It is tempting to judge any forecasting tool by its last few results. Three correct calls and it looks brilliant; three misses and it looks useless. Neither conclusion is justified. Evaluating a model fairly takes a little more patience.
Two measures, two difficulty levels
Predicta tracks two things. The main measure is whether the model pointed to the correct result: a home win, a draw or an away win. The secondary measure is the exact score. As explained in another article, the exact score is much harder to hit, so it should be expected to succeed far less often. Reading both together gives a more balanced picture than either alone.
Why sample size is everything
Because football contains so much chance, a small set of matches proves very little. Suppose a model correctly calls two of three results. That could be skill, or it could be a lucky weekend. Only across many matches does the proportion settle into something meaningful, in the same way that a coin flipped ten times can easily show seven heads but flipped a thousand times will land close to half.
A fair evaluation therefore needs:
- a large number of matches, not a handful;
- every prediction counted, including the awkward ones, not just selected examples;
- a comparison with a reasonable baseline, since a model that is only as good as always picking the home side is not adding much.
Avoiding self-deception
People remember the hits and quietly forget the misses. They also reinterpret events afterwards: "I knew that would happen." Recording the estimate before the match and checking it afterwards, automatically, removes that bias.
That is why Predicta compares its estimates with real results whenever the match appears in the calendar. The comparison is done by the app, not by memory or selective screenshots, so the record includes all the estimates, good and bad.
It is also worth remembering what the statistics cannot show. A match given 60% to one side that ends in defeat is not automatically an error, because 40% events are very common. Quality shows up in the long run, in how well the probabilities line up with what actually happens.
Key takeaways
- The correct result is the main measure; the exact score is a harder, secondary one.
- Judging a model on three matches is meaningless; large samples are essential.
- Automatic comparison with real scores prevents cherry-picking and hindsight.
- Even an excellent model will miss often, because football is uncertain.