Explainable e-sports win prediction through Machine Learning classification in streaming

• URL: http://arxiv.org/abs/2510.19671v1
• Date: Wed, 22 Oct 2025 15:18:16 GMT
• Title: Explainable e-sports win prediction through Machine Learning classification in streaming
• Authors: Silvia García-Méndez, Francisco de Arriba-Pérez,
• Abstract summary: This work contributes to an explainable win prediction classification solution in streaming in which input data is controlled over several sliding windows.<n> Experimental results attained an accuracy higher than 90 %, surpassing the performance of competing solutions in the literature.
• Score: 7.743320290728378
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The increasing number of spectators and players in e-sports, along with the development of optimized communication solutions and cloud computing technology, has motivated the constant growth of the online game industry. Even though Artificial Intelligence-based solutions for e-sports analytics are traditionally defined as extracting meaningful patterns from related data and visualizing them to enhance decision-making, most of the effort in professional winning prediction has been focused on the classification aspect from a batch perspective, also leaving aside the visualization techniques. Consequently, this work contributes to an explainable win prediction classification solution in streaming in which input data is controlled over several sliding windows to reflect relevant game changes. Experimental results attained an accuracy higher than 90 %, surpassing the performance of competing solutions in the literature. Ultimately, our system can be leveraged by ranking and recommender systems for informed decision-making, thanks to the explainability module, which fosters trust in the outcome predictions.

Related papers

• See, Think, Act: Online Shopper Behavior Simulation with VLM Agents [58.92444959954643]
  This paper investigates the integration of visual information, specifically webpage screenshots, into behavior simulation via VLMs.<n>We employ SFT for joint action prediction and rationale generation, conditioning on the full interaction context.<n>To further enhance reasoning capabilities, we integrate RL with a hierarchical reward structure, scaled by a difficulty-aware factor.
  arXiv  Detail & Related papers  (2025-10-22T05:07:14Z)
• Who is a Better Player: LLM against LLM [53.46608216197315]
  We propose an adversarial benchmarking framework to assess the comprehensive performance of Large Language Models (LLMs) through board games competition.<n>We introduce Qi Town, a specialized evaluation platform that supports 5 widely played games and involves 20 LLM-driven players.
  arXiv  Detail & Related papers  (2025-08-05T06:41:47Z)
• A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions [0.023301643766310366]
  Machine learning (ML) has played a pivotal role in the transformation of the sports betting industry. This review explores various ML techniques, as applied in different sports such as soccer, basketball, tennis, and cricket. Despite these advancements, challenges such as data quality, real-time decision-making, and the inherent unpredictability of sports outcomes remain.
  arXiv  Detail & Related papers  (2024-10-28T19:49:53Z)
• Unsupervised explainable activity prediction in competitive Nordic Walking from experimental data [6.817247544942709]
  This work focuses on achieving automatic explainability for predictions related to athletes' activities, distinguishing between correct, incorrect, and cheating practices in Nordic Walking. The proposed solution achieved performance metrics of close to 100 % on average.
  arXiv  Detail & Related papers  (2024-06-18T16:29:07Z)
• ShuttleSHAP: A Turn-Based Feature Attribution Approach for Analyzing Forecasting Models in Badminton [52.21869064818728]
  Deep learning approaches for player tactic forecasting in badminton show promising performance partially attributed to effective reasoning about rally-player interactions. We propose a turn-based feature attribution approach, ShuttleSHAP, for analyzing forecasting models in badminton based on variants of Shapley values.
  arXiv  Detail & Related papers  (2023-12-18T05:37:51Z)
• Graph Encoding and Neural Network Approaches for Volleyball Analytics: From Game Outcome to Individual Play Predictions [5.399740513992854]
  We introduce a specialized graph encoding technique to add contact-by-contact volleyball context to an already available volleyball dataset. We demonstrate the potential benefits of using graph neural networks (GNNs) on this enriched dataset for three different volleyball prediction tasks. Our results show that the use of GNNs with our graph encoding yields a much more advanced analysis of the data.
  arXiv  Detail & Related papers  (2023-08-22T02:51:42Z)
• Modeling Content Creator Incentives on Algorithm-Curated Platforms [76.53541575455978]
  We study how algorithmic choices affect the existence and character of (Nash) equilibria in exposure games. We propose tools for numerically finding equilibria in exposure games, and illustrate results of an audit on the MovieLens and LastFM datasets.
  arXiv  Detail & Related papers  (2022-06-27T08:16:59Z)
• What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience [63.75363908696257]
  computational reinforcement learning seeks to construct an agent's perception of the world through predictions of future sensations. An open challenge in this line of work is determining from the infinitely many predictions that the agent could possibly make which predictions might best support decision-making. We introduce a meta-gradient descent process by which an agent learns what predictions to make, 2) the estimates for its chosen predictions, and 3) how to use those estimates to generate policies that maximize future reward.
  arXiv  Detail & Related papers  (2022-06-13T21:31:06Z)
• ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton [18.524164548051417]
  This paper focuses on objectively judging what and where to return strokes in turn-based sports. We propose a novel Position-aware Fusion of Rally Progress and Player Styles framework (ShuttleNet) that incorporates rally progress and information of the players.
  arXiv  Detail & Related papers  (2021-12-02T08:14:23Z)
• Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport [51.20042288437171]
  We propose a Two-Stage Spatial-Temporal Network (TSSTN) that can provide accurate real-time win predictions. Experiment results and applications in real-world live streaming scenarios showed that the proposed TSSTN model is effective both in prediction accuracy and interpretability.
  arXiv  Detail & Related papers  (2020-08-14T12:00:58Z)
• Scalable Psychological Momentum Forecasting in Esports [0.0]
  We present ongoing work on an intelligent agent recommendation engine for competitive gaming. We show that a learned representation of player psychological momentum, and of tilt, can be used to achieve state-of-the-art performance in pre- and post-draft win prediction.
  arXiv  Detail & Related papers  (2020-01-30T11:57:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.