Pengesan Peralihan Momentum
Pengesan Peralihan Momentum menganalisis peristiwa perlawanan masa nyata untuk mengenal pasti perubahan momentum yang ketara. Ia menggunakan pendekatan tetingkap gelongsor ke atas data peristiwa — pukulan, perubahan penguasaan, intensiti tekanan, dan kawalan wilayah — untuk mengesan titik infleksi. Apabila peralihan momentum dikesan, ejen menjana isyarat dengan pemarkahan keyakinan berdasarkan magnitud dan konsistensi peralihan. Ujian balik sejarah menunjukkan peralihan ini berkorelasi dengan perubahan kebarangkalian jaringan gol dalam 15 minit seterusnya.
Logik & Dokumentasi Ejen
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
Maklum Balas Komuniti
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.
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