Application-Oriented Pattern Analysis Utilizing the SPTA Algorithm for Efficient Targeted Sequential Pattern Mining

Abstract

In the gaming business, esports, also known as competitive gaming, has ushered in a new age that has brought up some innovative issues. One of these challenges is determining the level of player competency based on their plans and talents. Using game traces from Rocket League, a one-of-a-kind "soccer with rocket-powered cars" game, this investigation focuses on the automatic recognition of skill shots, an essential component of player performance. Challenges arise for classic pattern matching algorithms due to the unique characteristics of each skill performance. A Player Skill Modeling through Data-Centric Patterns (PSM-DCP) strategy that uses pattern mining and supervised learning techniques offers a solution to this issue. To highlight the potential of our system for player modeling shows that it can efficiently detect a wide variety of Rocket League abilities and shots. Also confirm the efficacy of this technique by conducting a comprehensive set of tests, which reveals that it is able to effectively differentiate and evaluate skillshots. The study results have a wide range of implications for various applications within esports. These applications include the improvement of match-making algorithms, the provision of assistance to game commentators in the form of insightful assessments, and the development of learning systems that are intended to improve player abilities. This study not only tackles the unique context of Rocket League, but it also offers a framework for exploiting data-centric techniques in esports analytics. This allows for additional developments and applications in competitive gaming, which opens up new paths for possibilities.