I've been fascinated by their quilt for years. It's a nice graphical representation to show how random the ordering of "winners" is each year. For those initially pondering their AA, especially those who care about short-term volatility, it's a powerful visual. For those who believe " nobody knows nuthin' " and therefore hold something like the Total Market, it provides some confirmation.
For some, however, the ordering isn't as important as the magnitude. That data is there in the quilt, but the visual picture painted is more about the ordering than it is about the magnitudes of returns. For those who can tolerate more short term volatility but who want to try and do more to maximize their long term returns, you'll get more information by using something like Portfoliovisualizer or Simba's spreadsheet.
Still, others have created alternatives to the Callan Quilt, such as this one on seeking-alpha which also graphically shows the magnitudes as well as the ordering. Scroll down a bit on the page and it's there. Definitely not as easy to read as the ordering-only Callan version, but the information is there.
https://seekingalpha.com/article/4057532-alternative-callan-periodic-table-of-investment-returns
Here's another one that tries to do the same thing:
https://www.investmentaccountmanager.com/products/a-new-periodic-table-of-performance/
There are many roads to Dublin and there is no one-size-fits-all asset allocation. I'm a big backtester, but there are limits to its usefulness. There will never be enough data to ever find the "best" portfolio by looking backwards. So you're left with what is likely a decent range of choices, but with a heavy dose of philosophy on top of it to choose your path. For some, that philosophy leads them to a Jack Bogle style buy the whole market portfolio. For others, it could be any combination of US or International Stock, factors, non-traditional assets such as Gold and/or commodities, factors, real estate, etc. In the end, the only thing that matters is whether you're meeting your goals, not whether you've optimized with limited data and not whether you're following somebody else's idea of a great portfolio.