⚽ FootballData
Dixon-Coles/Serie B

Serie B

Dixon-Coles model — fitted 16 Mar 2026 on 2,345 matches since 2020-01-01

Home Advantage (γ)
1.3002
>1 = home favoured
ρ (rho)
-0.09896
low-score correction
Teams modelled
47
Log-likelihood
-674

Team Parameters

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

#TeamAttack (α)Defense (β)Matches
1Venezia
1.6341
0.7412
160
2Sassuolo
1.6300
0.8885
38
3Cremonese
1.3767
1.0016
173
4Monza
1.3767
0.7408
106
5Frosinone
1.3763
0.9041
196
6Parma
1.3438
0.8113
109
7Genoa
1.3356
0.6256
36
8Pisa
1.3243
0.9045
203
9Palermo
1.3096
0.8472
141
10Como
1.2712
0.8986
110
11Cagliari
1.2621
0.6225
36
12Catanzaro
1.2607
1.0504
106
13Lecce
1.2560
0.7869
76
14Pescara
1.1168
1.4375
83
15Reggina
1.0395
1.0205
109
16Modena
1.0245
0.8153
140
17FeralpiSalo
1.0166
1.3818
37
18Empoli
1.0150
1.2667
86
19Chievo
0.9962
0.9746
57
20Perugia
0.9863
1.0580
93
21Trapani
0.9779
0.9975
18
22Crotone
0.9658
1.2301
54
23Livorno
0.9641
1.0170
17
24Spal
0.9624
1.0773
109
25Cesena
0.9536
1.2238
69
26Ternana
0.9491
1.1086
112
27Carrarese
0.9435
1.1406
68
28Mantova
0.9389
1.2721
68
29Spezia
0.9300
1.1202
128
30Sudtirol
0.9288
0.8676
141
31Benevento
0.9124
1.0824
93
32Vicenza
0.9106
1.1412
74
33Brescia
0.8923
1.0313
187
34Juve Stabia
0.8744
1.0131
89
35Alessandria
0.8657
1.2054
37
36Padova
0.8582
1.0897
29
37Avellino
0.8448
1.2677
30
38Ascoli
0.8312
0.8778
167
39Bari
0.8163
1.2281
142
40Sampdoria
0.8151
1.0442
107
41Virtus Entella
0.7919
1.1348
85
42Reggiana
0.7837
1.2688
142
43Pordenone
0.7677
1.3874
91
44Cosenza
0.7199
1.2854
203
45Cittadella
0.6970
1.2008
203
46Lecco
0.6855
1.6868
36
47Salernitana
0.6745
0.9478
96