Quote:
Originally Posted by Cobby
Defined, but not all that useful unless we know more about the distribution. For example, if the distribution is highly skewed to the left and I would assume the mean is assigned to a rating of 50, then that would mean that the 30 point rating difference between a 20-rated player and 50-rated player wouldn't mean too much in absolute terms, but the 30 point rating difference between a 50-rated player and an 80-rated player would mean a great deal.
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I would think they need to explain it better, certainly. Because the net effect if you apply a normal distribution based rating system on this type of population is that the lowest end of the ratings set will have no members.
Quote:
Originally Posted by Cobby
And if the ratings were distributed somewhat normally, I think that would a problem too. They really should be skewed.
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Is that really the case, though? Major league players in whatever OOTP universe you're playing have been selected from that skewed end of the population, and the minors or reserve rosters or free agent pool are filled with the guys on the lower end. However, because a player's full value is based on dozens of components, you will have some players in the majors who have very low ratings in certain skills. Here are a couple of charts from an OOTP major league. The first is the number of players in each range in a certain skill.
It's not a normal distribution but it's also not the tail-end that you're envisioning. The problem, though, comes when you convert that to a 20-80 scale using the 3 standard deviations idea (every 10 rating points is based on a single standard deviation from the mean. That results in this ratings set:
There are no players out of a 400+ set who have 20 or 25 ratings in this skill. Obviously a study would need to look at more than just one skill, but it's an interesting starting point.