Statistical Targeting

A family of Targeting strategies that involve the accumulation of statistics relevant to aiming at opponents, particularly those strategies that process input on the fly and store them in a data structure for quick access at fire time, as opposed to log-based targeting methods that store raw data and analyze it at fire time.

The statistics gathered could be anything that can be used to reconstruct a firing angle, such as the success rates of different simple targeters in a Virtual Guns array, the last bearing offset that would have hit the enemy, or a segmented array of Visit Count Stats.

Forms of statistical targeting

• GuessFactor Targeting (traditional) - The most common and successful form of statistical targeting involves the combination of GuessFactors, segmentation, and Visit Count Stats. Few modern bots use any other form of statistical targeting.
• Neural Targeting - This method of targeting trains a neural network to output correct firing angles based on input given at fire time. While not a commonly used method, it has been proven to have strong potential by Engineer, by Wcsv.
• Averaged Bearing Offset Targeting - A simple approach to statistical targeting. This method works by tracking the bearing offset that would have hit the enemy each time a bullet is fired (using Waves) and then firing at that average of those offsets.
• Angular Targeting - The original form of Angular Targeting was the same as Averaged Bearing Offset Targeting. A more effective version was used in Gouldingi, called AngularTargeting/Factored, which multiplied the bearing change by a factor that was continually averaged and dependent upon the enemy's current direction and velocity.
• Laser Targeting - The next step up in complexity from Averaged Bearing Offset Targeting, this method kept a log of correct firing angles, from which it would randomly select one at fire time.
• BestPSpace - While not really a firing technique in itself, this method describes segmenting the probability graphs based on different variables to select the best probability.