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MoneyLaw The Art of Winning an Unfair Academic Game

MoneyLaw The Art of Winning an Unfair Academic Game. Moneyball and baseball’s sabermetric revolution Asymmetries and biases in evaluating talent Prophets and profits, pennants and playoffs MoneyLaw : The art of winning an unfair academic game. Jim Chen Dean and Professor of Law

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MoneyLaw The Art of Winning an Unfair Academic Game

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  1. MoneyLawThe Art of Winning an Unfair Academic Game • Moneyball and baseball’s sabermetric revolution • Asymmetries and biases in evaluating talent • Prophets and profits, pennants and playoffs • MoneyLaw: The art of winning an unfair academic game Jim Chen Dean and Professor of Law University of Louisville Brandeis School of Law

  2. From Moneyball to MoneyLawAnother way of bridging athletics and academics

  3. The sabermetric revolution • Baseball, like life, is unfair • Differences in talent • Differences in money • Conventional wisdom • E.g., speed kills • “Scouts’ honor” • Batting average, RBI, ERA, fielding percentage, saves • Real numbers offer real answers to real problems

  4. Moneyball: The story of Billy Beane • Billy Beane the bonus baby • Won the 270-foot dash and scouts’ love • Drafted fifth overall by the Mets • Billy Beane the bust • 80 strikeouts, 66 hits, and a .546 OPS • Quit playing and started scouting • Billy Beane the general manager • Consistently takes the A’s deep • Evaluates college players the right way • Arbitrages other GMs’ biases

  5. Sources of asymmetry and bias in the evaluation of talent in baseball • Talent and money are unbalanced. So is information, along three dimensions: • Heuristic bias: shortcuts that backfire • E.g., favoring “skinny” over “fat” talent • “CK genes”: Available, salient, vivid data • Social bias: herd instincts can kill • Statistical bias: bad stats can mislead • E.g., batting average versus OPS

  6. Example: Defense-Independent Pitching Statistics (DIPS) • Conventional pitching stats are misleading • Vörös McCracken: balls put into play follow no pitcher-specific pattern • Defense, ballpark effects, weather, and random factors matter more • DIPS = K, BB, HR

  7. MoneyLawApplying baseball’s lessons to academia • Talent is hungry, but money is unevenly distributed • We tout access, diversity, social justice, community engagement – values often overlooked elsewhere • The UofL and schools like it must do more with less • Academia needs its Oakland A’s and its Dean Beanes

  8. Performance 1, Pedigree 0 • Perhaps the deepest source of bias in academia is reputation • Unexamined reputation “wins” because it is . . . • Succinct • Socially safe • Repeated till it seems true • Like baseball’s scouts (but not Liz Phair), we overlook “potential with no credentials”

  9. RIPS: Reputation Independent Performance Statistics • No measure of academic performance is valid if it relies on reputation • A bibliometric manifesto • Citation counts • Impact factors • Arxiv, SSRN downloads • RSCA percentage • Use RIPS to hire talent • Use RIPS to rate schools

  10. Other ways to apply MoneyLaw • Post-graduation results • Job rates; starting salaries • Bar passage • Access and diversity • Financial aid • Philanthropy • Percentage participation • Dollars per graduate • Young alumni

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