4/15/2021 0 Comments Pivot Player Download
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![]() Is there anything in the player data that shows why the USA team won. They had won the previous 4 gold medals, and seemed like a sure bet. Meanwhile, David Napoli was confident that Team USA would win this time. Instead, here is my rudimentary player data analysis, based on the team rosters. If youd like to do your own analysis, you can find the hockey team rosters here. In the roster, the height was listed as text, with feet and inches, and a foot mark, such as 57. I thought it would be interesting to get a heightweight ratio, so I used the BMI calculation for that. That was a simple formula multiple the calculated foot height by 12. You could round to 1 or 2 decimals, if you needed greater precision. The first table shows player counts, for each position, plus age, height and weight. After you add the value, right-click on one of the numbers, click Summarize Values by, and choose one of the functions. On average, the USA team is a couple of years younger, a bit shorter and 4 pounds lighter. I added two more pivot tables, below the first one, connected to the same Slicer. The men have a substantially longer career, from what this shows. I changed the values to of Column total, and created a stacked bar chart to show the results. Otherwise, some of the BMIs would not be listed, when you switch been the data for Men and Women. That command is on the Insert tab, between Charts and Sparklines. Pivot Player Download My FileIf you have the 3D Map tool, you can build your own maps after you download my file. Theyre spread from coast to coast, with most cities close to the US border, and some further north, in the the prairie provinces. ![]() Maybe that gave them an advantage against our Canadian team. The zipped file is in xlsx format, and does not contain macros.
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