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Activity Number: 602 - Game Analytics: How Data Science Transforms the Game Industry
Type: Topic Contributed
Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #304792 Presentation
Title: Combining Advanced Statistics and Machine Learning to Improve Games at Ubisoft
Author(s): Antoine Rebecq and Jean-Michel Daignan*
Companies: and Ubisoft
Keywords: video games; data science; machine learning; A/B tests; recommendations

In this presentation, we will discuss a few examples of how analytics and technology teams at Ubisoft combine machine learning and statistics to create value and improve player experience. Among others, recommendation algorithms help us serve personalized content in the game to our players (customization items they might like, specific in-game content they might enjoy, etc.).

We make extensive use of statistics in our modeling process. Each model is first inspected using custom metrics and refined estimators of performance. Then a live experiment with a carefully crafted sample of users is run. Interestingly, performance of models between prototype and live phase can differ.

We also try to popularize causal inference methods to support decision making. We now routinely run A/B tests to test how new ideas fare once they’re put to the test in front of actual players. We also look for quasi-experiments or create estimates that are tailored for each application in order to provide decision-makers the best possible information.

These applications of statistic supported by a strong engineering stack created by our cross-disciplinary teams.

Authors who are presenting talks have a * after their name.

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