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How predictive models are built for betting?

By Serge Gorelikov | Published: April 13, 2026, 15:40

Modern betting has moved beyond “gut feeling” or intuition a long time ago. At its core are mathematical models that help identify undervalued probabilities and, as a result, gain an edge over bookmakers. To understand how this works, it’s important to break down the principles behind predictive modeling.

Predictive models for betting

What is a predictive model?

A predictive model is a mathematical algorithm that estimates the probability of an event outcome. Instead of saying “PSG is stronger than Marseille”, a model provides concrete probabilities, for example:

  • PSG win - 62%
  • Draw - 23%
  • Marseille win - 15%

The key task is to compare your estimated probabilities with the bookmaker’s odds. If your model assigns a higher probability than what is implied by the odds, you’ve found a value bet.

Main types of models

There are several approaches to modeling in sports betting:

1. Statistical models

This is the foundational level. These models rely on historical data, such as:

  • Match results
  • Number of goals
  • Team form
  • Head-to-head records

The most well-known example is the Poisson model, which is commonly used to predict the number of goals in football matches.

2. Rating models

In these models, teams are assigned strength ratings. The most popular system is Elo. The principle is simple:

  • A team gains or loses rating points after each match
  • The rating difference is converted into outcome probabilities

The advantage of these models is their simplicity and stability.

3. Machine learning models

This is a more advanced approach, as dozens or even hundreds of factors are used. These models are trained on large datasets with hidden patterns and relationships identified automatically. Examples of features:

  • Expected goals (xG)
  • Possession percentage
  • Shots on target
  • Fixture congestion
  • Player injuries

Stages of building a model

1. Data collection

This is the foundation. Without high-quality data, a model is useless. Sources include:

  • Statistical websites
  • Sports data APIs
  • Proprietary databases

It’s very important for data to be clean and up to date.

2. Data processing

At this stage, all errors are removed, metrics normalised, and new variables (features) created. The examples would be:

  • Team form over the last 5 matches
  • Average goals scored at home vs. away

3. Model selection

The choice depends on your level:

  • Beginner → simple statistical models
  • Intermediate → Elo + additional factors
  • Advanced → machine learning

4. Training and testing

The model is evaluated on historical data:

  • Train/test split
  • Accuracy assessment
  • ROI evaluation

Remember that high accuracy does not necessarily mean profitability.

5. Validation and adaptation

A good model must:

  • Adapt to changes
  • Incorporate new data
  • Be updated regularly

Football evolves, and your model must evolve with it.

Value as the main goal

The most common mistake is trying to “predict outcomes”. The correct goal is to identify overpriced or underpriced odds.

Example:

  • Your model gives a 60% probability
  • The bookmaker offers odds of 2.20 (≈45% implied probability)

This discrepancy represents value.

Limitations of models

It’s important to understand that a model is a tool, not a guarantee:

  • Data may be incomplete
  • Unexpected events (red cards, injuries) are hard to predict
  • The market reacts quickly to new information

Additionally, bookmakers themselves use highly sophisticated models.

In practice, successful bettors combine multiple models, add subjective analysis and specialise in a specific league. Most importantly, they focus on long-term performance rather than single bets.

In conclusion

Predictive models are the foundation of professional betting. They enable a shift from guesswork to systematic analysis based on probabilities. However, models alone do not guarantee profit.

The key to success lies in combining:

  • High-quality data
  • Proper interpretation
  • Discipline
  • A consistent focus on value

It is this exact combination that creates your real edge over bookmakers.

Serge Gorelikov is a pro bettor who writes for MightyTips weekly. To follow Serge's latest predictions and betting tips, join our free Telegram channel.

Serge Gorelikov

Serge Gorelikov

Serge Gorelikov anonymous user

Serge Gorelikov

Review Author

As a child, I couldn't find my sport for a long time. It all changed when I started watching the 1998 FIFA World Cup in France, and football has been my passion since. I played football myself, and also worked as a referee on an amateur level. I love to travel with my family and spend my free time with friends.