Autonomous agents for sports analytics
Abstract — We present a scalable, multi-agent architecture for the autonomous orchestration of feature synthesis and machine learning experimentation. By framing the ML lifecycle as a competitive evolutionary simulation, our framework systematically resolves heterogeneous sports data into high-fidelity predictive intelligence.
Evolutionary ML Framework
Our proprietary optimization engine treats AI agents as competing civilizations. Through a continuous cycle of Execution, Evaluation, Selection, and Reproduction, our agents evolve winning model strategies and features without human intervention.
Execution
Parallel agent civilizations explore distinct segments of the feature space, generating novel hypotheses and code mutations simultaneously.
Evaluation
Automated multi-perspective judges evaluate performance. We assess for innovation, reliability, and edge-case resilience.
Selection & Mutate
Top-performing civilizations survive and breed. Their winning traits cross-pollinate to spawn the next generation of predictive models.
End-to-end autonomous sports analytics
Agentic AI replaces the manual hypothesis-experiment-analysis cycle. Our agents discover features, design experiments, and evaluate models across multiple prediction targets.
Autonomous Feature Engineering
AI agents generate predictive features from multi-source sports data autonomously. A novel dual lag schema captures both weekly and per-game temporal patterns, scaling feature spaces far beyond what manual engineering achieves.
Automated ML Experimentation
End-to-end pipeline that generates hypotheses, designs experiments, tunes hyperparameters via Bayesian optimization, and evaluates results across multiple prediction targets and player positions — no human in the loop.
Autonomous Semantic Unification
A multi-layered pipeline ingests, validates, and aligns heterogeneous data sources utilizing zero-shot LLM entity resolution, achieving high-fidelity cross-source mappings without rigid deterministic rules.
From raw data to predictions
A layered pipeline where each stage transforms, validates, and enriches — fully automated, end to end.
Unstructured Ingestion
Raw heterogeneous sports data is ingested and subjected to autonomous schema inference.
Semantic Resolution
Cross-domain entities are unified via zero-shot probabilistic mapping, bypassing legacy ETL logic.
Autonomous Feature Synthesis
Agent swarms traverse high-dimensional spaces to synthesize and validate non-linear temporal features.
Agentic Experimentation
Decentralized execution of hypothesis generation, Bayesian tuning, and multi-metric model evaluation.
Predictive Biometrics & Markets
Longitudinal inference modeling biomechanical fatigue, performance ceilings, and dynamic risk profiles.
Active research areas
We work on genuinely unsolved problems at the intersection of autonomous AI and complex data environments. Overcoming these technological bottlenecks requires systematic investigation, where failed hypotheses are a necessary byproduct of pushing the boundaries of applied machine learning.
- Uncertainty 01Autonomous Feature Synthesis in High-Dimensional SpacesCan multi-agent systems autonomously generate, test, and validate non-linear feature interactions without relying on human domain-knowledge priors? We are exploring the mathematical ceiling of automated feature discovery and cross-season generalization without overfitting.
- Uncertainty 02Agentic Orchestration of the ML LifecycleCan a decentralized swarm of AI agents reliably replace the human hypothesis-experiment-evaluation loop? We are engineering frameworks where autonomous agents manage computational budgets, avoid local optima, and orchestrate complex ML pipelines end-to-end.
- Uncertainty 03Semantic Entity Resolution via LLMsCan heterogeneous, unstructured temporal data be automatically unified with ML-grade fidelity? Standard deterministic logic fails across diverse sports data sources. We are researching autonomous schema alignment and zero-shot entity resolution to solve this at scale.
- Uncertainty 04Evolutionary AI Code MutagenesisCan AI systems safely self-optimize their own underlying architecture? We are building an evolutionary framework where AI "civilizations" mutate codebases, compete, and improve through automated, multi-perspective judged evaluation without human intervention.
- Uncertainty 05Non-Standard Target Prediction via Multi-Task LearningCan deep learning models accurately map highly complex, non-differentiable human targets? We are utilizing quantile regression and temporal graphs to predict longitudinal outcomes—such as biomechanical fatigue thresholds, performance ceilings, and recovery optimization.
- Uncertainty 06Compute-Constrained Agentic SwarmsAs autonomous agents scale, can inference costs be dynamically optimized without degrading reasoning quality? We are actively researching dynamic routing—directing routine pipeline tasks to specialized local models while reserving heavy reasoning models strictly for complex architectural decisions.
Built for the sports industry
The sports analytics market is projected to reach $12B by 2028. We license our autonomous agent architecture to elite organizations, providing Tier 1 predictive modeling capability without the infrastructure overhead of a dedicated AI R&D team.
Professional Teams & Front Offices
Player evaluation, draft scouting, salary cap management, and in-game strategy for NHL, CFL, MLS, and other North American leagues. Most teams below the top tier lack dedicated data science staff.
Player Agencies & Talent Management
Data-driven player valuations for contract negotiations, career development planning, and identifying undervalued talent across leagues and positions.
Sports Media & Analytics Firms
Predictive analytics for broadcasts, content platforms, and data companies. Companies like Sportradar and Stats Perform seek better models and feature pipelines to enrich their products.
Biometrics & Human Performance
Applying evolutionary algorithms to player tracking and biometric data. Predicting fatigue thresholds, injury risk profiles, and recovery optimization for elite athletes.
Two technical founders
Both under 35. Complementary expertise in multi-agent architectures, semantic resolution, and evolutionary computation.
Olivier
Architect of the autonomous semantic resolution systems and agentic ML experiment frameworks. Designed the feature engineering schema and pioneered the agentic AI methodology for autonomous experimentation.
Garry
Built the AI development infrastructure — agent workflows, swarm delegation, ML auditing, observability. Designed the evolutionary AI framework where agents compete, mutate, and evolve code.
Accessibility-first, not accessibility-later
True innovation leaves no one behind. Legacy analytics platforms are notoriously hostile environments—relying on dense, mouse-only interfaces that lock out brilliant minds. We are building the first sports AI platform engineered with Universal Design principles from day one.
Driven by a founder's close friend living with Multiple Sclerosis (MS), and fiercely aligned with the Accessible Canada Act's mandate for a barrier-free digital economy, our grand mission is to democratize high-performance data science. We believe elite predictive intelligence must be accessible to everyone, regardless of physical or cognitive barriers.
Enterprise Compliance
Rigorous adherence to WCAG 2.1 AA standards ensures our complex data visualization pipelines and ML dashboards are fully parseable by modern assistive technologies.
Motor Independence
Full keyboard navigation, voice control, and predictive switch-access means zero mandatory mouse-clicks.
Vision-First
Native screen-reader optimization and fluidly scalable UI elements ensure massive tabular datasets remain accessible.
Cognitive Clarity
Agentic AI shouldn't be overwhelming. We utilize progressive disclosure and strict information hierarchy to drastically reduce cognitive fatigue during complex analysis. This ensures that critical decisions can be made without navigating through walls of hexadecimal noise.
Organizations seeking enterprise licensing, research collaborations, or deep-tech investment opportunities. Our principal investigators are actively evaluating strategic alignments.