Since its debut in 2009, League of Legends (LoL) has been on a rise in becoming an extremely favoured multiplayer online battle arena (MOBA) game. This paper presented a logic mining technique to model the results (Win / Lose) of the LoL games played in 3 regions, namely South Korea, North America and Europe. In this research, a method named k satisfiability based reverse analysis method (kSATRA) was brought forward to obtain the logical relationship among the gameplays and objectives in the game. The logical rule obtained from the LoL games was used to categorize the results of future games. kSATRA made use of the advantages of Hopfield Neural Network and k Satisfiability representation. The data set used in this study included the data of all 10 teams from each region, which composed of all games from Spring Season 2018. The effectiveness of kSATRA in obtaining logical rule in LoL games was tested based on root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and CPU time. Results acquired from the computer simulation showed the robustness of kSATRA in exhibiting the performance of the LoL teams.