ClarusC64/football-latent-cross-coupling-midfield-control-breakdown-v0.1

What this repo does

This dataset detects hidden instability in football midfield control before visible breakdown occurs.

It identifies when spacing distortion, press-resistance erosion, and second-ball loss pressure are interacting in a way that will produce midfield control breakdown.

Core structure

This dataset models:

• —latent instability in midfield control
• —spacing distortion under pressure
• —press-resistance erosion
• —cross-coupled control breakdown risk

Prediction target

Binary:

• —1 → midfield control breakdown likely due to hidden instability plus interacting pressures
• —0 → instability remains contained or below meaningful activation threshold

Target column:

• —label_midfield_control_breakdown

Row structure

Each row represents a team midfield state.

Columns:

• —observable_state
• —latentinstabilityscore
• —crosscouplingintensity
• —hiddenstateindex
• —activationthresholddistance
• —midfieldspacingdistortion
• —pressresistanceerosion
• —secondballloss_pressure
• —stabilization_buffer

Column meaning

midfieldspacingdistortion

How unstable midfield distances and passing lanes have become.

pressresistanceerosion

How much the team’s ability to receive, turn, and play through pressure has degraded.

secondballloss_pressure

How strongly repeated loose-ball losses are increasing control instability.

key dynamic

Breakdown occurs when:

• —midfield spacing begins to distort
• —press resistance erodes
• —second-ball losses increase pressure
• —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 midfield_spacing_distortion >= 0.68 AND press_resistance_erosion >= 0.68 AND second_ball_loss_pressure >= 0.70 AND second_ball_loss_pressure > stabilization_buffer else 0

Files

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

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 midfield breakdown after it has already become visible through loss of control, repeated turnovers, or territorial collapse.

This dataset class targets hidden instability before overt midfield control breakdown becomes active in match dynamics.

That makes it useful for:

live midfield stability monitoring

control-loss risk assessment

press-resistance analysis

second-ball pressure detection

tactical collapse prevention

License

MIT

Structural Note

This dataset belongs to the Clarus latent detection layer.

It is designed to detect instability during formation, before overt midfield control breakdown 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`