Combining Ethnography and Big Data to Understand the Esports Audience

Key Takeaways:
  • Data without context can be misused easily
  • Understanding esports cultures within their context is key to gathering good data
  • Ethnographic methods and “boots on the ground” field work provide context and insight into how esports cultures work
  • A mixed method approach provides a more robust and accurate picture of the esports audience


Data without context can be a dangerous thing. With the almost limitless potential of analytics it can be easy to tell whatever story you want by framing numbers within imagined contexts, particularly with preconceived notions about what the data represents. Esports is a wide and diverse space, encompassing many games, communities, ways of play, and people. Due to the digital nature of esports communities and fanbases, they also generate a large digital footprint in terms of social data.

Social media data can tell us a lot about who the audience are, demographically, where they browse, and what products they may be interested in purchasing. What that sort of data can’t tell us is who these people are. What makes them tick? What informs their decision making? How do the fans of different games interact? At FanAI, we use a combination of data science and rigorous ethnographic methods like participant observation, fan stories, and in depth interviews. This gives us a powerful combination of both Big Data and Thick Data to better understand the esports audience within its context and deliver powerful insights about the esports audience. These insights help brands and organizations better understand the broad and diverse world of esports communities to better monetize their audience and make smarter, more sustainable investments in the esports world.

The Culture Concept

At their core, ethnographic methods aim to understand culture. “Culture” is a word that gets tossed around a lot, and it can mean a lot of different things; it is often a catchall term for social phenomena and for the mysterious inner workings of groups of people. Within the context of our qualitative research, we rely on an anthropological definition of culture as laid out by Edward B Tylor, one of the founders of cultural anthropology who defined culture as: “that complex whole which includes knowledge, beliefs, arts, morals, law, customs, and any other capabilities and habits acquired by [a human] as a member of society.” While in the academic sense there are more detailed and specific definitions of the aspects that make up culture, for the sake of our discussion, Tylor’s definition is a good baseline for understanding why we analyze culture in gaming.

Esports communities have their own customs, rules, art, ways of knowing, and pretty much anything else you could label as part of a culture and those cultures can span from a whole genre of games to an individual game.

What we do with Ethnography

In order to understand cultures in esports we rely on ethnography. Ethnography in the most basic sense is the rigorous study of culture through an anthropological lens. Culture, as per Tylor’s definition, includes everything related to the ways in which people experience and navigate the social world. Everything a community does in the context of a game reflects that community’s culture. The linguistic rules of emote usage and memes reflect codes of communication while the ways people play and the shifting of the metagame may reflect meaning making behaviors. We can also look at influencers and pro players, and where social capital or credibility in a community stems from and what that credibility means to community members. With that in mind, we can start to get at why ethnography might be useful in understanding esports communities.

More so than just the study of culture, ethnography entails a certain degree of academic rigor. From a methodological standpoint, ethnography aims to investigate truth and get it at the more sticky subjects of human experience and involves commitments to ethical practice. Ethnographic methods commit to ethical practice and minimizing biases by reflecting the data collected back upon the community which helps us keep any assumptions we may have made in check. This reflexivity helps bring to light questions of access, social power, and issues facing a culture or community in a way that leaves data open ended. This differs from standard understandings of audiences in market research which have often strayed toward situating groups within the context of the product or service being studied rather than the context of the people who interact with it.

By building an understanding of audiences through exploratory research and reflexiveness, instead of making assumptions about the nature of a culture and working backward from those ideas, we gain clearer insight into esports communities as they exist within their own contexts. The degree of rigor and reflexiveness employed also aids us in avoiding the pitfalls of self-reporting bias present in data founded on assumptions and leading questions.

How does ethnography support data science

From a data science standpoint, FanAI analyzes fans using social physics principles, based on game and casual streaming data as well as social interactions between fans and professional esports teams. With that data, we can look at flows of human interaction to describe generalized behavioral patterns.

We also look at purchasing habits of those same fans to get a better idea of their spending habits and possible affinity with brands. By putting spending data through clustering algorithms we can define groups of interest. based on the types of spending. This allows us to see the categories fans are actually spending in such as quick serve restaurants, travel, or electronics.

You may be asking where all this spending and social data comes from, and the answer is simple. FanAI acquires all the Big Data directly from esports organizations, including teams and leagues, and data partners. We insist on complete transparency on how that data is used and who gets to see it and audit the whole process. On the same principles all the Thick Data we gain through ethnographic approaches follows the same research ethics and processes of informed consent, anonymization, and access to findings you would find in an academic institution. With that sort of direct line and commitment to data stewardship, we can produce some truly powerful data insights with confidence.

While these kinds of data outputs are powerful on their own, they lack definitions of why or context around the behavior observed in the data, and that is where ethnography further bolsters our understanding. By employing ethnographic methods, such as in person interviews with people at events or diary studies and participant observation, we get closer to understanding the experience of esports fans within the contexts of their communities and within the contexts of events.

The Thick Data produced by ethnographic methods give us insight into the ways they perceive events, and why they attend those events alone or with friends. We can begin to grasp why someone may like one genre of games and not another or why someone may enjoy CS:GO but not Overwatch. Ethnography allows us to situate people who make an audience outside of data points and situate data points within the social contexts of the audience we study. This kind of mixed method approach also grants us the opportunity to ask questions and further investigate the why behind trends we see in the Big Data processing stage.