Grits N Gravy, on 24 January 2014 - 11:54 AM, said:
You could just apply a modifier to the K factor based on each piece equipment and chassis. Easy to use items increasing your K factor, thus your Elo goes up, so you have to play superior teammates more quickly. Track live data in background and make changes to dial in the system before you launch.
Doing the same thing here would help too. Apply a modifier to K factors based on group size.
The beautyf of all these solutions is u can run them in background and tweak them before the go live, or use historical data and model them too. It doesn't take a lot of effort to do so, it's just a matter of focus.
Sorta, but you'd need to keep the results of each modified K factor separate. Use the base Elo as a starting point for the weight class but then grow and maintain a separate Elo value, or in this case modified Elo value, for all the variables a player generates by playing different versions. That your base Elo isn't modified by a K factor just for wins/losses but our predicted value (your value before the win, not after) is modified for chassis and loadout in addition to the composition of both teams.
RussianWolf, on 24 January 2014 - 12:21 PM, said:
I realize that you don't know me. But when you start off like that, you've lost your audience. I've aced Calculus, Trigonometry, Physics, Statistics and Geometry. Understanding complex equations and systems is something I also do for a living.
I'll restate it very simply.
Math works. Always. BUT math can give flaw results when placed in the wrong environment.
Simple equation
Input a constant greater than 0. X^2 = Y and Y will always be greater than 1
Simple. Easy. Always works.
Change the environment where you allow numbers larger than 0 and the equation stops being 100% accurate.
.5^2=.25 oops, that's not greater than 1
Math is wrong? No. You just put the equation into the wrong environment for what you were working on. So it gave you flawed data. If you use the data and continue, that's your problem.
You know what, I do owe you an apology. I've been debating with Rich and Abivard and it skewed my perspective so fair enough, totally my bad.
However what you're doing is mistaking algorithms and logarithms for probability theory and statistical analysis. You can identify the impact of a single variable with an influence in the hundredths of a percentile if you've got enough telemetry and enough samples. This is, essentially, how marketing works. It's also the same tool set that lets us identify the composition of distant solar systems and galaxies by measuring influences on gravitational lensing.
The changes in the environment in MW:O is never as great as what a completely random generation would create. The variability isn't anything close to random, it's not like it's trying to predict a lotto number. Every match results in either a 1/0 result and while at a granular level there's an incredible number of variables, if you want to get picky there are a good tens or even hundred million variables in a 12 v 12 match. You want to get into the variables impacting each player which can then theoretically impact their performance you could get into billions of variables.
The same could be said of rolling a six sided dice. Air viscosity, surface imperfections, lunar gravity impacting the dice differently based on how high it's thrown. Surface friction based on how much of the dice comes into contact with the table as it bounces to a halt. Billions of variables.
Truth is that unless you've modified the dice the odds of rolling any particular number on any throw of the dice is 1:6.
The reality is that the performance of every player in a match are going to fall within a very narrow range of performance (Elo average). Very high and very low performance will be statistical outliers and their impact will decline with a larger sample range. Taken as a strict aggregate you'll have an 8.33% impact on your teams odds of a win. In individual instances though it's going to swing based on the relative ability of your teammates, your value might swing from 3% to 12%.
Where the player skill impact comes in however is suppose you're 1% better, then your impact will be from 4% to 13% compared to an average players 3-12%. That 1% variance in your performance over average performance is magnified by the K factor. You'll increase your Elo more dramatically beating players who are better than average, you'll lose less when they beat you.
The composition of the opposite team may seem random but because they are balanced to an Elo target much like your own team the range of actual variables to team compositions are very narrow.
The reality is that most people are not that good. Almost all players are average. Your 'skill' and how it impacts Elo is reflected far more by your ability to pick, build and play effective mech builds and if you want a spectacular Elo you need to hone the skill of joining and coordinating with a skilled team of players. These two skills are going to be far, far more impactful to your win/loss than your aiming ability.
What's all this equate to? In the current system your Elo rating is a good ballpark estimate of your performance and it does serve to separate players into rough bands of terrible, bad, average, good, awesome. You need hundreds of drops in a weight class to get seated around where you should be.
So if you've studied statistics how are you saying that something like how big an impact one player in a 12 v 12 MW:O match after several hundred examples in a roughly skill and weight balanced environment? The only difference between 1 v 1 and 12 v 12 is scale. Accounting for scale is solved by increasing sample size and breadth of data. That's why statistical analysis exists as a science.
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