The Swedish Allsvenskan returns with a jam-packed schedule starting Friday, July 3, and the league is buzzing with dramatic storylines both on and off the pitch. With the transfer window heating up and surprise leaders at the top of the table, this round of fixtures promises high stakes and intense action.

Surprise package IK Sirius currently sits atop the table and will look to maintain their lead when they host Mjällby AIF, despite distractions surrounding the sale of André Alsanati to Mosul SC and heavy interest in starlet Isak Bjerkebo from FC Copenhagen. Meanwhile, Malmö FF enters a new era under newly appointed manager Kjetil Helstrup as they travel to face Degerfors IF. Another massive highlight is the classic rivalry between IFK Göteborg and AIK, with the visitors reeling from the shock departure of club director Fredrik Söderberg. Finally, heavyweights BK Häcken and Djurgårdens IF will go head-to-head in a crucial battle for the European spots. Let’s explore our AI-driven statistical breakdowns and predictions for this week’s matches.

Full Time Results and Asian Handicap

In the table below you will find our AI prediccions for Allsvenskan round 11. Asian refers to the recomended asian handicap line.

Both Teams to Score

BTS (Both Teams to Score) is a popular betting market in sports, especially in soccer. In a BTS bet, you’re wagering on whether both teams in a match will score at least one goal each. The table below shows the BTS stats for the home team at home and the away team away.

Totals

The table below shows the goal averages for each encounter based on home teams average performance (goals scored and conceded) at home, and away teams average performance away. We use poisson distribution to calculate probabilities for over and under 2.5 goal. For your convienence we have converted the probabilities to decimal odds.

We also have calculated the average goals scored and conceded for the teams. See the table below.

Correct Score Predictions

These projections are generated using advanced statistical models that evaluate team strength, expected‑goals data, and historical scoring patterns. The table below highlights the most probable scorelines based on current performance indicators and model‑driven probability weighting.