Sports LLM Training Data & Annotation

Sports

Our data labeling services cover the entire spectrum of sports LLMs. Experts are here to help annotate game events and player results, training models to deliver accurate sports analytics and commentary.

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LLM Data Types for Sport

Sentiment Annotation

Marking emotional tone (positive, negative, neutral) in the text. Analysis of fan reactions to the game/decision; monitoring of hate speech. This allows models to gauge audience sentiment and detect inappropriate or toxic content.

Named-Entity Recognition (NER)

Marking specific objects in the text: player names, team names, locations, match dates, sports terms. Creating structured data from unstructured news, automatic generation of match summaries.

Intent Annotation

Determining the goal or action expressed by the user in the query. Training chatbots to interact with fans and support services. Properly annotated intents increase the model's ability to respond correctly to user queries.

Question/Answer Annotation

Marking pairs of "question" and "correct answer" based on sports data. Teaching LLMs to answer complex questions about rules, statistics, and history. The model provides accurate and reliable information to fan queries.

Temporal & Event Sequence Annotation

Denotation of the sequence of sports events: attack phases, change of possession, tactical transitions, timing of key moments. Teaches the model to understand cause-and-effect relationships in the game and build accurate analytical descriptions.

Dialogue & Commentary Annotation

Annotation of dialogue, commentary, and commentary phrases. Helps LLMs learn to generate realistic sports commentary, interviews, and chat interactions during matches. Trains the model to create contextually accurate comments in real time.

LLM Data Services for Sports

Domain Data Collection and Cleaning

Generation, collection and standardization of large amounts of specialized data for model training.

Specialized Data Annotation

Engaging experts to label data to transform input into structured training material.

Model Fine-Tuning

Adapting generic LLMs to client-specific data so that the model better understands industry terminology and context.

Accuracy and Hallucination Detection

Systematically checking the model’s generated responses for factual inaccuracy and fabricated information (hallucinations) to ensure reliability.

Prompt Engineering

Development and optimization of prompts to maximize the quality and predictability of the model’s output.

LLM Monitoring and Support

Continuous monitoring of model performance in a production environment, tracking data drift and using feedback for regular updates.

LLM Use Cases in the Sport Industry

Sports Analytics & Prediction

Models analyze large amounts of text and structured data to identify trends and assess player performance. High-quality annotation provides accurate interpretation of statistics and historical records to predict match results.

Analysis of player statistics
Forecasting season and tournament results
Detecting anomalies in sports reports

Personalized Training and Coaching

LLM integrates data on the athlete’s physical condition, biomechanics, and psychological profile to create recovery and nutrition plans. By incorporating data from physical AI devices and embedded AI sensors, deep annotation of input data allows models to provide actionable recommendations tailored to the needs of a specific athlete.

Generation of individual nutrition plans
Development of recovery programs after loads
Correction of movement techniques based on biomechanical data

Sports Media Automation

Models automatically generate match reports, highlights, and commentary in multiple languages. Annotating sports terminology and tone ensures accuracy and consistency across media platforms.

Automate event reporting
Create video highlight descriptions
Generate multilingual game commentary

Advanced Fan Engagement

Interactive chatbots provide instant answers to fan queries about tickets, player stats, or game rules. Annotated conversational data ensures robust user engagement and a high level of contextual understanding.

Provide instant, real-time statistics
Support users with tickets and logistics
Personalized team news notifications

Coaching Decision Support

LLMs analyze opponents’ tactical schemes and a team’s own mistakes to optimize game strategy. Using insights from physical AI tracking systems and processed by embedded AI, structured annotations help models recognize complex patterns and suggest substitution or formation changes to coaches.

Analyze opponent tactics and weaknesses
Real-time strategic substitution suggestions
Game situation and scenario simulation

Health & Injury Monitoring

Models analyze workload, fatigue, and biometric data to predict injury risk. Data from physical AI wearables and embedded AI sensors enables continuous monitoring, and accurate labeling increases the reliability of prevention and rehabilitation recommendations for medical staff.

Predict injury risk
Analyze athlete fatigue biometrics
Create individual rehabilitation plans

FAQ

How to ensure the accuracy of annotation, given the high speed of game events and the subjectivity of decisions?

Accuracy can be achieved by involving human experts and using detailed instructions. To minimize subjectivity, it is imperative to measure the inter-annotator agreement (IAA).

What types of data are most difficult to annotate in the sports domain?

The most difficult are multimodal data, in particular, accurate annotation of key points (Pose Annotation) for analyzing the biomechanics of an athlete. It is also difficult to mark sentiment in sports commentary due to slang and emotional context.

Why do we need annotation if we already have statistics?

Statistics only record what happened, while annotation determines the cause. Annotated data is necessary for deep analysis and fine-tuning of LLM to understand the sports context.

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"Delivering Quality and Excellence"

The upside of working with Keymakr is their strategy to annotations. You are given a sample of work to correct before they begin on the big batches. This saves all parties time and...

"Great service, fair price"

Ability to accommodate different and not consistent workflows.
Ability to scale up as well as scale down.
All the data was in the custom format that...

"Awesome Labeling for ML"

I have worked with Keymakr for about 2 years on several segmentation tasks.
They always provide excellent edge alignment, consistency, and speed...