I agree there is evidence. I said there is "little evidence".
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PHDram
albeit not conclusive. moreover, you did say that its "clear from that article that there is little statistical significance once you get above 52% and really not much difference down to 48%". you can assign significance if you want, but its NOT clear.
Yeah, I stand by that. Between 52% and 59% there is only a .3 game difference in win totals. If you take it down to 48%, there is only a 1.2 game difference. With the averages not topping 9.2 wins.
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PHDram
moreover, small sample size? the sample covers every team from 2011-2017...thats 224 data points which is not huge but well beyond what would be considered a small sample study. how many points do you want?
Given the complexity of the system and the fact that this article only looks at one variable, I would say at least 10x. The fact that table 1 and table 2 have conflicting results tells us that there is not enough data. Just because this is the data we have doesn't mean there is enough of it to drawn any conclusions since the differences in the sweet spot are so minimal.
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PHDram
that being said, given the data provided i too would not be surprised to find that "win totals go down as 59% is approached and that the real sweat spot is in in the 54-57% range" but that is really just a guess.
thus overall the article does provide some useful information as to how teams have successfully managed the salary cap. but as the exceptions point out, i dont know that they are hard rules, but rather rules of thumb.
I agree with the first point. Fair enough on the second. IMHO, the second table is more telling than the first with the exception of the danger spending 60%+.
Although this article seems more like a guy with Excel, access to the internet, and too much time on his hands. It would be interesting to see the raw data and do some real analysis on it.
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CeeZar
I don't assume no significance. I said little evidence.
Given the small sample size and lack of supporting data. I don't think there is any way to draw the conclusion that getting close to 60% is in any way optimal. We can say with certainty that is NOT common as less than 14% of the teams go over 55% which works out to 4.5 teams/year on average with even less teams near the 60% mark.
The .3 win difference between the segment up to 59% and the next lower segment up to 55% seems meaningless given that we are still talking about slightly above average win totals. In the 56-59% range, average wins are only 9.2. That means there were a lot of average to below-average teams in that range. I realize that we don't have the distributions, but unless they are way off the bell curve that seems like a pretty safe statement
What the data does show somewhat clearly is that top heavy teams at 60%+ are not good teams - as in really bad teams
Since there is a HUGE drop off from 56-59% to 60%+, I would bet that within the 56-59% range the that win totals go down as 59% is approached and that the real sweat spot is in in the 54-57% range.
