I haven't compared those exact strategies, especially as I usually look at deeper screens.

But the reason for suggesting the (price/(high+low)) expression is that it works better than (tr1y) or (price/high) during those rare times that the market is rebounding strongly after a recent deep sell-off. So the returns relative to market are pretty good during those rare extraordinary times, meaning the odds of beating the market in any given rolling year rise even if the end-to-end CAGR measures the same as other momentum metrics. It often increases the fraction of positions that are profitable, as a different measure of the same effect.

That makes the screen easier to stick with, and you get earlier evidence on the degree to which the screen is behaving as intended: if you're expecting it to add value in (say) 70% of rolling years, and it doesn't do that in the first three years, you have reason to be suspicious sooner than if you're running a screen with the same overall CAGR that manages it with rarer very high returns.

In short, it's intended to make the screen add some value in a wider variety of market regimes. Whether it does or not is of course a separate question, but that was the intent : )

Here's a Nasdaq 100 screen I created in 2019:

Nasdaq 100 member
Price/(52-week high+52-week low) top 50
5-year sales growth rate > 0
ROE * (5-year sales growth rate) top N

The top 10 monthly before friction beat the S&P in ~82% of rolling years since then. Overall CAGR advantage relative to S&P 500 over 9%/year out of sample.
The time period covers the pandemic plunge and rebound, so it's a nice test for the idea. The backtest returns in 2019, 2020 and 2021 are remarkably similar. 2022 horrible, as with many things.

(actually I used the largest 105 stocks listed on Nasdaq rather than nas100. So it might have included some financial firms. The Nasdaq 100 is basically non-financial market cap top 105, hold-till-drop 100)

Jim