Arthur Parsons (Tt games)
Updated: May 14, 2018
The Use of Data Scientists in Games
Parsons (2018) stated during his lecture the rise and dependency of companies, such as his own, on data scientists, someone whose responsibility lays in their ability to “merge, manage, interrogate and extract data to supply tailored reports to colleagues, customers or the wider organization” (Bonner, 2017). Despite being a very academic field according to Bonner, working at Prospects, the games industry seems to need these data scientists more and more. With the current trend of eSports becoming more popular, even being called “the next world sport” by Fnatic founder and owner, Sam Mathews (2017), more games need to be balanced at its core, a job fulfilled by these data scientists. To reiterate the need for these people within the industry, companies like Riot are looking to hire people to fill these roles (Riot Games, 2018).
Alongside this, the rising prevalence of Live Games or “games as a service” (Lane, 2012) means that balancing over longer periods of time is increasingly integral to the industry. Games like Overwatch (Blizzard Entertainment, 2016) are constantly receiving balance updates, live testing with the public in test servers and entire shifts in gameplay with new characters. Behind the scenes, tracking all of the data that the players create, will be a team of data scientists. The findings will go on to help develop the current game as well as influence future projects, improving the industry as a whole. The hope would be that games with dedicated data scientists would be better games.
Data scientists will not just assist in the development of a game, they will help with the marketing of a game. By knowing sales values in certain locations, age ranges and gender of players, marketing can be targeted toward a more focused demographic, and promotions can be withheld when sales peter off despite heavy advertising. A data scientist will be able to pick apart a games entire life-cycle, and included marketing and build a more accurate and extensive post mortem than a designer could.
The issue with this is that a player base could, as a result, be reduced to numbers and statistics rather than actually people with personalities that change over time. The gathered data would then not be representative of what a consumer actually wants. However, good data scientists are already ahead of this, tracking the human trends and taking it to the next level, as data psychologists (Campise, 2017). As a data psychologist they are able to translate the raw data into human decisions (Shah, 2016). This provides a much more accurate look at the landscape that the data comes from, supplying some – often needed – context.
Blizzard Entertainment. (2016). Overwatch [game]. Santa Monica, CA, USA, Activision Blizzard.
Bonner, K. (2017). Data scientist job profile [online] Prospects.ac.uk. Available at: https://www.prospects.ac.uk/job-profiles/data-scientist [Accessed 5 Mar. 2018].
Campise, K. (2017). Why the Future of Data Science Is Data Psychology [online] RTInsights. Available at: https://www.rtinsights.com/why-the-future-of-data-science-is-data-psychology/ [Accessed 5 Mar. 2018].
Lane, R. (2012). To Protect or Serve? [online] IGN. Available at: http://uk.ign.com/articles/2012/03/02/to-protect-or-serve [Accessed 5 Mar. 2018].
Mathews, S. (2017). ESports: the digital revolution has arrived [interview].
Parsons, A. (2018). University of Bolton Guest Lecture [lecture]. Employability and Enterprise, 05/03/2018.
Riot Games. (2018). Data Scientist [online] Available at: https://www.riotgames.com/en/work-with-us/job/13287 [Accessed 5 Mar. 2018].
Shah, D. (2016). The Future of Data Science [blog] EDX. Available at: https://blog.edx.org/future-data-science-qa-mit-professional-educations-devavrat-shah [Accessed 5 Mar. 2018].