Tottenham vs. AZ Alkmaar Prediction
Rangers vs. Fenerbahçe Prediction
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Aston Villa vs. Club Brugge Prediction
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Portsmouth vs. Plymouth Argyle Prediction
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Watford vs. Swansea City Prediction
Pharco vs. NBE Prediction
Pyramids vs. Al Masry Prediction
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Jagiellonia vs. Cercle Brugge Prediction
AZ Alkmaar vs. Tottenham Prediction
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FCSB vs. Lyon Prediction
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Real Betis vs. Vitoria Guimaraes Prediction
PSG vs. Liverpool Prediction
Bayern München vs. Bayer Leverkusen Prediction
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Pyramids vs. Ceramica Cleopatra Prediction
Petrojet Suez vs. Ghazl Al Mahalla Prediction
Ahly Cairo vs. Tala'ea Al Jaish Prediction
Feyenoord vs. Inter Prediction
Real vs. Atletico Prediction
PSV vs. Arsenal Prediction
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Cardiff vs. Burnley Prediction
Hull City vs. Plymouth Prediction
Millwall vs. Bristol Prediction
Preston vs. Swansea Prediction
Club Brugge vs. Aston Villa Prediction
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Exact:
An exact score hit occurs when the predicted score matches the final result perfectly. For example, if the prediction was 2:1 and the final score is also 2:1, it’s considered a 100% correct prediction.
1X2
The 1X2 tendency is a broader way of evaluating predictions based on the match outcome rather than the exact score. If a prediction was 2:1, any final score resulting in a home win (e.g., 1:0, 3:2, or 4:1) is considered correct. This means the predicted match tendency (home win, draw, or away win) is accurate, even if the exact score isn’t.
False:
A prediction is considered completely wrong when neither the scoreline nor the goal difference aligns with the final result. For example, if the prediction was 2:1, but the actual score was 0:0 or 0:1, the outcome and match dynamics are entirely different, making the prediction incorrect.
Data Tracking Since: 05-03-2025
Tracked Leagues/Competitions
About xGscore

xGScore is a modern football prediction platform that sets itself apart by using Expected Goals (xG) data, machine learning, and advanced analytics to forecast match outcomes. Unlike traditional prediction sites like WinDrawWin or PredictZ, which rely mainly on past results and team form, xGScore evaluates the quality of scoring chances to give a more accurate reflection of team performance.
They check team performance in genereal
This means that xGScore doesn’t just look at who won or lost, but rather how well each team played based on chance creation and defensive strength. It is particularly useful for identifying misleading results (e.g., a team winning 1-0 despite being dominated) and finding betting value by highlighting teams that may be under- or overperforming.
History of xGScore
xGScore was founded as part of the modern analytics revolution in football, which has been heavily influenced by the rise of Expected Goals (xG) models.
The platform leverages data science and machine learning to go beyond simple past results, focusing instead on underlying team performances.
While xGScore is newer compared to sites like Forebet or WinDrawWin, it has gained traction among data-driven bettors and those looking for value bets.
How Does xGScore Work?
xGScore uses Expected Goals (xG) as its primary metric, but its methodology involves multiple layers of analysis. Here’s how it works:
Expected Goals (xG) – The Core Metric
xGScore doesn’t just count goals; it evaluates how many goals a team “should have” scored and conceded.
Expected Goals (xG) is a statistic that estimates the probability of a shot resulting in a goal based on:
- Shot location (closer shots have higher xG)
- Shot type (headers are lower xG than footed shots)
- Pass leading to the shot (e.g., through-ball assists improve xG)
- Defensive pressure (whether the shooter was marked)
- Game context (open play, set-pieces, counter-attacks)
Data Inputs Used by xGscore
Unlike traditional sites that use fixed formulas, xGScore employs machine learning (ML) to improve its accuracy
- xG data for each team (attack & defense)
- Recent form based on xG, not just results
- Expected Goals Against (xGA) to measure defensive strength
- Odds movement tracking (to see how the betting market shifts)
- Injury & suspension updates (to adjust predictions based on key absences)
- Home/Away performance differences
- Shot conversion efficiency (whether a team is over- or underperforming)
- C. Machine Learning Model
xGScore Features
xGScore provides a rich set of tools for users
- xG-based match predictions (home/draw/away, correct score, etc.)
- Advanced team statistics (xG, xGA, xPTS)
- Odds comparison & tracking
- Bankroll management tools (suggested stake sizes)
- Live xG tracking for in-game betting insights
- Transparency—past predictions and success rates are available.
Accuracy & Limitations
✅ Strengths
- Data-driven approach ensures higher predictive quality than form-based models.
- Better at detecting lucky/unlucky teams, helping find value bets.
- Machine learning improves predictions over time.
- Includes odds tracking, a feature missing from many prediction platforms.
❌ Weaknesses
- Limited league coverage due to the reliance on xG data (not available for all leagues).
- More complex interface compared to simple tipster sites like WinDrawWin.
- Requires user knowledge—casual bettors may find xG stats overwhelming.