Anders Drachen

Anders Drachen, Ph.D. is a veteran Data Scientist, Game Analytics consultant and Professor at the DC Labs, University of York (UK).

Telemetry one of the fundamental terms in game analytics, describing the collection of data over a distance. The collection, analysis and reporting of user-behavior telemetry is the foundation for current analytics in game development. There is some confusion about the term, however, so we wanted to provide a bit of information about what telemetry is and its properties.


The collection and application of telemetry has a history dating back to the 19th century where the first data-transmission circuits were developed, but today the term covers any technology that permits measurement over a distance (derived from Greek: tele = remote; metron = measure). Common examples include radio wave transmission from a remote sensor or transmission and reception of information via an IP network.

Game telemetry is the term we use to denote any source of data obtained over distance which pertain to game development or game research. There are many popular applications of telemetry in games, including remote monitoring and analysis of game servers, mobile devices, user behavior and production. The source of telemetry most strongly represented in current game development is user telemetry, i.e. data on the behavior of users (players), for example on their interaction with games, purchasing behavior, physical movement, or their interaction with other users or applications.

Game telemetry data can be thought of as the raw units of data that are derived remotely from somewhere, for example an installed client submitting data about how a user interacts with a game, transaction data from an online payment system or bug fix rates.

In the case of user behavior data, code embedded in the game client transmits data to a collection server; or the data is collected from game servers (as used in e.g. online multi-player games like Fragile Alliance, Quake and Battlefield).

Naming conventions

The actual data being transmitted follow different naming conventions depending on the field of research or application domain that people are applying the data to. This can cause some confusion when reading articles on game analytics. The essence is that telemetry is measures of the attribute of objects (or items) – the latter which should be understood broadly to include people and processes. For example, the location of a player character as it navigates a 3D environment. In this case the location is the attribute, the player character the object. Conversely, the length customer service calls generated from a newly released patch in an MMORPG sees the length of the calls as the attribute of the customer service calls.


In order to work with telemetry data, the attribute data needs to be operationalized. This means deciding a way of expressing the attribute data. For example, deciding that the locational data tracked from player characters (or mobile phone users) should be organized as a number describing the sum of movement in meters. Operationalizing attribute data in this way turns them into variables or features – the term varies depending on the scientific field: in Experimental Psychology variable is used, so this term is generally seen in articles and conference presentations on telemetry used in game user research, whereas feature is used in Computer Science, so this term is used in e.g. data mining articles.

This is just a general guideline – naming conventions vary considerably because game analytics is not a domain with established standards, so care must be taken when consulting the literature on game analytics (such as it is). Finally, variables/features have a specific domain. The domain is the set of all possible values – defining the domain is essentially what operationalizing attribute data is all about. For example, a binary domain allows only two values (e.g. 0 or 1).

 Raw telemetry data can be stored in various database formats, which are ordered in such a way that it is possible to transform the data into interpretable measures, e.g. average completion time as a function of individual game levels, average weekly bug fix rate, revenue per day, number of daily active users, and so forth. These are called game metrics. Game metrics are, in essence, interpretable measures of something, as long as this something is related to games.

Metrics is what we use in our analyses of player behavior, and a subject we will go into more detail with in future posts.


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Anders Drachen

Anders Drachen, Ph.D. is a veteran Data Scientist, Game Analytics consultant and Professor at the DC Labs, University of York (UK).

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