The action intensifies in Sweden’s Allsvenskan as we head into another high-stakes round of matches on July 18–20. With the transfer window in full swing and the title race tightening, clubs are desperate to establish momentum in this crucial summer stretch.

The marquee matchup kicks off on Saturday, July 18, as AIK hosts GAIS at the Strawberry Arena. GAIS enters the clash with immense confidence following reports of securing a blockbuster signing in striker Nikola Vasic. On Sunday, July 19, IF Elfsborg will lock horns with high-flying IK Sirius in what promises to be a tactically fascinating battle, while Hammarby IF looks to secure home points against Degerfors IF. Additionally, league heavyweights Malmö FF travel to face Kalmar FF in a challenging away fixture. Let’s delve into our AI-powered predictions and statistical breakdowns for this week’s matches.

Full Time Results and Asian Handicap

In the table below you will find our AI prediccions for Allsvenskan round 13. 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.