OLS Regression Results Dep. Variable: total_wins R - squared: 0. 878 Model : OLS Adj . R-squared: 0. 877 Method : Least Squares F-statistic: 1102. Date : Mon, 05 Dec 2022 Prob (F-statistic) : 3. 07e-278 Time : 14:07:29 Log-Likelihood : -1815.5 No. Observations: 618 AIC : 3641. Of Residuals: 613 BIC : 3663. Df Model : 4 Covariance Type: nonrobust coef std err t P> | t) [0. 025 0.975] Intercept 34. 5753 25. 867 1.337 0. 182 -16.223 85. 373 avg_pts 0. 2597 0. 043 6.070 0.000 0. 176 0. 344 avg_elo_n - 0. 0134 0. 017 -0. 769 0. 442 -0. 048 0. 021 avg_pts_differential 1. 6206 0. 135 12. 024 0.000 1. 356 1. 885 avg_elo_differential 0. 0525 0. 018 2.915 0. 004 0. 017 0 . 088 Omnibus : 193. 608 Durbin-Watson: 0.979 Prob (Omnibus ) : 0. 000 Jarque -Bera (JB) : 598. 416 Skew: -1. 503 Prob ( JB) : 1. 14e-130 Kurtosis : 6.769 Cond. No. 2. 11e+05 Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 2. 1le+05. This might indicate that there are strong multicollinearity or other numerical problems