Quote:
Originally Posted by Markus Heinsohn
Again, I would like to see a Tango-like study with a fictional 16-team OOTP league with all minor league levels. Start the league, sim 100 years, and then analyze the stats (not ratings). Sveein did that during OOTP 9 beta, and the results looked very good IMO. Also, my tests results showed that development is in great shape as well. I focused on: League average stats, league average age, league average career length, league average ratings, individual career leaderboards and single season leaderboards. Everything was extremely stable and feeling realistic, no matter if in year 1, 30, 50 or 100.  |
I understand the attraction of trying to use stats from
OOTP to compare to stats that Tango used in creating his aging chains. Luckily, though, it's really not necessary since you have ratings that match the context of the stats you're trying to use. Why "luckily?" Well, because in order to get stats to work properly out of
OOTP you need to adjust for ballpark--and you will need to adjust for ballpark in the same way as Tango did--not just apply
OOTP park values. This task is very, very difficult.
In fact, if you use stats without adjusting them for ballpark (and possibly league totals .. though I need to think about that one a bit), and do the same study as Tango, your data will be wrong.
So, luckily, you don't need to do that because all that work Tango is doing to distill the data down is done _specifically_ to model what
OOTP call "ratings." The one error in using
OOTP ratings directly is that
OOTP ratings are not quite used linearly in their mapping to performance rates. The error induced here is quite small, though, and if you get that close no one will really complain much. I have heard one valid complaint to the way I do the study, and that's that a pitcher's K-rate is not 100% equal to their Stuff rating. It is instead something like 85% stuff, 10% movement, 5% control (or whatever). Therefore, to be really perfect in my study I should add that weighting.
As far as taking injuries out of the study, I can see that having some issue, but since injuries are most likely fairly evenly spread out in the early years, the growth stage of the curve should be fine (since it's all relative--all early ages should get hurt at about the same rate). If there's an area of the study I would be concerned about regarding injury it would be the ages past 32 or so where I suspect injuries begin to happen more often--so I would expect players to fall off more rapidly after age 32-33 than my studies show.