This post is a bit of a capstone. It utilizes all of the tools to make video games scientifically that I covered in the Parts 1-6 of “Game Planning With Science”. Make sure you’ve reviewed those weighty tomes before digging in here. In this post, I’m going to walk you through how to utilize capacity charts, story points, user stories, variance, and the central limit theorem to forecast development time lines.
There’s a saying in data science: Garbage In, Garbage Out (or GIGO, if you prefer). The most advanced formulas and models won’t provide outputs worth a dead cat if you don’t have high quality inputs. When it comes to something as difficult and uncertain as feature planning and estimation, that’s quadruply so. In this post I’m going to walk you through the system I’ve used successfully, how it works, and why. And it’s all based on the counter part to the story points from Part 5, user stories.
In Part 4 of “Game Planning With Science!”, I covered the central limit theorem, and how we can use it for forecasting feature development. At the end of the post I acknowledged that it’s no mean feat to track the time per individual feature without some heavy duty project management software and a team that is superlatively disciplined about tracking their time. In Part 5, I’m going to give you my favorite tool for getting around this problem: Story Points.
In Part 4 of “Game Planning With Science”, I’m going to wrap up the statistics primer I started in Part 3. This time, I’ll cover one of the most fascinating aspects of statistics: the Central Limit Theorem. Why does one aspect of statistics deserve its own post? BECAUSE IT’S FRIGGIN’ RAD, THAT’S WHY! Also (and probably more importantly) it allows us to make predictions when planning games, even if we don’t have a lot of data.
In parts 1 and 2 of “Game Planning With Science!” I covered the basics of process management and capacity charts. Now, in Part 3, I’m going to step away from direct operations management to discuss some basic concepts of statistics. Riveting, I know. But also essential if you want to be able to forecast accurately and confidently. There will be some heavy lifting in this post, but hang in there. A better understanding of statistics will change the way you see and treat your own data. It will also make you a more informed consumer of the information the rest of the world vomits at you every day.
In Part 1 of Game Planning With Science, I covered the fundamentals of operations management: critical paths, bottlenecks, and Little’s Law. If you haven’t read Part 1 yet, I suggest you do. Unless you’re familiar with the equations behind those concepts, Part 2 will be a little tricky to follow. But if you’re up to speed, read on. In Part 2, I’m going to walk you through how to assemble a capacity chart. You can use capacity charts to optimize your character art pipelines and add resources where they will do the most good.
The fundamental tools of operations science (also called decision science) were designed with factories and warehouses in mind. But they are easily applicable to video game art asset pipelines. In this post, I’ll walk you through the basics of how operation science looks at pipelines, called “process flows” in operations speak.