This Has My Attention: New Questions

gk: I know you’re being sarcastic to lazily make a point. Instead of answering a question that’s not being asked, let’s try and figure out what is actually going on instead.

https://365.strat-o-matic.com/league/expanded/457932



I’m simply asking the question “Why would this be occurring?”

From your definition of the runs scored usage, they not only note that runs scored is not necessarily a natural choice for determining the quality of a team but also "If chance plays a very large role, then even a team with much higher quality than its opponents will win only a little more often than it loses." In other words as chance increases the "QUALITY" factor i.e runs scored becomes less of a factor and higher quality on average will have lower wins than expected continually due to an increase in the amount of chance in the distribution of the runs scored. That stratomatic is more dependent on chance than baseball should be obvious, the result is from a dice roll as opposed to actual play of the game, therefore the higher runs scored teams on average should have less wins than expected as a result of chance and the lower ranked teams a higher average, which is what is occurring. This is in the definition you provided. To expect a game based on dice to produce a quality difference through dice rolls versus the quality difference between Hall of Fame pitchers and minor leaguers should be obvious.

By the way, this is exactly what happened in your upside down league as all the teams with positive run differential underperformed while the terrible teams overperformed.

Being a small ball guy, I typically DON'T have high scoring teams, often times finishing in the bottom third of the league in runs scored yet having a good positive R/D. So there goes your High Scoring teams underperforming theory.

I’m kind of amazed at this discussion- and mathematically it’s well above my head.
However, the mathematical minds, while fairly amazing at modeling and constructing tend to miss
Anything outside of parameters they can understand And quantify.
Again, why I miss guys like Bruce here, who could kind of function in both mathematical and artistic minds.

BUT- lets go back to why you need actuals rather than just using card data-
Assuming there isn’t nothing in the engine of the black box nature;
And to be clear, its not an issue of being ‘nefarious’ - its obviously been designed to mimic
Certain things we see in MLB - in fact don’t take my word for it (obviously you won’t)

SOM themselves say it -
How do you think they achieve things like they mention, so that it reflects ‘historical performances’ ala Eck and Maddux?
IF you don’t know what I’m talking about and you are arguing with me, then we have an even bigger issue of ignorance.

Salty I think it is above my head too.

The answer lies within the following (giving full attribution here as I think dj, Max and Charlie may have said it earlier but I lost track since I was last on)

SOM is not real life baseball.

Real life baseball is played without extreme "coefficients" of performance like pitchers who go 9 innings let up 11 runs and get the win. Baseball generally operates on the same general rules every day with a HUGE sample density.

SOM has a huge sample of individually differing paramers like I mentioned above. That's the crux of it. Pythag as an estimation model depends on the selected coefficients--just like in a weibull. Those exponents have inherent meaning and assumptions--and like I said earlier--blindly using a formula can yield garbage if you don't understand how to derive the formula from first principles.

The probem is you are using coefficients for something with real life actuals--and applying it to a different system.

Like you designed a satellite and are using the reliability model of a blood pressure monitor.

I couldn't resist this. Anyone ever an Isaac Asimov fan? Well, this reminded me of Hari Seldon.

From the wiki...
"Hari Seldon, also called Raven Seldon or the Raven by his enemies, specifically the late Galactic Empire, is the intellectual hero of Isaac Asimov's Foundation series. In his capacity as mathematics professor at Streeling University on Trantor, he developed psychohistory, allowing him to predict the future in probabilistic terms. His ability to predict disasters is the reason behind his nickname "Raven" Seldon."

Math is never just numbers. In the wrong hands, it’s a weapon. In the right hands, deliverance.
—Hari Seldon

geeky and not very funny but kinda made me chuckle. I am not making light of anyone's forthright discussions here, but poking fun at my own occasional silliness.

BTW...point of order so no confusion... note i used "coefficients" and "exponents" interchangeably in the earlier post. Maybe I mixed contexts but after all, they are the same thing. Just depends if you are in log or exponential form. Old habit I just see them the same way but sorry if caused nay confusion.

I'm talking to myself now.

Ha

I'm talking to myself now.
Ha

Be quiet

I'm talking to myself now.
Ha

Be quiet

LOLLL!
Nice Asimov reference
Foundation series was a fave of mine.

I couldn't resist this. Anyone ever an Isaac Asimov fan? Well, this reminded me of Hari Seldon.

From the wiki...
"Hari Seldon, also called Raven Seldon or the Raven by his enemies, specifically the late Galactic Empire, is the intellectual hero of Isaac Asimov's Foundation series. In his capacity as mathematics professor at Streeling University on Trantor, he developed psychohistory, allowing him to predict the future in probabilistic terms. His ability to predict disasters is the reason behind his nickname "Raven" Seldon."

Math is never just numbers. In the wrong hands, it’s a weapon. In the right hands, deliverance.
—Hari Seldon

geeky and not very funny but kinda made me chuckle. I am not making light of anyone's forthright discussions here, but poking fun at my own occasional silliness.

“Scientific method, hell! No wonder the Galaxy was going to pot.” - Seldon.
...as it pertains to the hard core math part of this thread.

I found it. This puts the puzzle together for me in a way that is more satisfactory, although everyone here made good contributions in getting the pieces on the table. I still think the long tails remain somewhat of a question mark , but I’m much happier having found an explanation for the bigger picture.

http://spiff.rit.edu/richmond/baseball/ ... g_sim.html

A huge swing and a miss on my part was not realizing I’ve only played ATG a very short time as a percentage of my overall teams. Comparing early 200x seasons to ATG seasons was probably somewhat apples to oranges, now with the benefit of some hindsight.

I was somewhat bothered by the “Strat isn’t real life” argument, which did not intuitively seem right to me. A search of testing Pythagorean Theorem on baseball simulations led me to the link posted above.