Momentum Shift Detector
Momentum Shift Detector menganalisis kejadian pertandingan real-time untuk mengidentifikasi perubahan momentum yang signifikan. Ia menggunakan pendekatan sliding window atas data kejadian — tembakan, perubahan penguasaan bola, intensitas pressing, dan kontrol teritorial — untuk mendeteksi titik infleksi. Ketika perubahan momentum terdeteksi, agen menghasilkan sinyal dengan penilaian keyakinan berdasarkan magnitudo dan konsistensi perubahan. Backtesting historis menunjukkan perubahan ini berkorelasi dengan perubahan probabilitas gol dalam 15 menit berikutnya.
Logika & Dokumentasi Agen
Core Logic
Data Sources - Live event stream (goals, shots, fouls, corners, possession) - xG model output (rolling 10-minute windows) - Pressing intensity metrics - Territorial control zones
Algorithm 1. Calculate rolling event density per 5-minute window 2. Apply change-point detection (CUSUM algorithm) 3. Cross-reference with xG flow differential 4. Generate momentum score: -1.0 (away dominant) to +1.0 (home dominant) 5. Signal emitted when score changes by > 0.3 within 10 minutes
Confidence Scoring - Base confidence from change-point p-value - Boosted by xG alignment (+0.1 if xG flow confirms) - Reduced by low event density (-0.1 if < 5 events in window)
Known Limitations - Less reliable in low-event matches (0-0 tactical battles) - Early match signals (0-15 min) have lower accuracy - Weather conditions not yet factored
Umpan Balik Komunitas
3Really clean implementation of CUSUM for sports data. Have you considered adding a Bayesian changepoint detection as an alternative? Might handle the low-event problem better.
Been using this in my pipeline for 3 weeks. The xG alignment boost is a nice touch — catches a lot of false positives.
Great agent. We've noticed it pairs particularly well with the Set-Piece Agent for corner kick momentum cascades. Worth exploring.
Punya umpan balik untuk agen ini? Bergabunglah dengan komunitas pengembang.
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