Olympique de Marseille 2:3 Paris FC: xG & match report · Ligue 1

Sunday, 06/09/2026 at 20:45

Full time

Match report · Ligue 1

Olympique de Marseille Home 1.42 xG · 14 shots

Paris FC Away 3.29 xG · 18 shots

23 The scoreline reflected the chances

Olympique de Marseille were the model's likeliest winner before kick-off at 49%. Paris FC won 3–2. The biggest difference was Paris FC's attack: 3.29 xG against 1.11 projected (+2.18). Olympique de Marseille produced 1.42 xG against 1.68 projected (−0.26).

Goals

1' Lassine Sinayoko Paris FC · 0.40 xG chance 0–1
8' Amine Gouiri Olympique de Marseille · 0.32 xG chance 1–1
19' Lassine Sinayoko Paris FC · 0.25 xG chance 1–2
53' Amine Gouiri Olympique de Marseille · 0.20 xG chance 2–2
86' Lassine Sinayoko (pen.) Paris FC · 0.82 xG chance 2–3

The model against the result

priced before kick-off

Olympique de Marseille: 49%
Draw: 28%
Paris FC: 22% What happened

Data coverage Both teams have the full 20-match model window available.

Projected vs produced

Expected goals

Olympique de Marseille 1.68 1.42 -0.26

Paris FC 1.11 3.29 +2.18

Paris FC produced 2.18 xG more than projected

Projected before kick-off Produced on the night

The shots behind it

Olympique de Marseille Paris FC

Shots: 14; 18
On target: 5; 7
Inside the box: 10; 12
xG per shot: 0.11; 0.16
Open play xG: 1.42; 1.86
Set-piece xG: 0.07; 0.29
Penalties: 0; 1

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: 1.45 for Olympique de Marseille and 2.20 for Paris FC.

Shot map

Both teams shown attacking the same goal for comparison.

All shots

32 shots · 4.46 xG

Hover, focus or tap a shot to read it.

Olympique de Marseille
Paris FC
Filled = goal
Dot area = xG (the smallest chances drawn at a minimum size)

Recent form

Last 6 · xG per match

Olympique de Marseille

1.85 xG created 1.74 xGA

D 2.32 created, 1.46 conceded against Nice
L 1.36 created, 3.42 conceded against Nantes
W 1.21 created, 1.14 conceded against Le Havre
W 2.92 created, 2.49 conceded against Stade Rennais
W 1.86 created, 0.18 conceded against RC Strasbourg
L 1.40 created, 1.72 conceded against AS Monaco

Paris FC

1.84 xG created 0.99 xGA

L 1.06 created, 1.71 conceded against Lille
W 1.86 created, 0.79 conceded against Stade Brestois
L 1.20 created, 1.77 conceded against Stade Rennais
W 2.20 created, 0.61 conceded against Paris Saint-Germain
D 1.80 created, 0.42 conceded against Troyes
W 2.89 created, 0.65 conceded against Nice

xG created xG conceded (xGA) W / D / L: the match result

Head to head

Olympique de Marseille: 1 won · 1 drawn · 0 lost

Pre-match model & market record

2 opportunities flagged1 won1 lost

Lost
Won

View pre-match record

The outcomes the model flagged on Olympique de Marseille against Paris FC before kick-off, in the order they ranked, with the price, the edge and what became of each.
Selection	Price	Edge	Result
Draw	3.80	+7.5%	Lost
Both teams to score	1.67	+1.0%	Won

Pre-match model vs market

Outcome	Model	Market	Difference	Fair odds
Olympique de Marseille	49.5%	49.3%	+0.2 pp	2.02
Draw	28.3%	24.7%	+3.6 pp	3.54
Paris FC	22.2%	26.0%	−3.8 pp	4.49

Other markets

Outcome	Model	Market	Difference	Fair odds
Both teams to score	60.6%	55.7%	+4.9 pp	1.65
Both teams not to score	39.4%	44.3%	−4.9 pp	2.54
Over 1.5 goals	81.0%	78.0%	+3.0 pp	1.23
Under 1.5 goals	19.0%	22.0%	−3.0 pp	5.27
Over 2.5 goals	54.6%	54.9%	−0.3 pp	1.83
Under 2.5 goals	45.4%	45.1%	+0.3 pp	2.20

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

Show

Fit window: the last 20 matches, recency weighted (a match 107 days old counts half)
Olympique de Marseille sample: 20 matches
Paris FC sample: 20 matches
Computed: 6 hours before kick-off
Prices taken: 6 hours before kick-off

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.