This Has My Attention: New Questions

You can find all of that data out without having to look at individual actuals—
You can look at how often HAL pinch runs, hits, uses whatever without seeing how
Any particular card performs in actuals.
On top of which, thats NOT the data being mined by some of the people doing it-
They are. Specifically looking at how every player performs in actuals, which again,
Should not be dependent on anything but what’s on the cards, assuming of course there
Are no black box items.

Of course that would not explain a million things we see quite commonly-
Like players who largely get on base through walks tend to perform fairly worse in the OBP
Department than what’s on their cards, and than players who tend to have more hits on their cards.
This has been noted repeatedly by a number of managers- one of many examples.

It also wouldn’t explain what I happened to see in the original sample computer Rom - CD I got in the mid-90s that Ive mentioned a number of times- but hey, if you can’t statistically prove it, then it doesn’t matter yeah?

This is the Theoretical Explanation of Pythagorean Expectation:

Initially the correlation between the formula and actual winning percentage was simply an experimental observation. In 2003, Hein Hundal provided an inexact derivation of the formula and showed that the Pythagorean exponent was approximately 2/(σ√π) where σ was the standard deviation of runs scored by all teams divided by the average number of runs scored.[8] In 2006, Professor Steven J. Miller provided a statistical derivation of the formula[9] under some assumptions about baseball games: if runs for each team follow a Weibull distribution and the runs scored and allowed per game are statistically independent, then the formula gives the probability of winning.[9]

More simply, the Pythagorean formula with exponent 2 follows immediately from two assumptions: that baseball teams win in proportion to their "quality", and that their "quality" is measured by the ratio of their runs scored to their runs allowed. For example, if Team A has scored 50 runs and allowed 40, its quality measure would be 50/40 or 1.25. The quality measure for its (collective) opponent team B, in the games played against A, would be 40/50 (since runs scored by A are runs allowed by B, and vice versa), or 0.8. If each team wins in proportion to its quality, A's probability of winning would be 1.25 / (1.25 + 0.8), which equals 502 / (502 + 402), the Pythagorean formula. The same relationship is true for any number of runs scored and allowed, as can be seen by writing the "quality" probability as [50/40] / [ 50/40 + 40/50], and clearing fractions.

The assumption that one measure of the quality of a team is given by the ratio of its runs scored to allowed is both natural and plausible; this is the formula by which individual victories (games) are determined. [There are other natural and plausible candidates for team quality measures, which, assuming a "quality" model, lead to corresponding winning percentage expectation formulas that are roughly as accurate as the Pythagorean ones.] The assumption that baseball teams win in proportion to their quality is not natural, but is plausible. It is not natural because the degree to which sports contestants win in proportion to their quality is dependent on the role that chance plays in the sport. 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. If chance plays very little role, then a team with only slightly higher quality than its opponents will win much more often than it loses. The latter is more the case in basketball, for various reasons, including that many more points are scored than in baseball (giving the team with higher quality more opportunities to demonstrate that quality, with correspondingly fewer opportunities for chance or luck to allow the lower-quality team to win.)

Baseball has just the right amount of chance in it to enable teams to win roughly in proportion to their quality, i.e. to produce a roughly Pythagorean result with exponent two. Basketball's higher exponent of around 14 (see below) is due to the smaller role that chance plays in basketball. The fact that the most accurate (constant) Pythagorean exponent for baseball is around 1.83, slightly less than 2, can be explained by the fact that there is (apparently) slightly more chance in baseball than would allow teams to win in precise proportion to their quality. Bill James realized this long ago when noting that an improvement in accuracy on his original Pythagorean formula with exponent two could be realized by simply adding some constant number to the numerator, and twice the constant to the denominator. This moves the result slightly closer to .500, which is what a slightly larger role for chance would do, and what using the exponent of 1.83 (or any positive exponent less than two) does as well. Various candidates for that constant can be tried to see what gives a "best fit" to real life data.

The fact that the most accurate exponent for baseball Pythagorean formulas is a variable that is dependent on the total runs per game is also explainable by the role of chance, since the more total runs scored, the less likely it is that the result will be due to chance, rather than to the higher quality of the winning team having been manifested during the scoring opportunities. The larger the exponent, the farther away from a .500 winning percentage is the result of the corresponding Pythagorean formula, which is the same effect that a decreased role of chance creates. The fact that accurate formulas for variable exponents yield larger exponents as the total runs per game increases is thus in agreement with an understanding of the role that chance plays in sports.

This the from the Empirical Origin section, and this is what I would like to take a look at:

There are some systematic statistical deviations between actual winning percentage and expected winning percentage, which include bullpen quality and luck. In addition, the formula tends to regress toward the mean, as teams that win a lot of games tend to be underrepresented by the formula (meaning they "should" have won fewer games), and teams that lose a lot of games tend to be overrepresented (they "should" have won more). A notable example is the 2016 Texas Rangers, who overshot their predicted record by 13 games, posting a 95-67 record while having an expected win–loss record of just 82-80.

https://365.strat-o-matic.com/league/expanded/462980

I’ve already posted this team, but now that I lost a Game 7 Finals on the road, let’s take a closer look.

My run differential was greater than all the other three playoff teams COMBINED. I snuck in as the wildcard but as a result forfeited home field advantage. I can tip my hat to the Champs manager, who beat me in seven games despite a negative run diff, an 81-81 regular season record, and the fact that this was the 16th team he’s ever played (assuming it’s not an alias). The best team doesn’t always win a short series, amirite?!

What are the odds??

So imagine my surprise when my very next team does this:

https://365.strat-o-matic.com/league/expanded/463015

My run differential was greater than all the other three playoff teams COMBINED (again). I lost the semis to another manager with 27 games total experience and a .500 managerial record. Hat tip! The best team doesn’t always win a short series, amirite?!

But here’s the thing. According to the Pythagorean math, high run differential teams should be UNDERREPRESENTED by the formula. That is, my expected win loss record should be LOWER than my actual record, not the other way around.

Why is the exact OPPOSITE mathematical expectation consistently happening do you think

Do you not utilize settings on do not pinch hit, quick relief, quick hook or slow hook, how aggressive to steal bases or conservative (percentages of success rate can go from 60 percent to 85 percent) how aggressive to be on basepaths, set up for how long starters stay in, either by getting s5 pitchers or maybe s9 pitchers, how strong to put outfield arms in, where to put error prone outfielders? BP Hr's on cards depending on the team and league ballparks. When is anything the cards?

Anyone that expects in this game expected wins to represent how good their team is, why not just give points for margin of victory and be done with this win loss BS. Get 3 points for a 25-0 win and .1 point for a 1-0 win, or whatever the mathematical model would generate to get expected wins from run differential and actual results. This of course will mean people who get in a division with a truly terrible team will get a lot of points but it would be fair and equitable.

Proving my point that you don't need evidence to deny there's an issue. If Charlie says it isn't an issue, apparently that's sufficient.

Do you not utilize settings on do not pinch hit, quick relief, quick hook or slow hook, how aggressive to steal bases or conservative (percentages of success rate can go from 60 percent to 85 percent) how aggressive to be on basepaths, set up for how long starters stay in, either by getting s5 pitchers or maybe s9 pitchers, how strong to put outfield arms in, where to put error prone outfielders? BP Hr's on cards depending on the team and league ballparks. When is anything the cards?

Anyone that expects in this game expected wins to represent how good their team is, why not just give points for margin of victory and be done with this win loss BS. Get 3 points for a 25-0 win and .1 point for a 1-0 win, or whatever the mathematical model would generate to get expected wins from run differential and actual results. This of course will mean people who get in a division with a truly terrible team will get a lot of points but it would be fair and equitable.

Proving my point that you don't need evidence to deny there's an issue. If Charlie says it isn't an issue, apparently that's sufficient.

Why don't we just go to expected runs as the decider of who makes playoffs and Home FIeld advantage? Take the four best and that is that seed according to that. According to the experts here that is the official guide to the best teams. As a matter of fact the team with the best expected record should just be declared the champ as they have already proven they are best and anything else is to deny the obvious. The rest of the game is an obvious fraud that is unproveable but certain.

Charlie-

This is again, entirely a red herring.
No one is saying what you are asserting as the proverbial reducto ad absurdum answer.

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

Here’s my Upside Down league, where the goal is to force your OPPONENT to outscore YOU.

The teams with the most wins still tended to underperform expectations, while the teams with more losses overperformed expectations. According to the Pythagorean math, the EXACT OPPOSITE should be occurring. In fact, I won’t even demand opposite, I’d settle for half the time (ie, bell curve).

Although “quality” in terms of the equation means that larger positive run differentials equate to a “higher quality team”, no one is saying the game is a fraud or there should be no playoffs.

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

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.

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

Three somewhat related reasons:
1. Strat on line is not real life
2. The erudite professors you quote could be full of bunkum (N.B. I had to look up what a Weibull distribution is - maybe Frankie can explain to me why its more appropriate than a Poisson or Gaussian)
3. This is a game of chance and the pythagorean exponents may be very different than what's being assumed.

Also, a question for the usual suspects who always show up in the conspiracy theory threads - is this really about a conspiracy theory or victimization?