Case Studies

Case Studies

AI-Powered Sports Highlight Validation & Content Moderation

About

Our client is a sports technology platform that uses Machine Learning (ML) to automatically generate highlight moments from recorded sporting events. These highlights are generated using match video footage, play-by-play (PBP) data, commentary feeds, score updates, and AI-driven event detection across multiple sports including Baseball, Basketball, Football, Lacrosse, Softball, and Volleyball.
Sports Highlight Validation & Content Moderation

Challenges

While the AI system automates highlight generation at scale, it occasionally produced quality and accuracy issues that required human oversight. The key pain points included:
Incorrect Event Classifications

Incorrect Event Classifications

The AI system at times misidentified the type of event occurring in a game, leading to inaccurate highlight categorization.
Inaccurate Timestamps & Mismatched Clips

Inaccurate Timestamps & Mismatched Clips

Highlights were occasionally generated with incorrect timing or video clips that did not match the intended action.
Duplicate & Incomplete Highlights

Duplicate & Incomplete Highlights

The system sometimes created duplicate moments or generated descriptions that were incomplete or failed to reflect the actual event accurately.
Missing Key Game Events

Missing Key Game Events

Important in-game moments were occasionally not detected by the AI, resulting in gaps in highlight coverage.
Incomplete Play-by-Play Data

Incomplete Play-by-Play Data

In certain cases, PBP data was unavailable or incomplete, requiring highlights to be created entirely from alternative data sources such as commentary feeds and scoreboards.
Technoheight

Technoheight

Solution

A dedicated Content Moderation Team was established to review every AI-generated moment against multiple data sources and maintain a continuous feedback loop with engineering and machine learning teams. To tackle these, the team executed the following:

Multi-Source Highlight Validation

Every AI-generated highlight was reviewed against match video footage, play-by-play data, commentary feeds, and scoreboards to verify correct event identification, timing accuracy, clip-to-action alignment, and appropriate game context.

Manual Description Correction

When AI-generated descriptions contained errors or lacked sufficient detail, they were manually corrected to ensure consistency and accuracy across all sports content.

Manual Highlight Creation

When the AI missed important moments, the team manually created highlights by analyzing match footage and cross-referencing available commentary, scoreboards, and game context — including cases where PBP data was entirely unavailable.

AI Feedback & Continuous Improvement

Recurring issues such as missed events, false positives, incorrect classifications, duplicate moments, and timestamp inaccuracies were documented and reported to engineering teams, forming a human-in-the-loop quality layer that supports ongoing ML model refinement.

THE RESULT

THE RESULT

Business Impact

Improved AI Accuracy

Structured validation and correction workflows improved the accuracy and reliability of AI-generated highlights, reducing recurring detection errors over time.

Complete Match Coverage

Manual highlight creation ensured full coverage of significant game events, eliminating gaps caused by AI misses or missing PBP data.

Higher Content Quality

Enhanced highlight descriptions and corrected metadata improved the overall quality and consistency of sports content delivered to end users.

Continuous Model Improvement

A structured feedback loop with machine learning teams enabled ongoing refinement of event-detection logic and model performance across multiple sports.

More Engaging Viewing Experience

Accurate, complete, and well-described highlights delivered a more engaging and reliable experience for sports fans across all supported sports.

Scroll to Top