Couch Scholar
Professional Learning Experience

The Right Question

Moneyball: Data-Informed Decision Making.

You'll learn to ask the question that data should answer, combine evidence with experienced judgment, spot when numbers or stories mislead, and leave with an evidence plan for a real decision.

Moneyball

How do you use data to see what experience alone misses, without letting the numbers mislead you or dismissing the people whose judgment they challenge?

You'll be able to:

  • Reframe a decision so that data answers the question that actually matters, not the one that's easiest to measure.
  • Use data and expertise together, with each checking the other, rather than letting either dismiss the other.
  • Recognize when small samples, missing information, gamed metrics or selective success stories are distorting a conclusion.
  • Consider who a metric or model might disadvantage, and build in review and transparency.
  • Introduce data-informed change in a way that respects experienced colleagues and builds trust through small tests.

You'll leave with: My Evidence Plan

No need to have seen it first.

Begin the experience

You don't need to have seen the film. Choose "I haven't watched" to work from the premise and the record.

Moneyball

Choose whether to discuss the film or work from the premise and the record.

I haven't watched

The premise and the record only.

You don't need to have seen the film. We'll work from the premise, what the record shows, and hypotheticals clearly labeled as such.

Begin with I haven't watched

I've watched

Film moments are open, labeled as the film's portrayal.

We'll discuss approved moments from the film, each labeled as the film's portrayal, alongside the record, without quoting the film.

Begin with I've watched

You can change stories while you are still in Observe. After that, your chosen story stays with you for the rest of the experience.

The question at the center

How do you use data to see what experience alone misses, without letting the numbers mislead you or dismissing the people whose judgment they challenge?

Why it matters

In 2002, the Oakland Athletics lost three of their best players to wealthier teams and could not afford to replace them in the usual way. General manager Billy Beane leaned on a different way of valuing players, one that focused on overlooked skills, especially getting on base. The team won 103 games, including a record 20-game winning streak, then lost in the first round of the playoffs. The 2011 film dramatizes that season. We treat it as a case study in data-informed decisions, not as a lesson in baseball, and you don't need to have seen it.

The Learning Everywhere Framework™

Observe → Connect → Analyze → Apply → Reflect

  1. 1

    Observe: The Question Behind the Numbers

  2. 2

    Connect: Your Institution's Numbers

  3. 3

    Analyze: What the Numbers Can and Can't Tell You

  4. 4

    Apply: My Evidence Plan

  5. 5

    Reflect: Seeing What Others Miss

Potato, the Couch Scholar AI learning coach

Potato™, your learning coach

Potato is curious, precise and fair to both data and experience. It keeps the film, the record and your interpretation distinct, never quotes the film, gives no legal or compliance advice, and keeps your own words central in Apply and Reflect.

What you'll walk away with

  • Reframe a decision so that data answers the question that actually matters, not the one that's easiest to measure.
  • Use data and expertise together, with each checking the other, rather than letting either dismiss the other.
  • Recognize when small samples, missing information, gamed metrics or selective success stories are distorting a conclusion.
  • Consider who a metric or model might disadvantage, and build in review and transparency.
  • Introduce data-informed change in a way that respects experienced colleagues and builds trust through small tests.
Begin the experienceExplore the related catalog course