⚽ FootballData
Dixon-Coles/Serie A

Serie A

Dixon-Coles model — fitted 16 Mar 2026 on 2,368 matches since 2020-01-01

Home Advantage (γ)
1.1341
>1 = home favoured
ρ (rho)
0.01701
low-score correction
Teams modelled
31
Log-likelihood
-648

Team Parameters

Attack (α) — normalised attacking strength; mean ≈ 1.0. Defense (β) — defensive weakness; lower = harder to score against.

#TeamAttack (α)Defense (β)Matches
1Inter
1.9971
0.6410
240
2Juventus
1.6687
0.8196
237
3Como
1.5330
0.6991
66
4Milan
1.4306
0.6722
235
5Napoli
1.4279
0.8243
236
6Atalanta
1.4275
0.7575
236
7Roma
1.3585
0.6798
235
8Genoa
1.1943
1.0672
199
9Bologna
1.1723
1.0121
235
10Sassuolo
1.1287
1.1267
202
11Fiorentina
1.1230
1.1403
237
12Torino
1.0714
1.2956
236
13Udinese
1.0509
1.1355
238
14Cagliari
1.0102
1.2019
201
15Crotone
1.0057
2.0453
38
16Lazio
0.9955
0.8429
237
17Frosinone
0.9201
1.4282
38
18Brescia
0.9023
1.1305
20
19Benevento
0.8834
1.5777
37
20Spal
0.8747
1.1482
20
21Salernitana
0.8439
1.7153
113
22Empoli
0.8176
1.3090
149
23Venezia
0.7860
1.1612
76
24Pisa
0.7786
1.3539
29
25Parma
0.7596
1.0327
126
26Spezia
0.7375
1.3771
110
27Verona
0.7225
1.4415
239
28Lecce
0.7194
1.0637
162
29Monza
0.6969
1.5002
112
30Sampdoria
0.6670
1.6043
133
31Cremonese
0.6669
1.1445
64