Silkeborg IF
Silkeborg IF in the Superliga, 2026/2027 season. All figures on this page are calculated from completed fixtures only.
- Competition
- Superliga (Denmark)
- Season
- 2026/2027
- Position
- 8 of 12Provisional, from completed fixtures in our data
- Team Strength
- 36.7Internal contextual metric, 0–100
- Founded
- 1917
Season summary
Calculated from completed fixtures only. Scheduled fixtures are never counted.
- Matches played
- 4
- Wins
- 1
- Draws
- 1
- Losses
- 2
- Goals scored
- 3
- Goals conceded
- 5
- Goal difference
- -2
- Points
- 4
1.00 per match
Form
A plain sequence of results — no weighting or momentum model is applied here.
- Draw
- Loss
- Win
- Loss
Oldest to most recent, completed fixtures only. 4 matches shown.
Team performance
Aggregated player data: team volumes summed from the individual player statistics of 4 fixtures. These are not official provider team statistics.
| Metric | Total | Per match | Matches with data |
|---|---|---|---|
| Shots | 43 | 10.75 | 4 |
| Shots on target | 15 | 5.00 | 3 |
| Passes | 2,147 | 536.75 | 4 |
| Accurate passes | 1,837 | 459.25 | 4 |
| Passes into the final third | 155 | 38.75 | 4 |
| Key passes | 34 | 8.50 | 4 |
| Chances created | 34 | 8.50 | 4 |
| Successful dribbles | 25 | 6.25 | 4 |
| Tackles | 78 | 19.50 | 4 |
| Interceptions | 49 | 12.25 | 4 |
| Clearances | 108 | 27.00 | 4 |
| Ball recoveries | 206 | 51.50 | 4 |
| Duels | 342 | 85.50 | 4 |
| Duels won | 181 | 45.25 | 4 |
| Aerial duels | 41 | 10.25 | 4 |
| Aerial duels won | 31 | 7.75 | 4 |
| Goalkeeper saves | 13 | 3.25 | 4 |
Aggregated player data, summed from the individual player statistics of this team's completed fixtures. These are not official provider team statistics. A dash or "not available" means the provider sent no value — it never means zero.
Efficiency rates
Ratios calculated only where both the numerator and denominator exist in the provider data.
| Metric | Rate |
|---|---|
| Pass accuracy | 85.6% |
| Shots on target rate | 34.9% |
| Dribble success | 41.0% |
| Duel win rate | 52.9% |
| Aerial win rate | 75.6% |
| Tackle success | 57.7% |
Recent results
Most recent completed fixtures, newest first.
| Date | Opponent | Venue | Score | Result | Competition |
|---|---|---|---|---|---|
| 16 Aug 2026 | Nordsjælland | Away | 0–1 | Loss | Superliga |
| 10 Aug 2026 | Odense BK | Home | 1–0 | Win | Superliga |
| 02 Aug 2026 | FC København | Home | 1–3 | Loss | Superliga |
| 27 Jul 2026 | Randers FC | Away | 1–1 | Draw | Superliga |
Home and away
One or both splits cover fewer than four completed fixtures, so these records are indicative only.
| Split | P | W | D | L | GF | GA | GD | Pts |
|---|---|---|---|---|---|---|---|---|
| Home | 2 | 1 | 0 | 1 | 2 | 3 | -1 | 3 |
| Away | 2 | 0 | 1 | 1 | 1 | 2 | -1 | 1 |
Squad
Current-season squad ordered by minutes played. Impact Score appears only for players who meet the publishable sample and coverage rules.
| Player | Role | Apps | Minutes | Goals | Assists | Impact |
|---|---|---|---|---|---|---|
| Robin Østrøm | Centre back | 4 | 360 | — | — | Sample too small |
| Aske Andrésen | Goalkeeper | 4 | 360 | — | — | Sample too small |
| Mads Larsen | Defensive midfielder | 4 | 360 | — | — | Sample too small |
| Jens Martin Gammelby | Defensive midfielder | 4 | 360 | — | — | Sample too small |
| Callum McCowatt | Attacking midfielder | 4 | 331 | 1 | 2 | Sample too small |
| Villads Westh | Defensive midfielder | 4 | 321 | — | — | Sample too small |
| Alexander Priesborg | Centre back | 4 | 314 | — | — | Sample too small |
| William Kirk | Defensive midfielder | 4 | 272 | — | — | Sample too small |
| William Løkke Møller | Centre back | 3 | 261 | — | — | Sample too small |
| Oliver Ross | Attacking midfielder | 4 | 236 | 2 | 1 | Sample too small |
| Rami Al Hajj | Striker | 4 | 211 | — | — | Sample too small |
| Lucas Thorst Riisgaard | Attacking midfielder | 4 | 172 | — | — | Sample too small |
| Younes Bakiz | Role not established | 4 | 117 | — | — | Sample too small |
| Andreas Poulsen | Centre back | 1 | 90 | — | — | Sample too small |
| Julius Lorents | Defensive midfielder | 4 | 85 | — | — | Sample too small |
| Alexander Simmelhack | Role not established | 3 | 40 | — | — | Sample too small |
| Sofus Berger | Role not established | 2 | 32 | — | — | Sample too small |
| Melker Jonsson | Role not established | 1 | 18 | — | — | Sample too small |
| Malthe Hansen | Role not established | 1 | 12 | — | — | Sample too small |
| Pedro Ganchas | Role not established | 1 | 8 | — | — | Sample too small |
Appearances and minutes cover completed fixtures only. Players who represented more than one team this season keep their full season sample and are flagged accordingly.
Player contributions
Leading contributors within this squad. Each list states the sample it is based on.
Most minutes
All completed fixtures this season.
- 1Robin Østrøm360minutes
- 2Aske Andrésen360minutes
- 3Mads Larsen360minutes
- 4Jens Martin Gammelby360minutes
- 5Callum McCowatt331minutes
Team strength
| Team Strength Score | 36.7 | 0–100 scale |
|---|---|---|
| Points per match | 1.00 | Component input |
| Goal difference per match | -0.50 | Component input |
| Goals scored per match | 0.75 | Context |
| Goals conceded per match | 1.25 | Context |
| Matches included | 4 | Completed fixtures only |
Team Strength is a transparent internal contextual metric (engine version 1) built from points per match and goal difference per match across completed fixtures. It is used to describe the quality of opposition a player faced. It is not an official league rating and carries no governing-body status. See the methodology for the full definition.
Methodology
Data source
Match, lineup, event and player statistics come from Sportmonks and are stored per fixture. Only completed fixtures with recorded minutes are aggregated — announced lineups and future fixtures are excluded.
Per 90 minutes
A per-90 value scales a season total to a full match: total × 90 ÷ minutes played. It is not calculated when minutes are missing or zero.
Missing data
The provider omits some statistics instead of sending a zero. A missing value is shown as "not available" and never converted to 0, so rates and percentiles are only produced where the underlying fields genuinely exist.
Impact Score
The Football Performance Lab Impact Score is an experimental in-house model that combines role-relative percentile families with explicit weights. It is not an official rating and not a measure of transfer value.
Minimum samples
Values are calculated internally from 180 minutes, but a player is only shown publicly with a ranked Impact Score from 360 minutes and 5 appearances, with at least 90% metric coverage and an established role. A single appearance contributes a match impact observation from 30 minutes.