Pre-Match Predictions

Key forecasting methods

To improve accuracy, structured models are used to process historical data and predict likely outcomes. Here are the main techniques:

📊 GAP ratings (Generalised Attacking Performance)

– Predicts stats like shots or corners based on recent team performance.
-Uses weighted factors like λ (for recent form) and φ₁ and φ₂ (to adjust for home and away effects)
– Ignores early and late-season matches, since results are more unpredictable then.
– Based on research published in Sage Journals.

📈 Bivariate Poisson model

– Predicts goals for both teams while accounting for the relationship between them.
– Performed well on the Open International Soccer Database, RPS (Ranked Probability Score): 0.2103 and Accuracy: 48.54%
– Shared in research via arXiv.

🤖 Machine learning models

– Techniques like Logistic Regression and Gradient Boosting Machines (GBMs) find patterns in complex data.
– In one study using CatBoost, the model reached 70% accuracy for matches with predictable outcomes and outperformed traditional betting odds

These models use real data to improve prediction accuracy. For developers, analysts, and betting platforms, they offer powerful, data-driven tools for forecasting football outcomes.

Emerging AI & Machine learning models

New AI and machine learning methods are helping improve football match predictions by analysing complex data like lineups, player form, and recent trends.

Deep learning models

Advanced model types like CNN-BiLSTM with attention are showing strong results, especially in analysing time-based match data.

– CNNs (Convolutional Neural Networks) spot spatial patterns.
– BiLSTMs (Bidirectional Long Short-Term Memory) capture how things change over time.
– Attention layers help the model focus on key factors like crucial players or match momentum.

A similar model called AS-LSTM (Attention-based Short-term Long-term Memory) was used to predict matches during the 2018 FIFA World Cup; it reached 63.3% accuracy, according to a study in ScienceDirect.

Natural Language Processing (NLP)

NLP models read and analyse match previews, news articles and social media posts

They blend this text-based data with numbers (like stats), offering insights on things like injuries, team morale, and lineup changes that aren’t in traditional stats.

Using odds to boost football predictions

Betting odds offer valuable insights because they reflect what both bookmakers and bettors expect to happen. When used smartly, odds can help improve prediction accuracy and identify profitable opportunities.

Key strategies for using odds

1. Odds comparison tools

Platforms like OddsPortal gather odds from 80+ bookmakers. By comparing them, you can spot differences, pick the best possible odds for your prediction and improve your potential return

2. Value betting

This means finding bets where the odds are higher than the actual chance of the outcome. Over time, this can lead to better profits.

– Example tools: Oddschecker
– Goal: Find undervalued outcomes based on your own data or models

3. Monitoring odds drift

Watching how odds change over time can reveal:

– Shifts in market opinion
– Clues about player injuries or team news
– In-play betting opportunities

This is especially useful just before kick-off or during live games

AI-powered platforms for football predictions

AI is changing how football predictions are made, offering smarter tools that analyse big data and provide real-time insights before matches.

Leading platforms

Here are some standout tools using AI to predict football outcomes:

– NerdyTips: Uses its NT 4.0 engine, a Java-based dual-layer model, to make predictions across 160+ leagues, claiming over 75% accuracy.
– AIGoalie: Gives daily tips on match winners and total goals, learning continuously from large datasets.
– FOOTBOT.NET: Applies machine learning to offer high-accuracy betting advice.
– FootCast: Reports over 81% success rate using AI-based match forecasts.

Key features

These tools offer:

– Live match updates
– In-depth analytics on teams and players
– Real-time predictions using both historical and live data
– User-friendly dashboards tailored for bettors, analysts, and fans

Challenges to watch out for

While powerful, these platforms still face some hurdles:

– Data quality: Bad or missing data can hurt accuracy, so checks and validation are important.
– Easy of understanding: Some AI models are complex, making it hard for users to understand or trust their predictions.
– Ethical use: These tools must be used responsibly, especially to avoid misuse in match-fixing or unfair play.

Expert & tactical pre-match analysis

Expert analysis before a match helps teams prepare better and supports more accurate predictions. It combines mental, physical, and tactical preparation to give players and coaches a competitive edge.

Mental preparation

Players and coaches often use routines like visualisation, breathing exercises and mental rehearsal

These techniques help them stay focused, reduce stress, and build team confidence, all of which are vital for staying sharp under pressure.

Physical preparation

Before a match, training focuses on fitness, recovery and proper warm-ups

This ensures players are physically ready and helps the team play as a cohesive unit, executing strategies smoothly during the game.

Tactical analysis

Analysts break down video footage and player tracking data

They study the opposing team’s tactics, such as how they press, build attacks, or handle set-pieces. This helps coaches spot patterns and weaknesses, adjust formations and plans and make smarter strategic choices

Blending data and expertise

Together, these methods boost team readiness, improve prediction accuracy, balance human insight with data-driven models for a complete match preparation strategy

Integrating multi-source predictions

To improve the accuracy of football pre-match predictions, it’s best to combine multiple types of data. Strong prediction systems pull together insights from stats, market odds, AI models, and expert knowledge.

What goes into a multi-source framework?

1. Statistical models

– Tools like Poisson Regression (used by FootyStats) predict goal probabilities using historical match data.
– These models give a baseline prediction for expected scores and outcomes.

2. Market odds

– Betting odds reflect crowd wisdom and bookmaker insights.
– Models like Dolores combine these odds with other features and have achieved 1.09% ROI overall and over 20% ROI in the English Premier League (as reported by Springer)

3. AI and machine learning

– Algorithms like k-nearest neighbors and neural networks detect hidden patterns in data.
– These models enhance predictions by handling complex relationships and nonlinear trends (detailed in Springer studies).

4. Human insight

– Expert analysis adds context that data might miss, like tactical trends, team morale, or motivation.
– Mental and strategic preparation, as noted by Catapult, brings extra depth that helps refine predictions.

Sportmonks & pre-match data

When working at Sportmonks, you have access to a powerful suite of tools, automating and enhancing pre-match predictions with high-quality, real-time data. Here’s how the Sportmonks football API supports pre-match forecasting:

Pre-match odds endpoints

Sportmonks offers a robust Pre-match Odds feed under the Standard odds API, featuring four main endpoints:

– All Odds: fetches every pre-match odd across fixtures
– Odds by Fixture ID: specific odds for a given fixture
– Odds by Fixture + Bookmaker ID: allows filtering to a specific bookmaker (e.g. bet365)
– Odds by Fixture + Market ID: filter by particular betting markets (e.g. 1X2, BTTS)

Premium odds feed (Historical)

For high-fidelity analysis and model training, the Premium Odds feed offers:

– All premium odds: full access to pre-match odds with historical data
– Premium odds by fixture / bookmaker / market: granular filtering
– All historical odds: tracks odds evolution up to 7 days post-play, capturing opening odds and all updates.

This historical data is invaluable for analysing odds drift, model calibration, value bet identification, and validating pre-match models.

Pre-Match News

Sportmonks also supports a Pre-match news endpoint, enabling access to curated news articles (available at least 48 hours before kickoff):

– All Pre-match news: retrieves pre-match articles for upcoming fixtures
– By season or upcoming fixtures: supports advanced filtering
– Useful metadata includes associated fixture, league, and line events via include options.

Developer experience

– Rich documentation across API v3.0, with support for search, filters, nested includes, and structured JSON responses.
– Comments & guides for building match pages featuring live and pre-match data, including combo endpoints.
– Works smoothly with development tools: Postman collections, Python wrappers, and code snippets to accelerate integration.

Power your football predictions with Sportmonks’ pre-match data

Deliver smarter, faster, and more accurate pre-match insights using Sportmonks’ football API. Whether you’re building prediction models, running a betting platform, or creating data-driven match previews, our pre-match tools give you everything you need.

With live and historical odds, expert-curated pre-match news, and powerful endpoints covering over 2,500 leagues and competitions, including popular ones like Club World Cup, Serie A, Ligue 1, Spanish La Liga, Primera A, Europa League, Champions League, CONCACAF Gold Cup, Europa Conference League all in one API.

Start for free or upgrade to unlock historical odds and premium forecasting tools today.