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andre_beton last won the day on June 10 2020

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  1. andre_beton

    The History of the Best Rikishi of All Times (Video)

    @John Gunning Definitely there wasn't such a big jump in skill as the ratings would indicate. The reason for the 'inflation' is that for earlier years lower division bout results are not available to me. If I only train the model on top division results, inflation isn't an issue, but the ratings become slightly less accurate (in the out-of-time tests) because there is no information available for upcoming new top division wrestlers. The mean of the rating is at 1500. In the early days the mean skill is a average top division wrestler, later it's in the third or fourth division. This is also the reason for rating inflation in chess, I believe: more and more players (with less skill) were added to the system over time.
  2. andre_beton

    The History of the Best Rikishi of All Times (Video)

    @Amamaniac Thanks for the feedback. 1. The Sumo Strength Rating indicates the likelihood of winning a bout. You can find the exact formula in the description on youtube. 2. I create the ratings using all data available to me which includes more than just the top division since the 1950s and more and more of the lower divisions since then. Hence, even a dominant top division rikishi may lose rating points if the divisions get 'closer' to each other (because newly promoted and relegated rikishi perform in such a way). 3. One point of making this is exactly so we don't have to rely on winning records and the official rankings. They are misleading with regard to 'pure ability' because e.g. rikishi do not participate in tournaments due to injuries, or someone has an easier fight schedule.
  3. andre_beton

    The History of the Best Rikishi of All Times (Video)

    @Randomitsuki The approach I use is (maybe slightly counter-intuitive) also usable for predictive purposes. Of course, I cannot time-travel. However, the smoothing forward and backward also results in better current (as in today) ratings, as the past bouts can be estimated more accurately. So I optimised the hyperparameters (my equivalents of the k factor) by testing predictive power out-of-time (i.e. by not using 'future' bouts) from a certain cut-off date (I think it was Jan 1st 2018).
  4. andre_beton

    The History of the Best Rikishi of All Times (Video)

    Yep, something went wrong with Wajima Hiroshi & Arase Nagahide. My limited pre-Eurosport Sumo knowledge clearly shows here.
  5. andre_beton

    The History of the Best Rikishi of All Times (Video)

    My approach is inspired by Elo, Trueskill, WHR, etc. The main improvement conceptually vs Elo is that my algorithm 'smooths' back and forward until convergence, that is ratings depend on bouts before and after a certain date. This makes it much more useful from a historian's perspective. Would you not classify Elo as machine learning? Re inflation: the main reason here is that during the earlier years the data only includes the top division and later more and more divisions are included. This of course leads to more spread of the ratings. It is a very interesting problem to figure out whether today's rikishi are better than their predecessors, I think for chess there has been a lot of work done with regard to it.
  6. I spent some time trying to visualise the (modern) history of professional Sumo using machine learning and developed an algorithm (similar in idea to Elo etc.) that models it quite well in my opinion, although I am far from being an expert, especially for anything before the 1990s. I would welcome any feedback, so I can make further improvements in the future and perhaps use it to create predictions for tournaments, too. Anyway, the video: