Hello,
I am working on creating a statistic to predict players most likely to get a hit on any given day. In this statistic, I am using five different predictive factors, which I am going to then assign relative weights to in order to improve the accuracy of the prediction statistic.
For example:
Of course, outliers happen (and happen frequently with the smaller sample sets), but here are the approximate ranges of each factor:
Basically, I'm wondering what the best way to "normalize" or "standardize" or "whatever" each individual factor is so that I can then assign weights to each of them to calculate my final hit predictor statistic. I love doing things like this to occupy my time, but it's been entirely long since my last statistics class.
Thank you for your help!
Best regards,
Eric
I am working on creating a statistic to predict players most likely to get a hit on any given day. In this statistic, I am using five different predictive factors, which I am going to then assign relative weights to in order to improve the accuracy of the prediction statistic.
For example:
- Batter Hits/Game (Last 30 Games)
- Batter Hits/Game (Last 3 Games)
- Opposing Pitcher Hits/9 Innings (Last 15 Games)
- Opposing Pitcher Hits/9 Innings (Last 2 Games)
- Ballpark Hit Factor (Current Season)
Of course, outliers happen (and happen frequently with the smaller sample sets), but here are the approximate ranges of each factor:
- Batter Hits/Game (Last 30 Games): ~0-1.5
- Batter Hits/Game (Last 3 Games): ~0-3
- Opposing Pitcher Hits/9 Innings (Last 15 Games): ~6-15
- Opposing Pitcher Hits/9 Innings (Last 2 Games):~2-20
- Ballpark Hit Factor: ~0.5-2.0
Basically, I'm wondering what the best way to "normalize" or "standardize" or "whatever" each individual factor is so that I can then assign weights to each of them to calculate my final hit predictor statistic. I love doing things like this to occupy my time, but it's been entirely long since my last statistics class.
Thank you for your help!
Best regards,
Eric