For your reading enjoyment: https://appliedballisticsllc.com/wp-content/uploads/2020/08/BC_SD_Effects.pdf
Shot-to-Shot Variation in Muzzle Velocity (MV) and Ballistic Coefficient (BC)
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I'm assuming that this is 100% supersonic, and the shots used never came close to transonic?
I have to say that I don't like the look of those numbers. It makes me think that I ought to be sorting and pointing bullets.
To be honest I don't do it because I already feel like I spend a ton of money on bullets, and the thought of throwing some away is horrifying.
I have to say that I don't like the look of those numbers. It makes me think that I ought to be sorting and pointing bullets.
To be honest I don't do it because I already feel like I spend a ton of money on bullets, and the thought of throwing some away is horrifying.
This is incorrect, as we have a lot of data for bullets into subsonic. We have done testing at 2K+ facilities at times.
Also, you don't throw them away you use them for practice. Then you use the best of the best when it counts.
That particular bullets data, yes. However the spread tables are true into subsonic. Because of how much a BC changes when you go into subsonic you would realistically need a different bracket of data. BCs can change dramatically in subsonic flight. This is why we highly recommend the use of a CDM. A BC is only true at a specific velocity, so we average them as well to help better model predictions.
So knowing this, why do we not have inputs for SD and/ or ES in AB mobile, analytics, etc so the solutions can better account for this?
I don't think that entering SD & ES into a mobile solver would do anyone any favors. Most guys don't even have a large enough data set to understand what their SD/ES actually is. Plus, you don't know if the next round will be 1 σ from your mean, 1.5 σ from your mean, etc.
My understanding is that the WEZ runs a monte carlo simulation that accounts for these types of unknowns. You can also run these variables independently with any ballistic solver to get a better understanding of what is realistic with you, your rifle, your ammo, etc. In my mind, this level of statistical evaluation should not be performed in the field and should be left out of any 'mobile' solver. Depending on your data set size, using the mean or median is the best data that you can use to get a hit.
I know. That's part of my point. A mobile monte carlo simulation is not beneficial for vast majority of shooters IMO.
To be clear, this can only tell you how likely you are to miss. It’s not like you can input the SD of muzzle velocity and get a more accurate firing solution.
I'm not sure if you meant to quote me or not, but yes - this is part of my point.
Yes, I think we agree. You weren’t the one who seemed confused but I suspect others may be.
It is possible in Analytics and AB Tacitcal, you can see the spread by simply incrementing temp variance by 5 degrees for every 1% BC SD. We are also working on a way to incorporate this into all of the systems.
His point is still valid.
99.99999% of the standard deviations and ranges (what non-statistics people call extreme spread) that people claim to have are based on five or fewer data points. A few may be based on 6 - 10 data points. Which means they are mostly worthless.
When you have SD and ES based on 30 - 50 data points, then you have something useful. Until then it's just mental masturbation.
I prefer to use Single Data Points. - SD-0, ES-0. Pefrtecion.
I too go to the gun range and shoot a 20 shot string. Then delete the outliers and pretend my SD of zero is the normal
post it to social media. Check out my 0 SD three shot super statistically valid string.
So I thought when you purchased a bullet profile curve from AB it evolves the BC for the given distance thus refining the predicted correction NOT that is averages the BC for all distances, because a computerized solver can make fluid calculations and averages are for pencil and paper.
I don’t think that’s relevant to the original topic. You’re right that BC is an imperfect approximation to the drag of a bullet, but however you model drag, the point is that some bullet types have more variation in drag from bullet to bullet than others, and this is an important source of error at long distance. You cannot compensate for this with any ballistic solver, only by choosing a more consistent bullet.
Okay thank you for making sure we all stay on track. I promise I only reply the way you do, with discouraging self righteousness. Maybe I don’t understand how this works. Every new comment or questions should be a completely new thread?
Just trying to provide information. Tone is easy to misread on the internet. No disrespect intended to anyone.
When you use a CDM you are not using a BC. You are also not using averaged data. You are using the actual drag model as measured into subsonic. However, that data is not easily interpreted. It also provides no comparison between two different bullets for the average user in its raw form. In order for the average user to have understandable and relatable data we share the BC numbers for its simplicity. Internally the software is using the measured drag at different velocities.
I appreciate the response - even though my initial comment "was not relevant to the original topic" lol Thank you.
Interesting. So by using the CDM for my 135g SMK 308w at 2650fps, AB will accurately calculate my drops considering everything else was entered correctly? Asking because AB is very off for this projo in my velocity spectrum. I had to go back to a G1/7 drag model and tweak from there.
Have you used the calibrate feature already. Not once have I used any of the common apps, Shooter, StrelokPro nor AB without needing to tweet the MV by the truing functions. One thing I do not like about AB it requires angle instead of cosine. So it means I must use the angle indication in my Vortex Ranger 1500 at the same level as my rifle vs. just using the cosine indicator mounted and trued to my rifle.
Thank you very much! It is very interesting to see some actual experimentally measured figures of BC variation.
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