Body: <p>I was wondering if anyone had already tried building a regression model that could predict the ELO rating of a chess player based on his last N games? Inputs could be anything, from chess notation of the plays to Stockfish's analysis of the moves. (Excellent/Innacuracy/blunder/...).
  Basically, anything but the actual way to calculate ELO, which requires (if I understood correctly), to get the wins/loss of other players and their respective ELO. And I think it's impossible for a player to get to ELO 2500 (for example), in only 10 or 20 games.</p>
  <p>I might have misunderstood some intricacy of the ELO system which makes my question unfeasible, forgive me if so.</p>
  <p>Thank you.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-11-19T14:07:47.523Z
Comments.Text: Elo is relative accuracy and changes in Elo are zero-sum after a rating has stabilized. By the way, Elo is not an acronym but the name of the person who invented it. If you are interested in modeling this, I would be interested in collaborating! Lichess publishes all their game data freely. Regarding the input of Stockfish analysis, we would need to run it for every game (it's only their on Lichess if a given user has chosen to analyze it with a computer after the game ended).
Comments.UserDisplayName: TunaFishLies
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-11-20T02:53:10.040Z
Comments.Text: It was a kaggle competition - many of the participants wrote up their results. https://www.kaggle.com/competitions/finding-elo
Comments.UserDisplayName: Michael Higgins
Tags: <deep-learning><chess>
Title: Deep Learning model to predict ELO chess rating from games?
UserDisplayName: Adrien Nivaggioli
ThreadText: # Deep Learning model to predict ELO chess rating from games?
  
  (Asked by: username_0 on 2022-09-08)
  
  I was wondering if anyone had already tried building a regression model that could predict the ELO rating of a chess player based on his last N games? Inputs could be anything, from chess notation of the plays to Stockfish's analysis of the moves. (Excellent/Innacuracy/blunder/...).
  Basically, anything but the actual way to calculate ELO, which requires (if I understood correctly), to get the wins/loss of other players and their respective ELO. And I think it's impossible for a player to get to ELO 2500 (for example), in only 10 or 20 games.
  
  I might have misunderstood some intricacy of the ELO system which makes my question unfeasible, forgive me if so.
  
  Thank you.
  
  **Tags:** deep-learning, chess
  
  > **Comments:**
  > **username_1**: Elo is relative accuracy and changes in Elo are zero-sum after a rating has stabilized. By the way, Elo is not an acronym but the name of the person who invented it. If you are interested in modeling this, I would be interested in collaborating! Lichess publishes all their game data freely. Regarding the input of Stockfish analysis, we would need to run it for every game (it's only their on Lichess if a given user has chosen to analyze it with a computer after the game ended).  
  > **username_2**: It was a kaggle competition - many of the participants wrote up their results. https://www.kaggle.com/competitions/finding-elo  
  
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  ## 0 answers
  
  