Codeforces: Soon We Will Change the Rating Calculation for New Accounts

Hello everyone.

In the coming days I will finish the implementation of small changes in the rating calculation for new accounts. Here are the main innovations.

• The rating of the new account will be equal to for display (but it will be equal to when calculating rating changes).
• Suppose, after the first round, the participant gained rating points (remember that the rating was considered equal to in such calculations), then in the rating display after this round will be displayed. Thus, after the first participation, the rating is likely to increase from to a value in the region of (plus or minus approximately).
• Thus, before the second participation, the displayed rating is , and the rating for calculating changes is . Suppose a new change is , then the displayed rating becomes .
• Thus, before the third participation, the displayed rating is , and the rating for calculating changes is . Suppose a new change is , then the displayed rating becomes .
• And so on the first rounds. Promotions of the displayed rating will be equal to (in total exactly ). Therefore, after participating in rounds, the rating is “stabilized” and the displayed rating will be equal to the one used to recalculate the changes.

Thus, on average, for new accounts at an early stage, the rating will increase, getting closer to the real value. These innovations will help solve several issues:

• We are reducing the starting rating from to , so that new accounts make a smaller contribution to the overall rating.
• Currently, especially for inexperienced participants, there is an effect that their rating at the beginning of participation is constantly falling (from to the value that is a true assessment of their skills). This, of course, leads to discomfort: you try, participate, solve some problems, but the rating drops.
• The previous paragraph gives additional motivation to create new accounts. If your current rating is less than , then there is a temptation to start from scratch with a fresh account. After the changes, such a start will require a noticeable long-term effort to climb at least your current rating. It seems that the changes should slightly reduce the motivation to create new accounts.

Apparently, a similar idea is used on AtCoder, but I don't know the details.

What do you think about such improvement? Sounds good?

• 19 months ago, # ^ |

  I don't know how but It'll be better to have just one rating.

• 19 months ago, # ^ |

  API will contain displayed ratings. Probably, good idea is to add one more field (say, trueRating) to API.

  I think divisions should be assigned based on displayed ratings. It will be completely non-transparent to assign division by true ratings but show displayed ratings. I agree that it will make a few cases then a participant takes part in Div3 but shouldn't because of true rating. But I think it will not be not very often cases and "trusted participants" policy will hide them in most cases.

  • 19 months ago, # ^ |

    Do you plan on somehow gating the rating gains when true rating is higher than division cutoff? It seems to me like someone can reach 2100/2200 by only participating in div4/div3 contests.

    • 19 months ago, # ^ |

      ← Rev. 2 →  

      +52

      Waiting for "How I went from 0 to 3000 in just 2 weeks".

  • 19 months ago, # ^ |

    ← Rev. 3 →  

    +38

    What's happened? Vepifanov’s rating.

    • 19 months ago, # ^ |

      max. specialist, 1579 lol

    • 19 months ago, # ^ |

      He gave his first contest in 1975 LOLLLLLLLLLLLLLLL

    • 14 months ago, # ^ |

      how his rating grown so fast

  • 14 months ago, # ^ |

    Any news about the API and the trueRating field? It would be very useful for many extensions, etc...

  • 14 months ago, # ^ |

    Sorry to bump this again, but any chance you could comment on the state of the trueRating field? As is, it's pretty difficult for rating predictor extensions, which are used by a significant portion of the community, to accurately evaluate projected rating changes.

• 19 months ago, # ^ |

  It should be a quite easy fix, I'm going to update CF-Predictor as soon as we got the "trueRating" field in the API response.

  • 19 months ago, # ^ |

    Good to know, many thanks for maintaining this tool!

  • 19 months ago, # ^ |

    ← Rev. 2 →  

    0

    I don't think that is sufficient because now the displayed rating depends on both true rating and number of past rated contests.
    It is not convenient to get the second from the API for users in bulk.

    Edit: The number of past rated contests can be calculated from the difference between real and displayed ratings, so it would, in fact, be sufficient.

• 19 months ago, # ^ |

  ← Rev. 2 →  

  +69

  Bruh 500 is way too low. Some people started on cf after already having experience, and at least I don't wanna do 10 contests just to reach my actual starting rating. Even people with 0 cp experience but have programming experience are higher usually much higher than 500.

  Also, I think it discourages you to care if you start with a super low rating too, like on atcoder I tend to not care about the contests at all since I haven't done many so my rating usually goes up no matter how much I mess around.

  • 19 months ago, # ^ |

    Yeah, I get your point. But you will also see that there are very less fake/alt accounts on AtCoder.

    Anyways your arguments seem fair too, but there will be some tradeoff :/

  • 19 months ago, # ^ |

    But I think your rating will remain low for 6 rounds only. 500, 350, 250, 150, ... will be added to each round for free. New people will have to tolerate lowrating for 6 rounds only.

    • 19 months ago, # ^ |

      I am not arguing against mike's system, as it combats this by having the calculation rating be higher, so it quickly converges. I was arguing against having the calculation rating also starting at 500, as was suggested.

      • 19 months ago, # ^ |

        No it will be 500 + d1. So it will appear to be near 500. Of course unparticipated will be 0.

        • 19 months ago, # ^ |

          Did you even read it...

          The rating of the new account will be equal to 0 for display (but it will be equal to 1400 when calculating rating changes)

• 19 months ago, # ^ |

  It is a good idea indeed..

• 19 months ago, # ^ |

  Create a new account and vote again

  • 19 months ago, # ^ |

    There must be some sort of glitch, I can't see your comment!

    • 19 months ago, # ^ |

      Make a new account and see again

• 19 months ago, # ^ |

  And 251 is the answer

• 19 months ago, # ^ |

  I think if a participant is not rated yet, new rules will be applied. If a participant took part in at least one rated contest, old rules are applied.

  • 19 months ago, # ^ |

    Thanks Mike For Clarifiaction

• 19 months ago, # ^ |

  What if you see my rating graph?

  • 19 months ago, # ^ |

    I can see the pain of struggler from your graph....

    • 19 months ago, # ^ |

  • 19 months ago, # ^ |

    you clearly havent seen my!

• 19 months ago, # ^ |

  Same with my graph

• 19 months ago, # ^ |

  Same with me LMAO.

• 19 months ago, # ^ |

  Check mine...

• 19 months ago, # ^ |

  I can feel you. My graph is pathetic, especially at the starting.

• 12 months ago, # ^ |

  ← Rev. 2 →  

  0

  （deleted）

• 19 months ago, # ^ |

  I'd say this is a little bit dystopian.

• 19 months ago, # ^ |

  Yes force each account to also provide a credit card/bank account number to Mike to "guarantee no fake accounts".

  • 19 months ago, # ^ |

    The comment is hidden because of too negative feedback, click here to view it

  • 19 months ago, # ^ |

    The comment is hidden because of too negative feedback, click here to view it

    • 19 months ago, # ^ |

      it is a joke

      • 19 months ago, # ^ |

        ← Rev. 2 →  

        0

        Oh sorry....couldn't catch the joke

    • 19 months ago, # ^ |

      r/woooosh

  • 19 months ago, # ^ |

    That would not guarantee no fake accounts. It will just give an unfair advantage to credit card thieves.

• 19 months ago, # ^ |

  Inb4 your subsidy got credited into Putin's swiss account.

• 19 months ago, # ^ |

  I am 15 so I don't have a UID. Literally, a lot of OIers don't have UID. So you force all OIers not to participate and practice on codeforces? That is very rude!

  • 19 months ago, # ^ |

    Yeah, I kinda agree with that.

• 19 months ago, # ^ |

  I think maybe they will be rated according to 1400+d1+d2.. rating and not their shown rating until 6 rounds

• 19 months ago, # ^ |

  They will show displayed ratings. So for new participants most graphs will show increasing at the start, generally.

• 19 months ago, # ^ |

  i think he probably did and is just saying that so it doesn't count as copying lol.

• 19 months ago, # ^ |

  Low novelty, doesn't compare to previous state of the art baselines, strong reject.

• 19 months ago, # ^ |

  It seems (from Mike's response to my message above) that eligibility will be determined by displayed rating.

  • 19 months ago, # ^ |

    So, this is exactly what may cause the problem. So if you have big real rating and small displayed rating, if you win, you'll get your real rating change and additional boost to your displayed rating, so you will get higher then beofre

• 19 months ago, # ^ |

  suppose a smart person decided to join CF, gets top ranks, sees that he needs to write 3 unrated contests to get rating .he would never come back

  • 19 months ago, # ^ |

    I think a very smart person won't be discouraged by giving 3 extra contests.9/10 new accounts is either someone's fake or actually a new user who is new to competitive coding. Also, the reason I said 3 is it is not that high for someone to get fed up without getting a rank and high enough for people who make a fake acc for every new contest(like Dream Lolita) But I get your point too

• 19 months ago, # ^ |

  Virtual contests...?

• 19 months ago, # ^ |

  yesterdays contest actually felt like div3+div4 infact

• 19 months ago, # ^ |

  ← Rev. 2 →  

  +8

  every contest you gain a little bias (and the sum of those biases over 6 contests is 1400), but you participate as a 1400 anyways. essentially after 6 rounds it acts like you started at 1400 rating and all rating fluctuations from the past 6 contests (that act like you had 1400 to begin with) apply after that too

• 19 months ago, # ^ |

  You need to take a third grade math class first.

  • 19 months ago, # ^ |

    Sad. despite studying in one of the top universities in the world, I've to take third-grade math again

• 19 months ago, # ^ |

  +1, about 2-3 weeks and no D1 ╯︿╰

• 19 months ago, # ^ |

  I don't really see the problem here.

  If you don't attempt any problem, it doesn't count as a rated participation anyway.

  If you do attempt and don't manage to solve anything, you'll likely get a huge negative delta, which will balance out the displayed rating bias increase.

• 19 months ago, # ^ |

  I recall how in the past it wasn't unheard of to have your TopCoder rating increased after scoring 0 :)

  While the situation that you described may not sound right intuitively, I don't see a good solution that wouldn't add unnecessary complexity to the system.

• 19 months ago, # ^ |

  Your question is answered here.

• 19 months ago, # ^ |

  Is that magic function not disclosed?

  • 19 months ago, # ^ |

    It is, follow the link AtCoder's Rating System on atcoder.jp to find all the details.

• 19 months ago, # ^ |

  I think any radical change will make many veterans unhappy.

• 19 months ago, # ^ |

  Flakire can get Master in only 2 rounds. So orz Flakire.

• 19 months ago, # ^ |

  In fact,you can find a legendary grandmaster in 10 rounds.

  • 19 months ago, # ^ |

    Just look at zyy. @WZYYN

• 19 months ago, # ^ |

  Just be patient, the ratings will be updated soon. It takes some time.

• 19 months ago, # ^ |

  If you can't be patient, and are using Chrome, add the Codeforces Enhancer to Chrome. It will (among other things), give you an estimate of your rating change in the standings table.

• 19 months ago, # ^ |

  Well, I was expecting my rating to ++ since I solved 4 questions in yesterday's competition (#644 Div3) b'cos of the recent changes in rating system. Refreshing throughout the day to see my new epic rating like a boss...... and.....

  Dropped by -46 instead (1395->1349) :play coffin dance:

• 19 months ago, # ^ |

  Now, for some reason the initial ratings are set to be zero. The starters should start with a rating of 1400, right?

  • 19 months ago, # ^ |

    As per this blog you initial rating will start from 1400 but you will see it from 0. It's explained properly in this blog i think.

    • 19 months ago, # ^ |

      Yes, you are right, I misunderstood it. Thanks.

• 19 months ago, # ^ |

  Mac I schadenfreude in your face.

• 19 months ago, # ^ |

  In the past, someone always try to droping points and then get high rank in div2/div3 to get high rating,

  maybe the rating they get is not as higher as usuall, but I think it's good to increase single score growth, maybe 500 or 400 ? And maybe it's good to make different rating has different maximum score growth, maybe we should make someone who has high level to reach the rating they worth soon. In Atcoder you can get more than 1000 points in your first contest

• 19 months ago, # ^ |

  ← Rev. 2 →  

  0

  It appears to me that rating changes for the users who were rated before the previous Div3 round have been a bit unfair,as is evident in the case of Droping_points . @mikemirzayanov it would be better if you can make the rating calculation rules a bit more clear..as apparently someone with a negative initial rating before the round got a delta of +300 despite finishing 3rd in the standings table whereas some unrated user who came last in the standings table also got a delta of +300,which seems a bit harsh to me.

• 19 months ago, # ^ |

  It starts from 1400, but yes it will cause a decrease in rating for others

  • 19 months ago, # ^ |

    Ah... I don't really like this...

• 19 months ago, # ^ |

  It's possible that they are doing mashup contests, and since they are private others can't see their submissions

• 19 months ago, # ^ |

  Lmao he speaks the truth here.

• 19 months ago, # ^ |

  The starting rating matters, because it influences whether the total rating mass of active users is decreasing or increasing. Thus, lowering the starting rating is a step to combat rating inflation.

  (If most of the new accounts are overvalued in rating compared to the old accounts, then the new equilibrium is with higher old ratings.)

  • 19 months ago, # ^ |

    ← Rev. 3 →  

    +49

    No, it does not. That is how Elo works. -100 to all new users is EQUIVALENT to +100 to all old users. If you decrease starting rating, then all the ratings are decreased by the the same constant. If you decrease starting rating not for all users, then all the ratings are still decreased, but now it is unconverged. Just because in Elo starting rating stands for average player rating. 1500 is just a some nice number which is used as a legacy.

    With the same success we could change factor 400 (stands for variance) in recalculation formulas. It won't change anything. Moreover, after some time, when sufficient amount of 1400 users are created, we will have the same problems, but -100 rating.

    As a direct consequence, new rating numbers will mean nothing. For old rating I could say that 2300 player should win 2100 with probability 75% and that 1600 player is roughly top33%, but now it is impossible. It destroys all the good properties of Elo!

    That's why I think that 1400 is a stupid idea.

    P.S. The second reason to hate CF (after the killing of the rating system) is the button "Cancel" in the usual place of the "Send" button

    • 19 months ago, # ^ |

      What? One of us is misunderstanding the new rating update. I thought the update is essentially just making the new starting rating 1400 instead of 1500.

      The accepted answer here says something about the effect of starting rating values...

      • 19 months ago, # ^ |

        ← Rev. 2 →  

        +16

        Yes, I also think that the update changes starting rating to 1400 instead of 1500 (for new users).

        The accepted answer also correct and perfect. But, what the answer says is two things:

        1. When you add something to starting rating globally (for all players, not our case), it just adds this constant to all ratings and does not affect anything else.

        2. When you merge ratings of skilled group of players with average players, it is a good idea to add some constant to the ratings of skilled group to make Elo converge faster.

        Second one is also not our case, since there no reasons why users who registered after May 25 should be less skilled than those, who done it before. The idea of 1400 update, as I understand, to deal with rating inflation caused by one-contest players, but it is the exactly the case where it won't help. Unless we repeat it every year. Then probably it will help to maintain reasonable number of LGMs without changing division borders. But in this case we will be unable to estimate anything from this rating numbers (e.g. winning probability), which is worse IMO.

        The better way to deal with the same problem is, e.g. to calculate rating updates using only the players with at least x contests.

• 19 months ago, # ^ |

  Thanks bro, I am doing the same what you did.

• • 19 months ago, # ^ |

    I was trying to figure out the same, That's happening with my account only :(

  • 19 months ago, # ^ |

    mine is broken too.....

• 19 months ago, # ^ |

  So if i get the first place in some contest, my performance will approach infinite?

• 19 months ago, # ^ |

  Let me restate.

  The following comment of _BeginnerCoder — Can anyone briefly explain what Mike has written? I'm too stupid to understand the technical intricacies involved — has disappeared along with all three of its descendants. Does anybody know why?

  • 19 months ago, # ^ |

    Yeah, it was deleted by Codeforces. Now we both will get some notifications that'll include a warning to BNBR else face account suspension. Walking on thin ice I tell ya!

• 19 months ago, # ^ |

  ← Rev. 2 →  

  -16

  I made some random checks and I think your rating is inflating as bad as current CF one. Have you tried to model CF competitions and make computational experiments?

  P.S. Oh sorry, I read like every second paragraph. Start with the following: assume every user score like Gauss(x, x) distributed score for some x. Every round is participated by half of users. How many rounds will you need to stabilize the rating? I think it's like 50 or 100 for current CF with 1000 users.

• 19 months ago, # ^ |

  I will translate for you some notices about the origin of inflation that were made in Russian.

  1. The knowledge growth causes a lot of problems as time spent on training is reduced when user become less active.
  2. Two divisions without overlap model causes inflation of div1 and deflation of div2 as it provides one-way invariant transfer through the boundary. This effect shouldn't be visible when CF rating was initially expanding (before 2017 or so I think).
  3. If we peek top 10% or top 25% users before round, their rating sum after round should be less than before round. This is an effect caused by selection (top 10% is more likely overrated than underrated).

  • 19 months ago, # ^ |

    The current CF system might have less inflation due to recent anti-inflationary measures, which I considered to be somewhat of a hack. Compared to the CF system without these measures, Elo-R seems to have less inflation based on looking at the rise of top-rated members over the years... but I admit this is not a rigorous experiment.

    I suppose it would be helpful to create a testbed with various rating algorithms, so that we can easily compare them along each of the criteria you mention. I can't spend too much time on this project while working alone, but I might slowly try some of these things. It's open-source if others want to chime in with their own experiments. And since my system has a theoretical framework, we can change its assumptions to better match reality if needed.

    Thanks for the discussion and experiment ideas!

  • 19 months ago, # ^ |

    ← Rev. 4 →  

    +29

    For a very simple experiment, I just tried running my algorithm with an initial of instead of . This changed the mean true rating of all Codeforces users from (when ) to (when ). That's a big difference! Most of this seems to be due to the weakest members not having converged, since they participate less. Nonetheless, the top ratings also increased, by 14 to 19 points.

    I suspect that two things are happening:

    • When new users join, is usually an overestimate of their skill, so they lose more than the system expects, giving everyone else extra rating points. This is an inflationary pressure.

    • As users compete more, they usually improve and become better than their rating, so they win more than the system expects, taking away rating points from others. This is a deflationary pressure.

    By using a high starting , we can drastically reduce the first effect, altering the balance between inflation and deflation.

    Edit: with (where it would stay fixed), we get a mean Codeforces rating of . The top ratings... are actually less than they were with ! I suspect the population hasn't converged yet. A high starting accelerates convergence of the population rating distribution by allowing it to spread right away, from Round #1. With low , my system might become even slower than others since it puts less weight on surprising performances (outliers).

    • 19 months ago, # ^ |

      What is mean rating? As far as I understand (I haven't read your paper yet, sorry), generalisation of Elo to the case of large numbers of simultaneous participants should be still zero-sum, i.e. maintaining constant mean rating. And if I remember and understand it correctly, the thing you are reinventing is TrueSkill (apart from the displayed rating feature, which is nice, but not so important now).

      And, as some kind of a bonus, TrueSkill could be generalised to the case of team competitions (many thanks to S. Nikolenko for pointing it out to me), which is sometimes our case (and I do not see how you do that).

      I didn't find an implementation of this rating system and didn't tested it on CF history, but I think that I'll try to do it next week if it would be interesting.

      • 19 months ago, # ^ |

        Maybe I should write an explicit comparison with TrueSkill, after studying the Gaussian density filtering algorithm more closely to understand its properties. The models are similar, but I'll point out differences:

        • My model uses logistic distributions instead of Gaussians: a major theme of my paper is to look at the implications of this.

        • TrueSkill has a model for draws, which I think is inaccurate for programming contests. Elo-R doesn't model draws, but instead treats them as half a win + half a loss, similar to Elo. Actually, if we model draws as nearly-equal performances, the Elo-R math gives a result equivalent to 1 win + 1 loss, but it seemed strange to me to give draws double the weight...

        • I appeal to the limit of large numbers of contestants to allow for an efficient and easy computation of performance. As a result, I get formulas that are more Elo-like and human-interpretable. Many properties of Elo-R can be deduced just by looking at the formulas, even without the theoretical model! (The formulas give reasonable results for small numbers of contestants too, although the theory no longer justifies this.)

        Mean rating is the average "true rating" among all users, including inactive ones.

        My system is most directly inspired by Glicko. It can be made more Elo-like by keeping constant, as happens to be the case in my experiment. In that case, the mean rating stayed very close to , drifting points off due to the approximations involved. I believe a system which preserves the mean will be inflationary: as Codeforces becomes more popular, it's likely to welcome a lot of weaker members, so we should want the average rating to decrease. Perhaps the top should be allowed to increase very slowly, as the state of the art improves.

        I believe Elo-R can be extended to team matches. Thinking about it very briefly, we could compute performance scores for each team, by modeling team performance as a sum or max of random draws for each member (which amounts to one random draw per team, with a different distribution). Then, knowing the team performance, we have to reserve-engineer a measurement of each team member's skill.

        • 19 months ago, # ^ |

          In short: I spent last hours studying the improved variant of TrueSkill and think that it is exactly what CF needs.

          It is not the complete list of reasons why I think so, but at least the most important ones.

          • TBH, I don't see why logistic distributions are better than Gaussians. IMO, the main reasoning behind the Gaussians in all Elo-like models is CLT and I can't find any similar argument for logistic distributions.

          • The model for draws in TrueSkill is perfect for programming contests! Consider players' points as a rough measure of his performance and get exactly this model of ties. The case of multiway ties (which is one of problems of the original TrueSkill) was fixed in its generalised version from the second link. And I can't say that on CF ties are not very common.

          • Rating inflation. What we exactly need from the model is the correspondence between rating number and percentile (like, if your rating is at least , then you are in top ), which is exactly what TrueSkill does and what is seems to be impossible in your model (correct me, if I am wrong here, I think so because of non-constant mean rating). Of course, any model of this kind should have some rating inflation, but we don't need exactly ten LGMs, we want to have number of LGMs proportional to the total number of players. Moreover, when we say that current rating system has problems with rating inflation, we generally mean that average div.1 user rating is growing too much because new users first contests influence the rating a lot more than it should be. But in theory, if we don't have a multiple account problem (which the visible rating feature should take care of), any model with non-constant should do the trick.

          Surely, we need to test and compare our models to discuss it further, so I'll try to implement all this stuff, and post my results next week.

          • 19 months ago, # ^ |

            Cool. It's definitely worth doing more tests! The improved TrueSkill from St. Petersburg looks like a good alternative. Nonetheless, I want to reply to your latest points:

            • What exactly is your CLT-based argument? Are skills and performances the sum of very small contributions? Top players seem more spread-out than a normal distribution would predict, and all players seem to have more days that are very good or very bad, compared to normal draws. The logistic causes my model to put less weight on these unusual performances. We could try to measure the true distribution.

            • The problem with ties is that there is no fixed that works. The most obvious example are the massive ties at points, whereas performances drawn from logistic or normal distributions would have no such bias. Maybe that's ok in practice: without human-interpretable formulas, it's hard for me to guess, just as it was hard to foresee problems with the original TrueSkill model of ties.

            • In Elo-like systems, rating differences are based on win probabilities, not percentiles. As far as I can tell, TrueSkill doesn't enforce percentiles either: it merely suggests them in the prior, just as Elo-R does.

            • Fixing the average rating reduces our control over the inflationary and deflationary forces I mentioned. But now I have a question. It seems to me that fixing the average rating is inconsistent with non-constant , because users with large would gain/lose more points than their opponent loses/gains. Are you saying that TrueSkill manage to have both, and how?

            • 19 months ago, # ^ |

              • Yes, something like that. A least on top level. Your skill is like a sum of problems you know how to solve and your performance is roughly the sum of solved problems minus the sum of mistakes you made. I think that good and bad days happen because of the way we competing. You have almost no choice between problems, so it is pure luck to get problems you would like. In order to handle this effect you should use some recommender system as a rating system, which is definitely an overkill for our case. But anyway, I think it is not good idea to change something in rating system just in order to handle additional CF-specific noise.

              • If you want to be very precise, then you can choose as a function of score, but I think that for practical purposes should be fine (and maybe the case of score should be handled separately)

              • True, I meant percentiles in the Bayesian sense, not where you are in the actual rating. For all Elo-based rating systems you could compute your expected percentile based on your rating (like, if your rating is 1500, then you are in top ). It could be different from the percentile in the actual rating, e.g. about 60% of ranked lichess (uses Glicko-2) users have their blitz rating lower than 1500.

              • Forget about this part, I messed up. Mean rating should be constant if we compute ratings with proper weights, I believe that it is also true for your model.

• 19 months ago, # ^ |

  We need to make this the new rating system

• 19 months ago, # ^ |

  I spent some time learning Rust and implementing "TrueSkill from SPb" and now I have a couple of questions about your rating system.

  I spent some time reading your code and noticed that you have only strict formulas, no approximations or loops until convergence. It makes me think what you do is not a Bayesian inference rather something different. I think that because the posterior distribution should not be expressible in terms of logistic distributions and therefore should be approximated somehow (just as TrueSkill does so). So, if I am right, then why does your rating system converges fast enough? And if I am not right, how did you do this?

  Also, if I understood it correctly, you don't add additional noise to players performance as it usually done. Instead, you have a lower bound on players sigma. Why are these approaches equivalent?

  Meanwhile, I found some bugs in pseudocode from article, so the implementation would take more time (algorithm is still correct, I just need to get some formulas from scratch). I think that I will finish and post first results next week. Also I want to use your CF-parser if you don't mind.

  • 19 months ago, # ^ |

    Sure, use whatever you need!

    I derive the formulas in the paper: in short, I cheated in a few places to make the formulas tractable. As a result, I have human-interpretable formulas that approximately follow the Bayesian theory. In other words, my system can be interpreted in two ways: the Bayesian way, or as an exponentially weighted average of recent performances with reduced weight on outliers.

    I do add noise. The "lower bound" you're seeing is simply the limiting value of if the noise and measurement error per contest are constant. Actually, the noising calculation is where I had the toughest time sticking to the Bayesian derivation. In the end, I came up with four methods, each with different pros and cons which I'd be happy to discuss further.

    • 18 months ago, # ^ |

      What is the complexity of your code? My code processes all contests from the set in your repo less than in two minutes (if cached already) and I haven't optimized it yet. TBH, it gives some shit instead of results, but I know where is the problem and I think that it doesn't affect the performance.

      • 18 months ago, # ^ |

        ← Rev. 2 →  

        0

        Wow 2 minutes! I'd like to see. If each contestant recalls rounds of history, then a round with contestants should take using binary searches, with the log factors reduced if Newton's method is used instead.

        The term only applies to certain versions of my "noising step", but the term seems common to all the programming contest algorithms that compute pairwise win probabilities.

        • 18 months ago, # ^ |

          Well, I can't prove that, but it seems like my code processes a round with contestants in time . It is not a really fair bound since each multiway tie and each team now work in quadratic time, but it is not so important when running on CF history and it definitely could be optimized to linear time.

          I don't want to post code that does not work, but if you don't want to wait until I fix everything and make a post, you can find it on my github (username is the same).

        • 18 months ago, # ^ |

          Cool, it looks like TrueSkill doesn't need to compute the pairwise win probabilities! You can probably make it even faster using Rayon to parallelize as I did.

          Today I reverted my code to use my simplest noising method. While the fancier methods are interesting for a variety of reasons, the simple one is more straightforwardly comparable with the current system on Codeforces.

          Basically, it approximates the posterior, which is the most intractable part, by a Gaussian, while still using logistic distributions in other places. This method has no term nor a saved history, so it satisfies the same monotonicity properties as Codeforces. It also results in the simplest versions of my formulas (see page 8 of the paper as of today). The rating distribution might converge more slowly with this method, but it seems to end up in the same place.

          • 18 months ago, # ^ |

            TrueSkill computes win probabilities only for pairs of players on neighbouring places (up to the multiway tie case).

            Btw, I finished the algorithm and now it works in less than a minute, but somehow strange. I can't find the problem (it is either parameters or bugs), so I am curious how you tested your rating system.

            Also, I am refactoring my code and since I am fairly new to Rust, I'd be very grateful for any suggestions to reduce the boilerplate and improve readability.

            • 17 months ago, # ^ |

              Hey I saw your new blog post, great work! Sorry that I haven't had much chance to examine your code or engage in the discussion. I moved on July 1 and the internet company still hasn't set up WiFi where I live :( so I'll be on and off the grid for just a little while longer...

• 18 months ago, # ^ |

  ← Rev. 2 →  

  0

  Or even just remove this "new system" which just hides the real rating like some third-rate computer game would do

  I understand lowering default rating from 1500 because a lot of people are below it, but not this

• 18 months ago, # ^ |

  Not at all.

• 15 months ago, # ^ |

  If you were unable to submit anything or you submitted but failed on sample tests. Your rating will be unaffected/participation won't be considered.

  • 15 months ago, # ^ |

    Thankyou...

• 12 months ago, # ^ |

  It started from zero, it reached 366 after you gave your first contest.

  • 12 months ago, # ^ |

    Yeah, but why some people start from 1200, or 1400.

    • 12 months ago, # ^ |

      And I don't know why someone is downvoting me for asking question.

      • 12 months ago, # ^ |

        Since this is ratism

    • 12 months ago, # ^ |

      Older accounts' initial rating is 1500 though it isn't mentioned, after they give their first contest, if they do better than 1500, their rating would increase from there. Otherwise it would come down to 1400 or 1200 like you mentioned. If it's a new account and started with a rating of 1200 or 1400, they must have done extremely well in their first contest, but still i don't think getting 1200+ in the first contest with the new accounts is possible tbh.

      • 12 months ago, # ^ |

        Thank you for explanation!

• 3 months ago, # ^ |

  Just Go to PROBLEMSET Section and sort the questions according to increasing order of difficulty, and first grind 10-15 problems to get familiar here, rest u will understand with time gradually.

• 2 months ago, # ^ |

  What are you high on?