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Hierarchical Bayesian model with defensive effects for rugby rankings
This dashboard presents results from a hierarchical Bayesian model that captures: player effects (intrinsic ability), team offensive effects (scoring ability), team defensive effects (opponent scoring prevention), and position effects (base rates by jersey number).
Use global controls above to select competition and season
Use global controls above to select competition and season
| Pos | Team | Played | Won | Drawn | Lost | PF | PA | Diff | Tries For | Tries Against | Bonus | Match Pts | Total Pts |
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Use global controls above to select competition and season
| Team | Expected Points | Expected Wins | Expected Diff | Predicted Position |
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Use global controls above to select score type. Select team and metric below.
Use global controls above to select competition and season
Use global controls above to filter by competition, season, score type, and ranking limit
| Rank | Team | Effect | Uncertainty |
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| Rank | Team | Effect | Uncertainty |
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Predictions for upcoming fixtures across all competitions
| Date | Home | Prediction | Away | Win Prob | Competition |
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Tournament knockout stage predictions
No data available.
| Match | Mutual Information |
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| Position | Top Players | Strength | Depth |
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| Rank | Player | Effect | 95% CI |
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| Date | Home | Score | Away | Competition |
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This dashboard visualizes results from a hierarchical Bayesian model for rugby rankings. The model estimates:
The log-linear predictor is:
log(λ) = α + β_player + γ_offense[team] - δ_defense[opponent]
+ θ_position + η_home × is_home + log(minutes/80)Where scoring events follow a Poisson distribution: N_score ~ Poisson(λ)