Taking your own movement into account when targeting?

While in melee PIF is more popular, in 1v1 most people (I guess) is using something similar to GuessFactor, where lateral movement is assumed. And whether you are scaling based on theoretical MEA or orbital MEA or precise MEA, we always assume that enemy movement is only relevant to our initial position and scales as if it moves in a circle — which is not true for real battles.

Sometimes an opponent that always moves perpendicular to you moves in straight lines, not circles, when you keep moving perpendicular to him as well. And for anything that controls attack angle, they won't move in perfect circle either. We've been already adding lat, adv, distance etc. to help our gun distinguish these situations, but what is currently lacking mostly, is about our own movement (although I see DrussGT is adding mirror offset). The lack of attributes about our own movement cannot be compensated by more data, and it can neither be made up by using more prediction power, e.g. PIF.

If we can eliminate the effect of wall (e.g. the use of precise MEA) & our own movement to enemy movement, the accuracy of our guns could ascend to an even higher level, where only the weakness (the flaw of being flat) of enemy is learned. I see this as the holy grail of guns, where each choice affects greatly how good your learning algorithm could perform and with this holy grail, any learning algorithm should reach its theoretical limit.

What I came up with is, instead of scaling bearing offset arithmetically on GuessFactors and some form of MEA, we run some simulation taking walls and our own movement into account, then treat GuessFactor as the ratio of time of the simulation it will reach, e.g. 1.0 as running constantly, and 0.5 as it stops after half of the simulation time. Then 0.0 would be it hits the brakes immediately.

With this approach, GuessFactors could be treated closer as "run time after bullet fire", rather than just some "guess". We've been already using precise MEAs to better simulate this behavior, but imo we could go even further.

And even further — we expand GuessFactors to a series of velocity each tick before hit. And run the simulation to decide attack angle while using the series of velocity from log. This will be something between PIF and GuessFactor, but with better accuracy and learning speed.

In those situations of predicting my own movement I always wonder about two cases:

1. true surfing, not sure about my own movement 2. new data coming, decisions changing, but the bullet will be already fired, I cannot regret (this should not be that impactful, though)

I like this approach, the PIF thing would be like the simulation of lateral velocity 1v1 string matchers use, but on top of a more complex curve instead of a circle.

1. You are partly sure about your own true surfing movement, as you already know most of the information that determines the intersection point.

2. New information definitely can't affect past decisions, but you can always do your best with existing information.

At least before our bullet hit, our movement is mostly determined by the bullets already in air, which is a good news.

If the optimal point is somewhere between my current point and moving full speed clockwise, for example, I really don't know the intersection point if I allow hitting the brakes. I know the direction, though, which is sufficiently relevant for this use case IMO.

It doesn't assume orbital movement nor enemy circling you. The only thing Guess Factor Guns assume is generally symmetric movement. By the way, PIF with Lateral and Advancing Velocity works very well.

No, it assumes that, as the way it scales between different distance & bullet power. Anyway, that can be overcomed with relevant attribute & more data.

Yes PIF with lateral & advancing velocity should work very well for similar reason.