- Thread Starter
- #101
Appreciate the response and will get started on them taking notes on a boring airplane ride tomorrow.
Yeah I clicked on the YouTube video link last night for “part 3”. My family was sleeping and air pods were lost somewhere so couldn’t listen, I did read the top few comments and basically every comment was “please share the data” “please share the spreadsheet” “please make this data available on your site”.
So it made me feel less like an ass that those were the top comments I saw.
My intent here wasn’t to “attack” your thread. I’m a data and results driven type of person. In the results I’ve seen being a 20+ year cleaner and now a 3 year non-cleaner, to the tune of dozens of guns from .223 to .300 win mag and everything in between, using both hand loads and factory ammo, and with most guns being on their 2nd and some of them their 3rd or 4th barrels… I have not seen any sort of “early failures” “issues with carbon build up” or “loss of accuracy or barrel life” compared to the same guns and chamberings that used to regularly clean.
If somebody has some empirical data with larger sample sizes being measured, and have come to a different conclusion, I genuinely want to see what they’ve seen so I can learn and make an informed decision.
No worries. Part of the issue is there are varying levels of data involved. Many comments on YT want to see the entire spreadsheet he was showing. Which is fine. They showed a number of examples, but certainly not all 105 cartridges. Others want a singular answer, which they clearly articulate does not exist. Then there is the large data set that feeds the model (thousands of barrels and thousands of rounds per barrel), then the assumptions and calculations that ultimately drive his gonkulator. They do their best to say what this iteration of the model is, and more importantly what is NOT.
I appreciate the effort to build this model with their test data in an attempt to support or refute fudd-lore using their extensive lab derived data base.
I would enjoy seeing excursions using different assumptions and trying to break out some variables to see their impact on the ‘estimate range’.
Last edited: