⚽ FootballData
Dixon-Coles/Ligue 2

Ligue 2

Dixon-Coles model — fitted 16 Mar 2026 on 2,122 matches since 2020-01-01

Home Advantage (γ)
1.1273
>1 = home favoured
ρ (rho)
0.08097
low-score correction
Teams modelled
40
Log-likelihood
-436

Team Parameters

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

#TeamAttack (α)Defense (β)Matches
1Lorient
1.6999
0.8274
43
2Metz
1.6020
1.0240
72
3Toulouse
1.5914
0.7949
76
4Auxerre
1.4600
0.8339
123
5St Etienne
1.2793
1.0162
99
6Reims
1.2722
0.8242
23
7Paris FC
1.2595
0.9212
195
8Dunkerque
1.2530
0.9034
171
9Guingamp
1.2194
1.2442
218
10Rodez
1.2188
1.2774
218
11Pau FC
1.1676
1.4823
209
12Bordeaux
1.1546
1.2103
76
13Angers
1.1329
1.0555
38
14Montpellier
1.1163
0.9871
23
15Quevilly Rouen
1.0937
1.3023
114
16Annecy
1.0801
1.0042
133
17Troyes
1.0528
0.9039
142
18Le Mans
1.0517
0.8806
32
19Sochaux
1.0086
1.1007
123
20Nimes
1.0056
1.5973
76
21Red Star
0.9845
1.0585
56
22Amiens
0.9764
1.5763
209
23Boulogne
0.9645
1.2824
23
24Chambly
0.9099
1.4457
47
25Lens
0.9017
0.9997
9
26Clermont
0.8939
1.3059
104
27Orleans
0.8913
1.0230
9
28Grenoble
0.8817
1.1504
218
29Concarneau
0.8597
1.4337
38
30Dijon
0.8594
1.0448
76
31Caen
0.8171
1.5107
195
32Martigues
0.8057
1.3776
34
33Le Havre
0.7999
0.6050
123
34Niort
0.7607
1.5909
123
35Nancy
0.7487
1.3213
108
36Chateauroux
0.7478
1.3515
47
37Ajaccio
0.7434
1.1136
157
38Laval
0.7040
1.2416
133
39Valenciennes
0.6432
1.2803
161
40Bastia
0.6140
0.9456
170