Games analysis: how to stop history repeating itself

  • Authors:
  • Clive Chandler;Len Noriega

  • Affiliations:
  • Faculty of Computing, Engineering and Technology, Staffordshire University, Stafford, UK;Faculty of Computing, Engineering and Technology, Staffordshire University, Stafford, UK

  • Venue:
  • MIV'06 Proceedings of the 6th WSEAS International Conference on Multimedia, Internet & Video Technologies
  • Year:
  • 2006

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Abstract

It is a sad reflection on life that all our experiences still lead to the same circle of mistakes; this was never truer than in the games industry as a whole. Designers struggle to achieve what is called the "Triple-A (AAA)" game, the game that will be the new Pac-man and earn them accolades and a fortune before they reach thirty. One of the main problems is that each new game design follows a design route seemingly ignoring any of the lessons learned from the design of its predecessors. Discussions with a number of design groups at the Games Development Conference in 2004 revealed that understanding exactly what makes a triple-A game is based on a post-mortem review of its success or failure. Whilst this may well appear at first glance to be an analysis of the successes and failures of the offering, it is rather a nodding account of how much money the game made and how popular (how many units sold). Academic papers on games analysis focus mainly on specific areas such as Narratology, Behavioural Design or workshops on the newest techniques etc. Serious games have now become a popular approach to the use of games in education, training etc, however, there does not seem to be an overall treatise on how to analyse a game in general terms or why one would want to. The topic of this paper is to attempt to produce a framework where a success or fail criteria can be employed in order to gain stored knowledge of successes and failures in modern games. Two approaches are postulated and sample analysis case studies are presented. It is hoped that the development of these frameworks will lead to a better understanding of what works and what does not in order to shorten the design process and to enable time to be spent more on innovations rather than the basic functions.