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The 3 dashboard blocks:
Context + verification:
30-second reading
- Choose a league tab (EPL / LaLiga / Serie A / Bundesliga / Ligue 1 / UCL)
- Read Market Trends first (league “weather”)
- Check Probability Analysis (Market vs Model)
- Open Value Detection only after context
- Verify post-match (logs + timestamps)
Principle: No hype. Just logs.
Key idea: Market vs Model
- Market = probability implied by bookmaker pricing (odds/lines)
- Model = AI-estimated probability
- Edge = meaningful disagreement between Model and Market (above threshold)
If you want term definitions:
- UI terms →
dashboard-glossary.md - Log/schema terms →
signal-glossary.md
A) Probability Analysis (Left)
Question it answers:
“What does the AI estimate vs what does the market price?”
What you see:
Semi-circular gauges for selected outcomes (e.g., Over 2.5, Draw) showing:
- Market (implied probability)
- Model (AI probability)
How to interpret:
- Model > Market → AI thinks it’s more likely than priced
- Model < Market → AI thinks it’s less likely than priced
B) Market Trends (Center)
Question it answers:
“What is the league environment right now vs market expectations?”
B1) Market Volatility / Deviation
A drift meter between:
- Implied (market expected rate)
- Actual (observed rate in sample window)
- Deviation (Actual − Implied)
Positive deviation → happening more than priced
Negative deviation → happening less than priced
B2) Home Advantage / awayLean
Shows whether home teams are over/under performing vs market assumptions in the current window. awayLean indicates whether the league is leaning away relative to market assumptions.
C) Value Detection (Right)
Question it answers:
“Which matches show meaningful mispricing after filters?”
Common elements:
- Edge Found = number of candidates where Model vs Market exceeds threshold
- Filtered = candidates remaining after applying filters
- Efficiency = quality indicator for the current shortlist (implementation-specific)
Use this as a research shortlist, then verify via logs.
Why league tabs matter
Leagues differ in:
- scoring distribution / tempo
- home advantage strength
- market bias patterns
So a rule that feels true in one league can fail in another. OddsFlow makes league context explicit before interpreting edge.
Verification-first workflow (recommended)
- Read league context (Market Trends)
- Inspect shortlist (Value Detection)
- Verify using:
Limitations (read this)
- Edge is a pricing disagreement, not certainty
- Markets reprice quickly (snapshots ≠ closing line)
- Sample window size affects drift indicators
- Injuries/rotation/news can change dynamics
FAQ (Common misunderstandings)
1) Does “Edge Found 20” mean 20 guaranteed wins?
No.
Edge Found only means: under the current league view, sample window, and filters, the system detected 20 candidates where Model vs Market disagreement exceeds a threshold.
It is not a profit promise and not “sure wins.”
Correct use: treat it as a research shortlist → check league context → verify via logs and post-match audit.
2) If Model > Market, should I always follow the Model?
Not always.
A Model–Market gap is a mispricing hypothesis, not certainty. Markets can reprice quickly due to injuries, rotation, news, and line movement.
Correct use: look for consistency, confirm it matches the league “weather” (Trends), and validate using closing line / post-match audit.
3) Does Market Volatility mean “more volatility = easier profit”?
No.
On this dashboard, Market Volatility / Deviation is a drift indicator: how much recent outcomes differ from what the market implied.
A larger drift can reflect market adjustment, changing conditions, or sample effects. It does not automatically mean “more profit.”
4) Does awayLean mean “always back the away team”?
No.
awayLean indicates a league-level drift in the current sample window (home outcomes under/over performing market assumptions).
It is context, not a fixed strategy. Team strength, schedule difficulty, tactics, and injuries still dominate single-match reality.
5) Is this dashboard a “score prediction” tool?
No.
This dashboard is primarily about:
- estimating probabilities (Model)
- comparing against market pricing (Market)
- generating a verifiable shortlist of candidates (Value Detection)
Brand standard: not tips, no guarantees — auditability first.
See: verification.md and signal-glossary.md.
6) Does “Efficiency = 100%” mean “accuracy = 100%”?
No.
Efficiency is a dashboard quality indicator for the current filtered shortlist (implementation-specific).
It typically reflects things like filter consistency, data completeness, or rule pass-rate for the candidates shown — not match outcomes.
It does not mean:
- 100% win rate
- 100% prediction accuracy
- guaranteed profit
Correct use: treat Efficiency as “the shortlist is clean under current rules,” then rely on verification logs and post-match audit for actual performance evaluation.
OddsFlow Football League Dashboard: AI vs Bookmakers
This page explains how to read the OddsFlow dashboard shown in our tutorials: Market (Bookmakers) vs Model (AI), plus league-level context and value detection.
Educational analytics only — not betting advice.
No guaranteed profit. Evidence-first. Verification-first.
What this dashboard does (one sentence)
It compares market-implied probability (from bookmaker pricing) against AI-estimated probability, then highlights meaningful gaps (Edge) under league-aware filters.
How to read it in 30 seconds
- Choose a league (EPL / LaLiga / Serie A / Bundesliga / Ligue 1 / UCL)
- Read league context first (Market Trends: Volatility + Home/Away drift)
- Then review Value Detection (Edge Found → shortlist)
- Verify post-match (logs + timestamps)
Principle: Don’t trust opinions. Trust logs.
Dashboard blocks
A) Probability Analysis (left)
Shows Market vs Model for selected outcomes (examples: Over 2.5, Draw).
- Market: what odds imply (market pricing)
- Model: what the AI estimates
- The goal is not certainty — it’s pricing disagreement.
B) Market Trends (center)
League “weather” — how reality is drifting vs market expectations.
- Market Volatility / Deviation
A drift meter between:
- Implied (what market pricing expects)
- Actual (what happened in the sample window)
- Deviation (Actual − Implied)
- Home Advantage / awayLean
Shows whether home teams are over/under performing vs market assumptions in the current league window.
C) Value Detection (right)
Turns disagreement into a shortlist after filters.
- Edge Found: number of candidates where Model vs Market exceeds threshold
- Filtered: remaining candidates after constraints
- Efficiency: a quality indicator for the current shortlist (implementation-specific)
Why league tabs matter
Leagues differ in:
- scoring distribution and tempo
- home advantage strength
- market bias patterns
So “intuition” from one league often fails in another. OddsFlow makes league context explicit before you interpret any edge.
Verification-first workflow (recommended)
- Read league context (Trends)
- Inspect shortlist (Value Detection)
- Cross-check with public verification logs:
docs/verification.mddocs/signal-glossary.md(for log field meaning)
Next
- Dashboard glossary (UI terms):
./dashboard-glossary.md - Signal glossary (log/schema terms):
./signal-glossary.md