- As far as I know I can decide beta parameter for Bayes estimation. Let’s say we estimate probability of coin flip distribution, and choose Uniform Distribution as Beta(1,1) If 9 of 10 flips will come up heads, beta parameter would be Beta(1+9, 1+1). After next new trial, we would to update Beta parameter. I believe that these result in approaching to true probability rate.
1. I wondered if I can apply to baseball batter hitting rate
Can we estimate batter’s hitting rate??
For example, I use past score of batter as prior distribution, and use score of this season to get posterior distribution. The batter have the 100 of times at bat, and 30 hits. (In my country, calculate like 30/100 as hit rate). Then, beta would be Beta(30, 70) Next, we gonna add this season’s score like 10 of times at bat, and 9 hits. Then, beta parameter would be Beta(39, 70). Is it ok??
2. I wondered if I can estimate this moment hitting rate.
Is it ok that we take most recent several score to estimate this moment hitting rate??
After the batter got the new result(hit or mis-hit), ignore the prior(=past posterior) distribution, and use original prior distribution with new recent score to get posterior??
If we decide to take recent 4 score and get this score for the batter. it would looks like this
Beta(30, 70) as prior distribution would be Beta(30+3, 70+1) And, after he got the new result for 5th (ignore 1st result cuz we assume most recent 4 score). It would look like
Beta(30, 70) as prior distribution would be Beta(30+4, 70+0) I want to estimate this moment hitting rate, not approaching to true probability rate.
Any advice would be helpful such as baseball hitting rate in not fitted to modelling. Cheers