What is AI Radar?

Quick summary

How does AI Radar work?

AI Radar works as a statistical screening within Predora.si. Instead of only showing the full grid, it organizes matches that belong to Active Model slices with different simulated historical performance profiles.

In plain terms, it helps you compare which slices of the Active Model's history show different combinations of Base, Accuracy, and Return.

AI Radar uses performance history to identify segments and show related current games, giving you a structured starting point for deeper match analysis.

Historical trends help you form research questions. Compare results across periods and current match conditions to assess how well a pattern fits.

• Active Model: the lens that guides probabilities, readings, and key metrics.
• Selected market: 1X2, Double Chance, Over 2.5 goals, Both teams to score, or Over 9.5 corners.
• Filtered league: when there is a league filter, AI Radar works within that slice.
• Historical cuts and segments: combinations of market, league, game profile, and Active Model behavior.
• Return: the model's simulated historical result considering the odds, read together with Base and Accuracy.

Why does looking at historical trends make sense?

AI Radar starts from an idea common in data analysis: before trying to predict an isolated event, it makes sense to look at history and understand which cuts, segments, and patterns have been performing better.

This logic appears in several areas that use forecasting and quantitative analysis: history helps understand consistency, variation, and behavior over time.

Use historical results to compare hypotheses across segments and periods.

AI Radar connects a current match with a specific historical performance segment of the Active Model.

AI Radar is not EV+

This is one of the most important points on the page: AI Radar is not the same thing as EV+.

AI Radar organizes games by slices of simulated historical performance. EV+, on the other hand, brings together scenarios in which the Active Model calculates positive expected value at the time of analysis.

Think of it this way: AI Radar compares historical slices. EV+ compares the estimated probability with the observed price.

• A game can appear in AI Radar because it belongs to a specific historical segment.
• That does not mean it is automatically EV+.
• To compare scenarios filtered by positive EV, use EV+.
• After AI Radar organizes a game, compare odds, EV, factors, and context.

How to interpret Return in AI Radar

Return is the model's simulated historical result considering the odds used in the slice.

Accuracy alone does not tell the whole story. Two segments with similar accuracy rates can show different Return values because of the odds.

The combined reading is: Base shows the sample size, Accuracy shows the frequency of correct results, and Return incorporates the odds into the history.

• Sample: the number of games in the segment. Compare this with the period and results to assess the evidence.
• Accuracy: frequency of correctly analyzed results within that segment.
• Return: the model's simulated historical result considering the odds.

How to interpret AI Radar

AI Radar reduces the universe of games and organizes statistical slices for comparison.

It does not decide for you. The list allows you to review related signals before interpreting each scenario.

• Open the AI Radar.
• Check the selected Active Model.
• Identify the market being analyzed.
• Look at Base, Accuracy, and Return.
• Open the games from the slice you want to compare.
• Interpret odds, EV, factors, and context independently.

Formula or logic

AI Radar is not a manual formula like EV, implied probability, or fair odds. It is an organization logic based on historical performance.

This means AI Radar organizes comparable slices without replacing the user's interpretation.

AI Radar = Active Model + market + league/filters + slices of simulated historical performance

Example in football

Imagine you are analyzing a Brasileirão Série A round in the Over 2.5 goals market. In Upcoming Games, you could see all available games in that market and analyze them one by one.

In AI Radar, Predora.si does a screening first. It organizes games that belong to slices of the Active Model's simulated historical performance.

The match belongs to an identifiable historical segment. Compare its current odds and estimated probability to assess EV.

After that, open the game and compare the market selection, estimated probability, best odds, EV, factors, slice Base, and match context.

How it appears on Predora.si

Within Predora.si, AI Radar appears as a quick screening area. It uses the same grid style as the app, but organizes games belonging to historical performance slices in the Active Model.

Return reflects the model’s simulated historical result for the selected segment using the odds in that sample. Compare it alongside sample size and time period.

• Shows event, market selection, estimated probability, and value/EV when that metric appears on the card or in the grid.
• Keeps the Active Model card at the top to provide context for the reading.
• Helps compare Base, Accuracy, and Return of the slice.
• Can change when you switch Active Model, market, or league.

AI Radar vs other pages

AI Radar is not the same thing as EV+. Radar organizes games by slices of simulated historical performance. EV+ brings together scenarios in which the Active Model calculates positive expected value.

Think of it this way: AI Radar compares historical slices. EV+ compares the estimated probability with the observed price.

Common mistakes

Practical checklist

1. Check the Active Model.
2. Identify the selected market.
3. Understand the filter that took the match to AI Radar.
4. Compare the sample size for the filter.
5. Compare Accuracy and Return.
6. Open the game to review the data.
7. Compare odds, EV, and available price.
8. Interpret factors, statistics, and context.
9. Record convergences and divergences before concluding the analysis.