I’m sure some of the guys on here have advanced degrees in statistics and could provide more information than I am about to (I got an A in stats in undergrad, an A in a graduate level course, worked as a tutor for Econ and stats, and had an A- in my undergrad econometrics course) so I have some background but likely not as much as some people here.

I actually had not intended to share this, but maybe ash, dh or someone that has a bank of sim data saved could send me some files so I can produce more valid findings. I wish I had not been so lazy and had saved the sim data for the leagues I’ve played, but alas I did not, so the below just looks at the most recent PPL, which means that n=24 (below the central limit theorem of 30).

Basically what I did was run some correlations between different variables in the most recent PPL. Calculating the data was simple for all categories except team D (since the sim gives us the data in the team rankings section.) For team D I calculated a weighted average of total team D by taking the total number of minutes played for a team and dividing each player’s minutes in the sim by the team’s total, giving me the weight for each player, I then multiplied each player’s weight by that player’s D rating and added all of weighted averages together for a total team D.

Here are some of my findings:

The range for team D was 54.91 to 84.79 (the bottom three teams in D won 25, 28, and 30 games while the top three won 41, 49, and 57) however the correlation between team D and wins was only **.4099** (ash had the 4^{th} worst D and won 52 games while the team with the 2^{nd} best D went 41-41).

The correlation between efg and wins was **.5873.** The range was .4713 (same team with 2^{nd} best D that went 41-41) to .5437 (the same 57 win team (great team btw nc) that was also 3^{rd} in team D).

The mean D for the league was 68.58 with a stdeva of 7.73 while the total efg for the league was .5137 with a team stdeva of .0191.

I also calculated a t-score for each team’s D and efg and combined the two to see how this value would correlate with W’s. The value here was **.7296.**

Again n=only 24, but efg seems to be a better predictor than D when it comes to W’s and looking at BOTH efg AND Def is a better predictor than efg alone.

I will say this for team D, the correlation between team D and opponent efg was -**.8869**. So, yea a great team D will impact how well your opponent shoots, but that in itself does NOT = W’s in the sim.

Really want to know how to win, outscore the other guy. The correlation between team point differential and W’s was a not so amazing .**96997. **(HOW to outscore the other guy is the ever burning question of course, because we KNOW it = wins.)

Oh and in case you are wondering rebounds and W’s had a **.6387** correlation, which means by themselves they are more important that either D alone or efg alone.

Ash KNOWS EXACTLY what he’s talking about guys, heck I’d bet good money he has run his own models on the sim and has even better data than mine to produce more valid results. Final note is that team FT% and W’s had a **.1953** correlation, i.e, worthless (at least for this set of 24) just as ash maintains.