On Choosing Games and What Counts as a "Good" Game

Introduction

Why is it important to justify the choice of games being used as an example in a scholarly article or for the purposes of study? In the early days of games studies, there seemed little call for careful scrutiny of one’s game choices. We studied what we had handy and wrote about the games we were already playing. However, if we want to make the case that the game in question is good by some measure (however we decide to define “good”), then we really should have some evidence to back this up. When a single game or a small number of games are chosen as the subject(s) of study, they form part of the bounded system that is the case being examined, and also form part of what makes the case of special interest (Stake, 1995). If we are proposing the use of a game in the classroom or the study of some specific game to learn something applicable to our agenda, whether that agenda is to examine the educational potential of the game or to learn something else about the game that may inform other instruction, then as academics we have a responsibility to explain why that game is suitable for our purpose.

One reason for putting thoughtful effort into justifying the choice of a game used in a study is that it helps to make the study itself more credible. This has implications for the increased acceptance of games studies academically as well as for helping to improve relations between academia and the games industry. In a recent article offering suggestions for how the Academy of Interactive Arts and Science could build stronger ties with the games industry, Hopson (2006) argues that we should:

Critical and commercial success are key recognizable and accepted (albeit subjective) measures of a game’s popularity, and that popularity in turn gives some indication of that game’s perceived quality as judged by players, developers, and game critics. When it comes to resources that are primarily creative or artistic in nature, subjective measures are often the only ones we have. In sports for example, such as sprinting, determining who the fastest sprinter is can be done quite objectively—it is a matter of comparing competition times, and the runner with the fastest time wins; no such objective measure exists for most creative endeavors, and since games are creative designs, we can only produce subjective measures. To further compound the problem, lists of ‘top games’ tend to be quite unstable and change not only from year to year as new titles gain recognition, but sometimes from day to day as in review sites where players can contribute. One consequence of this is that no single list can reasonably be used to support claims about a particular game’s qualities. One solution is to combine multiple lists into one comprehensive one. By combining multiple lists, we can increase our confidence in the qualifications of games that end up on top. However, the challenge in combining measures from these various sources is that the criteria used to produce lists of ‘good’ games are often so divergent that they cannot be compared or combined directly. Categories and scores vary, the methodology used to rate and rank the games varies, even the contributors vary—in some cases they are paid professional critics; in other cases association members or even the public at large contributes votes and reviews. The data fusion technique described in this chapter offers a solution to this problem that is both verifiable and repeatable. Combining a number of different measures to come up with a single measure ensures that games that end up at the top of the final list qualify as successful by more than one measure and have been assessed by more than one source. Using a systematic approach to ranking games results in a list with which most (industry, gamers, and critics) could agree.