Expect fireworks as Sweden’s Allsvenskan kicks off Omgång 12 this weekend, July 11–12. With the summer transfer window in full swing, teams are battling not only on the pitch but also off it as key player movements begin to reshape the league’s competitive landscape.

The crown jewel of the weekend is the classic Swedish derby on Sunday, July 12, as Malmö FF hosts arch-rivals IFK Göteborg. Under newly appointed manager Kjetil Helstrup, Malmö will look to assert their dominance on home turf. Meanwhile, Mjällby AIF welcomes AIK on Saturday under a cloud of transfer speculation, with standout midfielder Elliot Stroud reportedly attracting a concrete bid from Italian club Genoa. Elsewhere, IF Elfsborg travels to face GAIS having just confirmed the departure of key defender Buhari Ibrahim to Austria. Finally, high-flying IK Sirius will visit IF Brommapojkarna in a bid to keep their momentum rolling. Let’s delve into our AI-powered stats and predictions for this round’s matchups.

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.