Anders Drachen

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

In a previous post we discussed what game telemetry is, i.e. the data we use as inputs in game analytics processes. Game metrics is what telemetry is transformed into, and what provides the direct value to development and monetization. In this post we try to set up a definition for what a game metric is.

While the term “game metric” has become something of a buzzword in game development in recent years; metrics have arguably been around for as long as digital games have been made, but the application of game telemetry and game metrics to drive data-driven design and development has expanded and matured rapidly in the past few years across the industry.

From telemetry to metrics

Game metrics start with raw telemetry data, which can be stored in various database formats, ordered in such a way that it is possible to transform the data into various interpretable measures, e.g. average completion time as a function of individual game levels or revenue per day. These are called game metrics. Game metrics are, in essence, interpretable measures of something. They present the same potential advantages as other sources of BI, i.e. support for decision making in companies. Metrics can be variables/features and vice versa, or more complex aggregates or calculated values, for example the sum of multiple variables/features.

To take an example: telemetry data from a shooter like Quake could include data on the location of the player avatar in the virtual environment, the weapons used, and information on whether every shot hits or misses, etc. These are different attributes, and they can be converted to variables/features such as “number of hits” or “number of misses” with a domain from 0-1000 (with 1000 being the biggest number of hits scored for a specific level). In turn, these simple variables/features can form the basis for analysis, e.g. calculating the hit/miss ratio for each level or map in Quake (e.g. “hit/miss ratio is 1.2 on average for the “Total Mayhem” map”). An alternative is to use the variables/features “playerID”, “session length” and “points scored” to calculate the metric “points scored per minute” for each player.

These kinds of measures, that are based on calculations involving several variables/features, is what is usually referred to when the term “game metric” is employed. However, there is no standard terminology widely accepted in game analytics, so be prepared for variations.

Variable, feature, metric

Just to make sure the confusion is complete, it is important to note that most types of analysis and analytics software do not separate between a simple variable/feature or metric, or a more complex metric – when it comes to inputting measures into an analysis, they will follow the same naming standard – as specified by the software. For example, in the statistics package SPSS (PASW in newer generations), all measures of an object or objects are called “variables”. It does not matter whether this variable is a simple operationalization or a number calculated using a dozen such variables.

Metrics and the time frame

Metrics are usually calculated as a function of something. The typical unit is time, but can also be game build, country, progression in a game, or players, to name a few – which is one of the reasons the GA tool provides the ability to view metrics against any of these factors. All metrics are bound to some sort of timeframe, and this will always be from a past period – we cannot collect telemetry from the future (sadly). However, it is possible to run predictive analyses based on telemetry data, which can generate metrics for future behavior, e.g. expected sales figures, expected churn rate, expected number of players, etc. – however, these will always be based on predictions with a specific uncertainty attached, whereas collected telemetry data – if collected correctly of course – are facts.

To sum up, and provide a tentative and sufficiently broad definition, a game metric is a quantitative measure of one or more attributes of one or more objects that operate in the context of games.

Translated into plain language, this definition clarifies that a game metric is a quantitative measure of something related to games. For example, a measure of how many daily active users a social online game has; a measure of how many units a game has sold last week; a measure of the number of employee complaints the past year; task completion rates in a production team for a specific title, etc. – these are all game metrics because they relate directly to some aspect of one or more games.

Conversely, metrics that are unrelated to the games context, for example the revenue of a game development company last year, the number of employee complaints last month, etc., are business metrics. The distinction can be blurry in practice, but is essential to separate what is purely business metrics with those metrics that relate to games, of which a number are unique to game development (in how many other IT sectors can “number of orcs killed per player” be a business metric?).

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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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