Chief Blur Buster wrote:I've even commissioned an article by a peer reviewed researcher on eSports-league reaction times (
Input Lag and the Limits of Human Reflex). The millisecond is often easy to dismiss, but there's a few effects behind the millisecond too, that are often easier to understand for laypeople.
That article is very incorrect in many places. Now that it's being referenced, I went back to it and decided to address each point.
the human benchmark reaction time test
The 150 ms results in their database are from cheating/guessing, there's no point in referencing them.
Given current home testing setups, average measurements of 140 ms - 160 ms are achievable from audio reaction times. For visual reaction times with current monitor/mouse input tech, 160 ms average measurements are probably possible, and 140 ms averages are not.
Here is a video of [flood] sniping bots in CSGO.
More than half the frames are dropped in that youtube video, which makes counting frames suspect. Access to the raw video is needed for that method to work. However, 180 ms is in line with other results. It's not the fastest, but is superb.
startle response improves reaction times
Startle reaction times are limited to startle responses, which appear not to be useful for practical game purposes. (i.e. they are likely skipping large parts of your brain's usual processing pathway)
there are humans out there who are on the extreme end of the spectrum and who may not have been represented in most studies of human reaction time
Scientists are aware of this and take it into account already.
One of the sprinters (female) had an average reaction time in the legs of 79 ms, and her fastest was 42 ms
It's not a good idea to cite those fastest times. 42 ms is below the theoretical limit and is from guessing. The extreme averages should also be viewed with caution.
The <100 ms numbers from the research papers are real, but they are caused by extremely good measurement systems, rather than really fast people.
Quake LG section
Optimal LG technique aims at the rear of the target rather than the centerline, so this entire section is bogus and should be retracted.
Simulation 1: The Quick Draw
Individual players also have their individual reaction time distributions. But since there's only a single match between players, this only affects the numbers, and the method is still ok. This effect makes a 55% number closer to the given assumptions, rather than 57%.
Simulation 2: Tracking a Dodging Enemy
Again, LG aims at the rear rather than the centerline.
For its goal as an expository article, I think the article has so many major errors that someone who accepts it blindly will end up knowing less than someone who didn't read it at all. Nobody should bring it up as a reference, especially to someone who is new to the subject.
For a separate goal as a research article, I think the parts that are useful are the citations to the research papers and the Quick Draw section. The author's interpretation of the research articles is very poor, but the underlying research articles are still good.
The Quick Draw section is just looking at the CDF of a normal distribution. The 4 ms frame time is sufficiently small compared to the 20 ms player latency standard deviation that the overall distribution is normal. It's easy to calculate: given 20 ms standard deviation of population latency, 4 ms frame time, and 15 ms standard deviation of an individual's latency (made up number), the total standard deviation is sqrt(20^2 + 15^2 + 4^2/12) = 25 ms. My CDF table gives 57.93% at 0.2 = (5 ms / 25 ms). That actually makes me question the validity of the article's simulation, since 58% > 57% and I used a higher standard deviation.
In fact, it looked so suspicious that I plugged in the article's conditions into Mathematica:
Code: Select all
f[x_] = CDF[TransformedDistribution[x + y, {Distributed[x, NormalDistribution[0, 20]], Distributed[y, UniformDistribution[{-4.17/2, 4.17/2}]]}], x]; f[5]
This gives 59.85%. So the author's simulation code is wrong.
A video similar to flood's video is useful in an introductory article such as this one, but better ones exist. (Either flood's raw, non-youtube video, or another source.)