ClarusC64/football-latent-cross-coupling-defensive-transition-collapse-v0.1

What this repo does

This dataset detects hidden instability in football defensive transition states before visible collapse occurs.

It identifies when recovery delay, transition exposure, and rest-defence fragility are interacting in a way that will produce defensive transition collapse.

Core structure

This dataset models:

• latent instability in defensive transition
• recovery delay under stress
• rest-defence fragility
• cross-coupled collapse risk

Prediction target

Binary:

• 1 → defensive transition collapse likely due to hidden instability plus interacting pressures
• 0 → instability remains contained or below meaningful activation threshold

Target column:

• label_defensive_transition_collapse

Row structure

Each row represents a team defensive transition state.

Columns:

• observable_state
• latent_instability_score
• cross_coupling_intensity
• hidden_state_index
• activation_threshold_distance
• transition_exposure
• recovery_delay
• rest_defence_fragility
• stabilization_buffer

Column meaning

transition_exposure

How vulnerable the team is when possession is lost.

recovery_delay

How slow the team is to regain defensive structure.

rest_defence_fragility

How unstable the team’s protective shape is behind the ball.

key dynamic

Collapse occurs when:

• transition exposure is high
• recovery is delayed
• rest defence is fragile
• stabilization buffer cannot compensate

Label logic

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 transition_exposure >= 0.70 AND recovery_delay >= 0.68 AND rest_defence_fragility >= 0.70 AND transition_exposure > stabilization_buffer else 0

Files

• data/train.csv
• data/tester.csv
• scorer.py
• README.md

Evaluation

Primary metric:

• missed_latent_activation_rate

Secondary metric:

• false_activation_rate

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 defensive transition collapse after it has already produced visible breakaways, overloads, or concession sequences.

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

That makes it useful for:

live transition-risk monitoring

rest-defence review

tactical vulnerability detection

match-state collapse prevention

team recovery structure analysis

License

MIT

Structural Note

This dataset belongs to the Clarus latent detection layer.

It is designed to detect instability during formation, before overt defensive transition collapse becomes active in match behavior.

Production Deployment

Applicable to:

elite football clubs

performance analysts

tactical AI systems

broadcast analytics

sports research workflows

Enterprise and Research Collaboration

Suitable for:

clubs

sports analytics companies

event-data providers

coaching staffs

performance labs


Label check

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