**First ask students to look at the table below. What do you notice? What does it make you wonder?**

Team | Team Payroll | Wins | Sport | Season |

Boston Red Sox | $227,398,860 | 108 | MLB | 2018 |

Tampa Bay Rays | $68,810,167 | 90 | MLB | 2018 |

Washington Capitals | $85,000,000 | 105 | NHL | 2017-18 |

Carolina Hurricanes | $62,000,000 | 83 | NHL | 2017-18 |

Jacksonville Jaguars | $224,857,017 | 5 | NFL | 2018 |

Buffalo Bills | $124,148,769 | 6 | NFL | 2018 |

Miami Heat | $153,171,497 | 39 | NBA | 2018-19 |

Atlanta Hawks | $79,180,081 | 29 | NBA | 2018-19 |

In this activity students compare team wins with team salaries in the four major North American sports ... the NBA, NFL, MLB and NHL. Students use data from recent seasons to create scatter plots comparing wins and salaries for each sport. You can run the lesson anyway you wish.

We recommend breaking the class up into small groups and having each group create a scatter plot for only one sport. Have a group or groups from each sport share their scatter plot using poster presentations or with a document camera. When setting up plots, students will need to carefully consider which variable goes on which axis - independent versus dependent variable and the scales for each axis.

Start the lesson by looking at the data table at the top of this post. Give kids time to think about the table, take questions ... why the big difference in payrolls? Is this fair? Hopefully through class discussion you can land on: Does spending a lot on the team translate to greater success? Before giving them the data, you might ask them what information would be helpful for answering that question. Let them generate ideas as to what would be helpful data.

The student handout guides students through this exploration: setting up the scatter plot, reflection questions to consider during the group task and finally reflection questions during the whole group sharing.

*Extensions:* It might also be interesting to look at how different groups plotted the same data set. How does the scale effect the plot and how we see the data? Another idea is to use this data for linear modeling. The MLB data sets itself up nicely for a line of best fit.

**We have two different activity guides. **

Data for both activities: HockeyStats2018.pdf FootballStats2019.pdf BaseballStats2019.pdf BasketballStats2019.pdf

**For middle school students,**the lesson focuses on the 8th grade CCSS around scatter plots, association, outliers and clusters. Payrolls-vs-wins-2019.pdf

**CCSS: 5.G.1, 5.G.2, 6.SP.2, 6.SP.5, 8.SP.1, 8.SP.2, 8.SP.3, HSS.ID.6**

**For high school students**the lesson focuses on high school CCSS, such as correlation coefficients and distinguishing between correlation and causation. HS-payrolls-vs-wins2019.pdf

**CCSS: HSS.ID.C.8, HSS.ID.C.9, HSS.ID.B.6**

For members we have an Excel sheet of the data, editable graph blanks, editable Word docs and solutions.

GenGrid.pdf Team Spending and Wins2019.xlsx Payrolls-vs-wins-2019.docx

Payrolls-vs-wins-solutions2019.pdf HS-payrolls-vs-wins2019.docx HS-payrolls-vs-wins2019-solutions.pdf