Quote:
Originally Posted by LeeD
Context matters. Ratings creep (in itself) has nothing to do with inconsistent outcomes. Sure, someone who's still playing Helton or Pujols at first base will have seen their numbers fall off a cliff; but that's consistent with general increases in ratings due to game trajectory. What's inconsistent is seeing 104 Miggy go from High Gold MVP one week to High Gold replacement level the next. It's still one of the best cards in the set and should consistently be a top performer, especially at Gold level.
To say that variance and volatility must rule over "strict ratings logic" is hand waving rather than argument. It's also surrendering to the notion that chaos is a desirable and intended result in what is supposed to be a simulation.
The bottom line is that players have spent a lot of time, effort, and/or money acquiring the cards on their rosters. They/we deserve better, and I reiterate that the dev team has lost control over the outcomes. It's not chaos by intent or inevitability. It's chaos through lack of control. Too many variables, all of them pushed to the upper reaches of their ranges. A runaway freight train.
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I think we’re actually talking past each other a bit.
I’m not arguing that chaos is desirable, or that strict ratings logic shouldn’t matter. I’m arguing that once you combine a simulation engine with a live-service collectible economy, perfect consistency becomes mathematically incompatible with the format, not morally optional.
The 104 Miggy example is exactly why I see this as structural rather than accidental. In a closed system, yes — a card like that should be a steady top-tier performer at Gold. But in a normalized, tiered ecosystem designed to prevent roster cloning and ladder stagnation, something has to give: either outcomes compress, variance increases, or ratings lose their predictive power.
That doesn’t mean the results feel good — often they don’t. It means the design has prioritized league diversity and economic longevity over deterministic outcomes.
Where we differ, I think, is intent. You see a runaway system that’s lost control. I see a system that made a set of trade-offs years ago and now can’t reverse them without breaking the model itself.
That’s why I don’t treat Perfect Team as a long-term simulation anymore. I enjoy it in seasons, and when I want consistency and accountability from ratings, I go back to the core game — where those goals are still central.
If the conclusion is simply that the devs have “lost control,” then the conversation is effectively over — because blame doesn’t tell us what levers exist, which trade-offs are real, or what could actually be improved within the constraints of the model.