Comments (4)
The way it can be modelled, is using two additional items:
- decreasing the mean rating of the team linearly over time, and increasing it by a constant after each match to compensate for that.
- Adding a positive feature "break from last match" or a negative feature "matches in the last 2 weeks", that will be added to a each team for that match (same as Home Field)
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Agreed with @amirbachar - you can encode things like "momentum" with additional items.
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Thank you, will try to test it in a few weeks. Also would love to improve the speed a bit of the Python module.
from kickscore.
Hi @franz101, if speed is an issue for you, please note that we also have a Go implementation of the Kickscore library, which makes computations more efficient. I hope this helps!
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Related Issues (11)
- Draw HOT 1
- Model never converges when events with multiples winners are observed HOT 3
- Delta updates HOT 1
- How to save a trained model HOT 5
- timestamp() error on Windows with dates before 1970 HOT 4
- parameter selection HOT 12
- plot_scores function returns 0 for all but first and last value HOT 4
- Produce ranking HOT 2
- GaussianObservation.probability always returns zero HOT 7
- How countdiff
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