A few thoughts that came into my head and I'm asking out of curiosity since you obviously know more about it than I do:
1. What if I'll change the argument- can I make a claim that he is worse than the average rookie PF/C for example? or even the general C? in that case his sample will be bigger as it relates to the overall sample.
From a statistical point of view, "evening up" the sample sizes doesn't actually help - in fact making any sample size smaller means that the differences between the two results has to be bigger in order to be statistically significant. I think what would be of more impact by looking only at rookie PF/Cs for instance, is that they may actually be worse than the league average of 37% when defending 15+ foot jumpers, both because they are rookies and because they are not typically used to defend on the perimeter. Compared to a smaller sample of big men, particularly rookie big men, WCS could well be better at his value of 39%. But by reducing the sample size, the difference would have to be even larger to be statistically significant.
And a follow up question (which you probably took into account)- the difference isn't really 2% it's just over 5%- because that's the difference between 37 and 39 in precentages.
I recognize that, but the value of 2% doesn't actually play in to the calculations for a z-test. The actual ratios and sample sizes are all that matter (e.g. 120/309 vs. 37000/100000).
2. Can't I make the exact same claim on the other side? since the average doesn't really mean anything (unlike say using dice) and it is only use as a reference point- aren't the odds the same for him being average and for him being 4% worse?
I know it won't effect validity but if there is nothing I missed it could serve as a argument for it since it seems to me there's an idea if it's wrong it will be wrong in favor of him.
I'm not sure I follow what you're asking. I think you're asking the question of why we should assume that WCS's true percentage allowed is the league average 37%, rather than say 39% or even 41%. If that's what you're getting at, I see what you're saying, but it's kind of coming at it from the wrong direction. The reason is that we have a specific question here, and that question is: Is WCS a worse than average defender against 15+ jumpers? We're comparing him to the average because that's what we're interested in - we don't want to assume that he has any particular value of percentage against. We could ask other questions, of course, such as is WCS's percentage allowed statistically better than 45%? (Answer: yes) or is WCS's percentage allowed statistically worse than 35%? (Answer: no. Almost, but not quite at p < 0.05).
As for the partial question of whether the odds are the same that he is average or 4% worse than average...generally, no. The underlying distribution of something like that is usually Gaussian (a "bell curve") where average is most likely and the further you are from average the less likely you'll find someone with that value. For instance if that question were reworded ("aren't the odds the same that WCS allows 37% and that WCS allows 100%?") I suspect you'd intuitively say that the odds that WCS is such a poor defender that he allows 100% is very unlikely. BUT, that's not actually very relevant, either way. We don't actually use knowledge of the underlying distribution of percentage allowed when we do the statistical test.
3. If we'll hold all stats to scientific demands won't it destroy our ability to use them? since we are talking here about men and not controlled environment and there is a ton of variables (injuries, travel, mental state, growth, training, aging, off court behaviour, luck...) that change from game to game and especially between season and season- could I really make a scientific claim that Cousins will score more points than the average player next year for example?
I think one thing that one finds when they play around with stats in terms of asking questions about statistical significance is that we can't be really rock-solid certain about most sports stats as we'd like to think. I mean, we don't really know who is going to lead the league in scoring next year (Curry? LeBron? Harden? Somebody else?) and sportsbooks take advantage of that uncertainty. But some things are easier than others. I'd suspect (without trying to run any numbers) that you could make a very good case for Cousins to have scored statistically more than average this year, especially if you're looking at per-game numbers. But there's a big difference there...figure a team scores about 100 points and plays about 10 guys, so the average player scores about 10 points more or less. Cousins was around, what, 25 PPG or so? That would be 250% of average - it's not hard to imagine that the data will show he's better than average. On the other hand, the defensive percentage against stats are much tighter. WCS was in ratio terms about 5% worse than average. And you need to have a REALLY big sample to be confident that 5% worse than average really is worse than average rather than maybe just bad luck.
But I don't think that we necessarily HAVE to hold stats to statistical significance. It's perfectly valid to say, hey, WCS was worse than average on defending 15+ foot jumpers this year. It's a small effect, but that's what happened. At the same time, if somebody says they don't think that's necessarily real and points to it not being statistically significant, well, they're right too. There's a good chance that WCS's slightly worse than average 15+ foot J defensive numbers this year were just the result of bad luck. "He was worse than average this year" is right. "There's not very convincing evidence that he's going to be worse than average next year" is also right.
I could say a lot more, but I've probably said way more than enough!