You created a simple linear regression model for the total number of wins in a regular season using the average relative skill as the predictor variable.

print ( modell . summary( ) ) OLS Regression Results Dep. Variable: total wins R-squared : 0. 228 Model : OLS Adj. R-squared : 0.227 Method: Least Squares F-statistic: 182.1 Date : Mon, 17 Oct 2022 Prob (F-statistic) : 1.52e-36 Time : 00:37:49 Log-Likelihood: -2385.4 No. Observations: 618 AIC : 4775. Of Residuals: 616 BIC: 4784. Of Model : 1 Covariance Type: nonrobust coef std err t P> t [0. 025 0.975] Intercept -85.5476 9.305 -9. 194 0.000 -103.820 -67.275 avg_pts 1. 2849 0. 095 13.495 0.000 1. 098 1.472 Omnibus : 24. 401 Durbin-Watson: 1. 768 Prob (Omnibus) : 0.000 Jarque-Bera (JB) : 11. 089 Skew: -0.033 Prob (JB) : 0. 00391 Kurtosis : 2.347 Cond. No. 1.97e+03 Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 1.97e+03. This might indicate that there are strong multicollinearity or other numerical problems