PandaSkill -- Player Performance and Skill Rating in Esports: Application to League of Legends

• URL: http://arxiv.org/abs/2501.10049v2
• Date: Mon, 20 Jan 2025 10:21:30 GMT
• Title: PandaSkill -- Player Performance and Skill Rating in Esports: Application to League of Legends
• Authors: Maxime De Bois, Flora Parmentier, Raphaël Puget, Matthew Tanti, Jordan Peltier,
• Abstract summary: PandaSkill is a framework for assessing player performance and skill rating. It uses machine learning to estimate in-game player performance from individual player statistics. PandaSkill updates skill ratings using the Bayesian framework OpenSkill in a free-for-all setting.
• Score: 0.0
• License:
• Abstract: To take the esports scene to the next level, we introduce PandaSkill, a framework for assessing player performance and skill rating. Traditional rating systems like Elo and TrueSkill often overlook individual contributions and face challenges in professional esports due to limited game data and fragmented competitive scenes. PandaSkill leverages machine learning to estimate in-game player performance from individual player statistics. Each in-game role is modeled independently, ensuring a fair comparison between them. Then, using these performance scores, PandaSkill updates the player skill ratings using the Bayesian framework OpenSkill in a free-for-all setting. In this setting, skill ratings are updated solely based on performance scores rather than game outcomes, hightlighting individual contributions. To address the challenge of isolated rating pools that hinder cross-regional comparisons, PandaSkill introduces a dual-rating system that combines players' regional ratings with a meta-rating representing each region's overall skill level. Applying PandaSkill to five years of professional League of Legends matches worldwide, we show that our method produces skill ratings that better predict game outcomes and align more closely with expert opinions compared to existing methods.

Related papers

• SkillMimic: Learning Reusable Basketball Skills from Demonstrations [85.23012579911378]
  We propose SkillMimic, a data-driven approach that mimics both human and ball motions to learn a wide variety of basketball skills. SkillMimic employs a unified configuration to learn diverse skills from human-ball motion datasets. The skills acquired by SkillMimic can be easily reused by a high-level controller to accomplish complex basketball tasks.
  arXiv  Detail & Related papers  (2024-08-12T15:19:04Z)
• "Can You Play Anything Else?" Understanding Play Style Flexibility in League of Legends [54.60542351417308]
  We calculate two measures of flexibility for each player: overall flexibility and temporal flexibility. Our findings suggest that the flexibility of a user is dependent upon a user's preferred play style, and flexibility does impact match outcome.
  arXiv  Detail & Related papers  (2024-02-08T17:57:03Z)
• Understanding why shooters shoot -- An AI-powered engine for basketball performance profiling [70.54015529131325]
  Basketball is dictated by many variables, such as playstyle and game dynamics. It is crucial that the performance profiles can reflect the diverse playstyles. We present a tool that can visualize player performance profiles in a timely manner.
  arXiv  Detail & Related papers  (2023-03-17T01:13:18Z)
• GCN-WP -- Semi-Supervised Graph Convolutional Networks for Win Prediction in Esports [84.55775845090542]
  We propose a semi-supervised win prediction model for esports based on graph convolutional networks. GCN-WP integrates over 30 features about the match and players and employs graph convolution to classify games based on their neighborhood. Our model achieves state-of-the-art prediction accuracy when compared to machine learning or skill rating models for LoL.
  arXiv  Detail & Related papers  (2022-07-26T21:38:07Z)
• Behavioral Player Rating in Competitive Online Shooter Games [3.203973145772361]
  In this paper, we engineer several features from in-game statistics to model players and create ratings that accurately represent their behavior and true performance level. Our results show that the behavioral ratings present more accurate performance estimations while maintaining the interpretability of the created representations. Considering different aspects of the playing behavior of players and using behavioral ratings for matchmaking can lead to match-ups that are more aligned with players' goals and interests.
  arXiv  Detail & Related papers  (2022-07-01T16:23:01Z)
• Collusion Detection in Team-Based Multiplayer Games [57.153233321515984]
  We propose a system that detects colluding behaviors in team-based multiplayer games. The proposed method analyzes the players' social relationships paired with their in-game behavioral patterns. We then automate the detection using Isolation Forest, an unsupervised learning technique specialized in highlighting outliers.
  arXiv  Detail & Related papers  (2022-03-10T02:37:39Z)
• Evaluating Team Skill Aggregation in Online Competitive Games [4.168733556014873]
  We present an analysis of the impact of two new aggregation methods on the predictive performance of rating systems. Our evaluations show the superiority of the MAX method over the other two methods in the majority of the tested cases. Results of this study highlight the necessity of devising more elaborated methods for calculating a team's performance.
  arXiv  Detail & Related papers  (2021-06-21T20:17:36Z)
• The Evaluation of Rating Systems in Team-based Battle Royale Games [4.168733556014873]
  This paper explores the utility of several metrics for evaluating three popular rating systems on a real-world dataset of over 25,000 team battle royale matches. normalized discounted cumulative gain (NDCG) demonstrated more reliable performance and more flexibility.
  arXiv  Detail & Related papers  (2021-05-28T19:22:07Z)
• ELO System for Skat and Other Games of Chance [1.3706331473063877]
  The evaluation of player strength in trick-taking card games like Skat or Bridge is not obvious. We propose a new ELO system for Skat to overcome these weaknesses.
  arXiv  Detail & Related papers  (2021-04-07T08:30:01Z)
• 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)
• Competitive Balance in Team Sports Games [8.321949054700086]
  We show that using final score difference provides yet a better prediction metric for competitive balance. We also show that a linear model trained on a carefully selected set of team and individual features achieves almost the performance of the more powerful neural network model.
  arXiv  Detail & Related papers  (2020-06-24T14:19:07Z)

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.