🏴 Premier League · Regular Season - 7
Brentford vs Liverpool
Oct 17, 02:00 PM · Brentford Community Stadium
✓ Recorded before kickoffNormal PREDICTION
Confidence ThresholdGoalence groups predictions into three tiers by the probability of the picked outcome: Elite (≥ 70%), Safe (55-70%), and Normal (< 55%). Accuracy is reported separately for each tier so users can see how the model performs at its strongest signal.→Brentford Home Win
40%
Predicted score: 2-1
1Outcome Probabilities
Match ResultMatch Result (1X2)The traditional outcome market: Home win (1), Draw (X), or Away win (2). Goalence outputs three probabilities summing to 100% for every fixture.→
Brentford Home Win
40%
Over/Under 2.5Over/Under 2.5A prediction market that resolves Over if the total goals in the match are 3 or more, Under if 2 or fewer. Computed from Goalence's two lambdas: P(home_goals + away_goals ≥ 3).→
Over 2.5 2.5
59%
Both Teams ScoreBTTS — Both Teams To ScoreA prediction market that resolves Yes if both teams score at least one goal in the match, No otherwise. Goalence outputs a BTTS probability for every fixture by integrating the Poisson distribution over scorelines.→
BTTS: Yes
57%
Confidence: Normal.
2Most Likely Scores
3Drama Profile
How both teams have handled pressure across the season.
Historical pattern, informational only.
Pre-match Outlook
Brentford hold a slim edge over Liverpool in this Premier League match — 40% confidence puts it in the Normal tier with a 2-1 projection.
In this Premier League context, small swings in form or selection can flip the projection — treat the narrow-confidence pick as a starting point rather than a lock.
How the Model Works
We use an iterative Pi-Ratings model: each team has separate home and away attack/defense ratings, updated after every match. Lambda values are converted to outcome probabilities via Poisson distribution. Match result, over/under, and BTTS predictions are all derived from a single coherent model.
Brentford vs Liverpool — Brentford Home Win: 40%.
Predictions are produced by statistical models and cannot be guaranteed. Use as reference only.