Integrating the library

[Wulfheart]

I would like to integrate the algorithm into an existing Web API. Therefore I thought I could send the data which is currently in a subfolder of the cache directory directly as JSON. Is there any way to integrate it quite seamlessly as a library?

[EbTech]

Sure, you can make an API that accepts JSON files. What exactly do you want this library to do?

If you want to implement it in Rust, the worldrank-api directory might give you ideas. It's still under construction, but it should work. It doesn't directly use the raw data under cache/, but rather, loads the ratings history under data/. These data files are not in the repository, but must be generated by running multi-skill. The serde library automatically handles the conversion between in-memory structs and JSON files.

[Wulfheart]

I currently have a PHP application which is able to generate the player rankings for some contests as defined in the cache directory. Now I would like to integrate the ranking calculation as easy as possible into the current system. However, I think the simplest way to achieve this is by building a release, deploy it to the system and run it from there.
You don't happen to have a reference implementation of mmr in Go, Python or PHP (generally any other language than rust. I am still having a hard time to write code productively in rust 😢 )?

[EbTech]

Oh if you just want MMR itself, it's not a lot of code: https://github.com/EbTech/Elo-MMR/blob/master/multi-skill/src/systems/simple_elo_mmr.rs

Would you be able to translate this to your preferred language? Feel free to ask questions.

[Wulfheart]

I think I should be able to do this. However, is there a difference between EloMMR and SimpleEloMMR? How much do they differ?

[EbTech]

They're the same. The bigger Elo-MMR just has a bunch of (for most purposes) unnecessary features that can be turned on, like approximations that would make it run even faster.

[Wulfheart]

I decided to use it as a web-api for now as it is simpler to use it that way for now.

Are there any specific values for drift_per_second needed? After a certain threshold (~ 0.5/(246060)) inactive players are preferred.

[EbTech]

Inactive players get a boost when the drift is above 0.5 per day? That's unexpected.

[Wulfheart]

I made a repo to reproduce it (including steps) with the dataset I am using here.

Below you can see the first ranked player, the display ranking, the last contest and the drift per day. For drifts between 0 and 0.25 (the threshold may be higher) it seems reasonable but for higher than 0.5 it seems off as a player which hasn't been active for some years shouldn't be rated the top player.

{'player': 'Sheath', 'display_ranking': 1910} 2018-02-04 14:40:59 0
======
{'player': 'Sheath', 'display_ranking': 1909} 2018-02-04 14:40:59 0.01
======
{'player': 'Sheath', 'display_ranking': 1907} 2018-02-04 14:40:59 0.05
======
{'player': 'Sheath', 'display_ranking': 1903} 2018-02-04 14:40:59 0.1
======
{'player': 'Sheath', 'display_ranking': 1888} 2018-02-04 14:40:59 0.25
======
{'player': 'johnny_low', 'display_ranking': 1929} 2012-07-03 10:36:01 0.5
======
{'player': 'johnny_low', 'display_ranking': 2024} 2012-07-03 10:36:01 1
======
{'player': 'johnny_low', 'display_ranking': 2088} 2012-07-03 10:36:01 1.4285714285714286
======

Do you have any idea? Did I do something wrong in the rust api or is my methodology wrong?

Thank you in advance.

[EbTech]

Oh hey, I just remembered something that should explain your situation (sorry I've been a bit ill). The decay feature, which I think was pioneered by Glicko, comes with a couple caveats:

• In the current implementation, rating updates are postponed until the user competes again, so you won't see the decay right away.

• Since the decay works by increasing a player's sigma, it actually gives future contests (after the period of inactivity) higher weight. The rationale for this is that a player who takes a break from the system, much like a newcomer, has unknown skill. Their decayed rating is the result of the lower bound on their skill having decreased, but behind the scenes the upper bound will also have increased. In practice, you'll probably want to cap sigma to the starting value of 350. You might also decide to decay a player's rating back to the default of 1500, so that a long-inactive player would asymptotically revert to newcomer status. A sample implementation of this is provided in https://github.com/EbTech/Elo-MMR/blob/master/multi-skill/src/systems/common/mod.rs#L26 which might eventually make it into the core update code.

If you disagree with this uncertainty-based approach altogether, you can hack in a rating penalty instead of messing with sigma.

[Wulfheart]

Thanks for your thorough explanation.
I don't disagree completely with the uncertainty based approach I don't think it is viable in my situation because I want the rating to decay automatically after a time t even if the player hasn't participated in a contest.

Currently I use the experiment.eval() to to get the ranking. Where can I hack in the rating penalty?

[EbTech]

You can use the uncertainty-based decay too, using the same formula to update the rating for any given time. It's just that the current implementation doesn't have a way to ask for an updated rating at a particular time (I may change the design to support that later). If you want to add that yourself, I could imagine several places and haven't thought carefully about which is best. Whenever you retrieve the ratings, if you also retrieve the player's last update time, you can compare that to the current time and compute an adjusted rating that way.

[stephankokkas]

Oh hey, I just remembered something that should explain your situation (sorry I've been a bit ill). The decay feature, which I think was pioneered by Glicko, comes with a couple caveats:

• In the current implementation, rating updates are postponed until the user competes again, so you won't see the decay right away.
• Since the decay works by increasing a player's sigma, it actually gives future contests (after the period of inactivity) higher weight. The rationale for this is that a player who takes a break from the system, much like a newcomer, has unknown skill. Their decayed rating is the result of the lower bound on their skill having decreased, but behind the scenes the upper bound will also have increased. In practice, you'll probably want to cap sigma to the starting value of 350. You might also decide to decay a player's rating back to the default of 1500, so that a long-inactive player would asymptotically revert to newcomer status. A sample implementation of this is provided in https://github.com/EbTech/Elo-MMR/blob/master/multi-skill/src/systems/common/mod.rs#L26 which might eventually make it into the core update code.

If you disagree with this uncertainty-based approach altogether, you can hack in a rating penalty instead of messing with sigma.

I have a tendency to dislike this approach - only because if a player is absent from competition for a while it is unfair to assume that their skill will decrease. A possible reason for why a competitor is not competing may be to practise a particular skill or improve in an aspect of the game / task. I am still thinking of a solution to this problem.

[EbTech]

@stephankokkas in that case, wouldn't the uncertainty-based approach be more suitable? That way, the display rating is temporarily lowered, but is quickly raised when the player returns. Maybe you specifically dislike mu returning to 1500? In that case, you might leave mu untouched, but gradually increase sigma^2 towards an asymptotic cap such as 350. If the community gravitates towards a particular approach, I might make that the default; for now, there appears to be room for competing philosophies.

[Wulfheart]

I have a tendency to dislike this approach - only because if a player is absent from competition for a while it is unfair to assume that their skill will decrease. A possible reason for why a competitor is not competing may be to practise a particular skill or improve in an aspect of the game / task. I am still thinking of a solution to this problem.

@stephankokkas I am unable to grasp the concept @EbTech suggests. However, lowering the score manually incentivizes users to play more often.

[Wulfheart]

@stephankokkas have you come up with a better solution?