About the AI model
Our AI-driven prediction engine combines proven machine learning techniques with sports-specific domain knowledge to deliver concise match insights. The model is built in stages: it begins with a base model that has learned general language and pattern recognition from large, diverse datasets. That base is then fine-tuned on structured sports data — historical match results, team and player statistics, head-to-head records, fixture contexts (home/away, travel, rest days), and relevant meta-data such as weather and competition importance. This layered approach helps the system understand both long-term patterns (for example, seasonal form shifts) and short-term signals (such as recent injuries or lineup changes).
For each upcoming fixture the engine calculates multiple market-specific predictions (win/draw/win, totals, handicaps, etc.). Predictions are probabilistic: rather than outputting a single "correct" result, the model estimates a probability distribution across possible outcomes. Those probabilities are presented to users as a top pick with an associated confidence percentage and, where applicable, complementary market projections. We also combine model outputs with simple business rules and sanity checks to avoid extreme or clearly implausible suggestions.
It’s important to understand limitations: the model cannot see the future and is dependent on the quality and recency of the input data. Last-minute changes (sudden injuries, unexpected line-ups, or weather events) can materially affect an outcome and may not be reflected immediately. The predictions are intended to help users make more informed decisions but should not be interpreted as guaranteed outcomes or financial advice.
We prioritise transparency and privacy. Aggregate performance metrics (such as historical success rates for specific markets) are shown where available so you can evaluate how the model performs over time. Personal data is not used to personalise predictions unless you explicitly enable a feature that requires linking your account; in that case, any data usage will be described in the plugin settings and privacy documentation. The model is regularly retrained and monitored to incorporate new data and reduce bias, and we review outputs to guard against systemic errors.
If you want to dig deeper, check the match insights for probability breakdowns, confidence bands, and the markets used to build each tip. Use these signals alongside your own research, bankroll rules, and responsible gambling practices.
- Probabilistic outputs: the model provides likelihoods, not certainties.
- Multiple markets: predictions cover top markets (1X2, totals, handicaps) with separate confidence scores.
- Data-driven: results depend on the recency and quality of input data; last-minute changes can affect accuracy.
- Performance metrics: historical success rates are shown where available to help you evaluate model performance.
The AI predictions are informational only and do not constitute financial or gambling advice. Betting involves risk; the site and its operators are not liable for losses incurred based on information provided here. Always gamble responsibly and adhere to local laws and age restrictions.
