Embedded Visualizations for Tracking Athlete Performance in Swimming Competitions
Competitive sports coverage increasingly includes information on athlete or team statistics and records, with visualizations embedded in the video stream to track this data. This preliminary research explores the design space of embedded, moving visualizations in the context of professional and amateur swimming competitions.
Analyzing Player Connections and Performance in the Indian Pro Kabaddi League: A Network Analysis Approach
The paper applies network analysis techniques to study the connectivity patterns, clustering, and information flow dynamics among players in the Indian Pro Kabaddi League. The analysis reveals the league's small-world properties and provides insights into player rankings based on network centrality measures.
Multi-Layer Multi-Input Transformer Network for Accurate Prediction of Future Badminton Shots and Landing Coordinates
A novel Multi-Layer Multi-Input Transformer Network (MuLMINet) that leverages professional badminton player match data to accurately predict future shot types and area coordinates, achieving runner-up position in the IJCAI CoachAI Badminton Challenge 2023.
Concluding Suspended Sports Leagues: A Data-Driven Methodology for Selecting Shortened Seasons
A data-driven model that selects a subset of remaining games to conclude a suspended sports league season, while producing an end-of-season ranking similar to that of the full season.
The Evolution of Football Betting: A Machine Learning Approach
Utilizing Machine Learning to forecast premier league football match outcomes and inform bookmaker odds.
Basketball Offensive Lineup Analysis Based on Shooting Style and Role Clustering
Analyzing the impact of playing style compatibility on scoring efficiency in basketball lineups.
Imitation Learning in Badminton Player Behavior with RallyNet
RallyNet, a hierarchical offline imitation learning model, adeptly imitates badminton player behaviors by leveraging experiential contexts and geometric Brownian motion.
Machine Learning for Soccer Match Result Prediction: A Comprehensive Analysis
The authors explore the current state and future potential of machine learning in predicting soccer match results, emphasizing the need for more comprehensive comparisons between models and features. They highlight the importance of interpretability in prediction models for effective team management.
Player Pressure Map: Evaluating Soccer Performance Under Pressure
The author proposes a Player Pressure Map (PPM) to quantify pressure on individual players and teams in soccer games, enhancing performance evaluation under varying levels of pressure.
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