The Game Outcomes Project, Part 1: The Best and the Rest
游戏专案为何成功系列之一：脱颖而出者背后的金矿
网志版：http://wp.me/pBAPd-pz
原文网址：
http://gamasutra.com/blogs/PaulTozour/20141216/232023/The_Game_Outcomes_Project_Part_1_The_Best_and_the_Rest.php
http://tinyurl.com/kwcza9y
撰文：Paul Tozour
繁体中文翻译：NDark
20141216
译按：本文是一篇统计学专业文章，若有翻译不正确的文句，请以原文为主。
This article is the first in a 4-part series. The remaining 3 articles will
be published in January 2015.
For extended notes on our survey methodology, see our Methodology blog page
(link).
The Game Outcomes Project team includes Paul Tozour, David Wegbreit, Lucien
Parsons, Zhenghua “Z” Yang, NDark Teng, Eric Byron, Julianna Pillemer, Ben
Weber, and Karen Buro.
本文共分为四个篇章，剩余三个篇章将在2015年一月发布。
关于问卷的方法论，请参阅我们的部落格页面 "Game Outcomes Project
Methodology":
http://intelligenceengine.blogspot.tw/2014/11/game-outcomes-project-methodology-in.html
http://tinyurl.com/kdmcv7t
游戏专案为何成功团队成员包含Paul Tozour，David Wegbreit，Lucien Parsons，
Zhenghua “Z” Yang，NDark Teng，Eric Byron，Julianna Pillemer，Ben Weber，及
Karen Buro。
The Game Outcomes Project, Part 1: The Best and the Rest
游戏专案为何成功之一：脱颖而出者的背后
What makes the best teams so effective?
Veteran developers who have worked on many different teams often remark that
they see vast cultural differences between them. Some teams seem to run like
clockwork, and are able to craft world-class games while apparently staying
happy and well-rested. Other teams struggle mightily and work themselves to
the bone in nightmarish overtime and crunch of 80-90 hour weeks for years at
a time, or in the worst case, burn themselves out in a chaotic mess. Some
teams are friendly, collaborative, focused, and supportive; others are
unfocused and antagonistic. A few even seem to be hostile working
environments or political minefields with enough sniping and backstabbing to
put Team Fortress 2 to shame.
是什么要素使得优秀的团队脱颖而出？
在不同团队工作过的资深的开发者，常谈论团队间的文化差异。有些团队的运作方式像精
准的时钟，能够产生世界级的产品，同时还有愉快的工作环境与充分的休闲生活。有些团
队则在无止尽的熬夜工作中挣扎前进，甚至一周工作八十到九十小时。更糟糕的是，成员
因此觉得自己油尽灯枯。有些团队气氛良好，合作愉快，专注，游刃有余。有些团队目标
摇摆不定，成员互相制衡。甚至有些团队是在仇视，背刺，针对性行为或充满政治的工作
环境下工作。难道大家都没玩Team Fortress 2（绝地要塞2）吗？。
What causes the differences between those teams? What factors separate the
best from the rest?
As an industry, are we even trying to figure that out?
Are we even asking the right questions?
到底什么要素使得这些团队不同？什么要素使得好团队脱颖而出？
游戏产业想要知道这些答案吗？
我们是否有问到关键问题？
These are the kinds of questions that led to the development of the Game
Outcomes Project. In October and November of 2014, our team conducted a
large-scale survey of hundreds of game developers. The survey included
roughly 120 questions on teamwork, culture, production, and project
management. We suspected that we could learn more from a side-by-side
comparison of many game projects than from any single project by itself, and
we were convinced that finding out what great teams do that lesser teams don’
t do – and vice versa – could help everyone raise their game.
对于这些答案饥渴就导致了"游戏专案为何成功"这个专案的催生。2014年的十月开始，我
们团队针对游戏开发者进行了大范围的问卷。问卷中包含了关于团队合作，文化，制程，
专案管理的一百二十个问题。我们预估可以从这些问卷资料的比对中找到差异，我们也确
信可以找到优秀团队到底有或没有做了其他团队做的什么关键动作，进而帮助其他团队。
Our survey was inspired by several of the classic works on team
effectiveness. We began with the 5-factor team effectiveness model described
in the book Leading Teams: Setting the Stage for Great Performances. We also
incorporated the 5-factor team effectiveness model from the famous management
book The Five Dysfunctions of a Team: A Leadership Fable and the 12-factor
model from 12: The Elements of Great Managing, which is derived from
aggregate Gallup data from 10 million employee and manager interviews. We
felt certain that at least one of these three models would surely turn out to
be relevant to game development in some way.
我们的问卷被几项团队效率的数个研究所启发：Leading Teams: Setting the Stage
for Great Performances的"团队效率的五个指标"；管理名著：The Five Dysfunctions
of a Team: A Leadership Fable；Gallup 公司所蒐集一千万员工与管理者面试资料所产
出12: The Elements of Great Managing的"十二个指标"；我们认为这三种模型中至少有
一种能真正套用在游戏开发上。
We also added several categories with questions specific to the game industry
that we felt were likely to show interesting differences.
On the second page of the survey, we added a number of more generic
background questions. These asked about team size, project duration, job
role, game genre, target platform, financial incentives offered to the team,
and the team’s production methodology.
我们也依照游戏产业的特殊性加入了几个我们认为会探索出有趣差异的类别问题。
在问卷的第二页是一般性的背景问题，关于团队人数大小，专案长度，工作角色，游戏类
型，平台，商业营收，以及开发方法。
We then faced the broader problem of how to quantitatively measure a game
project’s outcome.
Ask any five game developers what constitutes “success,” and you’ll likely
get five different answers. Some developers care only about the bottom line;
others care far more about their game’s critical reception. Small indie
developers may regard “success” as simply shipping their first game as
designed regardless of revenues or critical reception, while developers
working under government contract, free from any market pressures, might
define “success” simply as getting it done on time (and we did receive a
few such responses in our survey).
我们立即面临的问题，就是如何量化所谓的成功？
这个问题是因人而异，有些开发者标准不高，有些则关心游戏的名声。独立开发者希望游
戏照着他们所设计的方向完成，而不管市场营收，也不管是否引来负评。有些开发者是依
照合约规范下工作，不需在意市场压力，此时的成功就是专案完成。（而我们确实在问卷
中收到这样的回应）
Lacking any objective way to define “success,” we decided to quantify the
outcome through the lenses of four different kinds of outcomes. We asked the
following four outcome questions, each with a 6-point or 7-point scale:
"To the best of your knowledge, what was the game's financial return on
investment (ROI)? In other words, what kind of profit or loss did the company
developing the game take as a result of publication?"
"For the game's primary target platform, was the project ever delayed
from its original release date, or was it cancelled?"
"What level of critical success did the game achieve?"
"Finally, did the game meet its internal goals? In other words, to what
extent did the team feel it achieved something at least as good as it was
trying to create?"
由于缺乏定义成功的方式，我们决定用不同的指标来量化这个数据。我们问了四个关于产
出的问题，分别有六，或七个回答等级：
就你所知，游戏专案的回收状况如何？也就是公司开发此专案的获利与损失如何？
针对此游戏专案的首要平台，专案有延迟或被取消吗？
此游戏专案有被认定为成功吗？
此游戏专案是否有达到原本认定的目标？也就是，团队内部认为最终产出与原先预估
相契合？
We hoped that we could correlate the answers to these four outcome questions
against all the other questions in the survey to see which input factors had
the most actual influence over these four outcomes. We were somewhat
concerned that all of the “noise” in project outcomes (fickle consumer
tastes, the moods of game reviewers, the often unpredictable challenges
inherent in creating high-quality games, and various acts of God) would make
it difficult to find meaningful correlations. But with enough responses,
perhaps the correlations would shine through the inevitable noise.
我们希望我们能将产出的回答结果关联到问卷的其他问题项目。了解哪些问题项目对产出
帮助最高。我们也关心哪些问题其实是对于产出无关紧要（如玩家的品味，评论家的心情
，高品质游戏的突破），这些噪声会造成我们找不到真正有意义的关联。但有足够的回应
，也许这些关联性可以从噪声中被我们捡拾出来。
We then created an aggregate “outcome” value that combined the results of
all four of the outcome questions as a broader representation of a game
project’s level of success. This turned out to work nicely, as it
correlated very strongly with the results of each of the individual outcome
questions. Our Methodology blog page has a detailed description of how we
calculated this aggregate score.
接着我们就混杂了先前提到的四个产出问题，依此设计了产出的合计分数，来代表游戏专
案的成功度。结果运作的很棒，这个机制强烈的与各项问题连结。我们的方法论部落格页
面有这项分数的详细描述。
We worked carefully to refine the survey through many iterations, and we
solicited responses through forum posts, Gamasutra posts, Twitter, and IGDA
mailers. We received 771 responses, of which 302 were completed, and 273
were related to completed projects that were not cancelled or abandoned in
development.
我们小心的计算并重复定义这项问卷的数据，也从论坛，Gamasutra，Twitter，IGDA的邮
件群组中收到回馈。我们最终回收了七百七十一份的问卷，其中三百零二份有效，而没有
被取消或放弃的专案留下了两百七十三份。
The Results
So what did we find?
In short, a gold mine. The results were staggering.
结论
所以我们最后找到了什么？
简短来说，我们找到了惊人的金矿。
More than 85% of our 120 questions showed a statistically significant
correlation with our aggregate outcome score, with a p-value under 0.05 (the
p-value gives the probability of observing such data as in our sample if the
variables were be truly independent; therefore, a small p-value can be
interpreted as evidence against the assumption that the data is independent).
This correlation was moderate or strong in most cases (absolute value >
0.2), and most of the p-values were in fact well below 0.001. We were even
able to develop a linear regression model that showed an astonishing 0.82
correlation with the combined outcome score (shown in Figure 1 below).
在一百二十项的问题中超过百分之八十五都显示出与我们的产出分数有强烈的关联性（
correlation），其显著性（p-value，http://en.wikipedia.org/wiki/P-value，此值是
观察资料中变量是否独立的机率，因此此值越小代表可被解释为推论的资料是独立的）都
小于0.05。关联性很大（大于0.2），大多数的p-values甚至小于0.001。我们甚至能够建
立出惊人有0.82关联性的回归分析结果。
Figure 1. Our linear regression model (horizontal axis) plotted against the
composite game outcome score (vertical axis). The black diagonal line is a
best-fit trend line. 273 data points are shown. 图片请连原文：
http://gamasutra.com/db_area/images/blog/232023/regression_vs_outcome_normalized.png
。我们的水平轴回归分析对上垂直轴的产出分数。
To varying extents, all three of the team effectiveness models (Hackman's “
Leading Teams” model, Lencioni's “Five Dysfunctions” model, and the Gallup
“12” model) proved to correlate strongly with game project outcomes.
We can’t say for certain how many relevant questions we didn’t ask. There
may well be many more questions waiting to be asked that would have shined an
even stronger light on the differences between the best teams and the rest.
But the correlations and statistical significance we discovered are strong
enough that it’s very clear that we have, at the very least, discovered an
excellent partial answer to the question of what makes the best game
development teams so successful.
广义来说，三个团队效率的模型（Hackman's “Leading Teams” model, Lencioni's “
Five Dysfunctions” model, and the Gallup “12” model）全部都与我们的产出分数
高度相关。
我们不能声称是否我们没列出来的问题才更加与其相关，确实可能有更多问题是我们应该
问的，更加将好团队的要素发掘出来。
但统计的证据显示出来我们发觉了此问题的可能答案，可以让我们的游戏开发团队更加成
功。
The Game Outcomes Project Series
Due to space constraints, we’ll be releasing our analysis as a series of
several articles, with the remaining 3 articles released at 1-week intervals
beginning in January 2015. We’ll leave off detailed discussion of our three
team effectiveness models until the second article in our series to allow
these topics the thorough analysis they deserve.
This article will focus solely on introducing the survey and combing through
the background questions asked on the second survey page. And although we
found relatively few correlations in this part of the survey, the areas where
we didn’t find a correlation are just as interesting as the areas where we
did.
游戏专案为何成功的系列
由于篇幅所限，我们会依序释出一系列的分析文章，剩下的三篇会以一个礼拜为周期的方
式自2015年一月开始释出。我们会在第二篇中探讨三个团队效率模型，让它们能够被详尽
的分析及解释。
本篇文章会专注在介绍这个问卷专案，以及问卷第二页的背景问题。在其中我们发现一些
低关联性的问题，这些区域中我们未能找到如同其他区域一样显著的关联性。
Project Genre and Platform Target(s)
First, we asked respondents to tell us what genre of game their team had
worked on. Here, the results are all across the board.
专案的游戏类型与发布平台
首先，我们请填写者回答了他们团队开发游戏专案的类型。结果如图。
Figure 2. Game genre (vertical axis) vs. composite game outcome score
(horizontal axis). Higher data points (green dots) represent more successful
projects, as determined by our composite game outcome score. 水平轴的游戏类型
对垂直轴的产出分数。越高的数值代表越成功的案子。
We see remarkably little correlation between game genre and outcome. In the
few cases where a game genre appears to skew in one direction or another, the
sample size is far too small to draw any conclusions, with all but a handful
of genres having fewer than 30 responses.
我们在游戏类型与产出分数间没有发现显著的关联性。有些数据会出现一个方向的趋势，
但由于个别的游戏类型都只有不到三十份回应，因此这些数据无法让我们做出结论。
(Note that Figure 2 uses a box-and-whisker plot, as described here).
We also asked a similar question regarding the product’s target platform(s),
including responses for desktop (PC or Mac), console (Xbox/PlayStation),
mobile, handheld, and/or web/Facebook. We found no statistically significant
results for any of these platforms, nor for the total number of platforms a
game targeted.
我们也问了类似关于发布平台的问题（桌机，家机，行动，手持，网页），也都没有显著
的统计结果。
Project Duration and Team Size
We asked about the total months and years in development; based on this, we
were able to calculate each project’s total development time in months:
专案长度与团队人数
我们问了关于开发的资总年月数。
Figure 3. Total months in development (horizontal axis) vs game outcome
score (vertical). The black diagonal line is a trend line. 总月数对产出分数
As you can see, there’s a small negative correlation (-0.229, using the
Spearman correlation coefficient), and the p-value is 0.003. This negative
correlation is not too surprising, as troubled projects are more likely to be
delayed than projects that are going smoothly.
如你们可见，有一个负向的关联，p值是0.003。负关联并不令人意外，不顺利的专案都会
做比较长。
We also asked about the size of the team, both in terms of the average team
size and the final team size. Average team size was between 1 and 11 with an
average of 5.7; final team size was between 1 and 500 with an average of
48.6. Both showed a slight positive correlation with project outcomes, as
shown below, but in both cases the p-value is over 0.1, indicating there’s
not enough statistical significance to make this correlation useful or
noteworthy. We suspect that the small positive correlation can be explained
by the fact that a struggling project is less likely to receive additional
resources over time than one that’s going well. So the result is not too
surprising.
我们也问了关于团队大小的问题，包含平均的人数，与团队最后的人数。平均人数的数据
自1到11，平均5.7。最后的人数的数据自1到500，平均为48.6。对于专案的产出都有正相
关，但两者的p值都大于0.1，显示不出足够的统计特征，因此我们就不在此深入研究。我
们假设小的正相关可说是小团队都资源都不足。
Figure 4. Average team size correlated against game project outcome
(vertical axis).平均人数对产出分数
Figure 5. Final team size correlated against game project outcome (vertical
axis).最终人数对产出分数
Figure 6. Percent change in team size (final divided by average) correlated
against game project outcome (vertical axis).专案人数改变绿对产出分数
Game Engines
We asked about the technology solution used: whether it was a new engine
built from scratch; core technology from a previous version of a similar game
or another game in the same series; an in-house / proprietary engine (such as
EA Frostbite); or an externally-developed engine (such as Unity, Unreal, or
CryEngine).
The results are as follows:
游戏引擎
我们问了关于技术方案的问题。从自制引擎到市售引擎（如Unity，Unreal，CryEngine）
。结果如下。
Figure 7. Game engine / core technology used (horizontal axis) vs game
project outcome (vertical axis), using a box-and-whisker plot.游戏引擎对产出分
数
Average composite score
Standard Deviation
Number of responses
New engine/tech
53.3 18.3 41
Engine from previous version of same or similar game
64.8 15.8 58
Internal/proprietary engine / tech (such as EA Frostbite)
60.7 19.4 46
Licensed game engine (Unreal, Unity, etc.)
55.6 17.5 113
Other
55.5 19.5 15
The results here are less striking the more you look at them. The highest
score was for projects that used an engine from a previous version of the
same game or a similar one – but that’s exactly what one would expect to be
the case, given that teams in this category clearly already had a head start
in production, much of the technical risk had already been stamped out, and
there was probably already a veteran team in place that knew how to make that
type of game!
结果并不如期待中惊讶。显而易见最高分的专案是同样或类似系列的续作。同类型专案已
经将风险降低，成员也可能是个中老手。
We analyzed these results using a Kruskal-Wallis one-way analysis of
variance, and we found that this question was only statistically significant
on account of that very option (engine from a previous version of the same
game or similar), with a p-value of 0.006. Removing the data points related
to this answer category caused the p-value for the remaining categories to
shoot up above 0.3.
我们使用Kruskal-Wallis one-way analysis of variance来分析结果，我们发现只有沿
用同类型技术的专案才有显著的低p值（0.006）。除此之外的数据的p值都超过0.3。
Our interpretation of the data is that the best option for the game engine
depends entirely on the game being made and what options are available for
it, and that any one of these options can be the “best” choice given the
right set of circumstances. In other words, the most reasonable conclusion
is there is no universally “correct” answer separate from the actual game
being made, the team making it, and the circumstances surrounding the game's
development. That’s not to say the choice of engine isn’t terrifically
important, but the data clearly shows that there plenty of successes and
failures in all categories with only minimal differences in outcomes between
them, clearly indicating that each of these four options is entirely viable
in some situations.
我们对此数据的解释是沿用旧有技术的专案可能是该唯一可行的方案，换句话说最理性的
解答就是对于不同团队而言，制作游戏没有所谓正确的工具，所谓正确的工具其实就是团
队制造出来的工具。因此我们不能说引擎的选择并不重要，只能说从数据来看引擎的选用
未能造成产出的差异。
We also did not ask which specific technology solution a respondent’s dev
team was using. Future versions of the study may include questions on the
specific game engine being used (Unity, Unreal, CryEngine, etc.)
但我们没有以特别引擎来询问，也就是说没有细到问团队使用的是Unity，Unreal，或是
CryEngine。这点我们在未来可以加入问卷之中。
Team Experience
We also asked a question on this page regarding the team’s average
experience level, along a scale from 1 to 5 (with a ‘1’ indicating less
than 2 years of average development experience, and a ‘5’ indicating a team
of grizzled game industry veterans with an average of 8 or more years of
experience).
团队经历
我们也问了关于团队平均经历的问题，从少于两年，到八年以上。
Figure 8. Team experience level ranking (horizontal axis, by category listed
above) mapped against game outcome score (vertical axis)团队经历对产出分数
Here, we see a correlation of 0.19 (and p-value under 0.001). Note in
particular the complete absence of dots in the upper-left corner (which would
indicate wildly successful teams with no experience) and the lower-right
corner (which would indicate very experienced teams that failed
catastrophically).
这里我们可看到一个0.19的相关度（其p值小于0.001）。请特别注意左上角（没经验但成
功的团队）与右下角（很有经验但失败的团队）是完全没有资料的。
So our study clearly confirms the common knowledge in the industry that
experienced teams are significantly more likely to succeed. This is not at
all surprising, but it's reassuring that the data makes the point so clearly.
And as much we may all enjoy stories of random individuals with minimal game
development experience becoming wildly successful with games developed in
just a few days (as with Flappy Bird), our study shows clearly that such
cases are extreme outliers.
因此我们依照常识确认有经验的团队比较容易成功。也不意外。也因此关于那些极小团队
与专案的巨大胜利（如Flappy Bird）其实真的是偶发事件。
The Surprises: Production and Incentives
This first page of our survey also revealed two major surprises.
The first surprise was financial incentives. The survey included a question:
“Was the team offered any financial incentives tied to the performance of
the game, the team, or your performance as individuals? Select all that
apply.” We offered multiple check boxes to say “yes” or “no” to any
combination of financial incentives that were offered to the team.
The correlations are as follows:
令人意外：制程与激励因子
我们问卷的第一页给我们两个重要的意外结论。
第一个意外是金钱的激励。问卷里面问了一个问题：团队会依据团队，或个人产出给予金
钱方面的激励吗？填写者可以对给团队或个人来分别填写有及没有。
Figure 9. Incentives (horizontal axis) plotted against game outcome score
(vertical axis) for the five different types of financial incentives, using a
box-and-whisker plot. From left to right: incentives based on individual
performance, team performance, royalties, incentives based on game
reviews/MetaCritic scores, and miscellaneous other incentives. For each
category, we split all 273 data points into those excluding the incentive
(left side of each box) and those including the incentive (right side of each
box).奖励对产出分数，从左到右根据分别代表根据个人效率，团队效率，分成，根据网
页评论评分，或其他数据。我们在各项目中分别列出有与没有的情形。
Of these five forms of incentives, only individual incentives showed
statistical significance. Game projects offering individually-tailored
compensation (64 out of the 273 responses) had an average score of 63.2
(standard deviation 18.6), while those that did not offer individual
compensation had a mean game outcome score of 56.5 (standard deviation 17.7).
A Wilcoxon rank-sum test for individual incentives gave a p-value of 0.017
for this comparison.
在五种激励因子中，只有个人的奖励是有显著的统计指标。273份数据中的64份有给个人
的奖励，该些专案的平均产出分数是63.2，标准差18.6，而反过来没有给个人奖励的数据
则是平均56.5（标准差17.7）。在这个比较下给予个人奖励的数据透过Wilcoxon
rank-sum test方法可以得到0.017的p值。
All the other forms of incentives – those based on team performance, based
on royalties, based on reviews and/or MetaCritic ratings, and any
miscellaneous “other” incentives – show p-values that indicate that there
was no meaningful correlation with project outcomes (p-values 0.33, 0.77,
0.98, and 0.90, respectively, again using a Wilcoxon rank-sum test).
那么其他的奖励方式，基于团队，分成，网页评论评分，或其他的方式，都没有对于产出
分数有显著的相关度。（p值0.33，0.77，0.98，0.90）
This is a very surprising finding. Incentives are usually offered under the
assumption that they are a huge motivator for a team. However, our results
indicate that only individual incentives seem to have the desired effect, and
even then, to a much smaller degree than expected.
这发现令人惊讶。我们都认为奖励给予团队会有巨大的激励。然而，结果却显示，只有给
予个人的激励才会达到效果，即便如此，都比我们认为能达到的等级都来得小。
One possible explanation is that perhaps the psychological phenomenon
popularized by Dan Pink may be playing itself out in the game industry –
that financial rewards are (according to a great deal of recent research)
usually a completely ineffective motivational tool, and actually backfire in
many cases.
可能的解释是也许类似psychological phenomenon popularized by Dan Pink的解释，也
就是金钱的奖励可能反而会造成反效果。
We also speculate that in the case of royalties and MetaCritic reviews in
particular, the sense of helplessness that game developers can feel when
dealing with factors beyond their control – such as design decisions they
disagree with, or other team members falling down on the job – potentially
compensates for any motivating effect that incentives may have had. With
individual incentives, on the other hand, individuals may feel that their
individual efforts are more likely to be noticed and rewarded appropriately.
However, without more data, this all remains pure speculation on our part.
我们对于分成与网页评论评分特别深入思考，也许游戏开发者的不快乐是来自于无法掌控
的时候，如被迫接受设计，团队有人搞砸无法交件。这些不快乐抵销了能够获得奖励的因
子。只有针对个人的奖励却仍能够保持在成员身上。然而，我们的资料如果更多，才能够
真正证明这点推论。
Whatever the reason, our results seem to indicate that individually tailored
incentives, such as Pay For Performance (PFP) plans, seem to achieve
meaningful results where royalties, team incentives, and other forms of
financial incentives do not.
不管原因如何，我们的结论指向个人的奖励，如Pay For Performance所言，确实比其他
方式来的有效。
Our second big surprise was in the area of production methodologies, a topic
of frequent discussion in the game industry.
We asked what production methodology the team used – 0 (don’t know), 1
(waterfall), 2 (agile), 3 (agile using “Scrum”), and 4 (other/ad-hoc). We
also provided a detailed description with each answer so that respondents
could pick the closest match according to the description even if they didn’
t know the exact name of the production methodology. The results were
shocking.
另一个令人惊讶之处在于方法论，也是业界频繁讨论的问题。
我们问的问题是团队是属于没有使用特定的开发方法，使用瀑布式，使用敏捷式，或使用
其他随意的方式来开发。我们也在答案的旁边附注了最有可能的情境。结果令人震惊。
Figure 10. Production methodology vs game outcome score.制程方法论对上产出分
数
Here's a more detailed breakdown showing the mean and standard deviation for
each category, along with the number of responses in each:
这里是一个详细的数据表格，显示各项方法论的数据及标准差，与数量。
Average composite score
Standard Deviation
Number of responses
Unknown
50.6 17.4 7
Waterfall
55.4 17.9 53
Agile
59.1 19.4 94
Agile using Scrum
59.7 16.9 75
Other / Ad-hoc
57.6 17.6 44
What’s remarkable is just how tiny these differences are. They almost don’
t even exist.
看出什么了吗？答案是什么也没有。
Furthermore, a Kruskal-Wallis H test indicates a very high p-value of 0.46
for this category, meaning that we truly can’t infer any relationship
between production methodology and game outcome. Further testing of the
production methodology against each of the four game project outcome factors
individually gives identical results.
更进一步的说，透过Kruskal-Wallis H test方法测试，竟造成了一个高度的p值0.46。也
就是说我们完全无法找到方法论对于游戏产出的关系。
Given that production methodologies seem to be a game development holy grail
for some, one would expect to see major differences, and that Scrum in
particular would be far out in the lead. But these differences are tiny,
with a huge amount of variation in each category, and the correlations
between the production methodology and the score have a p-value too high for
us to deny the assumption that the data is independent. Scrum, agile, and “
other” in particular are essentially indistinguishable from one another. “
Unknown” is far higher than one would expect, while “Other/ad-hoc” is also
remarkably high, indicating that there are effective production methodologies
available that aren’t on our list (interestingly, we asked those in the “
other” category for more detail, and the Cerny method was listed as the
production methodology for the top-scoring game project in that category).
对某些游戏开发者来说，方法论被认为是圣杯，会造成巨大的差异，特别以敏捷式为标的
。但其造成的差异很微小。每个方法论与产出分数的相关度都很低（p值很高）。比之
Scrum，敏捷式，或其他方法，没有使用特定的方法与使用其他方法都比想像中来得高。
对此我们只能解释可能存在我们没有列出的方法才可能是正解。（有趣的是，在填写者的
回答中Cerny method得到了最高的分数）。
Also, unlike our question regarding game engines, we can't simply write this
off as some methodologies being more appropriate for certain kinds of teams.
Production methodologies are generally intended to be universally useful,
and our results show no meaningful correlations between the methodology and
the game genre, team size, experience level, or any other factors.
同时不像对于引擎的问题，我们无法直接了当写出对于某些团队来说，某些方法论有效与
否。制程的方法论原本被认为是放诸四海皆准，但我们的结果却没办法显示出方法论与游
戏类型，团队人数，经验，或其他项目有相关性。
This begs the question: where’s the payoff?
这就引出了问题，什么才是决定性的要素。
We’ve seen several significant correlations in this article, and we will
describe many more throughout our study. Articles 2 and 3 in particular will
illustrate many remarkable correlations between many different cultural
factors and game outcomes, with more than 85% of our questions showing a
statistically significant correlation.
这篇文章中我们以谈到几个决定性的相关度，而我们还会在之后的研究提出更多的结论。
第二与第三篇会提出更多关于文化与游戏产出分数显著的相关性，我们提出的问题中超过
八成五都有显著的相关性。
So it’s very clear that where there were significant drivers of project
outcomes, they stood out very clearly. Our results were not shy. And if the
specific production methodology a team uses is really vitally important, we
would expect that it absolutely should have shown up in the outcome
correlations as well.
But it’s simply not there.
所以，显然对于专案产出，我们的问卷已经找到决定性的驱动因子，非常明显。也因此我
们没必要藏私。假如此处特定的方法论很重要，我们深信必定可以在产出分数上产生相关
性。
但结果并不是如此。
It seems that in spite of all the attention paid to the subject, the
particular type of production methodology a team uses is not terribly
important, and it is not a significant driver of outcomes. Even the
much-maligned “Waterfall” approach can apparently be made to work well.
似乎尽管我们试图找到方法论的相关性，都没办法找到方法论对产出是重要的证据。即便
是大家最不喜欢的瀑布式也都运作的很好。
Our third article will detail a number of additional questions we asked
around production that give some hints as to what aspects of production
actually impact project outcomes regardless of the specific methodology the
team uses