The perfect rifle cartridge in the precision rifle community would be the one that has no, or very minimal, vertical deviation at distance, as well as a projectile that has the least deviance in the horizontal plane. Although there is no such thing as a perfect cartridge, we can take a look at a few cartridges and the characteristics that make them as good as they come.

A bullet with a high ballistic coefficient (BC) arrives at the target faster and with more energy than one with a low BC. Since the higher BC bullet gets to the target faster, there is also less time for it to be affected by any crosswind. Ammunition makers often offer several bullet weights and types for a given cartridge. Heavy-for-caliber pointed (spitzer) bullets with a boat-tail design have BCs at the higher end of the normal range, whereas lighter bullets with square tails and blunt noses have lower BCs.

The 6mm and 6.5mm cartridges are probably the most well-known for having high BCs, and are often used in long-range target matches of 300–1000 meters. The 6mm and 6.5mm have relatively light recoil compared to high-BC bullets of greater caliber, and tend to be shot by the winner in matches where accuracy is key. Examples include the 6mm PPC, 6mm Norma BR, 6x47mm SM, 6.5×55 Swedish Mauser, 6.5x47mm Lapua, 6.5 Creedmore, 6.5 Grendel, .260 Remington, and the 6.5-284. The 6.5mm is also a popular hunting caliber in Europe.

In the United States, hunting cartridges such as the .25-06 Remington (a 6.35mm caliber), the .270 Winchester (a 6.8mm caliber), and the .284 Winchester (a 7mm caliber) are used when high BCs and moderate recoil are desired. The .30-06 Springfield and .308 Winchester cartridges also offer several high-BC loads, although the bullet weights are on the heavy side. These rounds are not only known for their relatively flat trajectory and velocities, they also do not have a large variance in regards to fp/s. A good round will have a velocity variance of only +/- 4 fp/s.

Standard deviation (SD)

In the case of many handloaders and precision-rifle shooters like myself, we are always finding a way to lower our standard deviation (SD). Standard deviation, in statistics and probability theory, shows how much variation or dispersion exists from the average. A low standard deviation indicates that the data points tend to be very close to the mean; a high SD indicates that the data points are spread out over a large range of values.

Reaper Tips | Hand Load Your Ammunition

Read Next: Reaper Tips | Hand Load Your Ammunition

Simply put, in regards to bullets, standard deviation is just a fancy way of averaging each point’s distance from the mean, or how spread-out your bullets are. The higher the SD is, the more spread-out your bullet’s data is; a lower SD means the bullet’s data is closer together.

For example, if you shoot a string of five rounds, and your highest muzzle velocity is 2900 fp/s, and your lowest muzzle velocity is 2800 fp/s, then the SD of your muzzle velocity is calculated by (2900 – 2800)/2.326 = 42.99 fp/s.

The number 2.326 is derived from the following:

Bullets used: Divide your range by this:
2 1.128
3 1.693
4 2.059
5 2.326
6 2.534
7 2.704

Standard deviation is nothing more than a glorified average of how far each point in your collection of data is from the mean of data. Evaluate firearm accuracy based on many groups. Do not be distracted by changes in group size that are within plus or minus 50 percent of your firearm’s long-term average group size. Such variations are completely explainable by nothing but normal random variation, and do not indicate any change in the firearm, loads, or shooting technique.

The conclusion of standard deviation

For samples as small as five or so, use range instead of standard deviation. For small samples, standard deviation will almost always underestimate variation. Base estimates of standard deviation on small samples only if you are content to have a large amount of uncertainty in your estimate. It takes a lot of data to precisely estimate a standard deviation.

Do not interpret small changes in variation as real change, unless you have the large sample size required to support such a conclusion.

(Featured image courtesy of crispme.com)