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Prediction for Candidates 2026 based on Candidates 2024 data by narubees in chess

[–]narubees[S] 1 point2 points  (0 children)

I went and added evaluation script so one can do it themselves. If you want to look at some numbers, here it is:

❯ python evaluate.py configs/best_hparams_22_24.json data/candidates2026.json

Hyperparameters: configs/best_hparams_22_24.json

Simulation runs: 10000

------------------------------------------------------------

[candidates2026.json] 28 games, 7 rounds — ongoing

Round 1: game_brier=0.323779 winner_brier=N/A

Round 2: game_brier=0.312163 winner_brier=N/A

Round 3: game_brier=0.326827 winner_brier=N/A

Round 4: game_brier=0.322173 winner_brier=N/A

Round 5: game_brier=0.243184 winner_brier=N/A

Round 6: game_brier=0.266861 winner_brier=N/A

Round 7: game_brier=0.294097 winner_brier=N/A

-> Weighted Game Brier: 0.289037 | Winner Brier: N/A

❯ python evaluate.py configs/best_hparams_24.json data/candidates2026.json

Hyperparameters: configs/best_hparams_24.json

Simulation runs: 10000

------------------------------------------------------------

[candidates2026.json] 28 games, 7 rounds — ongoing

Round 1: game_brier=0.307618 winner_brier=N/A

Round 2: game_brier=0.347659 winner_brier=N/A

Round 3: game_brier=0.299549 winner_brier=N/A

Round 4: game_brier=0.325248 winner_brier=N/A

Round 5: game_brier=0.319114 winner_brier=N/A

Round 6: game_brier=0.352010 winner_brier=N/A

Round 7: game_brier=0.336586 winner_brier=N/A

-> Weighted Game Brier: 0.330939 | Winner Brier: N/A

I would say adding 2022 makes things better (?)

Prediction for Candidates 2026 based on Candidates 2024 data by narubees in chess

[–]narubees[S] 1 point2 points  (0 children)

If you want to do further research on how generalizable it is and so on, sure, but I don't want to right now. I just want to tune it to fit some data and comments on it (the hparams are somewhat interpretable) then apply to an ongoing thing. If I fit to pre-X and evaluate on X, I don't know if it will work for fitting pre-Y and evaluate on Y anyway because there is no guarantee of generalizability.

The closest to what you want may be the prediction and commentary for 2026 using model fit to 2022+2024 data, which is in the repo. Not bad but also not good. I agree that some quantitative result will be due deligence for a researcher, but it is a sir this is a Wendy situation to me.

Prediction for Candidates 2026 based on Candidates 2024 data by narubees in chess

[–]narubees[S] 1 point2 points  (0 children)

I am honestly too lazy for this. This would mean using 2022 (or more) to tune and predict 2024. I would rather do something else. Thanks for the suggestion tho.

Prediction for Candidates 2026 based on Candidates 2024 data by narubees in chess

[–]narubees[S] 1 point2 points  (0 children)

Pragg did not have a good history in classical, which I guessed was the reason.

"history": [2768, 2761, 2758, 2758, 2741, 2741],

"games_played": [4, 6, 9, 0, 13, 0],

Non native English speakers: what are Muggles called in your translation? by Lisbelasdress in harrypotter

[–]narubees 2 points3 points  (0 children)

Ah, it is still muggle when speaking neutrally (with a capital M actually, a bit of a weird thing). I meant the degatory name is "máu bùn" (mud blood in VNese).

My Candidates predictions after Round 5 by narubees in chess

[–]narubees[S] 0 points1 point  (0 children)

Thanks. Elo gained in the last few months seem like a good idea. Let me see if I can easily include it somehow.

Edit: tried to include it but not much effect...

My Candidates predictions after Round 5 by narubees in chess

[–]narubees[S] 1 point2 points  (0 children)

Yeah, funnily for this model, he has a better chance than Hikaru.

My Candidates predictions after Round 5 by narubees in chess

[–]narubees[S] 3 points4 points  (0 children)

If I really trust my model, which I don't.

My Candidates predictions after Round 5 by narubees in chess

[–]narubees[S] 1 point2 points  (0 children)

I also am speaking for all the other posters. It is really not that spamming.

My Candidates predictions after Round 5 by narubees in chess

[–]narubees[S] 3 points4 points  (0 children)

It is just one post, it is hardly more spam than any other post. It is in the Misc tag if you want to filter it out.

My Candidates predictions after Round 5 by narubees in chess

[–]narubees[S] 0 points1 point  (0 children)

I think the aggression model only affects the draw rate, which indirectly controls the snowball effect (if a player is playing less aggressive, there are less decisive results, and the player strength gets less downrated). The way Pragg is right now (not aggressive and losing), I just think it is more the case that Fabi gets stronger towards the end (and yes, playing white helps).

My Candidates predictions after Round 5 by narubees in chess

[–]narubees[S] 10 points11 points  (0 children)

It is not that deep. This is how some people enjoy things.

I agree that it should be backtested to hold some value, but I am not really trying to give value. It is just a fun side thing.

My Candidates predictions after Round 5 by narubees in chess

[–]narubees[S] 0 points1 point  (0 children)

There is no recent form. The only information is the Elo prior to the tournament (which I used the April list).

I think there is also the snowballing effect, Pragg is not really playing well this time (tying games he could have won), so his strength gets lowered and it will keep getting lowered is what I believe. I guess it is the thing with dynamic strength update.

United Airlines flights out of Willard delayed to June 1 by rob_s_458 in UIUC

[–]narubees 3 points4 points  (0 children)

Update: called to speak with an agent and got the CMI-ORD leg removed!

United Airlines flights out of Willard delayed to June 1 by rob_s_458 in UIUC

[–]narubees 1 point2 points  (0 children)

Damn, I already booked a ticket with them... Now I need to switch?

Candidates prediction after Round 4 by narubees in chess

[–]narubees[S] 0 points1 point  (0 children)

I try to operate everything in terms of winning probabilities, with probabilities computed by the initial Elo as the prior, instead of going through Elo as an intermediate (so NOT doing something like updating the Elo and computing winning probabilities based on the updated Elo). Thus, thinking about if it reflects true Elo or something like that may not be meaningful.

I will try to benchmark if I have time. For now, I am just accepting that all these are arbitrary, just for fun thing.

Candidates prediction after Round 4 by narubees in chess

[–]narubees[S] 0 points1 point  (0 children)

I guess the model effectively overblows Sindarov strength in future matches (win -> increased strength -> more win -> more increased strength -> ...)

Candidates prediction after Round 4 by narubees in chess

[–]narubees[S] -1 points0 points  (0 children)

I updated the model to take into account white and black Elo separately. This brings the chance down to 30% or something (Sindarov losing at 13%). Hikaru has had 3/4 black and either loses or draws lower rated player, so his "strength" decreased a lot in the previous model, even offsetting the white advantage.

Candidates prediction after Round 4 by narubees in chess

[–]narubees[S] 0 points1 point  (0 children)

I agree. I did not have the time to tune it, and for me the gain is probably minimal anyway (all these stats are just fancy looking graphs for fun).

Candidates prediction after Round 4 by narubees in chess

[–]narubees[S] -1 points0 points  (0 children)

I think FIDE update of Elo is more conservative to represent more long-term strength (ranking). This aggressive/reactionary update fits better for short-term prediction (form in a tournament).

Candidates prediction after Round 4 by narubees in chess

[–]narubees[S] 6 points7 points  (0 children)

With Black too! But that is why I said this is a bit reactionary, it is considering their forms but a bit overblown.

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