I don't research the software side of things too much, but I would assume that the amount of games doesn't matter as much as it does the amount of games in a genre that the player base of a console supports.
The PS4 might have more games than the Xbox One, but depending on the genre of said games, those 20% more games on the PS4 might not drive consumers to purchase them because it (genre) does not fill their needs.
While on the Xbox One side, while having less games overall, what it does have in plenty is shooters. Xbox owners love like 2 things, sports and shooters, so even with less games overall, because the core base of Xbox favors shooters (and sports), they'll pick those up in spades, while another genre (JRPG) is almost ignored on the platform.
As long as the player base of the console gets games in the genre that is favored the most, I think the tie ratio will be "good".
Or I can be completely off base with this.
Sure, genres will factor in. If the US PS4-owning population collectively has zero interest in JRPGs, then a bunch of JRPG releases isn't going to increase Sony's software sales. The weird thing is, the existence of the JRPGs seems to be causing PS4 owners to walk out of the store with
nothing; those extra games make them buy
less software per-console than Bone owners.
Queso has been arguing that more physical releases means more physical sales. No one really disputes that, but diminishing returns, etc. However, Queso seemed to be arguing and I may be completely misunderstanding him again that the returns are actually fairly linear. But it actually sounds like that as release counts go up, per-title sales actually go down. Sales-per-release seems to go down as release counts go up. PS4 has more games than Bone, but PS4 owners buy less games. Wii had twice as many games as the 360 did giving Wii owners far more choice but only 86% as many games sold per console. Analysis paralysis? I dunno; it's counter-intuitive.
Roughly, when there is only one variable, the coefficient of a regression line is the covariance between the two variables normalized by the variance of the independent variable (in this example, number of releases).
The R^2 is the total variation in the data (this case: sales) that can be predicted in a linear fashion by the independent variable (this case: number of releases).
Like I said, I never took Prob & Stat, so I looked up what covariance meant, and I'm guessing you're saying that we need to find the mean for the release counts, and the mean for the tie ratios, and then see how much those two means deviate from each other? How do we do that, using the numbers we have? Like, can you walk me through it? <3