Data study · HockeyAllsvenskan 2026/27
HockeyAllsvenskan 2026/27 — simulated 1,000,000 times
Who will win HockeyAllsvenskan? Which six teams make the play-offs and who ends up in the relegation battle? The supercomputer plays the whole season a million times — every match, every round — and counts how often each team finishes in each position.
Kalmar HC wins the league in 41.3% of the seasons, with an expected 99.0 points. The simulation was run on 16 September 2026, ahead of the first round of the season.
The title race
The chance of finishing top of the regular season once every round has been played. Kalmar HC is the favourite, but another team wins the league in 59% of the simulations.
Share of 1,000,000 simulated seasons that end with the team top of the table. Teams below 0.5% are not shown.
The final table — place by place
Each row is a team, each column a final position. The darker the square, the more often the team finishes there. Wide, pale rows mean an open race; a dark cluster means the model is confident.
| Team | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
 Kalmar HC | 41 | 24 | 15 | 9 | 5 | |||||||||
 BIK Karlskoga | 23 | 23 | 19 | 13 | 9 | 6 | ||||||||
 Leksands IF | 18 | 21 | 19 | 15 | 10 | 7 | ||||||||
 Modo Hockey | 9 | 14 | 16 | 17 | 14 | 11 | 7 | 5 | ||||||
 Södertälje SK | 8 | 11 | 14 | 15 | 14 | 11 | 8 | 6 | ||||||
 AIK | 8 | 11 | 13 | 14 | 13 | 11 | 8 | 6 | ||||||
 Mora IK | 6 | 9 | 11 | 13 | 13 | 12 | 10 | 8 | 6 | |||||
 Visby/Roma | 6 | 9 | 11 | 13 | 13 | 12 | 10 | 8 | 6 | |||||
 Västerås IK | 7 | 10 | 12 | 13 | 13 | 12 | 11 | 8 | ||||||
 IK Oskarshamn | 6 | 9 | 11 | 12 | 13 | 13 | 12 | 9 | 6 | |||||
 Nybro Vikings IF | 6 | 8 | 10 | 12 | 13 | 14 | 13 | 10 | 7 | |||||
 Östersunds IK | 7 | 9 | 12 | 14 | 17 | 17 | 14 | |||||||
 Almtuna IS | 7 | 11 | 16 | 23 | 31 | |||||||||
 Vimmerby HC | 7 | 10 | 15 | 23 | 34 |
Probability in per cent for each final position — darker = more often. The column colour shows the band: Title · Play-offs (2–6) · Mid-table (7–12) · Relegation (13–14).
How the season ends for each team
The same figures added up into what matters: title, play-offs, mid-table, relegation. Teams with more than ten per cent for both the play-offs and relegation are the wild cards of the season.
Kalmar HCBIK KarlskogaLeksands IFModo HockeySödertälje SKAIKMora IKVisby/RomaVästerås IKIK OskarshamnNybro Vikings IFÖstersunds IKAlmtuna ISVimmerby HCHow the season ends for each team, as a percentage of the simulations. The title is not included in the play-off band — the bands are mutually exclusive and add up to 100.
The points distribution
How many points do the teams finish on? The curves show the share of simulated seasons for each final points total — the width is the uncertainty, the peak the most likely outcome. Choose teams to compare.
- Kalmar HC: expected 99.0 points, 80% of seasons between 87 and 111
- BIK Karlskoga: expected 94.8 points, 80% of seasons between 83 and 107
- Leksands IF: expected 93.3 points, 80% of seasons between 81 and 105
- Modo Hockey: expected 88.8 points, 80% of seasons between 77 and 101
The full table in numbers
Expected points, the range 80 per cent of seasons land within, expected position and the probability for each band.
| # | Team | xPts | 80% range | Exp. pos. | Title | Play-offs | Mid-table | Relegation |
|---|---|---|---|---|---|---|---|---|
| 1 | Kalmar HC | 99.0 | 87–111 | 2.4 | 41% | 56% | 3.0% | <0.1% |
| 2 | BIK Karlskoga | 94.8 | 83–107 | 3.1 | 23% | 70% | 7.2% | <0.1% |
| 3 | Leksands IF | 93.3 | 81–105 | 3.5 | 18% | 72% | 9.5% | <0.1% |
| 4 | Modo Hockey | 88.8 | 77–101 | 4.5 | 9.0% | 72% | 19% | 0.3% |
| 5 | Södertälje SK | 84.3 | 72–97 | 5.7 | 4.0% | 61% | 34% | 1.0% |
| 6 | AIK | 81.6 | 69–94 | 6.4 | 2.3% | 51% | 44% | 1.9% |
| 7 | Mora IK | 76.6 | 64–89 | 7.8 | 0.7% | 32% | 62% | 5.2% |
| 8 | Visby/Roma | 76.5 | 64–89 | 7.9 | 0.7% | 32% | 62% | 5.3% |
| 9 | Västerås IK | 71.7 | 59–84 | 9.2 | 0.2% | 17% | 70% | 13% |
| 10 | IK Oskarshamn | 70.6 | 58–83 | 9.6 | 0.1% | 14% | 70% | 15% |
| 11 | Nybro Vikings IF | 69.9 | 58–82 | 9.7 | 0.1% | 13% | 70% | 17% |
| 12 | Östersunds IK | 65.4 | 53–78 | 10.9 | <0.1% | 6.0% | 63% | 31% |
| 13 | Almtuna IS | 60.0 | 48–72 | 12.1 | 0.0% | 1.9% | 44% | 54% |
| 14 | Vimmerby HC | 59.4 | 47–72 | 12.2 | 0.0% | 1.6% | 42% | 57% |
How the supercomputer works it out
Three steps — from match model to probability table.
Every match is replayed
Each remaining match is decided at random from the teams’ estimated strength — home advantage, goalscoring and defending weighed against the opposition. No match is a foregone conclusion, just as in real life.
A million seasons
The whole league is played through a million times. Points are awarded under hockey’s system: three points for a win in regulation time, two and one when the match is decided in overtime or a shootout.
The distribution is the answer
Once every season has been played, we count how often each team won the league, made the play-offs or ended up in the qualifier. That is the team’s probability — not a guess, but a frequency.
The model’s baseline forecast is supplemented with editorial strength adjustments ahead of the season (squad changes).
Frequently asked questions about the HockeyAllsvenskan simulation
Who wins HockeyAllsvenskan 2026/27 according to the simulation?
Kalmar HC wins the league in 41.3% of 1,000,000 simulated seasons, with an expected 99.0 points. The closest challenger is BIK Karlskoga with 23.1%. That is a frequency, not a forecast — in more than half of the simulations another team wins the league.
Which teams make the play-offs in HockeyAllsvenskan?
The best chances of a top-six finish belong to Kalmar HC (97%), BIK Karlskoga (93%), Leksands IF (90%), Modo Hockey (81%), Södertälje SK (65%), AIK (54%). The probability refers to a place among the top six of the regular season once all 52 rounds have been played.
Who risks relegation in HockeyAllsvenskan?
The biggest risk of finishing in the bottom two belongs to Vimmerby HC (57%), Almtuna IS (54%), Östersunds IK (31%). The battle at the bottom is often tighter than the race at the top — a few points separate several teams.
How does the simulation work?
Every remaining match gets a full set of score probabilities from the same match model as our predictions — team strength, form, shot quality, special teams and goaltending. The supercomputer plays out every match, settles overtime and shootouts, awards the points (3/2/1/0) and ranks the table. This is repeated 1,000,000 times, and the share of seasons with a given outcome becomes the team’s probability.
How often is the simulation recalculated?
After rounds have been played. New results change both the standings and the teams’ estimated strength, so the probabilities move throughout the season — most of all where a few points decide.