Methodology
How ProbaBall Predictions Work
Overview
ProbaBall uses advanced statistical models and machine learning algorithms to predict soccer match outcomes and final league standings. Our approach combines historical data, current form, and sophisticated mathematical models to provide accurate and reliable predictions.
Data Sources
Match Results
Historical match data including scores, possession, shots, and other key metrics from multiple seasons.
Player Statistics
Individual player performance data including goals, assists, minutes played, and advanced metrics.
Team Form
Recent performance trends, home/away form, and head-to-head records between teams.
Market Data
Transfer values, squad depth, and financial indicators that impact team performance.
Prediction Models
Match Outcome Predictions
We use a linear regression model to predict match outcomes based on key performance indicators:
- Home Form: Recent performance of the team playing at home
- Away Form: Recent performance of the team playing away
- Stadium Capacity: Accounts for the influence of crowd size on match outcomes
League Standings Predictions
Final standings are generated through Monte Carlo simulations:
- Run 10,000+ simulations of the remaining season
- Each simulation uses our match prediction model for every remaining fixture
- Results are aggregated to show most likely final positions
- Qualification and relegation probabilities are calculated from simulation outcomes
Model Updates
Our models are continuously updated to maintain accuracy:
- Weekly Updates: Team ratings and form adjustments after each matchday
- Transfer Window Adjustments: Model parameters updated when key players move
- Seasonal Recalibration: Full model retraining at the start of each season
- Performance Monitoring: Continuous tracking of prediction accuracy and model refinement
Limitations
While our models are sophisticated, soccer predictions inherently involve uncertainty:
- Injuries & Suspensions: Unexpected player absences can significantly impact results
- Tactical Changes: New formations or strategies may not be immediately reflected
- Motivation Factors: Cup competitions, relegation battles, and end-of-season scenarios
- Random Events: Weather, referee decisions, and other unpredictable factors
Our predictions should be viewed as informed estimates based on available data, not certainties.
Research & Development
We continuously work to improve our models:
- Incorporating new data sources like player tracking and tactical analysis
- Experimenting with advanced neural networks and deep learning approaches
- Collaborating with academic researchers in sports analytics
- Testing alternative modeling approaches and ensemble methods