This article introduces a frequentist analogue to the Bayesian SUCRA method for ranking treatments in network meta-analysis, called P-scores. Unlike SUCRA, which uses Bayesian posterior distributions, P-scores are based on frequentist point estimates and standard errors. They measure the mean extent of certainty that a treatment is better than competing treatments. The authors demonstrate that P-scores produce nearly identical numerical values to SUCRA in case studies of diabetes and depression data. Both methods induce rankings that mostly follow the point estimates, but take precision into account. However, neither SUCRA nor P-scores offer a major advantage compared to looking at credible or confidence intervals. The P-score is calculated as the mean of one-sided p-values and can be used without resampling methods. The study shows that in frequentist network meta-analysis, treatment ranking can be done without resampling, and P-scores provide a frequentist equivalent to SUCRA. The results confirm that the ranking mainly depends on the point estimates, with exceptions for treatments with different precision. The authors also note that P-scores have similar numerical values to SUCRA and that both methods are mainly driven by point estimates. The study concludes that P-scores are a good approximation to values generated by resampling methods.This article introduces a frequentist analogue to the Bayesian SUCRA method for ranking treatments in network meta-analysis, called P-scores. Unlike SUCRA, which uses Bayesian posterior distributions, P-scores are based on frequentist point estimates and standard errors. They measure the mean extent of certainty that a treatment is better than competing treatments. The authors demonstrate that P-scores produce nearly identical numerical values to SUCRA in case studies of diabetes and depression data. Both methods induce rankings that mostly follow the point estimates, but take precision into account. However, neither SUCRA nor P-scores offer a major advantage compared to looking at credible or confidence intervals. The P-score is calculated as the mean of one-sided p-values and can be used without resampling methods. The study shows that in frequentist network meta-analysis, treatment ranking can be done without resampling, and P-scores provide a frequentist equivalent to SUCRA. The results confirm that the ranking mainly depends on the point estimates, with exceptions for treatments with different precision. The authors also note that P-scores have similar numerical values to SUCRA and that both methods are mainly driven by point estimates. The study concludes that P-scores are a good approximation to values generated by resampling methods.