OLS Regression Results Dep. Variable : total wins R-squared: 0. 871 Model : OLS Adj. R-squared: 0 . 870 Method : Least Squares F-statistic : 1377. Date : Thu, 11 Aug 2022 Prob (F-statistic) : 4 . 45e-272 Time : 01 : 47 :16 Log-Likelihood: -1833 .6 No. Observations : 618 AIC : 3675. Df Residuals : 614 BIC : 3693. Df Model : 3 Covariance Type: nonrobust coef std err t P> t [0 . 025 0. 975] Intercept 38. 3929 26 . 603 1 . 443 0. 149 -13 . 851 90 . 637 avg_elo_n 0 . 0010 0 . 018 0 . 055 0. 956 -0 . 034 0 . 036 avg_pts_differential 1 . 8045 0. 135 13.355 0 . 000 1.539 2. 070 avg_elo_differential 0 . 0358 0 . 018 1. 955 0 . 051 -0 . 000 0 . 072 Omnibus : 251 . 562 Durbin-Watson : 0 . 844 Prob ( Omnibus ) : 0. 000 Jarque-Bera (JB) : 1028 . 080 Skew: -1. 868 Prob ( JB ) : 5 . 69e-224 Kurtosis : 8 . 096 Cond. No. 2. 1 1e+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