As a data scientist from Imperial College London, I analyzed 70+ La Liga matches using xG, defensive pressure indices, and expected goals. In this cycle, underdog teams outperformed expectations—especially in high-stakes away games. Here’s what the numbers revealed: low xG teams with elite defending won key fixtures. This isn’t luck—it’s logic.

As a data scientist with a passion for football analytics, I analyzed Barcelona’s record against the top five teams in La Liga from 2009 to 2018. The results are staggering: 50 wins, 16 draws, and just 6 losses in 72 matches. This isn’t luck—it’s structured dominance. Let’s dive into the numbers behind their elite performance and what it reveals about tactical superiority in modern football. For fans of xG, expected goals models, and real-world data insights, this is pure gold.

As a data scientist with a passion for football analytics, I dive into Tuesday's key matches: Valencia vs Espanyol in La Liga and Manchester City vs Aston Villa in the Premier League. Using Python-driven models and historical data, I'll share my predictions for outcomes, scores, and goal totals. Whether you're a stats geek or just love the beautiful game, these insights might give you an edge.

As a data scientist with a passion for football analytics, I crunch the numbers behind Valladolid and Valencia's desperate fight for survival. Using Python-driven insights, I dissect their defensive frailties, tactical dilemmas, and historical head-to-head data to predict who might escape the drop zone. Spoiler: one team's xG (expected goals) makes for grim reading.