Summary
I recorded this session to demonstrate how to turn a long‑format basketball dataset into a concise, wide‑format summary using pivot tables. I first explained why pivot tables are valuable: they let us aggregate raw rows (e.g., sales or game stats) into readable summaries without complex formulas. I then outlined the three components of a pivot table—rows, columns, and values—and applied them to our Adjusted Offensive Efficiency (ADJOE) data. Because the full dataset contains 1,759 rows and hundreds of teams, I filtered the table to the four teams we care about (Miami, Florida, Florida State, Georgia Tech) to avoid size limits. I walked through the exact steps in Google Sheets: inserting a pivot table, selecting the range A2:G1759, assigning Year to rows, Team to columns, and ADJOE (sum) to values, then applying the team filter. After creating the table, I highlighted how the grand‑total column is irrelevant and how the table automatically feeds a line chart that compares efficiency across years. The next actions are to repeat the process for Adjusted Defensive Efficiency (ADJDE) and ensure both charts appear on the final dashboard. I offered continued tech support for anyone who encounters issues.