Most football analytics frameworks are built around a single implicit assumption: that value in possession can be meaningfully reduced to how much danger an action creates . Whether expressed as expected goals, expected threat, possession value or packing-based measures, the dominant paradigm evaluates actions primarily by their offensive upside. This paradigm is powerful, but incomplete . Football possessions do not fail because they fail to create danger; they fail because

Why going backwards is often the most progressive decision in football Few actions in football are judged as harshly as the backward pass. It is booed in stadiums, criticised in television analysis, and quietly penalised in most analytical models. When a team plays backwards, the dominant interpretation is almost automatic: they are afraid , they lack ideas , they are wasting time . Modern football analytics, despite its sophistication, has largely inherited this intuition. M

Modern football analysis has largely moved beyond raw possession and shot counts. What truly separates teams today is how their decisions with the ball shape the probability of future danger . This is where Expected Threat (xT)  becomes one of the most powerful lenses available. In this article, we break down a full match (or sample of matches) using net xT added , spatial aggregation, and action-level context to answer a simple but crucial question: Where does this team crea

Modern football analytics has made enormous progress in valuing on-ball actions. Metrics such as xG, xT, possession value, and action-based VAEP-like models have become standard tools to evaluate decision-making when a player interacts directly with the ball. However, football is a continuous, spatial game , and the majority of tactical value is generated away from the ball. Players constantly move to create space, occupy threatening zones, stretch defensive structures, and p

Analytics has made remarkable progress in modelling on-ball actions: expected threat, expected possession value, packing indexes, progression value, field tilt.But football remains a continuous invasion game, and continuous games are rarely decided only by the player in possession. The real structure of a team emerges off the ball, in the geometry, occupation and manipulation of space. This is where a new layer becomes essential. Dynamic Occupation Value (DOV) A spatial metri

In modern football analytics, the prevailing visual language still operates almost entirely in two dimensions: scatterplots, radars, bar charts, percentile grids . These tools are effective, but they impose a hard constraint, only two continuous variables can be truly visualised simultaneously , unless the analyst resorts to colour-or size-encoding. 3D Cube Plot visualization with physical data As datasets become richer (tracking data, event granularity, physicality metrics,

Identifying players to monitor in an upcoming year requires balancing statistical output, role-specific behaviours, and developmental trajectories. The following six profiles highlight young players whose performances indicate clear upward trends. While metrics vary across leagues and data providers, the emphasis here is on repeatable actions: progression, chance creation, defensive impact, efficiency and adaptability. Momodou Sonko at K.A.A. Gent 1. Momodou Sonko (20, K.A.A.