Win At Any Cost (Updated!)

How should professional sports teams spend their money?

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Win At Any Cost (Updated!)

How should professional sports teams spend their money?

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2023-2024 Versions

In the fall of 2024, Citizen Math released updated versions of every lesson in our library, plus a few new ones! We know you may have already prepped an earlier version or planned a repeat of last year, so we're continuing to make these earlier versions available through Thursday December 5, 2024.

You can find the new lessons through the regular search, and we hope you love them as much as we do. You can read more about these updates in Our Community.

How should pro sports teams spend their money? Professional sports teams spend hundreds of millions of dollars on player salaries, and conventional wisdom says that teams with higher payrolls perform better. But maybe conventional wisdom is wrong.

In this lesson, use linear regressions and r-squared values to analyze data from professional sports leagues, evaluate how various factors correlate with wins, and debate whether a higher payroll is good business strategy.

REAL WORLD TAKEAWAYS

  • Professional sports teams often increase their total payroll or sign a high-priced superstar in an effort to win more games.
  • However, according to more than ten years’ worth of data from the NFL, MLB, NBA, and NHL, total payroll and the highest player salary each has a small impact on wins. Additionally, in each league, the relationship between salary and wins is weak.
  • In each league, if a team wanted to increase the number of games it won, it would expect to spend more on salaries than it would expect to receive in additional revenue.

MATH OBJECTIVES

  • Interpret the slope and intercept of linear regression in the context of real-world data
  • Interpret R-square value for linear regressions
  • Use linear regressions to justify decision-making

Appropriate most times as students are developing conceptual understanding.
Algebra 2
Bivariate Statistics
Algebra 2
Bivariate Statistics
Content Standards S.ID.7 Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data. S.ID.8 Compute (using technology) and interpret the correlation coefficient of a linear fit. S.ID.9 Distinguish between correlation and causation.
Mathematical Practices MP.3 Construct viable arguments and critique the reasoning of others. MP.5 Use appropriate tools strategically. MP.6 Attend to precision.

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