OLS Regression Results Dep. Variable: total wins R-squared: 0.837 Model : OLS Adj. R-squared: 0.837 Method: Least Squares F-statistic: 1580. Date: Mon, 17 Oct 2022 Prob (F-statistic) : 4.41e-243 Time : 00 : 48:24 Log- Likelihood : -1904.6 No. Observations : 618 AIC : 3815. Of Residuals: 615 BIC : 3829. Of Model : 2 Covariance Type: nonrobust coef std err t P> t [0. 025 0.975] Intercept -152.5736 4.500 -33.903 0.000 -161 . 411 -143.736 avg_pts 0. 3497 0. 048 7.297 0.000 0. 256 0. 444 avg_elo_n 0. 1055 0. 002 47.952 0.900 0. 101 0. 110 Omnibus : 89. 087 Durbin-Watson: 1. 203 Prob (Omnibus) : 0.000 Jarque-Bera (JB) : 160.540 Skew: -0.869 Prob (JB) : 1.38e-35 Kurtosis : 4.793 Cond. No. 3.19e+04 Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 3.19e+04. This might indicate that there are strong multicollinearity or other numerical problems