Enhancing Auto-Generated Baseball Highlights via Win Probability and Bias Injection Method

Title of the Paper

Enhancing Auto-Generated Baseball Highlights via Win Probability and Bias Injection Method

Paper Information

• Research Area: Application of Human-Computer Interaction and Artificial Intelligence in Sports Video Generation
• Keywords: Auto-generated sports highlights, baseball highlights, win probability, Win Probability Added (WPA), biased highlights, personalized highlights

Research Background and Problem Statement

• Problem or Challenge: Existing auto-generated sports highlights often rely on visual or audio cues, failing to comprehensively capture the dynamics of the game. For instance, focusing solely on scoring events may overlook critical defensive or strategic actions that influence the game's rhythm.
• Significance: Auto-generated sports highlights represent a rapidly developing research direction in sports entertainment and academia, particularly in enhancing viewer experience and information delivery.
• Research Motivation and Related Work: This study introduces a novel highlight generation method by leveraging baseball data's statistical metric—Win Probability Added (WPA)—to improve the performance of existing AI algorithms. Additionally, it addresses the issue of biased generation to cater to the personalized needs of diverse audiences.

Solution

• Proposed Method: Utilize the WPA metric to generate game highlights, capturing pivotal moments during the match. Furthermore, design a bias injection framework that adjusts WPA weights to produce highlights favoring specific teams.
• Innovations:
  1. Employ WPA, a highly context-sensitive statistical metric, instead of traditional visual/audio cues.
  2. Provide new pathways for personalized highlight generation by adjusting the direction and degree of bias.
• Implementation Steps and Key Technologies:
  1. Standard WPA Highlight Generation:
     • Extract key events with significant changes in WPA values, arrange them chronologically, and generate highlights.
     • Use algorithms to filter events with the highest absolute WPA values and select them based on user-defined highlight duration.
  2. Bias Injection Framework:
     • Adjust WPA weights to generate biased highlights for specific teams, increasing the weight of positive events and decreasing the weight of negative events.
     • Support two bias directions (favoring home or away teams) and two bias intensities (strong bias or weak bias).
     • Compare highlights generated under different bias conditions with standard highlights and existing AI methods.

Research Findings

• Specific Results:
  1. Compared to existing commercial solutions (e.g., NAVER AI highlights), WPA-based highlights were rated as more comprehensive and engaging.
  2. Evaluations of biased highlights showed that viewers' perceptions of the videos were closely tied to the match outcome (win/loss). Specifically, biased highlights combined with the supported team's victory received higher ratings from participants.
• Advantages:
  1. The WPA method excelled in capturing game dynamics and critical events.
  2. The bias injection framework provided opportunities for personalized viewing experiences.
• Experimental or Evaluation Results:
  1. User studies (involving 43 baseball enthusiasts) revealed that WPA highlights better reflected game dynamics compared to existing AI-generated highlights.
  2. Biased highlights were more popular when the supported team won the match, while they were less favored when the team lost.
  3. Fairness evaluations showed minimal differences, indicating subjective discrepancies in the definition of fairness.
• Limitations and Future Directions:
  1. The bias injection framework requires further exploration of modulation methods for bias intensity and the deeper impact of game dynamics on highlight generation.
  2. Automated highlight generation should further investigate individual user preferences (e.g., specific player performance) and diverse needs.
  3. The current study focuses on Korean baseball viewers, and future research should expand the sample scope to validate global applicability.

By introducing a WPA-based method and a bias injection framework, this paper demonstrates a scalable approach to improving the quality of automated sports highlights, catering to diverse audience needs, with significant application potential.