Leveraging LLMs for Aggregating and Ranking Sports Predictions
Client Background
- A US based fintech start-up wanted to build a big data analytics solution; and was looking for a suitable partner.
- Start-up was looking for an optimized solution to source 10-year financial data for 500 companies
- The data expected in a custom grid format to feed the analytics engine.
Approach To Solution
- AIMLEAP Automation Team leveraged AI driven data solution to build a semi-automated process to convert source data
- AI solution was trained to identify data points which needed manual intervention
- The AI solution was able to source and convert financial data with minimal manual intervention
- Automated data validation process assured that all data points are of expected quality
- Also, an automated process established to feed the final set of data to push to customer database on a regular interval
Approach To Solution
- AIMLEAP Automation Team leveraged AI driven data solution to build a semi-automated process to convert source data
- AI solution was trained to identify data points which needed manual intervention
- The AI solution was able to source and convert financial data with minimal manual intervention
- Automated data validation process assured that all data points are of expected quality
- Also, an automated process established to feed the final set of data to push to customer database on a regular interval
Challenges and Considerations
- 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.
Conclusion
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.
Keywords: LLM sports prediction case study, sports prediction aggregation, expert ranking, AI web scraping, sports analytics, prediction accuracy tracking
60%
cost saving
99%
data quality
Faster Go-To-Market
Quick turnaround helped faster Go-To-Market
Results and Insights
- 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.