作者：Ben Weber

很多游戏在开发和发行后都会使用遥测技术。而游戏开发工作中最让人激动的一刻便是使用游戏遥测技术维持游戏设计过程。游戏遥测分析能够帮助设计者回答以下几个问题：

*玩家如何与游戏进行互动？

*玩家在游戏中体验到了何种功能，模式和内容？

*为何玩家会选择退出游戏？

使用游戏遥测技术让你能够尝试与传统模式不同的游戏测试。比起让不同个体玩家到现场帮你测试游戏，遥测技术的采集功能能够让所有的用户群都成为你的测试者。这种方法的优点是可以大规模地收集数据，并且数据的时间维度很长。而缺点便是，遥测技术只能够提供关于玩家的数据资料，也就是设计者能够知道玩家在游戏中做了什么，但却不知道为何他们会这么做。

在游戏设计中使用遥测技术必须遵循一个特定的流程。这是一个交互式过程，包含了以下几个步骤：

1.问题：识别当前设计的一些问题

2.记录：列举出游戏需要哪些数据

3.分析：判断标记的数据是否达到预期值

4.改善：得出结果，分析设计并规划其它问题

为了在游戏设计中使用遥测技术，你就必须明确需要记录哪些游戏数据。阶段信息是一个普遍参数记录，如游戏的开始和结尾阶段。这个参数能够帮助设计者追踪玩家留存率，即一天，一周或者一个月后还有多少玩家仍继续玩游戏。并且为了回答上述问题，你还必须记录游戏的特定数据。这个参数还可以测试不同游戏模式和功能的使用率，并追踪玩家所接触的内容；它还能够用于追踪某项功能的集体和个体使用情况。另外一个数据来源来自于游戏中所发生的事件，如足球游戏中的实况报道。

我们可以使用各种方法去分析来自玩家的数据。使用功能参数能够帮助设计者判断玩家深入探索了游戏的哪些内容，留存率参数能够帮助设计者了解玩家在游戏中花了多少时间。这些数据都能够帮助设计者更好地优化游戏设计。然而，识别设计和数据结果的关联性却并非易事，因为其中并没有来自玩家的定性反馈。为了识别数据模式和关联性，我们必须克服这种局限，而其中一个方法便是使用具有预测性的建模技术，如机器学习。

遥测技术支持的游戏设计需要一个迭代过程，并且在这个过程中将会诞生出新的游戏版本。在网页游戏中这个过程很明确，而对于掌机游戏来说，这却是一个非常复杂的过程，不过在遥测技术的辅助下，DLC，修补程序以及游戏续集等都可以帮助设计师完善并更新游戏设计。

Madden NFL 11(from kotaku.com.au)

作为EA的技术分析家，我在《Madden NFL 11》中使用遥测技术去追踪玩家的行为。从而获得以下经验：

*为新手玩家提供简单的游戏攻略

*确保玩家清楚游戏控制方式

*根据游戏模式为玩家提供合适的挑战（本文为游戏邦/gamerboom.com编译，拒绝任何不保留版权的转载，如需转载请联系：游戏邦）

Telemetry-Supported Game Design

by Ben Weber

Game telemetry is being used both during development and post release. One of the most exciting applications of this work is the use of game telemetry to support the game design process. Game telemetry analysis can help a designer answer the following questions:

How do players interact with the game?

Which features, modes, and content are players experiencing?

Why do players quit playing the game?

Using game telemetry provides an alternative to conventional playtesting. Rather than bringing in individual participants for playtesting, collecting telemetry enables your entire user base to become playtesters. The advantage of this approach is that data can be collected at an enormous scale, from players in their natural environment playing with friends, across long periods of time. The main disadvantage of this approach is that telemetry provides only quantitative data about players. It enables a designer to identify what players are doing in the game, but not understand why players exhibit specific behavior.

There is a general workflow for using telemetry to inform game design. It is an iterative process consisting of the following steps:

1.Question: Identify questions about the current design

2.Record: Enumerate which data needs to be collected and deploy the game

3.Analyze: Determine if the recorded data matches expectations

4.Refine: Given the findings, analyze the design and formulate additional questions

In order to utilize telemetry to inform design, it is necessary to identify which game data needs to be recorded. One common metric recorded is session information, such as the starting and ending time of a gameplay session. This metric enables a designer to track retention rates for players, which can identify how many players continue to play the game after a day, week, and month. To order to answer all of the questions listed above, it is necessary to record game specific data as well. These metrics can measure usage of different game modes and features, as well as track which content is presented to players. It can be useful to track both aggregate and individual usage of features, in order to further refine queries about the data. Another source of data is events that occur in the game, such as play-by-play summaries in a football game.

A variety of different approaches can be used to analyze data collected from players. Game metrics such as feature usage enable a designer to determine which aspects of the game are being explored by players, and retention metrics enable a designer to find out how long players are engaged with the game. These data points can be used to provide recommendations for modifying the design. However, identifying correlations between the design and results found in the data is a non-trivial task, because there is no qualitative feedback from players. One approach to overcome this limitation is to apply predictive modeling techniques, such as machine learning, in order to identify patterns and correlations in the data.

Telemetry-supported game design requires an iterative design process, in which new versions of the game are deployed to the user base. For web-based games this process is straightforward. For console games, this process becomes difficult, but franchise titles, DLC, patches, and sequels provide opportunities for updating a design based on results found from telemetry.

While working as a technical analyst at Electronic Arts I used telemetry to track player behavior in Madden NFL 11. The work resulted in the following recommendations:

Simplify playbooks for novice players

Clearly present the controls to the player in game

Provide the correct challenge for players based on the game mode

The main outcome of this analysis is guidelines for playcalling conventions and the matchmaking systems based on feedback from telemetry. A description of the predictive modeling process is available in this paper and the application to other games is discussed in this paper. （source:gamasutra）