ClarusC64/football-latent-cross-coupling-team-structure-collapse-v0.1

What this repo does

This repository introduces a Clarus dataset for detecting latent instability under cross-coupled conditions in football team structure.

The goal is to identify match states in which a team may still appear organized or only mildly unstable but already contains hidden internal degradation that may activate into overt structural collapse once interacting pressures exceed containment.

Core structure

This dataset models a pre-failure geometry built from:

• —latent instability
• —cross-coupling intensity
• —hidden state accumulation
• —activation threshold distance
• —susceptibility and amplification dynamics

Prediction target

The target is binary:

• —1 means hidden instability plus interacting pressures are sufficient to make team structure collapse likely
• —0 means latent instability remains contained or below meaningful activation threshold

Target column used in this repo:

• —label_team_structure_collapse

Row structure

Each row represents a football team state described by:

• —observable state
• —latent instability score
• —cross-coupling intensity
• —hidden state index
• —activation threshold distance
• —time under exposure
• —susceptibility factor
• —amplification pressure
• —stabilization buffer

Column meanings

observable_state

What the team shape appears to show at surface level.

Examples:

• —stable
• —mild-anomaly
• —no-visible-failure
• —surface-normal

latentinstabilityscore

How much hidden structural instability exists beneath visible organization.

Range:

0.00 to 1.00

crosscouplingintensity

Strength of interaction between destabilizing variables such as spacing distortion, fatigue accumulation, role confusion, pressure resistance failure, transition imbalance, and opponent overload creation.

Range:

0.00 to 1.00

hiddenstateindex

Composite measure of concealed team-structure degradation or unseen tactical vulnerability.

Range:

0.00 to 1.00

activationthresholddistance

Distance from hidden instability becoming overt team structure collapse.

Lower means closer to activation.

Range:

0.00 to 1.00

timeunderexposure

Normalized duration score for how long destabilizing conditions have been present.

Range:

0.00 to 1.00

susceptibility_factor

How vulnerable the team structure is to hidden degradation.

Examples include compactness fragility, poor rotation support, weak rest defence, unbalanced midfield distances, and low recovery capacity.

Range:

0.00 to 1.00

amplification_pressure

External or internal force increasing the chance that hidden instability will activate.

Examples include sustained pressing, repeated transition stress, opponent positional overloads, late-game fatigue, and defensive disconnection.

Range:

0.00 to 1.00

stabilization_buffer

Capacity resisting activation.

Examples include tactical compactness, communication quality, recovery pace, role clarity, midfield control, and structural discipline.

Range:

0.00 to 1.00

Default label logic

Standard rule used for this dataset family:

label = 1 if latent_instability_score >= 0.60 AND cross_coupling_intensity >= 0.60 AND hidden_state_index >= 0.60 AND activation_threshold_distance <= 0.35 AND amplification_pressure > stabilization_buffer else 0

Files

• —data/train.csv — labeled examples
• —data/tester.csv — unlabeled evaluation examples
• —scorer.py — production scorer
• —README.md — dataset card

Evaluation

Primary metric:

• —missedlatentactivation_rate

Secondary metric:

• —falseactivationrate

Additional reported metrics:

• —accuracy
• —precision
• —recall
• —f1

The scorer expects binary predictions only.

No score threshold is applied.

The scorer is deterministic and includes audit metadata:

• —scorer version
• —scorer id
• —UTC evaluation timestamp
• —SHA-256 hash of reference file
• —SHA-256 hash of predictions file

Example scorer call

python scorer.py reference.csv predictions.csv

Where:

reference.csv contains a label_... target column

predictions.csv contains one of: prediction, pred, label, or output

Why this matters

Most football analysis detects structural collapse after it has already become visible in transition breaks, defensive gaps, or concession sequences.

This dataset class targets hidden instability before overt team collapse becomes active in match dynamics.

That makes it useful for:

tactical monitoring

pressing-risk assessment

transition-vulnerability detection

late-game structure management

team-shape stability analysis

License

MIT

Structural Note

This dataset belongs to the Clarus family of stability benchmarks.

It is designed to measure whether a football team that appears structurally stable is already internally unstable due to hidden degradation and interacting variable pressure.

This places it in a pre-failure layer of the Clarus architecture, concerned with concealed activation pressure before overt instability becomes active in match behavior.

Production Deployment

This benchmark can support systems that monitor hidden tactical risk before obvious structural collapse appears.

Use cases include live match analysis, coaching review, opposition exploitation mapping, tactical rehearsal, and concealed instability detection in elite team systems.

Enterprise and Research Collaboration

This dataset class is suitable for adaptation across football clubs, performance analysis teams, coaching staffs, sports research groups, event-data modeling, and tactical intelligence workflows.


Label check

- rows 1, 2, 5, 7, and 10 satisfy the default positive rule
- row 9 stays negative because `cross_coupling_intensity` is below `0.60`