OLS Regression Results Dep. Variable: total wins R-squared : 0 . 823 Model : OLS Adj. R-squared: 0 . 823 Method: Least Squares F-statistic: 2865. Date : Thu, 11 Aug 2022 Prob (F-statistic ) : 8 . 06e-234 Time : 01 : 39 :25 Log-Likelihood: -1930 .3 No. Observations: 618 AIC : 3865 . Df Residuals: 616 BIC : 3873 . Df Model : 1 Covariance Type: nonrobust coef std err t P> t [0 . 025 0. 975] Intercept -128 .2475 3. 149 -40 .731 0. 000 -134 . 431 -122 . 064 avg_elo n 0. 1121 0 . 002 53 .523 0 . 000 0 . 108 0. 116 Omnibus : 152 . 822 Durbin-Watson: 1 . 098 Prob (Omnibus ) : 0 . 000 Jarque-Bera (JB) : 393 .223 Skew : -1.247 Prob ( JB ) : 4. 10e-86 Kurtosis : 6 . 009 Cond. No. 2. 14e+04 Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 2. 14e+04. This might indicate that there are strong multicollinearity or other numerical problems