Identifying fighting, balanced, and territorial go player styles with deep learning

Using deep learning techniques, this research study offers a novel method for analyzing cricket shots. The goal of the project is to build a solid framework that can reliably classify and examine different kinds of cricket shots under various playing circumstances and player styles. The technique begins with gathering a large dataset of cricket shot films, which is then rigorously preprocessed to annotate different sorts of shots. The next step involves choosing and training a Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) model to identify and categorize cricket shots according to their visual attributes. The principal aims of this study are as follows: (1) Construct a deep learning model that can reliably categorize cricket strokes; (2) Assess the model's performance through extensive testing and contrast with conventional techniques and (3) Use deep learning techniques to improve your analysis of cricket shots. This research is important because it has the potential to transform cricket performance evaluation and sports analytics. This study intends to use deep learning techniques to offer more accurate and thorough insights into cricket shot execution, assisting players, coaches, and analysts in making strategic decisions and optimizing performance.

A major challenge for deep reinforcement learning (DRL) agents is to collaborate with novel partners that were not encountered by them during the training phase. This is specifically worsened by an increased variance in action responses when the DRL agents collaborate with human partners due to the lack of consistency in human behaviors. Recent work have shown that training a single agent as the best response to a diverse population of training partners significantly increases an agent's robustness to novel partners. We further enhance the population-based training approach by introducing a Hierarchical Reinforcement Learning (HRL) based method for Human-AI Collaboration. Our agent is able to learn multiple best-response policies as its low-level policy while at the same time, it learns a high-level policy that acts as a manager which allows the agent to dynamically switch between the low-level best-response policies based on its current partner. We demonstrate that our method is able to dynamically adapt to novel partners of different play styles and skill levels in the 2-player collaborative Overcooked game environment. We also conducted a human study in the same environment to test the effectiveness of our method when partnering with real human subjects. Code is available at https://gitlab.com/marvl-hipt/hipt.

Introduction This study explores how deep learning and interpretable modeling can reveal the impact of player styles on performance across basketball leagues. By examining stylistic features and their influence on cross-league adaptation, the research aims to provide a quantitative framework to understand performance mechanisms in diverse competitive environments. Methods Game data from players and teams spanning the 2019–2024 seasons were collected as samples. The study first clustered and modeled players' technical styles using principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and Gaussian mixture models. It then employed a branch-type multilayer perceptron (Branch-MLP), combined with the SHAP (SHapley Additive exPlanations) algorithm, to conduct interpretable analyses of mainstream tactical structures. Results Findings reveal that the NBA prioritizes offensive efficiency and coordinated team play, whereas the CBA emphasizes ball-possession control and physical confrontation. The Branch-MLP model demonstrated high accuracy in tactical recognition tasks. Quantitative evaluations further showed that interior defense-focused role players maintained more stable performance across leagues, while perimeter ball-handling players exhibited greater variability. Discussion/Conclusion This study advances the quantitative analysis of athletic performance by integrating deep learning with interpretable analytics. Its insights provide actionable references for training, player transfers, and youth talent development, supporting data-driven decisions in professional basketball management.

Feature Comparison for Classification of Kaustinen Fiddle Playing Style from Archived Recordings Using Deep Learning