xgEdge

Sunday, 23/08/2026 at 18:30

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Match report · Serie A

Frosinone 0:1 Juventus

Frosinone Home 0.41 xG · 11 shots
Juventus Away 2.18 xG · 20 shots
01 The scoreline reflected the chances

Juventus were the model's likeliest winner before kick-off at 43%. They won 1–0. The biggest difference was Juventus's attack: 2.18 xG against 1.43 projected (+0.75). Frosinone produced 0.41 xG against 1.08 projected (−0.67).

Goals

  1. 22' Bremer Juventus · 0.33 xG chance 0–1

The model against the result

priced before kick-off
Frosinone
26%
Draw
31%
Juventus
43% What happened

Data coverage The model has never seen one of these sides play. Every figure here rests on an exactly average stand-in for it, which is a guess in a projection's clothes.

Projected vs produced

Expected goals
Frosinone The model projected 1.08 Produced 0.41 Difference -0.67
Juventus The model projected 1.43 Produced 2.18 Difference +0.75

Juventus produced 0.75 xG more than projected

Projected before kick-off Produced on the night

The shots behind it

Frosinone Juventus
Shots
11
20
On target
2
5
Inside the box
7
13
xG per shot
0.04
0.10
Open play xG
0.33
1.01
Set-piece xG
0.12
0.99
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: 0.45 for Frosinone and 2.00 for Juventus.

Shot map

Both teams shown attacking the same goal for comparison.

Juventus · Francisco Conceição · 52' · No goal · 9 m · 0.46 xG Juventus · Bremer · 22' · Goal · 6 m · 0.33 xG Juventus · Randal Kolo Muani · 19' · No goal · 4 m · 0.29 xG Juventus · Kenan Yıldız · 42' · No goal · 14 m · 0.14 xG Juventus · Bremer · 17' · No goal · 9 m · 0.10 xG Juventus · Lloyd Kelly · 35' · No goal · 8 m · 0.10 xG Juventus · Weston McKennie · 71' · No goal · 8 m · 0.10 xG Frosinone · Antonio Raimondo · 13' · No goal · 15 m · 0.09 xG Juventus · Randal Kolo Muani · 7' · No goal · 18 m · 0.08 xG Juventus · Francisco Conceição · 7' · No goal · 15 m · 0.08 xG Frosinone · Luis Hasa · 87' · No goal · 21 m · 0.07 xG Juventus · Douglas Luiz · 38' · No goal · 19 m · 0.06 xG Frosinone · Giorgi Kvernadze · 40' · No goal · 12 m · 0.06 xG Frosinone · Ilario Monterisi · 4' · No goal · 7 m · 0.05 xG Juventus · Douglas Luiz · 31' · No goal · 27 m · 0.04 xG Frosinone · Seydou Fini · 53' · No goal · 13 m · 0.04 xG Frosinone · Gabriele Calvani · 90' · No goal · 10 m · 0.04 xG Juventus · Kenan Yıldız · 35' · No goal · 9 m · 0.03 xG Frosinone · Gabriele Bracaglia · 40' · No goal · 22 m · 0.03 xG Juventus · Pierre Kalulu · 42' · No goal · 14 m · 0.03 xG Juventus · Weston McKennie · 63' · No goal · 14 m · 0.03 xG Frosinone · Gabriele Calvani · 70' · No goal · 11 m · 0.03 xG Juventus · Teun Koopmeiners · 88' · No goal · 21 m · 0.03 xG Juventus · Randal Kolo Muani · 45' · No goal · 14 m · 0.02 xG Juventus · Douglas Luiz · 45' · No goal · 29 m · 0.02 xG Juventus · Francisco Conceição · 48' · No goal · 15 m · 0.02 xG Frosinone · Giorgi Kvernadze · 58' · No goal · 11 m · 0.02 xG Juventus · Francisco Conceição · 66' · No goal · 26 m · 0.02 xG Juventus · Edon Zhegrova · 83' · No goal · 32 m · 0.02 xG Frosinone · Giorgi Kvernadze · 74' · No goal · 14 m · 0.01 xG Frosinone · Aleksa Terzić · 90' · No goal · 27 m · 0.01 xG

All shots

31 shots · 2.45 xG

Hover, focus or tap a shot to read it.

  • Frosinone
  • Juventus
  • Filled = goal
  • Dot area = xG (the smallest chances drawn at a minimum size)

Recent form

Last 6 · xG per match
Frosinone

No match with expected goals behind it yet.

Juventus

1.77 xG created 0.62 xGA

  • W 1.31 created, 0.59 conceded against Bologna
  • D 0.52 created, 0.65 conceded against Milan
  • D 2.60 created, 0.30 conceded against Hellas Verona
  • W 2.29 created, 0.88 conceded against Lecce
  • L 1.98 created, 0.47 conceded against Fiorentina
  • D 1.92 created, 0.85 conceded against Torino

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

Pre-match model & market record

8 opportunities flagged2 won6 lost

  • Lost
  • Lost
  • Lost
  • Lost
  • Won
  • Lost
  • Lost
  • Won
View pre-match record
The outcomes the model flagged on Frosinone against Juventus before kick-off, in the order they ranked, with the price, the edge and what became of each.
Selection Price Edge Result
Frosinone to win 8.00 +105.8% Lost
Frosinone 2+ goals 5.00 +47.6% Lost
Juventus not to score 6.50 +39.0% Lost
Draw 4.20 +30.1% Lost
Juventus at most 1 goal 2.25 +28.3% Won
Frosinone to score 1.73 +19.2% Lost
Both teams to score 1.95 +9.4% Lost
Under 2.5 goals 2.00 +6.2% Won

Pre-match model vs market

Outcome Model Market Difference Fair odds
Frosinone 25.7% 11.9% +13.9 pp 3.89
Draw 31.0% 22.6% +8.4 pp 3.23
Juventus 43.3% 65.5% −22.2 pp 2.31

Other markets

Outcome Model Market Difference Fair odds
Frosinone to score 69.0% 53.7% +15.4 pp 1.45
Frosinone not to score 31.0% 46.3% −15.4 pp 3.23
Juventus to score 78.6% 85.4% −6.8 pp 1.27
Juventus not to score 21.4% 14.6% +6.8 pp 4.68
Both teams to score 56.1% 48.0% +8.1 pp 1.78
Both teams not to score 43.9% 52.0% −8.1 pp 2.28
Frosinone 2+ goals 29.5% 18.9% +10.6 pp 3.39
Frosinone at most 1 goal 70.5% 81.1% −10.6 pp 1.42
Juventus 2+ goals 43.0% 58.9% −15.9 pp 2.33
Juventus at most 1 goal 57.0% 41.1% +15.9 pp 1.75
Over 1.5 goals 75.7% 76.2% −0.5 pp 1.32
Under 1.5 goals 24.3% 23.8% +0.5 pp 4.12
Over 2.5 goals 46.9% 52.6% −5.8 pp 2.13
Under 2.5 goals 53.1% 47.4% +5.8 pp 1.88

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 warning
Show
Fit window
the last 20 matches, recency weighted (a match 107 days old counts half)
Frosinone sample
0 matches
Juventus sample
20 matches
Computed
4 hours before kick-off
Prices taken
2 days before kick-off

Warnings

  • There is not a single played match in the database for Frosinone — the estimate for that side is the league average and nothing else.

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.