26 Aug 2026
How to compare football predictions using method, transparency and track record
Comparing football predictions involves more than counting recent hits. Here’s how to assess each source’s methodology, sample size, probability calibration and long-term record.
1. Identify what each source is predicting
Before comparing results, put the predictions on an equal footing. One source may indicate only the winner, while another publishes probabilities for a win, draw and defeat. You must also separate markets and events: match result, goals, both teams to score and qualification have different levels of difficulty. Record the prediction date, competition, match and stated confidence level.
2. Examine the methodology and transparency
A reliable source explains how it arrives at its predictions. Look for information about the data used, lineup updates, recent form, home advantage, opponents’ strength and how injuries are handled. Statistical models should state, at a minimum, which variables are included in the calculation and how they are tested. Generic phrases such as “expert analysis” do not make it possible to assess the quality of the method.
3. Consider the sample size and quality
Ten consecutive hits may simply be a short streak. Compare sources with a sufficient number of predictions and separate results by competition, market type and period. A long series reduces the effect of chance, but it does not fix a poor method. Also check whether the sample includes difficult matches and opponents of varying quality, rather than only games selected after the results were known.
4. Assess probability calibration
A 70% prediction should come true roughly seven times in every ten cases when many predictions at that level are combined. This is the principle of calibration. To assess a source, group predictions into ranges such as 50%–59%, 60%–69% and 70% or higher, then compare the predicted frequency with the actual frequency. A source can correctly pick many winners while still assigning overly high probabilities.
5. Analyse the long-term record
Ask for a complete list of predictions, with a record published before the matches. The history should show hits, misses, total number of predictions, performance by market and the period analysed. Avoid placing weight on screenshots, deleted selections or reviews that show only successful picks. When comparing models that use probabilities, metrics such as log loss and the Brier score are more informative than a simple hit rate, because they also measure the quality of the confidence assigned.
6. Make a fair, up-to-date comparison
Use the same set of matches for all sources and define the evaluation rule before starting. Do not combine result predictions with goal predictions in a single hit rate. Update the analysis with new matches and check whether performance holds outside the original period. The best source is not necessarily the one leading a short streak, but the one with a clear method, a broad sample, well-calibrated probabilities and a verifiable record.
Related pre-match analysis
Keep reading
Analysis: PK Sport · our methodology
Analysis based on public data and market signals. For analysis only — not betting advice.