OLS Regression Results Dep. Variable: avg_pts R-squared : 0. 194 Model : OLS Adj. R-squared: 0. 191 Method : Least Squares F-statistic: 73.96 Date: Mon, 17 Oct 2022 Prob (F-statistic) : 1.65e-29 Time : 00 :53:20 Log- Likelihood : -1787.3 No. Observations: 618 AIC : 3581. Of Residuals: 615 BIC : 3594. Of Model : 2 Covariance Type: nonrobust coef std err t P> t [0. 025 0.975] Intercept 103.4609 7.715 13.410 0.000 88.309 118.612 avg_elo_n -0.0039 0. 005 -0.759 0. 448 -0. 014 0. 006 avg_pts_differential 0. 5474 0. 118 4.644 0.000 0. 316 0.779 Omnibus : 0.365 Durbin-Watson: 1.359 Prob (Omnibus ) : 0.833 Jarque-Bera (JB) : 0. 248 Skew: 0. 036 Prob(JB) : 0.883 Kurtosis : 3.067 Cond. No. 6.60e+04 Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 6.6e+04. This might indicate that there are strong multicollinearity or other numerical problems