Interpreting Prediction Accuracy and Volatility in Zow Ratings
Zow Ratings evaluates more than just rankings—it tracks how accurate and stable those rankings are throughout the season. Two of the most important signals on the Confidence Stats page are Prediction Accuracy and Volatility.
What Prediction Accuracy Represents
Prediction Accuracy reflects how often the model correctly identifies the winning team based on current ratings. No rating system is perfect, but tracking accuracy over time offers meaningful insight into model performance.
When accuracy increases, confidence in the model’s understanding of the sport grows. When accuracy drops, it signals the model is learning from surprising results and adjusting accordingly.
Understanding Volatility
Volatility measures how much ratings shift from one day to the next. Several factors drive volatility:
- Early-season uncertainty
- Inconsistent team performance
- Strength-of-schedule mismatches
- Overtime or penalty-kick outcomes
- Tournament play
High volatility means the model is absorbing new information rapidly. Low volatility means the season has stabilized and outcomes follow more predictable patterns.
Why These Metrics Matter
Prediction Accuracy and Volatility directly influence the Confidence Score. Together, they give a transparent view of how strongly the model understands each sport at a particular moment.
When accuracy is high and volatility is low, Zow Ratings becomes highly reliable for comparing teams and making predictions. When the opposite is true, users should expect more surprising outcomes and shifting rankings.
Related pages: Confidence Stats · Results Predictor