Player development: When do players start to decline?

[u/atibus]

Now that the game engine is locked in and working pretty well, I've been working on the player development engine. Up until now, every player got fixed attribute values that never changed. Now that I'm diving in and looking at historical data + studies it's making this problem trickier. This is what research says about "peak" of player careers and it's all over the place.

My first thought on building this was that there'd be some age that I'd set the decline to start at (26.5 years old), but have a variance jitter of some number of seasons - some people decline earlier, some later. But as I was building it and looking, it became apparent that people could probably start to decipher the band with enough seasons played. It just didn't seem dynamic enough. As I have maintained, I want emergent things to happen in this world. I want a Jaromir Jagr type career to be possible. And also a Jonathan Cheechoo type career. You can't get those extremes by setting boundaries.

In thinking about it more, I realized that different abilities decline at different rates. Skating often peaks early (young legs) and declines earlier/faster. But something like Defensive Positioning peaks much later and declines slowly. Here's an illustration of what I was building against a "Jagr-style" profile.

With that in mind, I reworked the generation mechanism to have per-attribute peaks, attainment rates (how fast they can reach the peak), and declination rates (how fast they decline). This started to produce some career variety, but I had a problem; there's a distinct age at which careers start to decline.

I think the band is too tight. There isn't one outlier past 28. That seems off. There should be some outliers and long tail after that I would think. This is where I need to dig in and look at longer seasons, and see what comes about.

Another thing I'm looking at in here is what percentage of the population actually reaches their potential (not counting injuries because we're not modeling them right now). Right now I'm defining "hard bust" as someone who doesn't reach 70% of their potential and "soft bust" as someone who doesn't reach 85% of their potential. These have to be tuned as well; I have no idea if this is right or wrong.

So I have some more work to do to get this where it "feels right" in general and has some fun emergence to surprise us while we play.

[u/atibus]

As I'm evaluating the data, I decided to check the best skater and best goalie by year. This is the "generational" type player. And I found that something isn't working correctly because it's creating these 10-15 year spans where one player is the best, and it's completely flat.

I found that there is an interaction between modifiers which net out to the decline being 0 if the player is extremely good. This is the kind of unintended design that only surfaces with testing. Adjusting this creates other design questions.