In my last post on this topic, I used linear regression to analyze my custom formula that attempts to predict each team's future success in the NFL. If you missed that, you can get caught up on it (and every post in my "Crunching the Numbers" series) by checking out this hub.
The previous article gave an overview of how linear regression works and the adjustments I made so that results will be easier to read. I won't go through that all again, but I'll sum up the highlights here:
- All numbers associated with each statistic are correlation coefficients, and they quantify whether or not that statistic has any sort of relationship to winning
- A positive number implies a positive relationship (for example, more points scored = more games won)
- A negative number implies a negative relationship (for example, fewer points allowed = moregames won)
- In order to have 95% confidence that a number is not coincidence, the valueof the number must be at least 1.