Leveraging LLMs for Aggregating and
Ranking Sports Predictions

The core workflow consists of:

• LLM-Powered Web Scraping: LLMs extract predictions from a variety of sports websites, forums, and analyst blogs, handling unstructured and varied formats with high adaptability.
• Centralized Database Storage: All predictions, along with actual match outcomes, are stored in a structured database for historical analysis.
• Automated Accuracy Tracking: After each match, the system compares predictions to real outcomes and updates each predictor’s accuracy score, generating transparent, up-to-date rankings.

This approach shifts the focus from building proprietary predictive models to objectively evaluating the performance of existing analysts and sources.

Information Aggregation: LLMs are tasked with parsing complex sports narratives, extracting structured prediction data, and linking it to specific matches and analysts. This requires nuanced understanding, context recognition, and the ability to resolve ambiguities in source material.

Analytical Reasoning: The system uses divide-and-conquer strategies, such as segmenting data by predictor or match, to streamline aggregation and minimize reasoning complexity for the LLM.

Evaluation Metrics: Predictor rankings are based on real-world accuracy-how often their published predictions align with actual outcomes. The system can employ metrics such as precision, recall, and F1 score to quantify performance.

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Objectivity and Transparency: By aggregating predictions and tracking actual outcomes, the system provides a transparent, unbiased leaderboard of expert performance, helping users identify the most reliable sources.

Adaptability: LLMs excel at handling unstructured data and adapting to new formats or sources, ensuring the system remains robust as the online sports prediction ecosystem evolves.

Scalability: Automation enables the system to process large volumes of predictions and results across multiple sports and competitions with minimal manual intervention.

Nuanced Performance Evaluation: The system can distinguish between predictors who consistently offer accurate insights and those who may be over- or underperforming due to chance, volume of predictions, or specialization.

Data Quality: The accuracy of rankings depends on the quality and completeness of scraped predictions and match results.

Ambiguity in Source Material: LLMs must resolve ambiguities and inconsistencies in how predictions are presented, which can affect aggregation accuracy.

Ranking Algorithm Selection: Different ranking algorithms (e.g., Elo, win rate, Markov Chain) may yield varying results, especially when matchups are unevenly distributed or when some predictors have far more predictions than others.

This LLM-driven approach to sports prediction aggregation and ranking offers a scalable, transparent, and adaptable solution for objectively evaluating expert performance. By focusing on information extraction, aggregation, and real-world accuracy tracking, the system empowers users to make informed decisions based on proven expertise rather than opaque statistical models. As LLM technology advances, such frameworks are poised to become foundational tools in the sports analytics ecosystem.

Also, check how AI-powered quality control is transforming code review pipelines with “QC Using AI”.