Esport games comprise a sizeable fraction of the global games market, and is the fastest growing segment in games. This has given rise to the domain of esports analytics, which uses telemetry data from games to inform players, coaches, broadcasters and other stakeholders. Compared to traditional sports, esport titles change rapidly, in terms of mechanics as well as rules. Due to these frequent changes to the parameters of the game, esport analytics models can have a short life-spam, a problem which is largely ignored within the literature. This paper extracts information from game design (i.e. patch notes) and utilises clustering techniques to propose a new form of character representation. As a case study, a neural network model is trained to predict the number of kills in a Dota 2 match utilising this novel character representation technique. The performance of this model is then evaluated against two distinct baselines, including conventional techniques. Not only did the model significantly outperform the baselines in terms of accuracy (85% AUC), but the model also maintains the accuracy in two newer iterations of the game that introduced one new character and a brand new character type. These changes introduced to the design of the game would typically break conventional techniques that are commonly used within the literature. Therefore, the proposed methodology for representing characters can increase the life-spam of machine learning models as well as contribute to a higher performance when compared to traditional techniques typically employed within the literature.

翻译：电竞游戏在全球游戏市场中占据显著份额，且是增长最快的细分领域。由此催生了电竞分析领域，该领域利用游戏的遥测数据为玩家、教练、广播方及其他利益相关者提供信息。与传统体育相比，电竞项目的机制与规则变化迅速。由于游戏参数频繁更新，电竞分析模型往往寿命短暂，而这一难题在现有文献中基本被忽视。本文从游戏设计（即补丁说明）中提取信息，并利用聚类技术提出一种新的角色表征形式。作为案例研究，我们训练了一个神经网络模型，采用这种新型角色表征技术预测《Dota 2》单场比赛的击杀数。随后将该模型的性能与两种不同的基线方法（包括传统技术）进行对比评估。该模型不仅在准确率上显著优于基线（AUC达85%），而且在游戏两次引入一名新角色及全新角色类型的更新迭代中仍保持准确率。这些游戏设计的变更通常会破坏文献中常用的传统技术。因此，所提出的角色表征方法既能延长机器学习模型的生命周期，相较于文献中通常采用的传统技术，还能实现更高的性能。