Scouting By Reward: VLM-TO-IRL-Driven Player Selection For Esports

TL;DR

This paper presents a novel IRL-based framework for esports player scouting that leverages multimodal data and VLMs to evaluate stylistic alignment with professional players.

Contribution

It introduces a new player selection system using reward functions learned from gameplay and broadcast footage, enhancing scouting accuracy.

Findings

The framework effectively captures mechanical and tactical signatures of elite players.

It enables scalable, data-driven roster construction and talent discovery.

The system integrates high-resolution telemetry and VLM-generated commentary for comprehensive evaluation.

Abstract

Traditional esports scouting workflows rely heavily on manual video review and aggregate performance metrics, which often fail to capture the nuanced decision-making patterns necessary to determine if a prospect fits a specific tactical archetype. To address this, we reframe style-based player evaluation in esports as an Inverse Reinforcement Learning (IRL) problem. In this paper, we introduce a novel player selection framework that learns professional-specific reward functions from logged gameplay demonstrations, allowing organizations to rank candidates by their stylistic alignment with a target star player. Our proposed architecture utilizes a multimodal, two-branch intake: one branch encodes structured state-action trajectories derived from high-resolution in-game telemetry, while the second encodes temporally aligned tactical pseudo-commentary generated by Vision-Language Models (VLMs) from broadcast footage. These representations are fused and evaluated via a Generative Adversarial Imitation Learning (GAIL) objective, where a discriminator learns to capture the unique mechanical and tactical signatures of elite professionals. By transitioning from generic skill estimation to scouting "by reward," this framework provides a scalable, workflow-aware digital twin system that enables data-driven roster construction and targeted talent discovery across massive candidate pools.