Here's how to sharpen your analysis:
Create a scatterplot of TB's passing yards vs. opponents' average passing yards allowed.
Determine the functional form of the relationship. Linear? Quadratic?
I assume the relationship is negative: Fewer passing yards when opponent is better at pass defending. But, you can see.
Look for weirdness. Outliers?
If you've taken a couple of stats courses, you could go on to try this:
Regress TB's yards on average yards allowed. Include a quadratic term if that will get the functional form right as per your exploration by way of scatterplot.
You really want to use autoregressive techniques because your independence assumption is shot to hell by chronological relationships,
Autoregressive model - Wikipedia, the free encyclopedia. But, I would squint past that "little" problem. Your bigger problems have to do with extrapolating beyond the range of your data into the cold weather etc.
Anyhow, once you fit your model, you can use your fitted model to generate predictions for the rest of the games. With regression you can always add more variables to your model. I would suggest game temperature if we had a few cold weather games, but it's been a very mild fall. You could consider dipping into past seasons, but then you have to deal with that non-independence with a multilevel model of games clustered within seasons, but it's probably worth the added complexity.
