# How the forecast works

###### The forecast uses game results to create an ELO rating, and then the season is run 10,000 times do get an average result. The reason it is run so many times is to get as many possible seasons as possible. I will go further in depth on that later. It then averages the results, to give us the final probabilities.

# Calculating ELO

###### Elo was created by Arpad Elo, a Hungarian-American physics professor. The index was first created for chess, but has been adapted for multiple sports. I found the win percentage of each game by the difference of ELO What makes ELO a good rating is that ELO points never disappear or appear. The dispersion of points always averages to 0. The winner takes from the loser, and it is that simple.

###### I created my ELO rating by taking the win loss records or each team’s preseason schedule. The win percentage was then inverted into an ELO, by finding the difference of ELO based on that percentage. The other factor strength of schedule index from MaxPreps.com. The strength of schedule was multiplied by 10 to get an ELO difference. Those two factors are added together to get the total ELO difference. The ELO difference is added to 1300 to get a composite ELO. Since the I had very little data to go by, I knew the starting ELO’s were going to be very volatile and not truly represent the teams talents. So in order to get a little more stable ELO rating I mean reverted the each team by 1/3. In statistics noise and signal are often used terms, signal represents the true population or data, but signal is smaller movement that if read incorrectly, could mess up the calculations. I reduced the noise from the data given to me by doing this regression, and made the teams starting ELO closer to each other. This was incase I over or under-estimated each team’s true ELO or skill.