Direct answer
AI Radar is the Predora.si feature that organizes football games into simulated historical performance segments of the Active Model.
Explore current games through the Active Model’s historical performance segments, then compare their odds, factors, and context.
Definition in one sentence: AI Radar is an intelligent screening that uses the Active Model's performance history to organize comparable game segments.
Quick summary
| Point | What it means |
|---|---|
| What is | A page that organizes games by simulated historical performance segments of the Active Model. |
| Analytical role | Reduce the universe of games and compare statistical segments. |
| What it shows | Game, market, analyzed result, chance, value, observed odds and Active Model card. |
| Depends on | Active Model, selected market, filtered league and historical performance by segment. |
| Common mistake | Reading a highlight without exploring its historical segment. |
| How to interpret | Comparing Base, Hit Rate, Return, EV, odds, factors and context in AI + Statistics. |
What is AI Radar?
AI Radar is an area of Predora.si designed to reduce noise. Instead of manually switching between league, market, chance, EV and filters, you start with a leaner list of games that belong to segments with good historical performance.
In practice, it answers a very direct question:
Which games should I look at first today?
This point is important: AI Radar does not exist to recommend an action. It exists to speed up your initial screening. A consistent analysis still involves interpreting odds, market, factors and match context.
How AI Radar works in practice
The logic combines four main pieces:
Active Model + selected market + league or segment + historical performance by segment = organized list in AI RadarIn other words, AI Radar respects the mathematical lens in use in the app. If you change the Active Model, the reading can change. If you switch the market, the question asked of the game changes. If you filter a league, the analyzed universe also changes.
AI Radar is not a loose AI opinion. It shows games that belong to segments where the model has been performing better, with a focus on comparison.
What you see on the AI Radar page
The page shows a grid similar to other areas of the app, but already with a more filtered selection for analysis.
| Element | How to interpret |
|---|---|
| Active Model | Shows which model calculates chance, EV, fair odds and game reading. |
| Sample | How many games are in the history of that segment. A larger base tends to be more reliable. |
| Hit | Percentage of analyzed results that hit in that historical segment. |
| Return | Historical result considering odds, not just hit rate. |
| Chance (%) | Estimated probability by the Active Model for the displayed analyzed result. |
| Value / EV | Measures the divergence between the observed odds and the estimated probability. |
| Event | Game, competition, date, and time. |
| Analyzed result | Market selection analyzed by the Active Model. |
| AI + Statistics | Area to compare game data with the mathematical reading. |
The screening reduces the initial universe and organizes scenarios into comparable segments. The highlight is analytical data, not an automatic conclusion.
How to interpret AI Radar in 7 steps
1. Open the AI Radar
Go to the AI Radar page and observe the list organized by performance logic.
2. Check the Active Model
Check whether the analysis is using Predora AI Pro, Predora Market Median or a custom model. The model is the app's mathematical lens.
3. Compare the selected market
1x2, Double Chance, Over 2.5 goals, Both teams to score and Over 9.5 corners ask different questions of the same game.
4. Compare Base, Hit Rate and Return
Base shows the sample size, Hit Rate shows the frequency of correct results and Return is the model's simulated historical result considering the odds. The three metrics describe the same segment from different angles.
5. Compare some games in the segment
Open some scenarios from the same segment and compare the data calmly. The filter loses value if every highlight is treated as a conclusion.
6. Compare with AI + Statistics
Check pre-match prediction, home/away, season, recent games, head-to-head, league average and sample size.
7. Reassess the EV when the odds change
Value depends on the comparison between the probability estimated by the model and the odds offered by the bookmaker. If the odds have dropped, the EV may have changed along with it.
EV = (estimated probability x odds) - 1Positive EV highlights a price above the fair odds implied by the model’s probability estimate.
Practical example in football
Imagine you opened AI Radar in a Brasileirão Série A round and selected the market Over 2.5 goals.
| Market | Over 2.5 goals |
|---|---|
| Chance | 54% |
| Odds | 1,95 |
| EV | +5,3% |
| Scope | Segment with good historical performance |
| Action | Open game and review |
The EV calculation would look like this:
Estimated probability: 54% = 0.54 Available odds: 1.95 EV = (0.54 x 1.95) - 1 EV = 1.053 - 1 EV = +0.053 = +5.3%The initial reading is positive, but it still needs context. Now you open the game and check whether the factors support the story: offensive creation, goals scored, goals conceded, shots, recent form, league average and odds availability.
If the factors differ from the model, explore what explains the gap. A tight game or low attacking output can add useful context to an AI Radar highlight.
AI Radar vs Performance vs Upcoming Games vs Upcoming Games with EV+
These pages complement each other. Each answers a different question.
| Page | Question it answers | Analytical role |
|---|---|---|
| AI Radar | How to organize the segments for analysis? | Compare games organized by simulated historical performance. |
| Performance | How do Sample, Hit Rate, and Return vary across leagues? | Compare segments by Sample, Hit Rate, and Return. |
| Upcoming Matches | Which games, leagues, markets, and odds are available? | Present the full catalog without a closed analytical filter. |
| Upcoming Games with EV+ | Which scenarios show positive EV under the estimate? | Compare estimated probability, observed odds and fair price. |
| Upcoming Games with High Probability | Which scenarios show the highest estimated probability? | Sort scenarios by estimated probability. |
A simple flow looks like this: AI Radar, open the scenario, compare factors and odds and interpret the data.
How the Active Model changes AI Radar
The Active Model is the engine that calculates probabilities, fair odds and EV in the app. When you switch this model, AI Radar can change.
Can change
- estimated probabilities;
- EV and fair odds;
- displayed games;
- order of highlights;
- performance by league and market.
How to evaluate
Do not switch models because of one lost game. Use base, hit rate and simulation to evaluate behavior with sufficient sample.
How to interpret AI Radar in the long term
AI Radar uses historical performance to organize segments. Compare sample size, hit rate, and Return to see what supports each pattern.
Small base fluctuates more
If the segment has few games, the numbers may look very good or very bad due to statistical noise.
Hit rate is not simulation
A segment can hit a lot with low odds and still have Return below zero. Read Hit Rate and Return together.
Compare historical patterns across periods
History shows trends for comparison. Football still has variance, red cards, penalties, injuries and unexpected lineups.
The goal is process
Build a repeatable analysis routine: compare price, probability, and match context, then review the results over time.
Common mistakes when using AI Radar
1. Skipping the segment behind a highlight
AI Radar connects a current game with a historical performance segment. Open the match to explore how its price and factors compare.
2. Ignoring the selected market
Chance, EV and analyzed result always depend on the chosen market.
3. Looking only at the chance
A high chance helps, but the value reading also depends on the price.
4. Looking only at the EV
A positive EV should be compared with context, sample, and other signals.
5. Ignoring the sample
An isolated Return value in a small sample can be misleading.
6. Not opening up the game for review
AI Radar is the beginning of the analysis, not the end.
7. Treating every highlight as a conclusion
If every highlight becomes a conclusion, the filter loses its analytical role.
Practical checklist for interpreting the scenario
- Active Model: identify which model calculates the scenario.
- Market: distinguish 1x2, Double Chance, goals, BTTS and corners.
- League: compare the sample size in the filter.
- Sample, Hit Rate, and Return: interpret the metrics together.
- Chance: identify the outcome to which the probability refers.
- EV: compare estimated probability, fair price, and observed odds.
- Odds: record which quote is being compared.
- Factors: compare convergences and divergences in AI + Statistics.
- Alerts: compare sample size, unusual match context, and any factors that differ from the model’s view.
- Summary: describe which signals support or contradict the reading.
How to apply this on Predora.si
A simple way to use it in daily life:
Quick 5-minute routine
- Open the AI Radar.
- Check the Active Model.
- Choose the market.
- Review some scenarios from the filter.
- Compare factors and the observed quote.
- Interpret the consistency of the scenario.
Complete 10-minute routine
- See Performance by league.
- Return to AI Radar filtering by league and market.
- Compare chance, EV, hit rate, and simulation.
- Compare factors by market.
- Record whether price, probability, and context converge or diverge.
FAQ about AI Radar
What does an AI Radar highlight tell me?
An AI Radar highlight connects a current match with a historical performance segment. Explore its sample, Return, odds, and match factors to assess that connection.
Is AI Radar the same as Upcoming Games with EV+?
AI Radar organizes games by historical performance segments. EV+ focuses on current prices above the Active Model’s estimated fair odds. Compare both views to explore different aspects of an opportunity.
Is AI Radar better than Upcoming Games?
They are different views. AI Radar organizes statistical filters; Upcoming Games offers a broad view of the games in the current filter.
Why do AI Radar games change when I switch the Active Model?
Because the Active Model defines probabilities, fair odds, EV, and the reading of the games. If the mathematical lens changes, AI Radar highlights can also change.
Can I use AI Radar in any market?
You can use AI Radar in the markets covered by the Predora.si analysis experience, such as 1x2, Double Chance, Over 2.5 goals, Both teams to score, and Over 9.5 corners.
How should I interpret a game that appears in AI Radar?
As part of a filter for comparative analysis. Open the game and interpret factors, observed odds, sample, and context independently.
What does simulation mean on the AI Radar card?
Return is the model's simulated historical result considering the odds. It complements Sample and Hit Rate in the comparison between filters.
Open AI Radar and use the list as a starting point to compare odds, factors, and context.
Closing
AI Radar organizes football games by filters of the Active Model's simulated historical performance for comparative analysis.
In football, variance always exists. One play changes the game, one odd changes the EV, and a small sample can be misleading. Therefore, the best use is to treat AI Radar as part of a process: review filters, compare factors and odds, and interpret each scenario.