Posted by crazystengel on 10/23/2010 4:59:00 AM (view original):
In WIS the very good teams win more than 7 out of 10 and the very bad teams fall short of 3 out out of 10, a sufficiently wide variance that in any given league, one or or more teams may be pushing real-world historical performance records.
Winning more than 7 out of 10 means 114+ wins; winning fewer than 3 out of 10 means 0-48 wins. Other than prog leagues (where everyone's team salary is different) I almost never see teams with that many or that few wins.
I've kept pretty detailed records of a league I've run for 13 seasons (Random Captain). Of the 312 teams that have played in that league, none has won 7 out of 10 games over a full season. Only one had a winning % under .300 (.296, to be exact). By your definition, that league's had no very good teams and one very bad team in 13 seasons.
Take a look at everyone's profile. How many owners do you see whose winning % is less than .400 or greater than .600?
It's hardly necessary to point out the logical error in extrapolating from "leagues I've run," with a limited number of owners pursuing a particular theme, to the larger WIS universe. Nor the basic mathematical flaw in assuming that a regression to mean of .400-.600, among those customers who regularly buy WIS products means none of their teams, or those of one-time or occasional users, surpass or fall short of those numbers.
Again, none of this answers Amy's original question about observed streakiness now compared to two years ago. The answer to that is yes, anecdotally.
I've certainly observed that, as well as comments by other plays complaining about the phenomenon. But that's strictly anecdotal, we lack sufficient and sufficiently rigorous data to reach a definite conclusion. Despite the best efforts of some customers, there just aren't enough numbers available yet to say, although presumably administration could provide more.
In turn, that would not answer the question of whether increased streakiness is a bad thing. Two other alternatives have been offered: that WIS would never do such a thing, or that streakiness is a normal part of real baseball. I tend to agree with the latter, but that does not bring us any closer to a comparison between WIS streakiness and real-life streakiness.
The flaws in the arguments advanced so far in defense are that it's "random" and "anything can happen." But of course, if the simulation is in any way accurate, it can't. This is not a random universe. The data sets drawn from historical baseball remove that element. We don't need to approximate the performance of the 1916 Giants, we know exactly what they did. Yes, running their data sets against those of every other professional team greatly increases the complexity and the variability of the possible outcomes. But "anything" is not possible. To the extent the algorithms allow for completely random outcomes, they would fail to model the results of real-life baseball adequately.
That's what is suggested by some of the admittedly limited data presented here. If a sampling of a "Tim Jordan" data set, run against the same opposition under the same circumstances, produces outcomes ranging from 51 to 121 runs, it suggests significant problems with the modeling. And also that, from a consumer perspective, among games of chance the odds are significantly better putting money on "black" than investing in the WIS "Tim Jordan."