Here's a line chart I put together from the 60 games Eli has played so far. (You might remember that I put all of his statistics into Excel shortly before Christmas.) This includes all of the wind and temperature information from the official NFL gamebooks and includes all of his playoff games, right up to his all time high rating of 132 in the Dallas game.
Enjoy:
If you would like a larger and more clear image, you can click on the link below.
Larger Chart Image - (
New Window )
Teams with a bye were 17-0 in the playoffs until Sunday right?
Cold is a state of mind... you can't tell me that when the temp drops to around zero, the Packers can handle that better then anyone else... thats just crazy. They get cold too! BUT, they will have to tackle a 265 pound MONSTER, we don't.... Favre is older and the sacks will hurt more...
Bring the cold, we are ready
Winds vs. Rating
and
Temp vs. Rating
Then evaluate the correlation based on those results
If you want to post the data, I can run the true analysis for you
When looking at the relationship between QB Rating vs. Temp, there is very little correlation. An R-squared value of .0871 means that based on the data we have, if we were to make a prediction of Eli's QB Rating based on the temperature, we would only be accurate about 8.8 times out of 100. This translates into little or no statistical relevance.
When looking at the relationship between QB Rating vs. Wind Speed, there is also very little (thought slightly more) correlation. An R-squared value of .1481 means that based on the data we have, if we were to make a prediction of Eli's QB Rating based on the Wind Speed, we would only be accurate about 14.8 times out of 100. This translates into little or no statistical relevance, though it does carry more statistical relevance than QB Rating vs. Temperature.
;)
And as in the first post, here is the link to the larger image:
Link to Larger Image - ( New Window )
Nice work!
Am I getting the jist? Do you work for a job that incorporates stats?
I wonder if it's just a different way of looking at it. But as I said the way I learned it, it means that 14.8% of the variation from the mean is due to the regression model and 85.2% is in fact RANDOM.
I am a strategy analyst in the logistics field. Graduated college in 06. I do a lot of data analysis, but don't do too much advanced analysis like this (yet).
For instance, compare Eli to Ben.
Would Ben's R-squared value be lower or higher? If higher, it would mean that Ben is "more affected" by temperature changes than Eli.
You just have to be careful when making statements like that because when you are dealing with such small r-squared values, there isn't that much of a difference between .088 and .21 -- they would still be considered statistically insignificant in predicting QB rating from temperature.
The best conclusion to draw is that Eli's QB Rating CANNOT be accurately predicted based on Temperature or Wind Speed.