Damallsvenskan

Sweden• Stockholm

November 9, 24

0 - 3

Main Tip

1 - 2

or? - ?

(31%)

Djurgården vs Rosengård W - AI Football Prediction for November 9, 24

Our AI-powered preview for the Damallsvenskan clash between Djurgården and Rosengård W on November 9, 24 in Sweden delivers a comprehensive look at both teams' recent form and their head-to-head history. Djurgården have managed 3 win(s), 1 draw(s), and 1 loss(es) in their last 5 matches, averaging 2.0 goals per game. Meanwhile, Rosengård W have recorded 0 win(s), 2 draw(s), and 3 loss(es) in the same period, with an average of 0.6 goals per match. Looking at their previous encounters, these teams have faced each other 0 time(s): Djurgården have won 0, Rosengård W have won 0, and 0 match(es) ended in a draw. On average, their meetings have produced 0.0 goals per game. Factoring in current form, historical results, and advanced statistical models, our AI provides actionable insights to help you make smarter betting decisions for this match.

Team Performance Metrics

Average Goals per Game

2.00

Average of 2.00 goals scored per match

Formation

4-2-3-1

Most commonly used formation

Average Goals per Game

0.60

Average of 0.60 goals scored per match

Formation

3-4-3

Most commonly used formation

Recent Matches

Djurgården

Insights

Won 60% of last 5 matches

Highest score in last 5: 4 goals

Rosengård W

Prediction Insight

1 - 2

or? - ?

This prediction carries a speculative 31% probability rating. While our models identify some favorable patterns, underlying metrics show increased volatility and uncertainty. Historical data suggests occasional success in similar scenarios, but approach with caution. If considering this bet, we recommend reduced stake sizes and careful monitoring of pre-match developments.

Speculative Option

Confidence Level: Low

31%

Frequently Asked Questions

General, technical, and compliance information about Superior Tips' AI-powered football predictions.

: Superior Tips is a football prediction platform that uses advanced artificial intelligence models to forecast match outcomes. It analyzes data from over 200 leagues worldwide, factoring in recent form, player stats, and team dynamics to generate high-accuracy predictions. These forecasts help users make more informed betting decisions using a data-driven approach.
: We cover all major football leagues, including the English Premier League, La Liga, Bundesliga, Serie A, and Ligue 1, as well as dozens of lower-tier and international competitions. Our system updates daily to ensure full coverage of ongoing matches across over 200 leagues worldwide.
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: Predictions are updated daily, usually before noon GMT. Updates account for lineups, injuries, and other real-time events that impact accuracy.
: Our prediction engine is built on machine learning models trained with historical match data, team statistics, and player-level metrics. The system continuously learns and adapts, identifying hidden patterns and outcome probabilities far beyond what manual analysis can achieve.
: We feed the AI with over 40 parameters per match: team strength, win/loss streaks, player injuries, home/away stats, historical matchups, expected goals (xG), and even weather data. The model then weighs these dynamically to output accurate forecasts.
: xG helps quantify goal-scoring opportunities based on shot quality and position. We integrate xG to better estimate team dominance and attacking efficiency, which improves the reliability of our final predictions.
: Our AI achieves up to 96.8% accuracy on specific match types, tracked using outcome validation against real match results. Accuracy is calculated per market (e.g. match winner, BTTS, over/under) and continuously monitored to improve reliability.
: We use ensemble models, including random forests and gradient boosting, along with neural networks for deeper insights. These models work together to evaluate multiple scenarios and reduce prediction error.
: The model adapts in real-time to data like confirmed injuries, lineup changes, and match-day conditions. This ensures the most up-to-date forecasts for every fixture.
: We source data from premium providers, validate it with cross-checking techniques, and continuously monitor input for inconsistencies. Quality control is built into our pipeline.
: Each percentage reflects the model's confidence in a specific outcome (win, draw, loss). A 70% win chance doesn't mean a guaranteed result but indicates strong statistical advantage.
: 'Accumulator' refers to a group of individually predicted matches bundled into one bet for higher combined odds. 'Bet of the Day' is our highest-confidence single pick, selected using confidence scoring across all models.
: Use the homepage filters or sidebar buttons for quick access by league (e.g. Premier League) or by bet type. Each match card includes sorting options.
: Our xG section shows the likelihood of each team scoring based on shot quality, attacking positions, and past performance. It helps users understand whether a team is underperforming or overperforming relative to expected output.
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: No. While our models provide statistically strong predictions, football matches are inherently uncertain. We do not offer guaranteed wins and recommend responsible use.
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: Structured data allows search engines and AI models to directly parse content like FAQs and match data. This improves visibility in search features like rich snippets.
: Yes, we provide a public widget for embedding match predictions. API access is in development for B2B use.
: We maintain XML sitemaps, optimized meta tags, fast-loading pages, and ensure mobile-first indexing. We also support llms.txt and robots.txt directives for crawler guidance.

Damallsvenskan

Djurgården

Rosengård W

Djurgården vs Rosengård W - AI Football Prediction for November 9, 24

Team Performance Metrics

Djurgården

Rosengård W

Recent Matches

Djurgården

Insights

Rosengård W

Prediction Insight

Frequently Asked Questions

GENERAL

AI & DATA

USER GUIDANCE

COMPLIANCE

TECHNICAL CRAWLABILITY