I like the way it works in AtCoder, there my rating went down the first time after 7 contests. Assuming it will work here similar it is a good change. Still the problem that top 10 of Div3 contests make a huge jump in rating, but that is another story. Tysm, I was getting frustrated by new accounts of established users in div3s. Amazing Work yet again by my favorite CP platform.
Thanks a ton, Mike. However, both the submitted questions were accepted in the first test as well as post the open hack and system test session. But i haven't got any updates regarding my performance or ratings. I am new to this. Please tell me what am i supposed to do? 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.
Why not simply add Performance like on AtCoder to resolve the issue with understanding why rating goes up or down. Most of users are programmers. And all programmers knows magic constants are bad. Are there any research why it is need to be why not or ? I solved a lot of very easy problems and participated in my first contest Div3. I was able to solve only 2 questions still happy that I got the courage to start ;.
I checked my ratings and saw that it got dropped So from a beginners perspective, I can tell that it is going to raise the motivation a lot for new comers. Refreshing throughout the day to see my new epic rating like a boss People with new account are becoming master and candidate master.
That does not seems to be fair imo. This could promote people to make new account. Where can I get the comprehensive information about how the ratings are calculated in CodeForces by the way? MikeMirzayanov I think there are few bugs in the new rating system as many people who gave their first contest yesterday have become candidate master with just problems solved. Now, for some reason the initial ratings are set to be zero.
The starters should start with a rating of , right? As per this blog you initial rating will start from but you will see it from 0. It's explained properly in this blog i think.
I have observed that problems in CF Round Div 3 were quite easy. Due to this, the rating of participant in exploded. Adding more to this, new accounts became masters just by solving 2 problems last night. This might be due to new rating system.
However, this completely destroys the motive of the new rating mechanism, that was to reduce creation of new accounts. Make prev. As the problems were indeed suitable for Div 4 contests only. 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 points in your first contest.
I have given seven contests till date and in each one my rating has only fallen, even though ive solved at least two problems in each contest apart from one. Its quite disencouraging tbh. Still waiting for even a slight increase in my rating. I'm a bit confused with the rules apparently. Will the ratings for other users decrease if starting a new calculation from zero? I think with the fast growing number of users, even will still make the rating inflated at a super high rate.
As for most newbies, I think a starting number of or less is more appropriate It doesn't affect the rating showed anyway.
Guys I was curious about one thing, but I don't want to spam Codeforces with a new blog so asking here I am seeing some high-rated guys from my country being online almost continuously. They aren't submitting anything most of the time, not even in practice except in a few days. So what are they doing? Is this some kind of browser connection bug? I have no interest in their life, just want to satisfy my curiosity. It's possible that they are doing mashup contests, and since they are private others can't see their submissions.
What is the point of this update? And moreover, it does not even solve the problem of one-contest profiles, just moves it points lower. Btw, AtCoder uses its own crazy rating system, which is definitely not from Elo family, so everything above does not apply to it.
Besides the implementation, I agree that current rating system is not the best for CF purposes and maybe should be changed, but it requires a lot more research. 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. No, it does not. That is how Elo works. 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. With the same success we could change factor stands for variance in recalculation formulas. It won't change anything. Moreover, after some time, when sufficient amount of users are created, we will have the same problems, but rating. As a direct consequence, new rating numbers will mean nothing.
It destroys all the good properties of Elo! 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.
One of us is misunderstanding the new rating update. I thought the update is essentially just making the new starting rating instead of The accepted answer here says something about the effect of starting rating values Yes, I also think that the update changes starting rating to instead of for new users. 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.
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 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.
The better way to deal with the same problem is, e. When I started comp. I feared my rating would drop and didn't participate in contest, rather solved problems later. Although, I now understand that rating is just a depiction of your skills and their was no point of doing what I did, I really appreciate this step and it will be really in making sure that the ultra-beginners don't get demotivated in the starting itself.
Whats wrong with ur graph? Just do what we have in chess. I'm too stupid to understand the technical intricacies involved — has disappeared along with all three of its descendants.
Does anybody know why? 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! Separation of the real rating and the displayed rating does not seem right to me You could just drop the initial rating to or 0 or whatever would solve the problem.
For me when i started codeforces, I had almost no idea about cp and knowing just some basics of C I was in the same position of rapid rating loss, but I think those initial rating drops made me work harder and improve myself, had it been the new rating system I might not have given my best because I HAD NOTHING TO LOSE, reason for that may be: even if I perform bad my rating will increase initially.
Can anyone tell me why we new users are not assigned 0 rating? So if he solve a single question he will get motivated. So, after 2 contests since the rating calculation change there are Candidate Masters "TrueRating" that can participate in Div 4 contests. I am facing some problems related to my cf ratings. In a couple of last contests I've solved 1 problems each.
I can find any specific reason for that. Can anyone help me to understand whats going on? For not that applicable comparison, AOE2:DE uses several ranked ladders such that if you have good rating in one, it's easy to get good rating in another in a few games, and it requires you to play 10 games before you're visibly ranked — before that, only you can see your hypothetical rating.
Here's how everyone on Codeforces ranks according to it, including the performance and rating change of their most recent participation. It reduces inflation but doesn't eliminate it , reduces the influence of new members, limits the size of rating jumps for experienced contestants, and produces I believe a more reasonable distribution of ratings see the table here for a comparison.
The math does all this naturally, without special hacks. As a bonus, I recently optimized the implementation so that the entire history of Codeforces can be simulated on my laptop in 25 minutes. I haven't run many experiments or detailed comparisons, but I'd gladly assist anyone who wants to try! 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?
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 for current CF with users. 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 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.
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. 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. The top ratings I suspect the population hasn't converged yet. 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. 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. 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.
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.
My system is most directly inspired by Glicko. 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. TBH, I don't see why logistic distributions are better than Gaussians. 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. 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.
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.
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. Introduction The star rating is a quantitative measure that ranks funds each month based on their trailing three-, five-, and year risk-adjusted returns versus their Morningstar Category peers. Until late , the star rating calculation also adjusted returns for the effects of sales commissions paid to own a fund.
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