Football Analytics How to Analyse Football Injuries and Team News A practical framework for assessing player availability, replacement quality, tactical consequences, uncertainty and the betting market’s response.
Football Analytics How Professional Bettors Separate Process from Results Professional bettors judge decisions through probability, price, execution and market evidence rather than allowing individual wins or losses to define the process.
Football Analytics Reverse Engineering the Betting Market A practical framework for extracting implied probabilities, expectations and information from football betting-market prices.
Football Analytics Expected Points in Fantasy Premier League Explained A practical guide to expected FPL points, including current scoring rules, a complete worked player example and the limits of xP projections.
Football Analytics Correlation vs Causation in Football Analytics Correlation shows that two football variables move together. Causation requires evidence that changing one variable produces a change in the other.
Football Analytics Backtesting a Football Betting Model Explained A practical guide to testing a football betting model on historical matches without data leakage, unrealistic prices or misleading performance conclusions.
Football Analytics Expected Minutes Explained: Why Playing Time Matters in Football Analytics and FPL Expected minutes turn uncertain starts, substitutions, injuries and rotation into a realistic playing-time projection for analytics, FPL and recruitment.
Football Analytics How to Identify Value in Fantasy Premier League A reusable framework for identifying FPL value through expected points, minutes, role, replacement quality, captaincy and whole-squad opportunity cost.
Football Analytics Why Possession Percentage Can Be Misleading Possession percentage can describe who had more of the ball without revealing who controlled the dangerous areas. Learn how territory, game state and action value change the interpretation.
Football Analytics Set-Piece Analytics Explained Set-piece analytics measures how teams create and prevent danger from corners, free kicks and throw-ins by separating delivery, first contact, shot quality and outcomes.
Football Analytics How to Build a Simple Football Betting Model Build a simple football betting model using team goal data, Poisson probabilities and fair odds, then compare its estimates with bookmaker prices.
Football Analytics The Most Undervalued FPL Players for 2026/27: A Data-Driven Pre-Season Analysis A data-driven assessment of potentially undervalued Fantasy Premier League players, separating genuine value from cheap prices and last season’s points.
Football Analytics Finishing Overperformance Explained Scoring more goals than expected can indicate finishing skill, favourable variance or weaknesses in the xG model. Learn how analysts distinguish between them.
Football Analytics Sample Size in Football Analytics Explained Learn how sample size affects football analysis, why no single threshold works for every metric, and how to avoid mistaking short-term noise for repeatable performance.
Football Analytics Shot Maps Explained: How to Read Football Shot Data Learn how to interpret football shot maps, compare shot quantity with chance quality and recognise patterns distorted by game state or weak attempts.
Football Analytics Possession Value Explained: How Football Actions Create Attacking Value Possession value estimates how football actions change a team’s attacking and defensive position. Learn how state-value, action-value and xT models work.
Football Analytics Football Betting Syndicates Explained How professional football betting syndicates combine modelling, trading, execution, risk and operations without turning probability into certainty.
Football Analytics Game State in Football Analytics Explained Learn how scores, time, red cards and competition context change football behaviour—and how to correct misleading performance statistics.
Football Analytics Signal vs Noise in Football Data Football data contains both meaningful patterns and random variation. Learn how sample size, context, repeatability and testing help analysts distinguish genuine signals from noise.
Football Analytics The Moneyball Timeline of Football Analytics: From Notebooks to AI Trace the evolution of football analytics from handwritten match records and early computer models to xG, tracking data, data-driven recruitment and artificial intelligence.
Football Analytics How Professional Football Bettors Build Their Own Odds Learn how professional football bettors turn team data, expected goals and contextual evidence into independent probabilities, fair odds and value assessments.
Football Analytics Beyond xG: What Betting Syndicates Measure Next Expected goals is only one layer of football modelling. Explore the additional signals sophisticated betting syndicates may use to evaluate teams, players and prices.
Football Analytics Field Tilt Explained: How to Measure Territorial Dominance in Football Field tilt measures each team's share of final-third activity. Learn how to calculate it, interpret it and avoid mistaking territory for attacking quality.
Football Analytics PPDA Explained: How Football’s Pressing Metric Works A practical guide to calculating and interpreting PPDA, including pressing intensity, provider differences, game state, opponent style and tactical context.
Football Analytics Expected Threat (xT) Explained: How Football Teams Create Danger Expected Threat values how passes and carries move possession into more dangerous areas. Learn how xT grids work, with examples and limitations.
