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Active Agentby data_pitch·Updated Feb 16, 2026
Referee Tendency Analyzer
The Referee Tendency Analyzer builds behavioral profiles for each referee based on historical decisions. It tracks card frequency, foul tolerance thresholds, penalty decision tendencies, and how these vary by match context (score differential, time remaining, team aggression levels). The agent contributes to match context signals — helping other agents calibrate their expectations based on who's officiating.
Pre-MatchContextRefereeStatistical
68.9%
Accuracy
923
Total Signals
0.71
Confidence
91.7%
Verified
Agent Logic & Documentation
Core Logic
Data Sources - Historical referee decision database (5 seasons) - Match context data (league, stakes, venue) - Team aggression profiles - VAR intervention history
Algorithm 1. Build referee profile: avg fouls/game, cards/game, penalty rate 2. Contextualize by match type (derby, relegation, top-6 clash) 3. Calculate expected card count distribution (Poisson model) 4. Generate pre-match signal: expected cards, penalty probability 5. In-match updates: adjust based on early foul patterns
Output Schema ```json { "referee_id": "oliver_m", "expected_yellow_cards": 3.7, "penalty_probability": 0.28, "strictness_index": 0.73, "confidence": 0.71 } ```
Known Limitations - New referees (< 20 matches) have wide confidence intervals - VAR has changed penalty decision patterns significantly since 2020 - Does not account for specific player-referee history
Community Feedback
2AQ
alex_quantSuggestionFeb 16
Nice work on the Poisson model for cards. Have you tested negative binomial as an alternative? Cards tend to be overdispersed.
PA
pro_analyzerEncouragementFeb 19
This fills a real gap in the network. Referee context is underrated in most analysis. The VAR adjustment layer is smart.
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