

Leadership Through Marketing

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Course 4 of 6 in the Specialization Organizational Leadership

The success of every organization depends on attracting and retaining customers. Although the marketing concepts for doing so are well established, digital technology has empowered customers, while producing massive amounts of data, revolutionizing the processes through which organizations attract and retain customers. In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence.

From the lesson

Leadership’s Role in Analytics with Florian Zettelmeyer

Why the data explosion in marketing has made an understanding of Big Data and Analytics essential for Leadership through Marketing.

Overview of Modules 3 and 43:06

The Moneyball Story Revisited4:44

Why Analytics Requires Managerial Judgment5:41

Why Analytics Requires Process and Incentive Changes7:58

Why Analytics Has To Be Problem Driven5:00

Why You Need a Working Knowledge of Data Science4:54

Meet the Instructors

Greg Carpenter
James Farley/Booz Allen Hamilton Professor of Marketing Strategy, Director of the Center for Market Leadership, Faculty Director, Kellogg Markets and Customers Initiative (KMCI)
Kellogg School of Management
Florian Zettelmeyer
Nancy L. Ertle Professor of Marketing, Director, Program on Data Analytics at Kellogg
Kellogg School of Management
Sanjay Khosla
Adjunct Professor of Executive Education
Kellogg School of Management

[MUSIC]

There's a good chance you may have heard the story of baseball manager Billy Beane.

The story's well known thanks to Michael Lewis's best selling book Moneyball.

And also because Brad Pitt played Billy Beane in the movie of the same name.

But the story is almost as widely misunderstood as it is well known.

To many executives, the Moneyball story begins the era of big data and analytics.

This is an era in which organizations that are not driven by big data and

analytics will lose out.

Now, it is true that data and analytics play a big role in the Moneyball story.

In 2002 the Oakland A's had one of the smallest budgets for

player salaries of any team in baseball.

And because he was frustrated that he couldn't outbid other teams for

good players, Billy Beane hires Paul DePodesta.

Now Paul DePodesta is an economics major from Harvard and

he's very good at analytics and at baseball statistics.

When Beane and DePodesta started doing analytics, they discovered two statistics

that are predictive of how many runs a player scores, which

is how you win games in baseball, and they are on-base percentage and slugging.

Where things get interesting is that these statistics are not

what baseball scouts had traditionally valued.

What Beane and DePodesta realized, is that this meant that players who

ranked highly on the statistics were likely to be undervalued by the market.

And so Billy Beane began looking for player bargains, players whose statistics

suggested they would score runs but who were under the radar of other teams.

When Beane began to hire such players, the A's started to win a lot.

As an aside, there's an interesting story what happens

in the years that follow what the Oakland A's did, but that is not important for

the point I want to make today.

Now many executives interpret this story as baseball's big data and

analytics moment, but that really misses its key message.

You see, none of the data analytics elements of the Moneyball story,

were new in 2002.

Detailed data on baseball players have been available for over 100 years.

Regression analysis, a statistical method that the Oakland A's used,

was invented more than 200 years ago,

and could have been implemented with 1980s computer power.

Even the idea of applying analytics to baseball wasn't new.

There is a famous baseball statistician,

Bill James, who coined this approach sabermetrics, and

who published yearly books since 1977 about data analytics in baseball.

And so the question is, why did the Moneyball story happen in 2002?

Why did it not happen 1992 or even 1982?

What was new in 2002 was that a leader, Billy Beane, had the courage

to use the insights from data analytics to drive the way he ran his business.

It took courage both because what the analytic said went against

the conventional wisdom of how to run a successful baseball team and,

because Beane had to overcome a lot of reluctance inside the organization

to change to this new approach.

The reason Moneyball succeeded for

the Oakland A's was not because of the success of the data analytics themselves.

It succeeded because the leader understood the potential of analytics for

his business, and because he changed the organization so

that it could deliver on that potential.

You see just as many executives misunderstand Moneyball, they also

misunderstand the key to transforming into an analytics driven organization.

Too often big data and analytics is seen as a data science and

a technology problem.

Of course analytics requires investments in technology, in infrastructure, and

in data scientists but putting analytics to work for

the business is mostly a leadership problem.

You cannot simply hire a room of data scientists and hope to be successful.

They can't leverage analytics in a vacuum.

And so, in this module I want to explain in more detail

the three reasons why analytics is a leadership problem.

[MUSIC]

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