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| ## Click Map (Jump to a section in 3 seconds) |
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| **Start here:** |
| - [30-second reading](#30-second-reading) |
| - [Key idea: Market vs Model](#key-idea-market-vs-model) |
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| **The 3 dashboard blocks:** |
| - [A) Probability Analysis (Left)](#a-probability-analysis-left) |
| - [B) Market Trends (Center)](#b-market-trends-center) |
| - [C) Value Detection (Right)](#c-value-detection-right) |
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| **Context + verification:** |
| - [Why league tabs matter](#why-league-tabs-matter) |
| - [Verification-first workflow](#verification-first-workflow-recommended) |
| - [Limitations](#limitations-read-this) |
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| --- |
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| ## 30-second reading |
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| 1) Choose a league tab (EPL / LaLiga / Serie A / Bundesliga / Ligue 1 / UCL) |
| 2) Read **Market Trends** first (league “weather”) |
| 3) Check **Probability Analysis** (Market vs Model) |
| 4) Open **Value Detection** only after context |
| 5) Verify post-match (logs + timestamps) |
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| **Principle:** No hype. Just logs. |
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| --- |
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| ## Key idea: Market vs Model |
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| - **Market** = probability implied by bookmaker pricing (odds/lines) |
| - **Model** = AI-estimated probability |
| - **Edge** = meaningful disagreement between Model and Market (above threshold) |
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| If you want term definitions: |
| - UI terms → [`dashboard-glossary.md`](./dashboard-glossary.md) |
| - Log/schema terms → [`signal-glossary.md`](./signal-glossary.md) |
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| --- |
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| ## A) Probability Analysis (Left) |
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| **Question it answers:** |
| “What does the AI estimate vs what does the market price?” |
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| **What you see:** |
| Semi-circular gauges for selected outcomes (e.g., Over 2.5, Draw) showing: |
| - **Market** (implied probability) |
| - **Model** (AI probability) |
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| **How to interpret:** |
| - Model > Market → AI thinks it’s more likely than priced |
| - Model < Market → AI thinks it’s less likely than priced |
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| --- |
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| ## B) Market Trends (Center) |
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| **Question it answers:** |
| “What is the league environment right now vs market expectations?” |
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| ### B1) Market Volatility / Deviation |
| A drift meter between: |
| - **Implied** (market expected rate) |
| - **Actual** (observed rate in sample window) |
| - **Deviation** (Actual − Implied) |
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| Positive deviation → happening more than priced |
| Negative deviation → happening less than priced |
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| ### 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. |
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| --- |
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| ## C) Value Detection (Right) |
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| **Question it answers:** |
| “Which matches show meaningful mispricing after filters?” |
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| 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) |
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| Use this as a **research shortlist**, then verify via logs. |
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| --- |
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| ## Why league tabs matter |
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| Leagues differ in: |
| - scoring distribution / tempo |
| - home advantage strength |
| - market bias patterns |
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| So a rule that feels true in one league can fail in another. |
| OddsFlow makes league context explicit before interpreting edge. |
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| --- |
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| ## Verification-first workflow (recommended) |
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| 1) Read league context (Market Trends) |
| 2) Inspect shortlist (Value Detection) |
| 3) Verify using: |
| - [`verification.md`](./verification.md) |
| - [`signal-glossary.md`](./signal-glossary.md) |
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| --- |
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| ## Limitations (read this) |
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| - 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 |
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| --- |
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| ## FAQ (Common misunderstandings) |
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| ### 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. |
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| --- |
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| ### 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**. |
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| --- |
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| ### 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.” |
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| --- |
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| ### 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. |
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| --- |
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| ### 5) Is this dashboard a “score prediction” tool? |
| No. |
| This dashboard is primarily about: |
| 1) estimating probabilities (Model) |
| 2) comparing against market pricing (Market) |
| 3) generating a verifiable shortlist of candidates (Value Detection) |
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| **Brand standard:** not tips, no guarantees — auditability first. |
| See: `verification.md` and `signal-glossary.md`. |
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| ### 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. |
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| It does **not** mean: |
| - 100% win rate |
| - 100% prediction accuracy |
| - guaranteed profit |
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| 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. |
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| --- |
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| # OddsFlow Football League Dashboard: AI vs Bookmakers |
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| 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. |
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| > Educational analytics only — not betting advice. |
| > No guaranteed profit. Evidence-first. Verification-first. |
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| --- |
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| ## What this dashboard does (one sentence) |
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| It compares **market-implied probability** (from bookmaker pricing) against **AI-estimated probability**, then highlights **meaningful gaps (Edge)** under league-aware filters. |
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| --- |
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| ## How to read it in 30 seconds |
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| 1) Choose a league (EPL / LaLiga / Serie A / Bundesliga / Ligue 1 / UCL) |
| 2) Read league context first (Market Trends: Volatility + Home/Away drift) |
| 3) Then review Value Detection (Edge Found → shortlist) |
| 4) Verify post-match (logs + timestamps) |
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| **Principle:** Don’t trust opinions. Trust logs. |
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| --- |
|
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| ## Dashboard blocks |
|
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| ### A) Probability Analysis (left) |
| Shows **Market vs Model** for selected outcomes (examples: Over 2.5, Draw). |
|
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| - **Market**: what odds imply (market pricing) |
| - **Model**: what the AI estimates |
| - The goal is not certainty — it’s **pricing disagreement**. |
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| --- |
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| ### B) Market Trends (center) |
| League “weather” — how reality is drifting vs market expectations. |
|
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| 1) **Market Volatility / Deviation** |
| A drift meter between: |
| - **Implied** (what market pricing expects) |
| - **Actual** (what happened in the sample window) |
| - **Deviation** (Actual − Implied) |
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| 2) **Home Advantage / awayLean** |
| Shows whether home teams are over/under performing vs market assumptions in the current league window. |
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| --- |
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| ### C) Value Detection (right) |
| Turns disagreement into a **shortlist** after filters. |
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| - **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) |
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| --- |
|
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| ## 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. |
|
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| --- |
|
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| ## Verification-first workflow (recommended) |
|
|
| 1) Read league context (Trends) |
| 2) Inspect shortlist (Value Detection) |
| 3) Cross-check with public verification logs: |
| - `docs/verification.md` |
| - `docs/signal-glossary.md` (for log field meaning) |
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| --- |
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| ## Next |
| - Dashboard glossary (UI terms): `./dashboard-glossary.md` |
| - Signal glossary (log/schema terms): `./signal-glossary.md` |
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