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So to start with I had to do a little bit of data prep. This involved using a select tool to rename the column headers appropriately and then a select records tool to only use the relevant parts of the data. I then used a summarize tool to group the data by player name and sum the events, money and victories per player.
To calculate the top 5 highest earners per event I first used a formula tool just to calculate the earnings per event. Then the data was sorted so the highest earners were at the top of the data set before select records tool was used to isolate the top 5.
To find the top 5 highest earners who have never won an event I started off by using a filter to remove any victorious players from the data set. Then the data was ordered with the highest earners at the top and again select records was used to isolate the top 5.
These 2 data sets and a singular null row were combined using the union tool before a final select tool was used to rename the column headers so that it matched the given output.
To calculate the top 5 highest earners per event I first used a formula tool just to calculate the earnings per event. Then the data was sorted so the highest earners were at the top of the data set before select records tool was used to isolate the top 5.
To find the top 5 highest earners who have never won an event I started off by using a filter to remove any victorious players from the data set. Then the data was ordered with the highest earners at the top and again select records was used to isolate the top 5.
These 2 data sets and a singular null row were combined using the union tool before a final select tool was used to rename the column headers so that it matched the given output.
Fun one. I don't know if we needed to add the null row divider, but I arrived at the same answer for both groups.
