Iddaa Analiz Excel May 2026
1. Main Dashboard (Overview)
| Feature | Description | |---------|-------------| | Today’s Matches | Auto-import or manual entry of match fixtures, leagues, and dates. | | Confidence Rating | User rating (1–5 stars) based on analysis. | | Recommended Bet | Suggests MS (1-X-2), Ü/İ (Over/Under), or Çifte Şans (Double Chance). | | Stake Suggestion | Calculates optimal stake based on Kelly Criterion or fixed % of bankroll. | | Win/Loss Tracker | Real-time P&L update. |
3. Key Excel Techniques for Analysis
| Technique | Purpose |
|-----------|---------|
| AVERAGEIFS / SUMIFS | Team form aggregation |
| Poisson Distribution | Goal expectancy modeling |
| Expected Value (EV) calculation | = (Probability * Decimal Odds) - 1 |
| Conditional Formatting | Highlight value bets |
| PivotTables | League table simulation |
| Regression (LINEST) | Predict scorelines |
📥 Useful Excel Templates (Free to Reference)
- Poisson Goal Predictor – [Example link: spreadsheetfootball.com]
- Betting Tracker – [Action Network’s free bankroll tracker]
- ELO Rating System in Excel – ClubELO’s methodology adapted for sheets
3. Statistical Calculators (Auto Formulas)
| Tool | Formula / Logic | |------|----------------| | Poisson Distribution | Predicts match score probabilities using avg goals. | | Elo Rating Update | Dynamic rating based on match results. | | Expected Goals (xG) | Approximated from shots on target (manual input). | | Form Weighted Average | Gives more importance to recent matches. | | Over/Under Probability | Calculates % for >2.5 goals based on historical data. | iddaa analiz excel
3.3 Poisson Distribution Model (Goal Expectation)
This is the holy grail for football Iddaa analysis. To predict the exact score (e.g., 1-0, 2-1), you need average goals.
- Step A: Calculate average home goals per game for Team A.
- Step B: Calculate average away goals conceded for Team B.
- Step C: Calculate attack/defense strength.
- Step D: Use the
=POISSON.DIST(goals, expected_goals, FALSE)formula to get the probability of each scoreline.
Example: If the model says Home win probability is 55% and odds are 2.20 (implied probability 45.5%), you have found Value. Validate required fields on import
❌ Error 1: Overfitting Data
You find a pattern that "Team X never loses on a rainy Tuesday in February." This is statistical noise. Excel allows you to slice data too thinly. Fix: Use a minimum sample size of 20-30 matches before drawing conclusions.
Validation & error handling
- Validate required fields on import; report missing rows
- Normalize team names via fuzzy-match suggestions with a review table
- Date parsing with multiple locale formats and timezone option
5. Value Bet Strategy
The ultimate goal of the Excel sheet is to find "Value." you need average goals.
Scenario:
- Your Excel model calculates that Team A has a 50% chance of winning.
- The Iddaa odds for Team A are 2.20.
- Bookmaker Probability = $1 / 2.20 = 45.4%$.
Conclusion: Your model (50%) rates the team higher than the bookmaker (45.4%). This is a Value Bet. If your model is accurate, betting on this discrepancy repeatedly will yield profit over time.