The new dimensions though, need to be looked about their information flow. If the only information about a player, is about its games outcomes within a specifiable (but rarely reminded) pool of players, AND pool of games (player pairng event), mutliplying new building blocks, would not use any information about the player. unless as, it is my understanding the K factors are introduced, ad-hoc, one problem identified at a time.. to fix the population distribution assumption constraining all the individuals ratings trajectories, non-locally (population to individual, global to local).
Also, without the first dimension or module or measure of 3, which makes the same population distribution assumption (right?), Glicko (or such population behavior agnostic rating statistics) do also construct stochastic models (1 and 2 being different there, precisely), about the minimal individual assumptions of rating uncertainty as the cloud (pool) of players buildup up through games events (single games, or clumps of them as in tournaments, not sure about how OTB works besides tournament events, or meta tournament pools of games and players, but not necessary, glicko can input those).
The third one might be interesting and also about modelling something from some knowledge about population structure. I understand that the K factors were some sort of age structure, perhaps confused about age or chess-age, and whether rating over some history, might be changing the individual K factors. It did not seem to look at the whole human lifespan age structure (on the other side of performance and age, or if it is not age, but declining practice whatever the reasons, or even interest and motivation). I think this might be a research area. the modelling of improvement to different from "noise" uncertainty.. But not a proposition of measure yet..
So, the first dimension or measure, reproduces a problem from ELO (for me it is the population pressure cooker cover being tightly sealed)
the second. is some kind of glicko model (correct me, in spirit of the letter).
and the third. is a research proposal. which i am also curioius about. but i would not take it as done deal. ChatGPT is a great tool for smoothing out discourse (might want to massage my post with it, if i was making some sense it will protrude it better than i did), and also for generating hypotheses otherwise (I am not consistent for example, or I did not complete an idea, or I missed some component, or else). The problem then, is figuring out which part is convergence and syntax reformatting, and the part that is creative. but you have the source it is my post.. In case of chess player "strength", which for me is better seen as mutlidimension skill set, being explored through experience digested and other means of acquiring knowledge individually that make it to the digested skill set, but we are far from being able to measure any of that, we do not have any other data than the personal profile of the player, which is likely irrelevant (maybe not?) to its strengths or ability to play chess in any positions (ok. i just mentioned what strength should be in my opinion)
Also, without the first dimension or module or measure of 3, which makes the same population distribution assumption (right?), Glicko (or such population behavior agnostic rating statistics) do also construct stochastic models (1 and 2 being different there, precisely), about the minimal individual assumptions of rating uncertainty as the cloud (pool) of players buildup up through games events (single games, or clumps of them as in tournaments, not sure about how OTB works besides tournament events, or meta tournament pools of games and players, but not necessary, glicko can input those).
The third one might be interesting and also about modelling something from some knowledge about population structure. I understand that the K factors were some sort of age structure, perhaps confused about age or chess-age, and whether rating over some history, might be changing the individual K factors. It did not seem to look at the whole human lifespan age structure (on the other side of performance and age, or if it is not age, but declining practice whatever the reasons, or even interest and motivation). I think this might be a research area. the modelling of improvement to different from "noise" uncertainty.. But not a proposition of measure yet..
So, the first dimension or measure, reproduces a problem from ELO (for me it is the population pressure cooker cover being tightly sealed)
the second. is some kind of glicko model (correct me, in spirit of the letter).
and the third. is a research proposal. which i am also curioius about. but i would not take it as done deal. ChatGPT is a great tool for smoothing out discourse (might want to massage my post with it, if i was making some sense it will protrude it better than i did), and also for generating hypotheses otherwise (I am not consistent for example, or I did not complete an idea, or I missed some component, or else). The problem then, is figuring out which part is convergence and syntax reformatting, and the part that is creative. but you have the source it is my post.. In case of chess player "strength", which for me is better seen as mutlidimension skill set, being explored through experience digested and other means of acquiring knowledge individually that make it to the digested skill set, but we are far from being able to measure any of that, we do not have any other data than the personal profile of the player, which is likely irrelevant (maybe not?) to its strengths or ability to play chess in any positions (ok. i just mentioned what strength should be in my opinion)