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NHL 19 THREES Data Deep Dive

NHL 19 THREES Data Deep Dive

Analyzing game development using data science

By Brian Maresso & Sean Finlon


Are multiplayer video games fair to all parties?  Most players can probably think of a time where it sure seemed like the odds were stacked against them.  We may never know for sure what the developers had in mind when they created these games.  However, is it possible to derive these patterns and use them to predict game outcomes solely by observing games?  This is the question we set out to answer in this series of articles we’re calling our NHL 19 THREES Data Deep Dive.  Our data and analysis is centered around the Electronic Arts video game NHL 19- specifically the minigame called THREES.  Whether you are a THREES player, a video game enthusiast checking whether the dice are weighted, or if you just stumbled here on accident, we hope that you enjoy reading about our findings.  For those looking to reproduce our results or apply our methodology to another game, our data sets and important snippets of code are linked in each article.


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