4 ms·
I've worked on games, huge productions, which ended up receiving really low ( and well-deserved) review scores. Everyone knew it was about to happen, but this
by Agentlien 4y ago
I've worked on games, huge productions, which ended up receiving really low ( and well-deserved) review scores.
Everyone knew it was about to happen, but this was a multi-year product where everyone had been pointing out the issues more than early enough but management simply wouldn't hear it. Concerns about unrealistic scope, poor design, etc. were met with demands that we believe in the game and put our backs into it.
Even then, I did find myself enjoying the game and every now and then lost track of what I was supposed to be testing (as a software engineer testing my changes, not QA) and simply lose track of time playing the game.
- dagw 4y agowhere everyone had been pointing out the issues more than early enough The other variation I've experience is everything starts out great with a clear design and reasonable time line. Then a couple of years later, when you are almost done, some people start worrying that what you're making might be a bit too 'niche' and start adding some 'minor' changes to make the it appeal to a larger demographic.
- Agentlien 4y agoI originally wrote but removed another paragraph (because I didn't want to make the comment too long) about another wrinkle... The worst production I was part of, which lead to a full year of crunch for my team, started with everyone saying we were way over scoped and then the studio leadership decided, halfway through production, to change the entire campaign to be built around five huge set pieces each of which required months of work to develop bespoke stuff only used in one mission.
- aliswe 4y agoWhat baffles me is that the data-driven mindset is supposed to counter this, but the people in charge in these cases were probably already thinking they are daa driven by essentially following assumptions.
- Agentlien 4y agoThe problem is that the studio leadership made the deliberate decision to aim for something our data showed we could not deliver, relying on overtime to compensate for the overambitious scope. Which is not a feasible solution and, unsurprisingly, did not work out.