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The ability to inform your decisions with data is only as good as the data available to you. For example, tracking an opponent’s setter distribution can be instrumental in deciding where you want to direct your blockers’ focus. But looking at total attempts for an opponent’s hitters may be misleading – how many of a player’s attempts were in system vs. out of system? What about in serve receive or transition? Is one player favored when running a certain attack pattern? Quite a bit of context can be lost if certain factors are not accounted for. The ability to filter data meaningfully lies in your ability to record data well, and this session aims to demystify this process, especially for those who do not have access to data accumulation tools beyond pen and paper. In summary, the goals of this session are to: 1. Provide an example of how different conclusions might be reached when looking at filtered vs. unfiltered data. 2. Introduce the concept of “tidy data” and recording data in that format 3. Provide a simple, step-by-step guide of how to view and subsequently use that data to better inform your decision making Excel file: https://github.com/natengo1/2020_avca... R for Data Science (https://r4ds.had.co.nz/tidy-data.html)