Italy2025/26 season

Serie A Table

Live standings refreshed hourly. The xPtscolumn shows the model's expected points based on goal-scoring rates; the Δ column is actual minus expected — positive means over-performance, negative means under-performance.

Fixtures Finish-position probabilities Top scorer race xPts methodology

#	Team	P	W	D	L	GF	GA	GD	Pts	xPts	Δ	Form
1	Inter	37	27	5	5	86	32	+54	86	84.3	+1.7	WDWWD
2	Napoli	37	22	7	8	57	36	+21	73	65.5	+7.5	LWDLW
3	AC Milan	37	20	10	7	52	33	+19	70	64.4	+5.6	WDLLW
4	AS Roma	37	22	4	11	57	31	+26	70	69.3	+0.7	DWWWW
5	Como	37	19	11	7	61	28	+33	68	74.2	-6.2	LWDWW
6	Juventus	37	19	11	7	59	32	+27	68	69.8	-1.9	WDDWL
7	Atalanta	37	15	13	9	50	35	+15	58	61.4	-3.4	DLDWL
8	Bologna	37	16	7	14	46	43	+3	55	52.6	+2.4	LLDWW
9	Lazio	37	13	12	12	39	39	0	51	50.0	+1.0	WDWLL
10	Udinese	37	14	8	15	45	47	-2	50	49.1	+0.9	LDWWL
11	Sassuolo	37	14	7	16	46	49	-3	49	48.5	+0.5	WDWLL
12	Torino	37	12	8	17	42	61	-19	44	38.2	+5.8	DDLWL
13	Parma	37	10	12	15	27	46	-19	42	35.2	+6.8	WWLLL
14	Genoa	37	10	11	16	41	50	-9	41	44.0	-3.0	WLDDL
15	Fiorentina	37	9	14	14	40	49	-9	41	43.9	-2.9	DDLDW
16	Cagliari	37	10	10	17	38	52	-14	40	40.5	-0.5	LWDLW
17	Lecce	37	9	8	20	27	50	-23	35	32.8	+2.2	DDWLW
18	Cremonese	37	8	10	19	31	53	-22	34	34.4	-0.4	DLLWW
19	Hellas Verona	37	3	12	22	25	59	-34	21	26.5	-5.5	LDDLD
20	Pisa	37	2	12	23	25	69	-44	18	22.1	-4.1	LLLLL

How xPoints is computed: for each team we average goals scored and conceded per match across the season, feed those rates into a Poisson model, derive a per-match expected-points value, and multiply by matches played. It's a goals-rate proxy — true shot-xG xPoints would require per-fixture stats aggregation that's outside the API budget right now. The methodology is consistent across every team in the table so the rankings remain meaningful.