Uncovering Soccer Teams Passing Strategies Using Implication Rules

使用机器学习改进 Soccer Simulation 2D 游戏中的盘带、传球和盯人动作

Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning

本文介绍了 RoboCup 2021 2D 足球仿真冠军 CYRUS 的三大核心功能：多动作盘带、传球预测和盯人决策，分别提升了盘带成功率、团队协作和防守策略。

工程化特征以改进 Soccer Simulation 2D 比赛中的传球预测

Engineering Features to Improve Pass Prediction in Soccer Simulation 2D Games

本研究提出基于 DNN 和 RF 模型及嵌入式数据提取模块，预测 SS2D 比赛中传球行为，经对阵 RoboCup 2019 顶尖队伍验证，将传球预测性能提升 5% 至 10%。

QPass: a Merit-based Evaluation of Soccer Passes

Quantitative analysis of soccer players' passing ability focuses on descriptive statistics without considering the players' real contribution to the passing and ball possession strategy of their team. Which player is able to help the build-up of an attack, or to maintain the possession of the ball? We introduce a novel methodology called QPass to answer questions like these quantitatively. Based on the analysis of an entire season, we rank the players based on the intrinsic value of their passes using QPass. We derive an album of pass trajectories for different gaming styles. Our methodology r…

Leaving Goals on the Pitch: Evaluating Decision Making in Soccer

Analysis of the popular expected goals (xG) metric in soccer has determined that a (slightly) smaller number of high-quality attempts will likely yield more goals than a slew of low-quality ones. This observation has driven a change in shooting behavior. Teams are passing up on shots from outside the penalty box, in the hopes of generating a better shot closer to goal later on. This paper evaluates whether this decrease in long-distance shots is warranted. Therefore, we propose a novel generic framework to reason about decision-making in soccer by combining techniques from machine learning and…

面向四足机器人团队的真实世界合作与竞技足球研究

Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams

本研究提出分层多智能体强化学习框架，结合底层运动控球技能与高层MAPPO+FSP战略策略，实现四足机器人自主足球竞技，经消融实验验证优势并完成真实场景部署。