Serie A: Predictions, Odds & Tips

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Model-based predictions for Serie A — Inter, Milan, Juventus, Napoli and the rest of the Italian top flight, analysed by the same match model that powers PlaymakerAI. Ahead of every round the model computes 1X2 probabilities, a full score matrix, expected goals and over/under lines, built from hundreds of data points per team: strength, form, playing style, squad and weather.

The predictions are held against live odds from multiple bookmakers, with positive expected value (EV+) flagged where the model’s probability beats the implied odds. Played matches are graded openly — hit or miss — and everything updates continuously until kickoff.

Upcoming matches

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League table2025/26

New season with no matches played yet — the table shows the most recently completed season until the rounds get going.

Pos	Team	S	W–D–L	GF–GA	P	xP	xG	xGA	xGD	FT	Style
1	Inter	38	27–6–5	89–35	87	62,8	74,9	37,6	+37,3	71 %	Possession & high press
2	Napoli	38	23–7–8	58–36	76	54,4	56,6	41,4	+15,2	62 %	Possession control
3	Roma	38	23–4–11	59–31	73	62,8	63,5	33,8	+29,7	66 %	Possession control
4	Como	38	20–11–7	65–29	71	56,5	62,3	36,5	+25,8	68 %	Possession & high press
5	AC Milan	38	20–10–8	53–35	70	56,4	60,0	48,3	+11,7	52 %	Balanced & physical
6	Juventus	38	19–12–7	61–34	69	62,1	68,0	37,2	+30,8	66 %	Possession & high press
7	Atalanta	38	15–14–9	51–36	59	54,9	64,6	50,2	+14,4	60 %	Possession & high press
8	Bologna	38	16–8–14	49–46	56	54,5	49,4	40,7	+8,7	60 %	Possession control
9	Lazio	38	14–12–12	41–40	54	46,5	43,3	55,1	−11,8	52 %	Balanced & physical
10	Udinese	38	14–8–16	45–48	50	44,1	39,9	57,0	−17,1	44 %	Low block & direct
11	Sassuolo	38	14–7–17	46–50	49	49,5	44,5	54,3	−9,8	41 %	Balanced & physical
12	Parma	38	11–12–15	28–46	45	46,1	37,2	58,5	−21,3	32 %	Low block & direct
13	Torino	38	12–9–17	44–63	45	53,8	50,5	59,3	−8,8	40 %	Low block & direct
14	Cagliari	38	11–10–17	40–53	43	47,6	42,2	55,5	−13,3	36 %	Possession control
15	Fiorentina	38	9–15–14	41–50	42	48,6	53,6	60,4	−6,8	45 %	Balanced & physical
16	Genoa	38	10–11–17	41–51	41	45,8	46,0	59,3	−13,3	42 %	Possession control
17	Lecce	38	10–8–20	28–50	38	47,7	36,5	56,2	−19,7	42 %	Low block & direct
18	Cremonese	38	8–10–20	32–57	34	47,4	39,1	60,0	−20,9	37 %	Low block & direct
19	Hellas Verona	38	3–12–23	25–61	21	53,1	36,1	48,3	−12,2	39 %	Low block & direct
20	Pisa	38	2–12–24	26–71	18	48,2	41,8	60,4	−18,6	32 %	Low block & direct

Latest articles

All articles

2026-05-19 · 1 min

League Pulse: Serie A, 2025

Inter Milan dominates Serie A 2025 with the league's best attacking statistics at 86 goals and tops the table together with Napoli and AS Roma. Lautaro Martinez is highlighted as the league's most decisive player with his combination of goal-scoring instinct and game understanding, while Pisa and Verona struggle against relegation.

2026-04-26 · 1 min

Player Profile: Luka Modrić (AC Milan)

Luka Modrić continues to impress as AC Milan's regista with 425 meters of progressive passes per match and 1.3 goal-creating actions, making him one of Serie A's best build-up players despite being 39 years old. His offensive production is however limited with only 0.04 xG per match due to his deep playing position, but he compensates with strong defensive reading and 6.1 interceptions per match.

2026-04-03 · 1 min

Player Profile: Nikola Krstović (Atalanta)

Nikola Krstović is a goal-hungry striker at Atalanta who stands out with 5.2 shots per match and strong physical presence in the penalty area. Despite his impressive finishing abilities and work rate, he lacks creativity in build-up play, which makes him a specialized goalscorer rather than an all-around forward.

Played matches

Prediction track record

No settled matches yet for this selection — the scorecard fills in after every played round.

How the predictions work

Every prediction comes from PlaymakerAI's match model — the same data platform professional clubs use for scouting and match analysis. The model is trained on event data from hundreds of thousands of matches and weighs over 200 factors per team: strength ratings, current form, playing style, squad player profiles, fixture congestion, travel and even matchday weather.

For each fixture the model produces a full goal distribution. From it we derive the 1X2 probabilities, the score matrix, expected goals, over/under lines and both-teams-to-score. Predictions are frozen before kickoff and never changed afterwards — once a match finishes we grade it openly, hit or miss.

Odds are collected continuously from several bookmakers. When the model's probability for an outcome exceeds what the odds imply, we flag it as value (EV+). That is the model's assessment, not a guarantee: even positive-EV bets lose often. Gamble responsibly.

Predictions come from PlaymakerAI's match model and reflect the model's assessment — not betting advice. Gamble responsibly.