FOR DEVELOPERS
A scorer you can build around.
Bring a data model. Leave with an interface your application can use. Start in Python, run a local workflow, or call the same engine over HTTP.
Bots & applications
Publish rankings or score-backed responses in the interface your users already use. Keep integration logic in your application.
Dashboards & reports
Expose the profile scores and the signals behind them. Give readers a way to understand the model's interpretation.
Services & pipelines
Reuse an explicit adapter configuration across jobs and consumers. Choose local library calls or a remote Gateway boundary.
One engine.
Choose your way in.
Local tools, the Python DevKit, and the HTTP API all use the StatLine scoring model. Start with the workflow that fits your team.
Compare the three pathsSTART IN PYTHON
from statline import load_dataset, score
rows = load_dataset('EBA_Elevate302/eba_players_gp3_plus.csv')
results = score('eba.players', rows, mode='batch', weights='pri')
ranked = sorted(zip(rows, results),
key=lambda pair: (-pair[1]['pri'], -pair[1]['pri_raw']))
for row, result in ranked[:3]:
print(row['PLAYER'], result['pri'])