START HERE Flagship guide

The Ultimate Guide to Padel Betting

Everything you need to understand padel betting.

Built from 100+ InfoBets padel guides • Free • Continuously updated

Start with the Ultimate Guide →

A betting model is the most reliable way to make consistent, data-driven predictions in padel. Instead of guessing outcomes or relying on intuition, a model helps quantify the true probability of each result — letting you instantly identify value.

This guide walks you through a simple, beginner-friendly approach to building your first padel betting model.

---

What Is a Betting Model?

A betting model is a structured system that:

✔ assigns probabilities to outcomes

✔ compares them to bookmaker odds

✔ identifies value bets automatically

Your goal is NOT to be perfect — your goal is to be more accurate than the market in specific situations.

---

Step 1 — Collect the Right Data (Only What Matters)

Padel doesn’t require huge datasets. You only need high-impact statistics.

Core Data Inputs:

1. Recent form (last 10 matches)
2. Star Point performance
3. Net points won %
4. Long rally success %
5. Break-point conversion & saves
6. Unforced error rate
7. Indoor vs outdoor performance
8. Court speed suitability
9. Weather impact (outdoors)
10. Partnership chemistry score

You can track these in a simple spreadsheet.

---

Step 2 — Standardise Each Metric (Score 1–10)

To combine metrics from different sources, convert everything into a 1–10 scale.

Example:

• Star Point win rate 60% → score = 8
• Break-point save % 40% → score = 4

This makes comparisons easy.

---

Step 3 — Weight Each Variable by Importance

Not all stats matter equally.

Here is a recommended weighting system:

Metric	Weight
Form (last 10)	25%
Star Points	20%
Net % won	15%
Long rallies	10%
Break-points	10%
Court speed suitability	10%
Weather suitability	5%
Chemistry score	5%

This produces a composite rating for each team.

---

Step 4 — Calculate Team Strength Score

Use a simple weighted formula:

TEAM SCORE =
(Form × 0.25) +
(Star Points × 0.20) +
(Net % × 0.15) +
(Long rallies × 0.10) +
(Break-points × 0.10) +
(Court speed × 0.10) +
(Weather × 0.05) +
(Chemistry × 0.05)

This gives each team a number between 1–10.

Example:
Team A score = 7.2
Team B score = 5.9

---

Step 5 — Convert Scores Into Win Probabilities

A simple approach:

Probability Team A Wins = Team A Score / (Team A Score + Team B Score)
Probability Team B Wins = Team B Score / (Team A Score + Team B Score)

Example:
Team A: 7.2
Team B: 5.9

Total = 13.1

Team A win probability = 7.2 / 13.1 = 55%
Team B win probability = 5.9 / 13.1 = 45%

---

Step 6 — Compare to Bookmaker Odds (Finding Value)

Bookmaker odds imply probabilities.

Example:
Team A odds = 1.80 → implied probability = 55.5%
Team B odds = 2.00 → implied probability = 50%

Compare bookmaker % to your model %:

• Your model: Team B = 45%
• Bookmaker: Team B = 50% → no value

But if a bookmaker offered:
Team B odds = 2.40 → implied probability 41.6%

Your model gives 45% → value exists.

---

Step 7 — Build a Confidence Score

Not all predictions have equal reliability.

Confidence increases when:

✔ Data on both teams is complete

✔ Court speed matches the model’s prediction

✔ No injuries or fatigue factors

✔ Consistent statistical edges (3+ metrics)

Confidence decreases when:

❌ Weather is unstable

❌ New partnerships (small data sample)

❌ Form is inconsistent

❌ Indoor/outdoor split is dramatic

---

Step 8 — Expand the Model With Additional Markets

Once comfortable, expand to:

✔ Over/Under models

✔ Handicap models

✔ Set betting probabilities

✔ Player-specific stats (e.g., left vs right side)

This brings your model closer to professional standards.

---

Optional: Add Machine Learning (Advanced)

If you want to go deeper:

• Logistic regression
• Random forest classifiers
• XGBoost models

These can predict based on historical data + contextual conditions.

But start simple first.

---

Example of a Simple Model Output

Match:

Team A score: 7.5
Team B score: 6.0

Calculated probability:

Team A win chance = 56%
Team B win chance = 44%

Bookmaker odds:

Team A = 1.65 (60.6%)
Team B = 2.20 (45.4%)

Model says:

• Team A overpriced → no value
• Team B undervalued → small value bet

---

Quick Model Checklist

✔ Did you standardise all stats?

✔ Did you weight metrics correctly?

✔ Did you adjust for court speed?

✔ Did you adjust for weather?

✔ Did you check tactical matchup manually?

✔ Did you compare to bookmaker odds?

If yes → you have a working model.

---

Summary

Building a padel betting model is easier than it sounds. You only need:

• the right stats (Star Points, net %, long rallies, form)
• a simple weighting system
• a probability formula
• comparison to bookmaker odds

A structured model gives you a repeatable, objective way to find value & avoid emotional decisions.