Correlation coefficient percentage is low considering relationship between variables I am creating a model in R Studio for NCAA football games and am encountering values that seem lower than they should be.  Here is my function and returned R-squared value.
lm(formula = rushing_yards ~ yards_per_rush_attempt, data = games.box)  

This is the end of the model summary:
Multiple R-squared:  0.778, Adjusted R-squared:  0.7779 

While 78% is nothing to scoff at I would have thought it would have been higher considering how closely related rushing_yards and yards_per_rush_attempt are to each other.  Am I missing something?  Is something wrong with my dataset or should I not worry about the results?


Yards per rush explains a lot of the variation in rush yards per game but leaves some of that variation explained. Another source of variation, not considered in your model, is how many rush attempts per game, so it is unsurprising that you leave some of the variation unexplained.
I think I agree with the comment that your $R^2$ is surprisingly high for a model that does not consider rush attempts, but you have such a simple model that I don’t believe there to be any overfitting concerns.
