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Super League
Referee: Luca Piccolo
Lausanne
S T T T T
1–1
Full-time
Grasshoppers
T S S U S

Retrospective analysis — the model is trained only on matches played before this one.

🔒 Who wins? Pro

The model's probabilities for a home win, draw and away win — with reasoning and recommended markets.

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Team trends

Lausanne Grasshoppers
Last 6 — all competitions · 6/6 matches
Win Ø 34%
17%
50%
Points per game
0,5
1,7
Over 2.5 Ø 75%
83%
67%
Both score Ø 59%
50%
67%
Average goals
3,2
2,8
Scored
1,0
1,5
Conceded
2,2
1,3
Goal diff per game
-1,2
+0,2
xG created Pro
xG conceded Pro
xG diff Pro
🔒 Goal markets Pro

Expected goals, over/under and both teams to score — priced by the model before kick-off.

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🔒 Most likely result Pro

The model's top final scores with the best bookmaker odds.

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Match stats

Half-time score: 1–0

0,80 xG 0,74
52% Possession 48%
8 Shots 13
2 Shots on target 3
2 Blocked shots 3
2 Goalkeeper saves 1
5 Corners 2
1 Offside 1
13 Free kicks against 22
3 Yellow cards 4
0 Red cards 0

Recent head-to-head meetings

16. May -26 Lausanne – Grasshoppers 1–3 8. Mar -26 Grasshoppers – Lausanne 2–3 24. Jan -26 Lausanne – Grasshoppers 1–1 14. Sep -25 Grasshoppers – Lausanne 3–1 22. Feb -25 Lausanne – Grasshoppers 2–2

Match report

Lausanne Grasshoppers
27′
🟨 Dorian Paloschi
1–0 I. Bah Mendes ⚽
O. Janneh
33′
Half-time 1–0
46′
🔁 A. Diaby
↦ D. Paloschi
46′
🔁 L. Plange
↦ O. Clemente
K. de la Fuente 🔁
↦ I. Bah Mendes
59′
66′
🟨 Nikolas Muci
Konrad de la Fuente 🟨
67′
Morgan Poaty 🟨
67′
F. Mollet 🔁
↦ O. Custodio
69′
S. Kone 🔁
↦ N. Butler-Oyedeji
69′
69′
🔁 S. Bengondo
↦ N. Muci
69′
🔁 D. Sahin
↦ A. Abrashi
Sekou Kone 🟨
72′
77′
🟨 Samuel Krasniqi
79′
🔁 A. Conateh
↦ H. Imourane
81′
⚽ S. Bengondo 1–1
D. Sahin
84′
🟨 Luka Mikulic
A. Ajdini 🔁
↦ B. Lekoueiry
85′
S. Traore 🔁
↦ O. Janneh
85′
Full-time 1–1

Odds comparison

Green columns are the outcomes that came in.

1X2
H
–
🔒
U ✓
–
🔒
B
–
🔒
Over/under goals
O1,5 ✓
–
🔒
U1,5
–
🔒
O2,5
–
🔒
U2,5 ✓
–
🔒
O3,5
–
🔒
U3,5 ✓
–
🔒
Both teams to score
BLS ja ✓
–
🔒
BLS nei
–
🔒

Recent matches

The analysis is a statistical estimate based on the teams' results and underlying performances (xG) — not a guarantee. See the model's results.