01 · 2 legs
Safe
- Double chance AGF or draw (1X)68%
- Over 1.5 goals81%
Danish SuperligaCeres Park, Aarhus14°0,3 mm29 km/h
Läs på svenskaErik Lindberg · · Written from the model's numbers · How the predictions work
Two games are hardly enough to paint a complete picture, but AGF Aarhus holds a perfect head-to-head record against Odense Boldklub since 2022. Yet, don't let this minor sample distract you from the broader narrative: Odense Boldklub has demonstrated a sharper edge in both points and expected metrics this season. Their 3-point haul eclipses AGF’s modest 2, and their expected points tally of 5.9 outstrips their hosts' 3.9. In a match tinged with Danish drizzle, goals may just splash down like rain.
AGF Aarhus, despite their penchant for a counter-attacking style, will need to be wary of Odense Boldklub's high press and possession-based game. The expected goals model pegs AGF at 1.64, but Odense isn’t far behind at 1.39. Both teams show a capacity to find the back of the net, a notion supported by a 61% chance of both sides scoring. The over 2.5 goals market is leaning towards a favourable 58% — despite the weather's dampening potential.
AGF Aarhus may enter this match with the model nudging them as slight favourites at 43%, but Odense Boldklub's 30% chance can't be discounted, particularly given their superior xG and xGA figures this season. Expect AGF to test Odense’s defense with crosses and counter-attacks, though it’s Odense who might just nick the advantage, leveraging their tighter defensive setup — a 1.1 xGA per match tells a compelling story.
The most likely scorelines are tantalisingly close: 1-1 at 11% and 2-1 to AGF at 9% — suggesting a tight contest. However, the value bet lies in a nod to Odense Boldklub sneaking a win. The bookmaker odds on a 1-2 scoreline, valued at 8%, could offer an edge for the bold punter. With AGF's tendency to concede matched by Odense's scoring ability, backing an Odense victory at the right odds might be the shrewd move.
01 · 2 legs
02 · 3 legs
03 · 4 legs
No odds available yet.
Head-to-head based on Danish Superliga data since 2022.
Solid line = created (xG), dashed = conceded (xGA).
Last 5 matches. Circle size = pass volume, line width = combinations between players. Attacking left to right.
The same network weighted by offensive threat (xT): circle size = threat created by the player, line width = threat of the combinations. Last 5 matches, attacking left to right.
| Performance | AGF | Odense |
|---|---|---|
| Points | 2 | 3 |
| xPoints | 3.9 | 5.9 |
| xG per match | 1.6 | 1.7 |
| xGA per match | 1.6 | 1.1 |
| xG within 8s of winning the ball | 0.38 | 0.43 |
| xGA within 8s of losing the ball | 0.29 | 0.15 |
| Playing style | AGF | Odense |
|---|---|---|
| Build-up efficiency | 0.3 | 0.32 |
| Field tilt | 0.51 | 0.64 |
| xT per match | 1 | 1.5 |
| xTA per match | 1 | 0.88 |
| Won balls, offensive half | 20 | 28 |
| Pressing intensity | 0.24 | 0.23 |
| Pressing efficiency | 0.29 | 0.28 |
| Pressing efficiency, offensive half | 0.21 | 0.23 |
| Entries into the box per match | 16 | 14 |
| Entries into the box against | 14 | 10 |
| Pass completion % | 0.75 | 0.79 |
| Pass completion % under pressure | 0.69 | 0.73 |
| Passes per match | 362 | 456 |
| Passes against per match | 305 | 362 |
| Switches of play per match | 23.1 | 25.4 |
| Long balls per match | 27 | 27 |
| Set pieces | AGF | Odense |
|---|---|---|
| xG from free kicks | 0.02 | 0.01 |
| Corners per match | 7.4 | 7 |
| Corners against per match | 4.4 | 6.3 |
| xG per corner | 0.04 | 0.02 |
| xGA per corner against | 0.04 | 0.01 |
| First touch, offensive corners % | 0.36 | 0.21 |
| First touch, defensive corners % | 0.54 | 0.6 |
| Other | AGF | Odense |
|---|---|---|
| Throw-in control | 0.75 | 0.71 |
| The goalkeepers | Mouhammade Camara (AGF) | Viljar Myhra (Odense) |
|---|---|---|
| Saves | 6 | 8 |
| Save % | 100% | 73% |
| xG prevented | 100% | 69% |
| Claims | 0 (0%) | 0 (0%) |
The model gives AGF a 43% win probability. Full 1X2 picture: AGF 43%, draw 27%, Odense 30%.
The model's most likely final score is 1-1 at 11% probability.
The model rates over 2.5 goals at 58% and under at 42%.
The probability of both teams scoring is 61% according to the model.
The same rules as the bookmakers’ builders: legs that contradict or already follow from your slip cannot be combined — for example correct score 2–1 with under 2.5 goals. The difference is that we don’t look the rules up in a table; we compute them straight from the score matrix.
Predictions come from PlaymakerAI's match model and reflect the model's assessment — not betting advice. Gamble responsibly.