Only the opponent is fast

I am experiencing the same problem. My players often feel very slow and sluggish, performing far below their stats and playstyles, while my opponents’ much lower-rated players appear significantly faster, with better reaction time, balance, and overall responsiveness.

This seems to be caused by unbalanced game mechanics. At times, the game appears to assist one player while placing clear disadvantages on the other. Within a single match, this balance can shift several times. In one moment, my players feel fully optimised and responsive, and moments later they become slow, heavy, and unresponsive.

This appears to be an uncalibrated balancing process that is intended to equalise gameplay but instead has the opposite effect. It creates unfair advantages for one player while artificially limiting the other, undermining competitive integrity and skill-based outcomes.

This appears to be a long-standing systemic issue within the FIFA/FC gameplay framework that has persisted across multiple releases. Despite repeated and well-documented community feedback over several years — and in fact decades, given that the first FIFA title was released in 1993 — the underlying balancing and momentum systems remain poorly calibrated and inconsistent in live online environments. This suggests that the current implementation is either fundamentally flawed at an architectural level or lacks sufficient diagnostic, monitoring, and tuning mechanisms to ensure stable and predictable behaviour. As a result, the issue continues to recur without a reliable long-term resolution.

@ developers: 

It is recommended that a full technical audit be conducted on the core gameplay systems, including player attribute scaling, dynamic difficulty adjustment, momentum modelling, and input-response pipelines. This review should include detailed instrumentation and telemetry analysis to identify instability, latency, and desynchronization points in live online environments.

In parallel, the AI architecture should be re-evaluated and refactored to ensure deterministic decision-making, consistent behavioural states, and predictable performance under network variance. Improvements should focus on state management, transition logic, and fail-safe mechanisms to prevent abrupt degradation in AI performance.

Implementing robust diagnostic tooling, automated regression testing, and continuous balance calibration pipelines would help ensure long-term system stability, reduce unintended gameplay variance, and restore competitive integrity.