Quantitative Analytics Platform
Probabilistic models, decision-intelligence systems, and quantitative research across football analytics and financial markets.
Current Work
Evidence-based systems designed for action. Every output includes confidence intervals. Every recommendation includes its conditions.
Football Analytics
Real Oviedo
La Liga Survival
Squad diagnostics powered by xG, npxG, and PPDA metrics. Identifies finishing inefficiencies, defensive vulnerabilities, and positional gaps to guide in-season decisions.
→ LiveFootball Analytics
Global
Scouting Engine
Cross-league player search covering Europe, South America, and beyond. Built to support recruitment decisions by identifying statistically similar profiles under budget constraints.
→ LiveFootball Analytics
Match Decision
Intelligence Engine
Probabilistic match forecasting using a Bayesian Poisson model tuned with walk-forward cross-validation. Outputs win/draw/loss probabilities and expected goals for pre-match preparation.
→Financial Analytics
Quantitative
Finance Models
Factor models, yield curve analysis, and probabilistic portfolio decision support. Applying the same evidence-based rigor to financial markets.
About
Alfonso Hidalgo — quantitative analyst building decision-support systems that translate uncertainty into actionable intelligence. Professional background in forecasting, driver-based modeling, and probabilistic risk analysis at Ebury (multi-currency treasury), now applying the same statistical rigor to build Quixote Analytics.
The methodology is grounded in the same principles used for liquidity forecasting and scenario analysis in finance: probabilistic modeling, uncertainty quantification, and evidence-based decision frameworks. Every output includes confidence intervals. Every recommendation includes its conditions and failure modes.
Currently focused on Segunda División / La Liga with expanding work in quantitative finance — two domains where data-driven thinking consistently outperforms intuition.
Stack: Python · pandas · NumPy · scikit-learn · Plotly · SQL · Git · Docker · Bayesian inference · time-series forecasting
Background: MSc Economics, BSc Physics (University of Oviedo) · 6+ years in finance analytics & treasury operations
Resources
Concise, practical reference guides written to consolidate the core concepts behind the tools used in this work. No fluff — only what matters.
Data Science & Algorithms
Python
Study Guide
Comprehensive reference covering pandas data manipulation, algorithmic thinking with complexity analysis, collections (defaultdict, Counter, deque), and essential data structures. Written for practitioners who work with real data.
Database & Query Design
SQL
Study Guide
Focused reference on analytical SQL: CTEs and recursive queries, all JOIN types with edge cases, window functions (RANK, LAG, LEAD, partitioning), and query optimization strategies used in production analytics workflows.
Contact
Available for analytics consulting, research collaboration, and quantitative roles in football or finance. Always interested in interesting problems.