Saturday, 29/08/2026 at 20:30
Full timePanetolikos 2:0 PS Kalamata
PS Kalamata were the model's likeliest winner before kick-off at 34%. Panetolikos won 2–0. The biggest difference was Panetolikos's attack: 2.46 xG against 1.10 projected (+1.36).
Goals
- 26' Gustav Granath Panetolikos · 0.57 xG chance 1–0
- 90' Salifou Soumah Panetolikos · 0.16 xG chance 2–0
The model against the result
- Panetolikos
- 32% What happened
- Draw
- 34%
- PS Kalamata
- 34%
Data coverage One of these teams has only 1 match in the model window, short of the 10 the model wants. Read the projection as a direction rather than as a number.
Projected vs produced
Panetolikos produced 1.36 xG more than projected
Projected before kick-off Produced on the night
The shots behind it
- Shots
- 20
- 11
- On target
- 4
- 3
- Inside the box
- 15
- 4
- xG per shot
- 0.12
- 0.09
- Open play xG
- 1.51
- 0.42
- Set-piece xG
- 0.96
- 0.62
- Penalties
- —
- —
Shot-level xG may differ from the published match total
The provider publishes the match total separately from its shot-by-shot values. Those values are rounded to two decimals and, leaving out penalties and own goals, add up to its non-penalty xG: 2.49 for Panetolikos and 1.02 for PS Kalamata.
Shot map
Both teams shown attacking the same goal for comparison.
All shots
31 shots · 3.51 xG
Hover, focus or tap a shot to read it.
- Panetolikos
- PS Kalamata
- Filled = goal
- Dot area = xG (the smallest chances drawn at a minimum size)
Recent form
1.13 xG created 1.28 xGA
- L 0.15 created, 1.38 conceded against AE Kifisia
- W 1.08 created, 0.95 conceded against Atromitos Athens
- D 1.56 created, 0.90 conceded against AE Larisa
- D 1.27 created, 1.40 conceded against MGS Panserraikos
- L 2.23 created, 1.86 conceded against Asteras Aktor
- W 0.50 created, 1.19 conceded against Asteras Aktor
xG created xG conceded (xGA) W / D / L: the match result
Pre-match model & market record
7 opportunities flagged1 won6 lost
- Lost
- Lost
- Lost
- Lost
- Lost
- Won
- Lost
View pre-match record Hide pre-match record
| Selection | Price | Edge | Result |
|---|---|---|---|
| PS Kalamata to win | 3.40 | +16.7% | Lost |
| PS Kalamata 2+ goals | 3.50 | +12.6% | Lost |
| PS Kalamata to score | 1.50 | +6.5% | Lost |
| Draw | 3.10 | +4.2% | Lost |
| Both teams to score | 2.00 | +3.2% | Lost |
| Over 1.5 goals | 1.44 | +1.2% | Won |
| Panetolikos at most 1 goal | 1.44 | +0.5% | Lost |
Pre-match model vs market
| Outcome | Model | Market | Difference | Fair odds |
|---|---|---|---|---|
| Panetolikos | 32.1% | 41.9% | −9.8 pp | 3.12 |
| Draw | 33.6% | 30.4% | +3.2 pp | 2.98 |
| PS Kalamata | 34.3% | 27.7% | +6.6 pp | 2.91 |
Other markets
| Outcome | Model | Market | Difference | Fair odds |
|---|---|---|---|---|
| Panetolikos to score | 69.8% | 70.9% | −1.1 pp | 1.43 |
| Panetolikos not to score | 30.2% | 29.1% | +1.1 pp | 3.32 |
| PS Kalamata to score | 71.0% | 62.5% | +8.5 pp | 1.41 |
| PS Kalamata not to score | 29.0% | 37.5% | −8.5 pp | 3.45 |
| Both teams to score | 51.6% | 46.7% | +4.9 pp | 1.94 |
| Both teams not to score | 48.4% | 53.3% | −4.9 pp | 2.07 |
| Panetolikos 2+ goals | 30.4% | 35.5% | −5.1 pp | 3.29 |
| Panetolikos at most 1 goal | 69.6% | 64.5% | +5.1 pp | 1.44 |
| PS Kalamata 2+ goals | 32.2% | 26.9% | +5.3 pp | 3.11 |
| PS Kalamata at most 1 goal | 67.8% | 73.1% | −5.3 pp | 1.47 |
| Over 1.5 goals | 70.1% | 64.5% | +5.6 pp | 1.43 |
| Under 1.5 goals | 29.9% | 35.5% | −5.6 pp | 3.35 |
| Over 2.5 goals | 39.5% | 39.0% | +0.6 pp | 2.53 |
| Under 2.5 goals | 60.5% | 61.0% | −0.6 pp | 1.65 |
Prices and probabilities are the ones stored before kick-off; nothing here is recalculated. Edge is the model's probability times the price, minus one, so a flagged outcome that lost is the ordinary case rather than a contradiction.
Model details
· 1 warningShow Hide
- Fit window
- the last 20 matches, recency weighted (a match 107 days old counts half)
- Panetolikos sample
- 20 matches
- PS Kalamata sample
- 1 match
- Computed
- 11 hours before kick-off
- Prices taken
- 11 hours before kick-off
Warnings
- PS Kalamata has only 1 match in the database. Below 10 matches the estimate is very unreliable.
Limitations
The model reads expected goals and nothing else. It does not know the lineups, who is injured or suspended, what either side has to play for, the weather, or how congested the schedule around this match is.