Game data science is in many ways a relatively young domain, especially when viewed through the lens of academic research. However, the application of data science methods to collect data from games or game companies has expanded so fast and evolved so rapidly that it’s easy to overlook the fact that a decade ago, using machine learning algorithms on game data was largely unheard of. The history of game data science can thus be thought of as being shallow but broad.

Challenges faced in the history of game data science

In general, there are several challenges to mapping the history of game data science:

  • First, the substantial amounts of knowledge generated aren’t recorded anywhere that’s publicly available. Companies invest resources in business intelligence, and the results are often treated as confidential due to their business value. Similarly, early academic research in the area is published across a dozen or more domains and thus is extremely fragmented.

  • Second, there has been substantial parallel growth in different sectors and countries, and thus it’s hard to say when a specific technology was developed or how it influenced the development of the field.

  • Third, any account of the historical perspective will naturally be biased by the specific area of focus or community that the author comes from.

Get hands-on with 1200+ tech skills courses.