作者：Tom Matcham

90%的任务

关于游戏分析我经常遇到的一个短语是“简单的事物将让你只完成90%的任务。”不管何时我听到或看到这一短语时，我马上想到的就是“你是否知道这点？你是否在学习机器以及进行统计时获得足够的现代研究，并将这些工具所提供的额外洞察力整合到你的数据中，虽然它们只占你的最终报告的10%，而直方图和柱状图将告诉你你的公司利益相关者所需要了解的关于你的数据集的90%的内容是什么。或者你有可能根据其他人的说法而假设这是正确的？此外，你所谓的‘简单的事物’是什么呢？”

我并不是在声明这一说法在很多情况下是错的：我的确看到有些游戏并不能有效地执行逻辑回归，但是当游戏产业拥有一些最佳资源时，我便会因为游戏公司对待数据的方式以及其它产业的区别感到惊讶。我想说的是，根据与开发者和制作人的交谈经历中，从整体上看来应用游戏分析并不能有效发挥作用。

现在我完全能够理解为什么分析调查质量会存在如此多限制因素：时间和金钱都非常有限，但开发者仍然想要深刻理解数据。问题在于糟糕的报告是非常危险的事。样本偏差，数据挖掘技术的滥用，对于结果的误解等等元素都有可能导致报告结论破坏了游戏设计和制作过程。这并不是任何人的错，只是因为进行“适当的”数据科学研究很困难，并且在执行分析时做到统计与计算机科学间的平衡也非常重要。

一直出现的问题

在认真研究了游戏分析后，我发现了我们在分析游戏数据时经常会忽视的4个领域：

数据清理

玩家是多种多样的，并且经常会做出不同的反应。所以如果你不能清理数据以删除一些异常值，那么你所设计的的玩家统计将会遭到扭曲。你必须仔细思考自己感兴趣的是谁以及什么：获得高质量的数据去分析特定的问题比任何有意义的总结都重要。

怎样的概率分布适合我的数据

通常情况下游戏分析统计数据是依赖于正态分布的数据。如果你的数据不能进行正态分布，并且你基于这一假设进行统计测试，那么你所获得的记过将有可能导致糟糕的设计决策并最终创造出一款失败的游戏。你必须仔细思考关于数据所作出的假设：这些假设是否能够通过测试？

database(from veerchina)

过度依赖于数据可视化

想要可视化数据是能够理解的，特别是当游戏开发就是这么一个可视化过程。此外，数据可视化是分析过程一个非常重要的环节。然而，如果你所执行的所有报告都被分解为一张图表，你便有可能遗漏掉许多关于你的数据集的潜在看法。在统计中，箱线图和直方图等都是探索型数据分析的重要组成部分，通常情况下是统计员为了在进行书面工作前获得数据“感”才去进行这项工作。很有可能你的数据集包含了比图表所呈现的更多内容。

行为模型

注：对于这一内容我的看法是存在偏见的，因为我个人对行为模型具有很大的兴趣。

在游戏分析学术中，人们经常说我们很难去推断用户的动机。这在很多情况下是没错的，如果你愿意创造一个玩家行为模型的话，你便会理解为什么在游戏过程中会发生某些事件。显然拥有这些动机数据将会让设计师和投资者同时受益，然而不管是在学术界还是制作领域，这仍是游戏分析中一个未被探索的领域。如果开发者拥有资源，那么模型化与分析玩家行为将对解释其它游戏行为具有很大的帮助。

结束语

尽管这篇文章会让你觉得我并未对游戏分析的使用留下深刻印象，但事实其实不是如此。通过比较，我相信许多公司所设置的用于收集并分析数据的系统都是最先进的。然而，我也相信我们可以进一步去获取工作室所收集的大多数数据集。理解你的用户到底想要什么是游戏开发问题的真髓：跨越整个产业所进行的更透侧的分析将帮助你更轻松第解决这一问题。

（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）

Big Data, Big Problems: A Mathematician’s Take on the Current State of Game Analytics

by Tom Matcham

90% Of The Way There?

A phrase I come across quite frequently with regards to Game Analytics is that ‘the simple stuff will get you 90% of the way there’. Whenever I hear or read this phrase, my immediate thought is ‘do you personally know that? Have you personally gone as far as modern research in machine learning and statistics can take you and concluded that the additional insight into your data provided by such tools was only worth 10% of your resulting report, and that bar charts and histograms and heatmaps told you 90% of what your company’s stakeholders needed to know about your dataset? Or are you presuming this to be true from what you’ve read from other people? Furthermore, what do you mean by “The Simple Stuff?”’

I’m not claiming that this statement isn’t true in many cases: I’ve definitely seen games where there really wouldn’t be much point in performing a logistic regression for example, but when the games industry has some of the best data sources available, I’m regularly surprised at the contrast between how game companies treat data compared to other industries. What I’m trying to say is that from my experience of talking with developers and producers, Applied Game Analytics, as a whole, is not done well.

Now, I completely understand that there are many constraints on the quality of an analytics investigation: time and money are extremely scarce, but developers still want insight into data. The problem is that a bad report is a very dangerous thing. Sample bias, misuse of data mining tools, misinterpretation of results and many other factors can lead to report conclusions that actually harm the game design and production process. It’s very rarely anyone’s fault, it’s just that doing ‘proper’ data science is difficult, and it’s incredibly important to have the right balance of statistics and computer science whilst performing an analysis.

Recurring Problems

Having investigated Game Analytics quite extensively, I’ve found 4 recurring areas that are frequently overlooked when game data is analysed:

Data Cleaning

Gamers are highly variable creatures and regularly act in bizarre ways. As such, it’s likely that if you don’t clean your data to remove outliers, the statistics of the gamers that you’re actually trying to design for will become distorted. Make sure you think very carefully about who and what you’re interested in: getting good quality data to analyse your particular problem is paramount to a meaningful conclusion.

What Probability Distribution is Appropriate for my Data

Far too frequently are statistics in game analytics computed on the basis that the data is normally distributed. If your data isn’t normally distributed, and you perform statistical tests based on this assumption, you’re going to get duff results that could lead to bad design decisions and ultimately a flop game. Think carefully about the assumptions you’re making about your data: can these assumptions be tested?

Over-reliance on Data Visualisation

It’s understandable to want to visualise data, especially when game development is such a visual process. Furthermore, data visualisation is an absolutely key component of the analytics process. However, if all the reporting you’re performing can be boiled down to a pretty picture, then it’s likely that you’re missing out on a lot of potential insights into your dataset. In statistics, box plots, histograms and the like are all part of Exploratory Data Analysis, which is usually performed by a statistician to get a ‘feel’ of the data they’re working with before they do the proper work. It’s quite likely that your dataset contains more insight than is purely representable by graphs.

Behavioural modelling

Note: my opinion on this subject is biased as I have a personal interest in behavioural modelling.

In game analytics academia, it’s frequently stated that it’s very difficult to infer the motivations of a user. Whilst this is true in many contexts, if you’re willing and able to creating a model of the player’s behaviour, it’s likely that you can do a fairly decent job of understanding why certain events took place in a playthrough. Obviously having such motivational data would be extremely beneficial for designers and moneymen alike, yet it’s a largely unexplored area in game analytics in both academia and production. If a developer had the resources, modelling and analysing the behaviour of a player would go a long way in explaining other game behaviours.

Closing Remarks

Although this article may give you the impression that I’m not impressed with the use of game analytics in practice, that’s not the case. By contrast, I believe that the systems that many companies have set up to collect and analyse data are state of the art. However, I do believe that more can be done to get the most of the datasets that studios collect. Understanding what our users want is the essence of the game development problem: better analytics across the industry will make solving that problem a little bit easier.(source:gamasutra)