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People with large sessions

10k+ solves sessions:

3*3: 31.5k (since Feb 2022)
2*2: 11.9k (since Oct 2021, first 2*2)
Pyra: 40k (since Jan 2022, first pyra)
Practice: 14k (sine May 2023, do warmup solves)

37 sessions, 139k solves in total.
 
I'm the opposite. I make a new session every time so I can see the session averages. But since I don't practice I don't have that many sessions. 2x2 I probably only have 1k solves total.
 
Hello guys, I'm currently making a study about just this: speedcubing data trends and statistics. I would like to ask you to export your sessions as a file from CSTimer, CubicDB, Twisty Timer or some others that you might use and send it to me, in order to have a decent amount of data. Thanks in advance. The "better" way to do it is through this survey, but you also can send it through my email or CubicDB group of Telegram.

Thanks a lot guys.
 
Hello guys, I'm currently making a study about just this: speedcubing data trends and statistics. I would like to ask you to export your sessions as a file from CSTimer, CubicDB, Twisty Timer or some others that you might use and send it to me, in order to have a decent amount of data. Thanks in advance. The "better" way to do it is through this survey, but you also can send it through my email or CubicDB group of Telegram.

Thanks a lot guys.
I have several reasons to do that research:

- To find behaviour patterns in the resolution time, that might help to predict the future expected development.
- To see if there's a reliable way to detect fraud from the dataset.
- It will be nice to see if the data fits on a statistical distribution.
- I want to check if there are some correlated metrics, like time vs hour, or basically any pair of different data.
- To analize outliers, that are those points inside the data, that doesn't fit with the "normal" behaviour. I want to know if there's a ratio between normal data and outliers. That way I can safely remove them to get a better result when extracting some other stats.
- There is something called seasonality that is related to cicles of data. I've seen from the time charts, that there are like mountains of data, going up and down and it will be cool if that actually happens and what does it means.
- To generate different charts from the data, like time series, histogram, distribution and so on.
 
I have several reasons to do that research:

- To find behaviour patterns in the resolution time, that might help to predict the future expected development.
- To see if there's a reliable way to detect fraud from the dataset.
- It will be nice to see if the data fits on a statistical distribution.
- I want to check if there are some correlated metrics, like time vs hour, or basically any pair of different data.
- To analize outliers, that are those points inside the data, that doesn't fit with the "normal" behaviour. I want to know if there's a ratio between normal data and outliers. That way I can safely remove them to get a better result when extracting some other stats.
- There is something called seasonality that is related to cicles of data. I've seen from the time charts, that there are like mountains of data, going up and down and it will be cool if that actually happens and what does it means.
- To generate different charts from the data, like time series, histogram, distribution and so on.
I submitted mine
 
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