Hello everybody. This is my first post. I just joined in hoping to commune with other handicappers, specifically ones focused on machine learning and manual models using variables and simple bedmas. I have developed successful models for NBA, but I would like to build a model for MLB.
My first question is, what would be a standard sample size for you to use for a statistic in MLB. Normally I use 1000-1500+ events, but with baseball there are a lot of teams with younger players now with stat samples that are not reliable due to their high variance. In your experience, what would be the minimum number of events that you would be happy with that provided a reliable statistic that was at least 75% correct in providing your model with accurate data. More superficially, pitching statistics in exit velocity, RPM, hard and soft contact, and launch angle.
If anybody needs help in algorithms or programming, or general physics feel free ask. Cheers and thank you,
Christopher
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To remove first post, remove entire topic.
Hello everybody. This is my first post. I just joined in hoping to commune with other handicappers, specifically ones focused on machine learning and manual models using variables and simple bedmas. I have developed successful models for NBA, but I would like to build a model for MLB.
My first question is, what would be a standard sample size for you to use for a statistic in MLB. Normally I use 1000-1500+ events, but with baseball there are a lot of teams with younger players now with stat samples that are not reliable due to their high variance. In your experience, what would be the minimum number of events that you would be happy with that provided a reliable statistic that was at least 75% correct in providing your model with accurate data. More superficially, pitching statistics in exit velocity, RPM, hard and soft contact, and launch angle.
If anybody needs help in algorithms or programming, or general physics feel free ask. Cheers and thank you,
Hello everybody. This is my first post. I just joined in hoping to commune with other handicappers, specifically ones focused on machine learning and manual models using variables and simple bedmas. I have developed successful models for NBA, but I would like to build a model for MLB. My first question is, what would be a standard sample size for you to use for a statistic in MLB. Normally I use 1000-1500+ events, but with baseball there are a lot of teams with younger players now with stat samples that are not reliable due to their high variance. In your experience, what would be the minimum number of events that you would be happy with that provided a reliable statistic that was at least 75% correct in providing your model with accurate data. More superficially, pitching statistics in exit velocity, RPM, hard and soft contact, and launch angle. If anybody needs help in algorithms or programming, or general physics feel free ask. Cheers and thank you, Christopher
I see you have another handle.
Welcome to a beatable sport with decent sample size. NFL is for amateurs who think 16 games is enough history.
To answer your questions, go with what you can get. Just because younger players have less AB and other events under their belts doesn't mean that that history should not be counted - just weigh less the earlier events with the mindset that they are coming into their own and more recent is more accurate. This is pretty standard.
The pen is mightier than the pigs
1
Quote Originally Posted by sharpmatrix:
Hello everybody. This is my first post. I just joined in hoping to commune with other handicappers, specifically ones focused on machine learning and manual models using variables and simple bedmas. I have developed successful models for NBA, but I would like to build a model for MLB. My first question is, what would be a standard sample size for you to use for a statistic in MLB. Normally I use 1000-1500+ events, but with baseball there are a lot of teams with younger players now with stat samples that are not reliable due to their high variance. In your experience, what would be the minimum number of events that you would be happy with that provided a reliable statistic that was at least 75% correct in providing your model with accurate data. More superficially, pitching statistics in exit velocity, RPM, hard and soft contact, and launch angle. If anybody needs help in algorithms or programming, or general physics feel free ask. Cheers and thank you, Christopher
I see you have another handle.
Welcome to a beatable sport with decent sample size. NFL is for amateurs who think 16 games is enough history.
To answer your questions, go with what you can get. Just because younger players have less AB and other events under their belts doesn't mean that that history should not be counted - just weigh less the earlier events with the mindset that they are coming into their own and more recent is more accurate. This is pretty standard.
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