Interconnectors play a crucial role in electric power systems. They contribute to balancing demand and supply in real-time, guaranteeing efficient dispatch in wide geographic regions, and increasing competition by creating large markets. However, interconnector capacity is a scarce resource because vertically integrated utilities were required to have generating capacity enough to supply most customers within their operating region under a regulated monopoly. Hence, identifying the efficient allocation method is essential, particularly after recent electricity market restructuring. This study evaluates the competitive effect of the implicit auction on the interconnector transmission capacity. The implicit auction allocates all the interconnector capacity simultaneously with electric energy in the day-ahead market. This method prevents market participants from strategically withholding the physical interconnector capacity ex ante to exercise market power, as allowed under the first come, first served rule. This study empirically shows how the capacity was withheld from the day-ahead market under the first come, first served rule using detailed reservation data. Next, I show that the implicit auction increases interconnector capacity available at the day-ahead market and trade volume. I use machine-learning methods, such as random forest and deep neural networks, to predict the counterfactual market outcomes without implicit auction. I find that the gain from trade under the implicit auction is more than US$55 million per year in Japan, which is more than 100 times the implementation cost of the implicit auction.