Drama and high stakes sweep across Sweden’s Allsvenskan as Round 22 takes center stage from September 13–15. Off the pitch, relegation-threatened Mjällby AIF made headlines by sacking Karl Marius Aksum and welcoming back head coach Andreas Brännström in a bid to spark survival. At the top of the standings, in-form Djurgården and high-flying IK Sirius continue their fierce pursuit of European places and championship contention.

The marquee spectacle takes place on Sunday, September 14, headlined by the fiery Stockholm derby between Djurgården and Hammarby, alongside a heavyweight clash between IF Elfsborg and Malmö FF. Earlier on Saturday, September 13, Andreas Brännström makes his return to the bench as Mjällby AIF travels to Degerfors IF, while Halmstads BK hosts IFK Norrköping. The action wraps up on Monday, September 15, with AIK meeting IF Brommapojkarna and the Gothenburg derby pitting BK Häcken against IFK Göteborg. Check out our AI-generated match predictions and tactical breakdowns for every fixture this week.

Full Time Results and Asian Handicap

In the table below you will find our AI prediccions for Allsvenskan round 22. 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.