Datathon cases

Data Science and The Gaming Industry

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Companies within the gaming industry play an enormous role in the global community, with the likes of Rockstar, Microsoft, and Sony being extremely influential in the space. With people spending more time playing games, these companies have risen to become masters of the industry. Data science comes into the mix to help gaming giants work out player habits, which lets them create strategies and hit the market in the right place; continue reading to find out more.

Gaming: A Data Science Playground

Data science is used to analyze the market and inform product development. There are two primary ways of analyzing game performance:

  • Data mining technique. This is used by companies to detect a game’s efficiency in the market.

  • The mathematical model. These gaming companies work out the optimal point of a game.

These methods are being transferred into the machine learning tools, which are using data science algorithms to elevate gaming companies above the competition. For example, people use a quality gaming tower to enjoy their favorite titles, and games perform at different levels depending on the specs; manufacturers use this data to improve their products and create more powerful computers.

Data Scientists in The Gaming Industry

The role of a data scientist includes investigating theories, designing, and testing developments. As well as this, creating algorithms to suit the techniques outlined above will form a large part of their remit. To become a data scientist attached to the gaming industry, you need to have an active interest in mathematical models, data mining, and the mobile gaming world. In terms of qualifications, you will need to have university-level education or the ability to prove your worth.

Deep Learning and Data Science

Deep learning involves mining enormous amounts of data, and typically takes place on an Advanced Analytics Team. They solve problems and create prescriptive, predictive, and descriptive models rooted in machine learning and deep learning algorithms. Data scientists that enjoy thinking outside the box and consistently learning will thrive in this role.

Game Data Analysis

Game performance levels need to be analyzed in relation to the consumer funnel, which will highlight areas to focus on retention and customer engagement. This job is performed by a game data analyst, and their goal is to service user roadmaps and decrease friction within the market. As well as developing the customer funnel, these data scientists are responsible for creating automated anomaly detection systems.

Use Case Examples

Activision, the studio behind Call of Duty, uses data science to improve the online play in the game. An issue they were faced with was “player boosting”, which involved teams working together to empower a single player’s rating, by dying on purpose. Unfortunately, this impacts the wider community scores and doesn’t reflect fair play. The Game Science Division (GSD) at Activision created a machine-learning algorithm to detect instances of player boosting.

Data science is important to the gaming industry, and helps manufacturers to develop better products and games to operate more efficiently. As the gaming industry continues to grow, data science will only play a more prevalent role.

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