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Serie A
Referee: M Ferrieri Caputi
Frosinone
U T S T T
2–3
Full-time
Monza
T S T T U

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

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

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Team trends

Frosinone Monza
Last 6 — all competitions · 6/6 matches
Win Ø 17%
17%
17%
Points per game
0,7
0,8
Over 2.5 Ø 58%
83%
33%
Both score Ø 50%
67%
33%
Average goals
3,0
1,7
Scored
1,3
0,5
Conceded
1,7
1,2
Goal diff per game
-0,3
-0,7
xG created Pro
xG conceded Pro
xG diff Pro
This season — Serie A · 18/18 matches
Win Ø 28%
28%
28%
Points per game
1,1
1,2
Over 2.5 Ø 48%
67%
28%
Both score Ø 59%
78%
39%
Average goals
3,0
1,9
Scored
1,3
0,9
Conceded
1,7
1,0
Goal diff per game
-0,4
-0,1
xG created Pro
xG conceded Pro
xG diff Pro
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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: 0–2

1,16 xG 1,34
60% Possession 40%
10 Shots 7
5 Shots on target 2
2 Blocked shots 2
0 Goalkeeper saves 3
5 Corners 1
2 Offside 0
11 Free kicks against 16
3 Yellow cards 5
0 Red cards 0
The table before the match Frosinone 14. · Monza 11.
# Team K V U T +/− xG +/− P
1 Inter 19 15 3 1 +35 +25.5 48
2 Juventus 18 13 4 1 +16 +15.9 43
3 Milan 18 11 3 4 +12 +4.0 36
4 Fiorentina 18 10 3 5 +9 -1.6 33
5 Bologna 19 8 8 3 +6 +5.4 32
6 Atalanta 18 9 2 7 +9 +5.4 29
7 Roma 18 8 4 6 +10 +7.5 28
8 Napoli 18 8 4 6 +7 +12.2 28
9 Lazio 18 8 3 7 +2 +4.5 27
10 Torino 18 6 6 6 -3 +0.0 24
11 Monza 18 5 7 6 -2 -5.7 22
12 Genoa 19 5 6 8 -4 -3.2 21
13 Lecce 18 4 8 6 -5 -11.0 20
14 Frosinone 18 5 4 9 -8 -6.7 19
15 Udinese 18 2 11 5 -10 -4.0 17
16 Sassuolo 18 4 4 10 -8 -6.8 16
17 Verona 19 3 5 11 -10 -8.0 14
18 Cagliari 18 3 5 10 -15 -4.7 14
19 Empoli 18 3 4 11 -20 -10.1 13
20 Salernitana 18 2 6 10 -21 -18.6 12

Calculated from our results data. “xG +/−” is expected goal difference — a positive number with a low league position suggests an undervalued team. Ties on points are sorted by goal difference; some leagues use other rules.

Match report

Frosinone Monza
Pierluigi Frattali 🟥
-5′
18′
Dany Mota 0–1
V. Carboni
28′
🟨 Roberto Gagliardini
41′
🔁 A. Sorrentino
↦ M. Di Gregorio
45′
V. Carboni 0–2
Dany Mota
Half-time 0–2
L. Mazzitelli 🔁
↦ Reinier
46′
G. Caso 🔁
↦ Pol Lirola
46′
51′
🟨 Luca Caldirola
Ilario Monterisi 🟨
54′
55′
M. Soulé 1–2
own goal
2–2 A. Harroui
I. Monterisi
56′
Kaio Jorge 🔁
↦ W. Cheddira
60′
60′
🔁 Pablo Marí
↦ R. Gagliardini
60′
🔁 J. Akpa Akpro
↦ A. Colpani
Mateus Lusuardi 🟨
73′
74′
🟨 Danilo D'Ambrosio
3–2 M. Soulé
76′
77′
🔁 S. Birindelli
↦ Pedro Pereira
77′
🔁 L. Colombo
↦ Dany Mota
81′
🟨 Lorenzo Colombo
A. Ibrahimović 🔁
↦ Mateus Lusuardi
84′
M. Çuni 🔁
↦ A. Harroui
85′
Enzo Barrenechea 🟨
89′
93′
🟨 Jean-Daniel Akpa Akpro
Full-time 3–2

Line-ups

Frosinone

3-4-3
80 S. Turati G
30 I. Monterisi D
5 C. Okoli D
47 Mateus Lusuardi D
20 Pol Lirola M
45 E. Barrenechea M
21 A. Harroui M
14 F. Gelli M
18 M. Soulé F
70 W. Cheddira F
12 Reinier F
Bench (12)
36 L. Mazzitelli
10 G. Caso
9 Kaio Jorge
27 A. Ibrahimović
11 M. Çuni
29 F. Ghedjemis
16 L. Garritano
17 G. Kvernadze
1 P. Frattali
31 M. Cerofolini
24 M. Bourabia
6 S. Romagnoli

Coach: E. Di Francesco

Monza

3-4-2-1
16 M. Di Gregorio G
33 D. D'Ambrosio D
6 R. Gagliardini D
5 L. Caldirola D
84 P. Ciurria M
32 M. Pessina M
38 W. Bondo M
13 Pedro Pereira M
28 A. Colpani F
21 V. Carboni F
47 Dany Mota F
Bench (15)
23 A. Sorrentino
22 Pablo Marí
8 J. Akpa Akpro
19 S. Birindelli
9 L. Colombo
18 D. Bettella
77 G. Kyriakopoulos
7 José Machín
11 F. Carboni
4 A. Izzo
66 S. Gori
80 S. Vignato
2 G. Donati
61 A. Ferraris
46 G. Cittadini

Coach: R. Palladino

Odds comparison

Green columns are the outcomes that came in.

1X2
H
2,66
U
3,38
B ✓
2,68
Over/under goals
O1,5 ✓
O2,5 ✓
1,87
U2,5
1,96
O3,5 ✓
Both teams to score
BLS ja ✓
BLS nei

Referee

M Ferrieri Caputi · 46 kamper i vår database

🟨 Yellow cards
4,2 / match
Average
⚽ Free kicks
28,0 / match
Above average

🟥 Red cards: 0,11 per match

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