Our Five Football Betting Strategy Secrets
This season, we’re setting our sights even higher. Here’s how we’ve taken our betting strategy to the next level for our fourth Season. We prepared for you best football wagering platforms.
Evolving our Strategies
Staying ahead of the curve in sports betting means continuously refining your strategy. Over the last five years, Systematic Sports has significantly enhanced its betting models for each season.
For Season 4, we continued to improve three key metrics:
- Annualised return: Average yearly profit of an investment over time.
- Sharpe Ratio: The higher, the better. Sharpe measures how much return you get for the risk you take. Top fund managers in finance aim for a Sharpe ratio above 1.5.
- Maximum drawdown: The lower, the better. The maximum drawdown shows the most significant drop from a portfolio’s peak to its lowest point. Top managers aim to keep the maximum drawdown below 20%.
We’ve added five new signals to leverage a more granular data set to improve these metrics.
1. Factoring in Player Skill: Adjusting for xG Outliers
Expected Goals (xG) is a powerful metric, but it doesn’t always tell the whole story. Some players consistently outperform their xG due to superior finishing skills. In Season 4, we’re adjusting xG figures for these players.
By recognising and rewarding consistent over-performers, our model now accounts for individual player skill levels, providing a more accurate reflection of likely outcomes.
2. Home-Away Advantage: Going Granular
Home-field advantage isn’t a one-size-fits-all metric. Some teams thrive at home, while others benefit less from the home crowd.
This season, we’ve introduced a more granular approach to factoring home-away advantage into our predictions. Instead of applying a generic adjustment, we now analyse team-specific home and away performance since 2015 (excluding 2020 for COVID), allowing for a more tailored prediction model.
3. Factoring Team ELO: Betting Smarter Against Favorites
In Season 3, we often bet too heavily against favourites, targeting the value in the odds offered for the underdog.
We’ve integrated a team ELO rating into our model to address this. ELO is a powerful indicator of a team’s strength relative to others. By incorporating it, we now have a more balanced view of matchups, particularly when assessing underdogs against top-tier teams.
4. Decay Factor: Adjusting for Time
Teams evolve throughout the season and dip in and out of form. A game played three months ago doesn’t have the same relevance as one played last week. That’s why we’ve introduced a decay factor to our model.
This means the older a match, the less weight its statistics have in our current predictions. Doing so ensures that our models stay sharp, focusing on the most relevant and timely information.
We previously weighed the last six gameweeks equally in our calculations. This new approach prevents outdated data from skewing predictions, ensuring our strategy is always grounded in the most current and relevant performance metrics.
5. Recalibrating Value and Probability Thresholds with Backtesting
Finally, we’ve recalibrated our value and probability thresholds using our backtesting engine. This has fine-tuned our “value bet” criteria, ensuring we’re not just picking winners but doing so with calculated profitable precision.
This recalibration ensures that every bet we take meets stringent criteria, reducing the noise and focusing on bets with the highest expected value.
Ready to Upgrade Your Strategy?
Our model now consumes more metrics and information than ever for Season 4, and the backtests are the best they have ever been.