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Extended Ease Metric (2): Variation Ease

@Noobmasterplayer123

Intriguing stuff. Gonna be unpacking this in my mind over the next couple of days.

In the meantime I have a question:

How did the chess event go! Cos u said u were late by 45 mins.

@Noobmasterplayer123 Intriguing stuff. Gonna be unpacking this in my mind over the next couple of days. In the meantime I have a question: How did the chess event go! Cos u said u were late by 45 mins.

@RuyLopez1000 said:

@Noobmasterplayer123

Intriguing stuff. Gonna be unpacking this in my mind over the next couple of days.

In the meantime I have a question:

How did the chess event go! Cos u said u were late by 45 mins.

It was great, I lost a game on time because of coming late, haha, went on winning the last few games, it was a casual tournament, so it was fun, being late allowed me to find something cool for ease variation function!

@RuyLopez1000 said: > @Noobmasterplayer123 > > Intriguing stuff. Gonna be unpacking this in my mind over the next couple of days. > > In the meantime I have a question: > > How did the chess event go! Cos u said u were late by 45 mins. It was great, I lost a game on time because of coming late, haha, went on winning the last few games, it was a casual tournament, so it was fun, being late allowed me to find something cool for ease variation function!

what website did you use to do these tests?

what website did you use to do these tests?

@krithin317 said ^

what website did you use to do these tests?

chessagine

@krithin317 said [^](/forum/redirect/post/vR9HwWBl) > what website did you use to do these tests? chessagine

Interesting ideas! It would be interesting to evaluate these scores, for example by looking at how often players make mistakes in positions of different ease

Interesting ideas! It would be interesting to evaluate these scores, for example by looking at how often players make mistakes in positions of different ease

plugin these discrete summaries into linear regression model y=β0​+β1​x+β2​x+βnx+ε

plugin these discrete summaries into linear regression model y=β0​+β1​x+β2​x+βnx+ε