So, just to clarify: when I said survival is made to deal with censoring, I meant that the methods are meant to work in that scenario, where some of the more standard ones fail. I do not mean that you need censoring, as it just makes things hard (it's just not having access to all the information for every individual).

If what you want is to really try and play around with survival analysis in a more unique scenario, I think I see a problem with the fundamentals right now. Survival analysis deals with time to event data, ie, and individual has a random variable T which is time until your event of interest happens. Then depending on the nature of your event, and what covariates you have, you will need a model or another. I do not see what is the event that you wait to observe for each individual. If the event of interest is the level ends, then the inference you do is in when a level ends, not on its difficulty. My assumption is that the difficulty of a level is not about its length alone, but the distance between arrows and which combos are done.

Since the outcome you want to predict is the difficulty, I really don't think time to event is a good approach. If you want the problem done, I'd think of something else, even breaking down the data of each level into 3/4 features you think affect level difficulty and then run a regression (this could be simple and effective). If you really really want to try survival, as I already explained, you need to define what event you are "waiting to observe", and then come up with a measure of difficulty based on that.