Term-Project: Most Valuable Teams in the NFL

Most-Valuable Teams in the NFL

In the final project of the semester, I chose to create an Excel workbook as well as an Access database to display statistics based on the top-10 most valuable teams of the NFL. Overall, the numerical data displays information based on the past year; these particular NFL teams are not considered valuable based on their records, but on profits and revenue earned by fans, media and other sources throughout the year. As well as the top teams, I included the highest-paid players on each team as well. These players vary in position, size, and background, however they proceed to earn the most in salary on their individual teams. With that, viewers of both the workbook and database will learn of the team value (in millions of dollars) as well as revenue made by the team, operation expenses, and value percent change in the past year. The National Football League is a major franchise in the United States that is valuable to the sports industry; players as well as teams are responsible for bringing in extremely large amounts of revenue and profit, and one will observe who the top bread-winners in the league are at this time. Some teams may not be winning teams, however loyal fans as well as media proceed to bring in profits despite any winning or losing record.

The Microsoft Excel Workbook includes:                             

  • Top ten most valuable teams in the NFL
  • Numerical data based on revenue, operation expenses, team value, value-percent change
  • Additional example of how to maximize revenue selling a constrained amount of Dallas Cowboy tickets
  • 3-D Column-chart displaying teams corresponding to their particular value

The Access Database includes:

  • Top ten most valuable teams in the NFL table
  • Highest-paid players corresponding to the top-ten teams
  • Team information including conference, records, coach names, etc.
  • Queries, forms, and records corresponding with the numerical and categorical data

Resources used for data:

 

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