作者：Danc

在上一篇文章中我提到了不同生产流程以及它们与各种风险间的关系。而在接下来的文章里我将具体阐述减少这些风险的技巧。

第一个技巧–受数据驱动的游戏开发，通过投入于低风险游戏功能和进程能够降低执行风险。这种降低执行风险的功能是很多游戏开发者以及当前众多开发公司所采取的减少风险的主要技巧。与简捷的产品开发相比，这种方法更有优势。但是也存在着一定的影响因素。

如果我们把受数据驱动的开发置于复杂过程的范围来看，那么我们将会想把这一过程清出复杂和混乱过程区域外而转向简单的区域里。简单的开发过程容易控制，并且更容易扩大规模，且不大可能出现灾难性的失败。

从概念上看，受数据驱动的开发依赖于两大技巧

·专注于现有的低风险行动

·把中等风险行动中转变成低风险行动

专注于低风险行动

首先你需要定义一些低执行风险项目，并专注于它们。以下我将列出一些按照风险等级排序的普通游戏元素：

1.新的核心游戏机制：高风险

2.新的世界性技术如人工智能：中高等风险

3.新设置/IP/品牌：中等风险

4.关卡设计：中等风险

5.更多相同技术如经过改良的效果图：中低风险

6.图形/过场动画：低风险

如果你着眼于当代游戏开发所需要的资源，那么你将会发现游戏经费优先问题是与它们的执行风险相违背。在整个游戏开发过程中，投入于核心游戏机制的人员较少，而图形制作的人员最多，关卡设计人员趋于中间等等。这是一个粗略的经验法则，但是却非常有效。

把中等风险行动中转变成低风险行动

下一步便是简化复杂或复合任务并将其转变成简单的生产过程。过去的游戏开发总是负担着一些非常令人讨厌的任务：

硬件平台上关于自定义代码编写的最后期限仍然摇摆不定。

美工人员在摸索新技术的过程中总会碰上一些让人讨厌的计划风险。

生产周期最后组合游戏元素便意味着，关卡设计和分分秒秒的游戏玩法只有在游戏开发的最后阶段才能接受测试。

完全简化这些区域并不适合？

从历史观点说，复杂过程的简化曾经出现于新型大众媒体生命周期的初期。让我们以动画作为发展的典例加以说明。很久以前，每一个艺术家都有自己独特的艺术风格。如果你想要Norman Rockwell（游戏邦注：20世纪早期的重要画家，作品横跨商业宣传与爱国宣传领域）的插画，你便可以去寻找他的作品前来观摩。在早期的动画制作中，你经常会要求动画作者创造出许多动画框架。那时候的动画都很短，而且主要属于劳动力密集型工作。

迪士尼（以及其他公司）也开始致力于高度重复的动画工作中。他们不再生产一系列具有相同视觉特征的图像，因此减少了风险。而是让一名美工人员画出一特定图，然后有好几百人同时按照这一幅图进行临摹，画出无数的米老鼠。通过定义特定的游戏角色并将生产过程趋于标准化，使得早期的动画工作室减少了一定风险，同时那些存在的风险也都是位于同一个平面上。

在游戏开发中也出现了相同的风险缓解过程。

美工人员太不灵活了？通过使用一些标准化道具，如Maya，Max以及Photoshop等便能解决这一问题。当这些工具集中在一起变成一个统一的工具链时，它们将能推动复杂图像的创作并降低计划风险。

令人崩溃的自定义代码？利用第三方渲染技术如Unreal或Renderware以减少你自己运行技术的风险。

在生产周期后期组合游戏元素？我研究了《God of War》所涉及的一些情况，该游戏的7个程序员创造了一个非常协调的工具链，让设计者能够变成一个高产但无需大量技能的生产齿轮。游戏机制的所有问题都简化为“按钮和播放动画”这一类问题。你也可以尽早或者反复进行游戏测试。

受数据驱动的游戏开发

这些简化过程所做的努力作为一种生产模式被广泛定义为“受数据驱动”的开发。那些低风险开发元素，如创造图形，模式和其它静态内容等都成为了游戏的主要成本。以下是一些普遍做法：

开发公司寄希望于几名程序员负责创建的集中引擎。开发工具扮演着一个前所未有的角色。

游戏机制经常效仿现有的一些游戏类别。

团队通过使用一些明确的内容渠道将数据流简化成游戏引擎。使那些没有变成技术的人也能够制作一些简单的美术道具。而这些数据将能够迅速转变成游戏引擎让玩家在进行游戏时一目了然。

团队将增加美工人员/设计者以完成游戏内容。

通过提供最刺激且优美的静态内容，以此包装核心游戏机制。

虽然受数据驱动的开发能够优化许多内容，但是却不能彻底完善游戏开发的所有方面。一些游戏因素如新游戏机制便还未被优化成简单的生产过程，它们仍然固执地停留在复杂，甚至混乱的过程中。公司总是尽可能地挑选一些经过实验的游戏机制，以尝试着缓解这些风险。

《God of War》便是这种发展过程的典例。他们通过运用一些经过证明的游戏机制，建立了一个强大的内容渠道，以此获得巨大的生产效益。如今的游戏设置与早前的差别不大，但是它们中一些华丽的内容却让整个游戏体验瞬间焕然一新。每当你按压按钮打开一扇门的时候，你都将会看到一个新的动画形象。每当你杀了一只怪兽时，你也将看到一个新的动画。这种机制让这款游戏显得更加有趣。比起过去那种简捷的游戏开发过程，受数据取得的开发显然更加优秀。这种开发有利于减少各种执行风险：

技术风险：因为绝大多数游戏报酬都是来自于内容而非复杂的系统，所以将大大减少技术性风险的出现。当动画师能够冒着较低的风险制作出相同的动画时，你也就不需要使用复杂的物理引擎了。这时候整个代码长度将会缩小，而你便不再需要大量的程序员加入。《God of War》中可执行的代码长度是1.5兆，所以仅需要7名程序员便能完成任务。

质量风险：通过在游戏开发初期创建一个紧凑的内容渠道，便能让你早些发现错误并及时纠正错误。因为这时你是在处理数据而非代码，所以代码的错误也将会急剧减少。

计划风险：因为游戏内容是按照低风险的线性规模产生，你将可以让更多人去解决问题，或者在出现一系列计划风险后缩减游戏范围。

人员风险：确立容易执行的内容与易于掌握的制度，这种迪士尼式的管理控制模式可以用于关卡设计过程中。这种模式让游戏公司能够使用一些技术较低的设计者扮演“生产齿轮”的角色。这些设计者可被替换，很容易训练且可以安排到大规模工作团队中。

这是一个让人印象深刻的壮举。如果你曾经经历过挫败的游戏开发过程，那么当你看到《God of War》出现在游戏开发者大会上时，你一定会觉得这种感觉太棒了。我们都想要创造一款高质量的游戏，让我们能在开发过程的最后一天愉快庆祝。如果你不去理解开发中的技巧和过程问题，你的技能将被掩埋，而你的游戏将遭受重创。

受数据驱动的开发所带来的机遇

通过执行受数据驱动的开发过程将能获得巨大的利益，以下将其分成两类加以说明：

王牌类型与成熟类型的比较

“故事类游戏”主要依赖于优秀的静态内容

那些处在某个游戏类型生命周期末尾的作品便是最后的赢家。这些团队主要是服务于一些定义明确的市场，而这些市场主要受众是“出道较晚”的用户。那些服务于硬核类型玩家的游戏处于不利的高风险中，但让游戏采纳一些低风险功能是没有问题的。但是那些购买了《Halo 2》的玩家就会愿意接受游戏机制的根本性转变（就如《Animal Crossing》）吗？这就不见得了。

还有一些特殊的游戏类型受益于数据驱动的游戏开发。日本的角色扮演游戏，图形冒险游戏等都提供给玩家强大的线性叙述方法，使用完整的内容渠道以减少执行风险。

这种“故事类游戏”与其它用线性叙述格式（如电影或书籍等）描述故事的方法展开了激烈的“肉搏战”。每一个游戏新续作的出现，都会让电影体验（如《Advent Children》）和游戏体验（如《FFXII）之间的鸿沟开始慢慢缩小。这些作品使交互性不再成为其提供给玩家的主要价值所在。而如果游戏能够提供越多高质量的静态内容，那么玩家将能获得更棒的体验。

受数据驱动的开发带来的问题

受数据驱动的开发将在未来的游戏产业中占据非常重要的一部分。比起过去的那些古老方法，它更能有效地处理一些风险问题。但是我们也应该注意这种开发中存在的一些问题。

市场问题：产品差异化不大

成本问题：生产成本较高

游戏玩法问题：玩家倦怠率过高

市场问题

新游戏如何获得竞争力？这真的是一个大问题。我想声明的是，游戏中唯一的价值定位是关于互动性的革新问题，而非那些围绕于玩家与游戏间静止的交互性因素（如图像或者级别设计）。举个例子来说把，在《任天狗》中，游戏的价值是来源于玩家与“狗”的互动而非游戏图像的分辨率。甚至在游戏《God of War》中，游戏的核心价值是源于游戏中的战斗而非那些华丽的动画角色。

受数据驱动发展依赖的一些低风险行动刚好也是一些低价值的行动。你可以在游戏中适当加入一些图像，级别设计和动画因素，因此提高玩家的游戏体验。如果一家游戏公司只是通过图像和关卡设计等因素进行竞争，那么可以肯定，这家公司将会因为大量无差异化产品而衰弱。

在中间件刚出现的早期，Epic便开始授权Unreal游戏引擎的使用了。获得授权者替换了图像和关卡设计元素，但是却未能改革新游戏机制，他们认为自己获得了新的且具有吸引力的内容。但玩家却认为那是一些充斥着无聊游戏机制的劣质第一人称射击游戏。它们与由Quake和Unreal主宰的市场背道相驰，所以这些游戏最后都遭遇失败。这是件多么讽刺的事啊，差异化内容的游戏也不足以应对激烈的市场竞争。

成本问题

另外一个问题便是，缓解风险的策略并不能帮助降低生产成本。可以确定的是，线性生产规模遭遇的风险较小，所以开发团队必须尽可能地进行反复劳作以应对竞争。很多公司盲目地选择那些最明显且风险性较低的竞争选项。

这一做法造成了一种恶性循环：

大家都争着推出拥有“最佳视觉效果”的游戏

开发团队将所有资金投入于低风险的生产因素以赢得竞争。花钱来降低执行失败的风险并生产出仍然不失竞争力的游戏。

当出现其他拥有“更佳视觉效果的”竞争者时，游戏制作人并不担心，因为他可以召集更多新的美工“齿轮”生产出更多富有竞争力的产品。这不会耽搁了游戏的制作，并且能让游戏更有吸引力。可以说这是一种简单的管理对策。

发行产品并已取得不错的销量。不幸的是，因为额外的生产成本，你反而会因此遭到损失。

虽然你的游戏因为财政亏损而失败，但是那些竞争公司也仍然在往自己的游戏里投入更多的资金，以提高其游戏的质量。

这种恶性循环让游戏公司不能快速地获得市场利益，并导致市场上充斥着大量低利润的游戏。虽然有一小部分因为类型突出而盈利，但是亏损的公司还是占大多数。

游戏玩法问题

如果我们着眼于动作类/反应类游戏模式中的数据驱动因素，那么我们能够预测数据驱动游戏中的一些关键特性。

数据驱动的游戏通过两种游戏设计因素吸引玩家：

情感反馈：指通过影响大脑而做出反应，就像自己被置身于一个真实的世界，深刻感受到周边的环境。你可以关注于一些情感内容与现实行为。《Final Fantasy》便在这方面做得很出色，这款游戏几乎在每一个画面中都提供给玩家强烈情感画面体验。

唯一反馈：这种反馈是指玩家在每次完成行动后都会经历不同的反馈。这一说法源自《God of War》之父David Jaffe追求“各种各样的特殊情况。我不希望从任何两扇门走出去都是同一个地方。”这时候你将仍然按压相同的按钮，但是你将会看到不一样的结果。

情感反馈的倦怠率较高，特别在反复使用这一机制时。当你第一次消灭了“Minotaur”（游戏邦注：《英雄无敌》系列中的牛头怪一族）时你会觉得很兴奋。但是越往后当你接触到更多怪物时，这种兴奋感便会渐渐淡灭。

唯一反馈看起来是个不错的游戏构思，但是它存在着一些弊端。反复进行游戏后，玩家便能够凭直觉摸清“唯一”反馈后的游戏模式。就像玩家在《God of War》中，当他们看到一些歪斜状的雕塑后，总会出现另外一只动画角色一样。当玩家对游戏模式一目了然后，他们的倦怠期将会先于设计者的预测出现。

而这将重蹈《Quake IV》的覆辙。因为玩家发现游戏中过多的相似点而导致这款游戏最终惨败，他们对其采用的昂贵新图像引擎和过多内容嗤之以鼻。

从游戏设计的角度来看，受数据驱动的游戏能更快吸引玩家的注意，但是也会很快地被他们遗弃。这是一种易接触和消耗性的娱乐产品，但是却不能建立更深程度的用户关系。当你不能与玩家建立深层次的交流时，游戏品牌也将受到严重的影响。《Crash Bandicoot》，《Sonic the Hedghog》和《Lara Croft》等游戏都未能与玩家进行较好的交流。

与这些游戏对比，《马里奥》这款游戏更多地依赖于不断更新的游戏机制。任天堂通过提供给玩家一系列独特的且高价值的游戏体验，以更好地衔接起游戏角色与玩家间的关系，进一步推动其更多游戏的发展。任天堂不只为玩家提供了更多差异化内容，也始终关注于游戏设置，以避免玩家对游戏产生倦怠或者游戏出现任何意外风险。

结论

受数据驱动的游戏开发是关于游戏开发当前发展状况的一大观点。它能够缓解困扰游戏开发多年的众多问题。

如果你关心自己现在所做的事，那么你就应该理解受数据驱动的游戏开发的相关实践和影响。这可以说是当前电子游戏行业未来的发展方向，同时这种观点也将影响大部分专业游戏开发者的工作环境。我们中有许多人已经接触数据驱动开发多年，但是我们所期待的华丽内容却不再适用于游戏后续作品，就像我们在《God of War》所看到的那样。

如果你能合理使用这种技巧，那么将获得巨大的帮助。纯粹的受数据驱动的开发能够推动那些成熟的游戏在市场上占据重要的份额。这种受类型主导的游戏对于游戏发行商来说至关重要，同时也有利于减少潜在的执行风险。但是，游戏公司若不想品牌价值受损，就要谨慎考虑将同一个游戏机制应用于2至3款游戏的做法。

总的来说，受数据驱动的游戏开发可以说是现今市场上很欠缺的一个环节。如果你是初次涉及游戏开发，那么你也许会面临较低的执行风险，但是设计风险和财政风险却不容小觑。如果你不能创造出独树一帜的游戏类型，那么仅仅依靠有趣的静态内容是远不能建立起属于自己的利基市场。这时，不论你前期在市场上投资了多少，都不能帮助你最终获利。

《God of War》对于索尼来说也是一个巨大的风险挑战，毕竟索尼也向其投入了高额的市场预算和开发预算。虽然这款游戏受到了强烈的好评，但是销量却不尽人意，远不及AAA级游戏的水平。在2005年4月份，即游戏发行的第一个月，该款游戏在美国的销量仅达20万，而到6月份其在全球的销量也才达到50万。但是对于那些AAA级游戏来说，销量都是在90万至120万之间浮动。

随着索尼继续推广该游戏的行动，我只希望他们能够从中达到收支平衡或者赚的一小点利润。无可否认，索尼正在尝试着创造《GOW》后续款。他们想借此弥补第一款游戏的损失。显然这是一个很简单的策略。创造一款高价游戏，让这款游戏吸引广大公众的注意，能够让他们重新提起对第一款游戏的注意力。

我一直很好奇，如果世界上前五大游戏发行商能够为了获得长远的盈利而以昂贵的游戏产品为代价，那么小型的游戏开发商该怎么办呢？但是不管怎么说，那些依靠早前的游戏机制而发行以内容为基础的游戏产品都是一种昂贵且有风险的策略。

受数据驱动的游戏开发是一种减少风险的策略。如果每个人都来到这个“庇护所”规避风险，那么显然这会造成市场失控的局面。所以受数据驱动的游戏开发只是一种辅助工具，所有的开发者都应该合理使用这一方法。

游戏邦注：原文发表于2006年4月5日，所涉数据和事件均以当时为准。（本文为游戏邦/gamerboom.com编译，如需转载请联系：游戏邦）

Managing game design risk: Part II – Data Driven Development

Read part Managing game design risk: Part I here. It provides an overview of different classes of production processes and their relationship to various forms of risk. The next two essays will talk about common techniques for reducing risk.

The first technique, data driven development, involves lowering execution risk by investing in lower risk product features and processes. This low execution risk strategy is the predominant technique used by game developers to reduce risk and is currently considered state of the art by most development houses. Compared to na?ve product development there are considerable advantages. However, there are also some unexpected side effects worth taking into account.

If we think about data driven development in terms of our process complexity spectrum, we want to drive processes out of the complex and chaotic zones down into the simple zone. Simpler development processes are easy to manage, easier to scale to larger teams, and far less likely to fail catastrophically.

Conceptually, data driven development relies on two techniques

Investing in existing low risk activities

Converting moderate risk activities into low risk activities.

Investing in existing low risk activities

The first step is to identify low execution risk items and invest in them. If we were to rank some common game elements according to risk, they would look something like this:

1.New core game mechanics: High risk

2.New to the world technologies such as better AI: Moderate-high risk.

3.New Setting / IP / Brands: Moderate risk

4.Level design: Moderate risk

5.More of the same technologies such as improved rendering: Moderate-low risk

6.Graphics / Cut scenes: Low risk

If you look at the resources spent on modern games, you’ll find that game funding priorities are roughly the inverse of their execution risk. Core game mechanics have the fewest number of people working on it during the length of the project, graphics of various types have the most, level design tends to sit someplace in between and so forth. It is a rough rule of thumb, but it works reasonably well.

Converting moderate risk activities into low risk activities

The next step is to simplify complex or complicated tasks in the hope of turning them into simple production processes. Game development in the past was saddled with some truly unpleasant risks.

Crazy custom code written under tight deadlines for hardware platforms that are still in flux.

Artist fumbling their way around new technology often introduced unacceptable schedule risk.

Assembling the game elements at the end of the production cycle often meant that level design and minute-by-minute game play wasn’t testable until very late in the development cycle.

Wouldn’t it be nice to simplify these areas dramatically?

Historically, the simplification of complex processes has occurred during the early stages of any new mass media life cycle. Let’s consider animation as an example of the typical evolution. Once upon a time, each artist drew in a unique style. If you wanted a Norman Rockwell illustration, you pretty much had to go to Norman Rockwell. In the case of early animation, you typically required one animator to create most of the frames of the animation. Animations were short, labor intensive affairs

Walt Disney (and others) came along with production techniques for the highly repetitive task of animation. They reduced the risk of producing a series images with the same visual characteristics. Suddenly, instead of having one artist working in a particular style, you could have hundreds, all laboriously following style sheet, all drawing Mickey Mouse. By defining specialized roles and standardizing the production process, early animation houses reduced risk and enabled the task to scale in a linear fashion.

The same process of risk reduction is occurring in game development.

Artist fumbling? This issue is solved by using standardized tools like Maya and Max and Photoshop. When they are tightly integrated in a unified tool chain, they increase the speed of creating complex graphics while reducing schedule risk.

Crazy custom code? Replaced by 3rd party rendering technologies like Unreal or Renderware which reduce the risk of rolling your own technologies.

Assembling game elements at the end of the production cycle? I sat through a lovely talk on God of War in which 7 programmers created a highly tuned tool chain that allowed designers to act as highly reproducible, lower skill production cogs. The entire issue of game mechanics was reduced to a ‘push button and play animation’ sort of affair. You could also play test levels early and often.

Data driven development

The result of these process simplification efforts is a production model known broadly as ‘data driven’ development. By focusing on low risk development elements, tasks like creating graphics, models and other static content becomes the primary cost center for the title. The following practices are common.

The company invests in a centralized engine maintained by a relatively small number of programmers. Tool development takes on a hitherto unprecedented role.

Game mechanics are usually borrowed from an existing genre.

The team streamlines the flow of data into the game engine using a well defined content pipeline. This allows people without programming skills to build much of the using artist friendly tools. The data is rapidly translated into the game engine where it can be viewed running in the actual game.

The team then ramps up the number of artist / designers to fill the game with content.

The focus of the title is on providing the most exciting, highly polished static content to wrap the core game mechanics.

Data driven development optimizes many but not all aspects of game development. Some gameplay elements such as new game mechanics have not yet been reduced to simple production processes. These remain stubbornly in the land of complex or even chaotic process. Companies try to reduce these risks as much as possible by selecting well explored game mechanics.

God of War is a great example of this development process. They gained impressive production efficiencies by building a strong content pipeline around the proven gameplay. The core game play differed only mildly from beat ‘em ups from ages past. However, they make the experience feel fresh by skinning the mechanics with flashy content. Every time you pressed a button to open a door, you saw a new animation. Every time you killed a monster, you saw another new animation. It looks great and it doesn’t play all that badly either.

Benefits of data driven development

Data driven development is clearly superior to na?ve game development processes of the past. It results in dramatic reductions in all forms of execution risk:

Technical risk: Because most of the game rewards come from content instead of complex systems, the need for taking on technically risk work decreases. There is no need to put in a complex physics engine when an animator can produce the same results with less risk to the overall project. The overall code size is much smaller and you need fewer programmers. The God of War executable was only 1.5 MB in size and this was produced by a mere 7 programmers.

Quality risk: By create a tight content pipeline that is regularly exercised early in the development process, errors are found early and fixed early. Because you are dealing with data instead of code, code errors decrease dramatically.

Schedule risk: Because game content is produced in a manner that scales linearly at low risk, you can easily throw more people at the problem or cut back scope if there is a threat of schedule risk.

People risk: By having content readily playable and instituting style guides, Disney-style directorial control can be imposed on areas like level design. This lets companies use relatively low skill designers as ‘production cogs’. They are replaceable, easily trained, and scalable to large work teams.

This is an impressive feat. If you’ve ever lived through a poorly run game development process, the God of War presentation at GDC sounded like heaven. We all want to create a high quality game that lets us spend the last day of development on the beach. If you don’t understand the techniques and processes involved, your skills are behind the curve and your games will suffer.

Opportunities for deploying data driven development

There are two classes of game that benefit strongly from implementing a data driven development process

Genre king brands competing in mature genres

‘Story games’ that rely primarily on quality static content

The obvious win are products that are at the end of the genre life cycle. Such teams are serving a well-defined market populated primarily by late and laggard adopters. Since the hardcore ‘genre addict’ customers they server are highly risk adverse, it is okay for the product to sport low risk features. Does anyone who buys Halo 2 really want a radical shift in game mechanics like you find in Animal Crossing? Not likely.

There are also very specific genres of game that benefit greatly from data driven development. Japanese RPG titles, graphic adventure games, and other games whose primary value comes from giving the player a strong linear narrative can use a robust content pipeline to cut their execution risk.

These ‘story games’ compete head-to-head with the tales in other linear narrative formats such as movies or books. The gap between the experience of a movie like Advent Children and a ‘game’ like FFXII diminishes with each new sequel. Such titles have forsaken interactivity as the primary method of providing players with value. The more high quality static content, the better.

Problems with data driven development

Data driven development is the future for a large portion of the industry. It is simply so much more effective at managing risk than older methods. However, there are issues that the smart development team should take into account.

Market issues: Low product differentiation

Cost issues: High production costs

Game play issues: High player burnout

Market issues

How do new games compete? This is a big question. I humbly submit that the unique value propositions in games typically are centered on innovation in interactivity, not the static fluff wrapping the interactivity. For example, in Nintendogs, the value comes from interacting with a dog, not from the resolution of the wallpaper in the room. Even in God of War, the core value comes from the combat, not the pretty animations.

The low risk activities that data driven development relies upon also happen to be low value activities. You can easily add considerable graphics, level design and animation to a product and only add incremental value to the customer. When companies compete using fluff elements such as graphics and level design, you end up with a slew of undifferentiated products.

In the early days of middleware, Epic started licensing their Unreal engine. Licensees replaced the graphic and the levels, but failed to innovate in terms of new game mechanics. The licensees believed they were offering new and exciting content. Players on the other hand, saw this flood of titles as inferior FPS with tire game mechanics. They ran smack into markets dominated by genre kings Quake and Unreal. These titles failed. Ah, the irony. Differentiated content is not enough to compete.

Cost issues

The other issue is that the strategy of risk reduction is not a cost reduction technique. By ensuring that production activities scale in a linear fashion at low risk, all that happens is that development teams are encouraged to do more of the same in order to compete. Most companies blindly invest in the most obvious and lowest risk competitive option.

This is creates a vicious cycle:

The competition releases a title with ‘The best graphics ever’

Your team chooses to compete by putting money into the lowest risk production element. By simply spending more money, they can keep their risk of execution failure low and still mount a competitive product.

When another competitor comes out with ‘Even better graphics’, then your producer doesn’t mind at all hiring a baker’s dozen of new art cogs to up the ante. It won’t delay the product and could give the game an edge. This is an easy management decision.

The product releases and sells a very reasonable amount. Unfortunately, due to the extra production costs, it posts a loss.

Even though your title is a financial failure, it provokes other competing companies to spend more on the quality of their titles.

The cycle slowly drives profitability out of the marketplace. The results are low profitability games that launch in intensely competitive markets. A small percentage remain profitable by capturing the genre king crown, but most lose money.

Gameplay issues

If we look at data driven games from our action / reward game play model, we can also predict some key characteristics of data driven games.

Data driven games encourage player addiction by relying on two classic game design elements:

Visceral rewards: Visceral rewards are ones that attempt to trick the brain into reacting as if it were put into a real world, high intensity situation. You see a strong focus on emotional content and realism. Final Fantasy does this quite well by providing highly emotional drama in almost every cut scene.

Unique rewards: Unique rewards are rewards that are different each time the player successfully completes an action. The classic quote from David Jaffe is his desire for “Lots and lots of special cases. I don’t want any two doors to open the same way” You are still pressing the same button, but the results appear unique.

Visceral rewards create very intense rewards. This can be very delightful to jaded players. Unique rewards, in the form of new animations, ensure that the rewards give the player a solid buzz each time they are encountered.

Visceral rewards have a high burnout rate, especially if they are reused. When you kill the Minotaur for the first time, it is quite exciting. After dealing with several of the beasties, the thrill of the kill wanes.

Unique rewards might seem like a good game idea, but they too have a dark side. With repetition, players will eventually grok the deeper patterns behind the ‘unique’ rewards. Ultimately, they see the impressively tumbling statue in God of War as yet another animation that opens a door. When the gameplay patterns become visible, players can prematurely burn on the title far sooner than the designer predicted.

This can lead to situations like Quake IV. Here the franchise was damaged because players focused on how the mechanics are ‘more of the same.’ They dismissed the new expensive graphics engine and the plethora of content that was intended to differentiate the product in the market place.

From a game design perspective, data driven games are a quick jolt of brain candy to be consumed and forgotten. They are low touch, consumable entertainment product that fails to build a deep and loyal customer relationship. When your ability to connect with your customer at a meaningful level is threatened, you have surprisingly high franchise churn. Crash Bandicoot? Sonic the Hedghog? Lara Croft? These titles’ relationship with the customer was much less secure than they imagined.

Contrast this to Mario, a franchise that relies much more heavily on constantly updated game mechanics. By offering players a series of unique high value game play experiences, Nintendo creates a strong customer bond with the character that primes the channel for future titles. Nintendo generally avoids player burnout and accidental damage to their highly valuable franchise by focusing on gameplay, not merely lobbing undifferentiated content at their customers.

Closing thoughts on Data Driven game development

Data driven development represents one vision of the current state of the art in game development. It eases a large number of issues that have plagued game development for many years.

If you care at all about why you do the things you do, you need to understand the practice and repercussions of data driven development. It is the current future of the console industry and its driving philosophies will shape the work environment of most professional game developers. Many of us have been doing flavors of data driven development for years, but expect extremely polished pipelines like the ones found in God of War to become the default for the upcoming generation.

It is a potent technique if used correctly. Pure data driven development is a solid strategy for titles in mature genres that have a brand capable of capturing the first or second place in the market. Such genre king titles are the bread and butter of any publisher and reducing execution risk is of paramount importance. However, companies should be careful of producing more than two or three titles that use the same game mechanics or they risk devaluing the franchise.

For all others, data driven game development tends to be a very expensive way fail in the marketplace. The execution risk may be low, but design risk and financial risk are actually higher than if you did na?ve game development. If you can’t capture the genre crown, exciting static content is rarely enough to carve out your own niche within the marketplace. You’ve invested a lot up front and you won’t get the sales on the backend.

Even a title like God of War is a huge risk for Sony. They’ve supported it with a large marketing budget and a decent sized development budget. It certainly has gotten them rave reviews, but sales have been disappointing for AAA title. First month US sales are reported at only 200k for the month of April and by June of 2005, they had reached 500k world wide. The mythical typical AAA title needs to sell anywhere from 900k to 1.2 million depending on how the books are kept.

With continued promotion, I’m hoping they’ll at least break even or turn a small profit. Admittedly, Sony is attempting to create a new next generation IP. They are willing to take a moderate loss on the first title. It is a simple strategy. Create an expensive splash, get the new brand in the public consciousness and then scoop up the preorders for GOW2.

I have to wonder though. If we’ve already gotten to the point where one of the world’s top five publisher has to release a very expensive title as a loss leader in order turn a future profit, where does this leave the smaller companies? Screwed and tattooed. At the very least, it suggests that launching original content-based IP that relies on older game mechanics is an expensive and risky strategy.

Data driven game development is one risk reduction strategy. If everyone invests in this ‘safe path’, then there will be some remarkable market failures. This is just another tool and should be used with care and wisdom. （source:lostgarden)