Your nick is my reaction
How much CPU time are we talking about here? Surely leaving the computer overnight would do the trick. As for RAM usage, I'm pretty sure there can be a way to avoid too much RAM usage by doing it in C++ non recursively.FullTablet wrote:
I can't use a very large amount, since the algorithm is expensive in RAM and CPU use
The pp value shown in the table is the overall pp from all keymodes (adding those columns were a last minute idea, so I didn't store pp values from the scores beforehand), in the next calculations I will store the pp of each play so the pp value shown for comparison will only include maps from the respective keymode. Since you play both 4K and 7K (being better at 4K), the rank difference is quite high.snoverpk wrote:
owie i think i have the largest rank drop in the entire sheet
That's a long-term goal.Aqo wrote:
Instead of putting all of this work into recreating ppv1 why not work on a more accurate diffcalc to improve stars?
You are among the first players in the database (the 6th one), so the data is about 1 month old (it takes a long while to collect all the data, so the data of the first players is already old when the calculation is done). Take into consideration that the pp amount in the table only considers pp from 4k maps, and doesn't consider the bonus pp from having many plays.Khelly wrote:
My stats were done when I had 1,000 less ppv2 so am sad
According to osu track you took it ~7th July. Is that about right?
While star rating seems to be highly correlated with the difficulty of maps, because of how the pp system works, that is not good enough for it's purposes.coldloops wrote:
Hello there,
have you tried to compare your difficulty measure with star rating ? I made a few correlation plots to illustrate this:
http://imgur.com/a/F6HjL
the "rank" is calculated by ordering the difficulty values, 1 will be the lowest, 2 the second lowest and so on.
I found it pretty interesting that it correlates with star rating so well given that they are different methods, what do you think ?
actually I made a similar analysis of beatmap diff and player skill using only score data and also got a high correlation (~0.88), so I was wondering is it really worth the effort to do this if star rating seems to be giving the same results ?
don't get me wrong, analysing score data to derive actual difficulty seems to be the best shot at getting that "true difficulty" people want but when I see those correlations I can't help but conclude that star rating seems to be pretty good already, despite not taking patterns into account.
While star rating seems to be highly correlated with the difficulty of maps, because of how the pp system works, that is not good enough for it's purposes.yes thats a good point, I hadn't really considered the pp weighting thing, but for the outliers to be useful I need to figure out which one is closer to being "right", there must be some sort of validation.
Since the overall rating of a player puts a heavy weight on the plays that give the most pp, the error in the difficulty rating of maps that are overrated has a big influence in the overall quality of the determination of the rating of the players. In this case, the outliers in the data are more important than what correlation tests indicate.
The problem is determining how to assign uni-dimensional ratings in cases where the higher skilled players don't always get higher scores than lower skilled players; changes in the algorithm used here concern mostly how to judge those cases.why does that happen ? is it lack of effort from high skilled players ? I thought about using the number of times a user has played a map to give some sort of "trustworthiness" to the score but this data is not available on the API.
Take me as an example. I used to be able to S 4.7* 4k a year ago. Now I can barely do a 4.4* S. People get rusty, magically get input lag, or some other shit happens where they can't play as good as they once used to.coldloops wrote:
why does that happen ? is it lack of effort from high skilled players ? I thought about using the number of times a user has played a map to give some sort of "trustworthiness" to the score but this data is not available on the API.
Take me as an example. I used to be able to S 4.7* 4k a year ago. Now I can barely do a 4.4* S. People get rusty, magically get input lag, or some other shit happens where they can't play as good as they once used to.skill decay is something I have considered, actually my initial idea was to only use multiplayer scores, that way I can get recent scores of all players regardless of them being best scores or not ( and as a bonus we also get unranked scores), the problem is that people don't play multiplayer as much as I hoped, specially high level players, some don't play multi at all...
Additionally, some people are simply REALLY good at specific things, but not at others. Some people can read SVs like they're not there, while other players might require quite a few tries to get a decent score on something with particularly nasty SVs.yea I guess thats what Fulll Tablet was talking about when he mentioned unidimensional ratings, different types of skill complicates things, but I think the ideal "best" player should be the one that can maximize the score of all types of maps.
It took a bit more than a month to retrieve the scores from the osu! servers using the API. The calculation after retrieving the scores then took several months (next updates would take less, considering several optimizations done to the algorithm meanwhile).snoverpk wrote:
nice update but all of the scores are from february
Here are some graphs of the number of notes in beatmaps, and ratio of plays that pass certain score milestones (800k, 900k, 990k, 1M) in the scores in the data, for several star rating ranges.abraker wrote:
I have been wondering, is there any correlation between the length of the map and the number of people who get a higher score when comparing maps of similar SR (tom stars)?