Data science in sport

How machine learning learned to predict sporting events more accurately than experts

How machine learning learned to predict sporting events more accurately than experts We explore the data science, algorithms, and statistics that are changing sports. Try for free
3D sports analytics cube with charts and data panels

Why do experts get it wrong so often?

Why do experts get it wrong so often? Every prediction we make is based on experience and intuition. But the brain isn't designed to process large volumes of data. It's susceptible to cognitive biases that interfere with objective assessment.
  • Anchor effect. We rely too much on initial information.
  • Confirmatory distortion. We notice only what confirms our expectations.
  • Recency effect. Reassessing recent events and their impact on the outcome.
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How predictive models work in sports

How predictive models work in sports Modern models combine statistics, algorithms, and big data.
  • Regression analysis. We identify relationships between factors and results.
  • Machine learning. Algorithms are trained on historical data and find hidden patterns.
  • Real-time processing. The model adapts to new data from different sources.

We tested the models on real data

We tested the models on real data Comparison of model forecasts with changes on a sample of 500+ events.
Analyzed events
500+ analyzed events
Processed data rows
120M+ processed data rows
Features used
150+ features used
Algorithms in an ensemble
6 algorithms in an ensemble
Comparison chart showing model forecasts against expert forecasts

What our users say

What our users say
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