Latest Serie A AI Predictions: Results, Under/Over & Shots

Also available in Claude and Perplexity. Query these predictions in plain language: our native connector lets your AI assistant call our models on demand, no code.

Set it up

2026-2027 model warm-up

First Serie A predictions after Matchday 5

We are waiting for five complete matchdays before publishing the first match forecasts. This gives the model enough current-season evidence to meet our performance standard.

Estimated forecast window October 11, 2026 Counting down to Matchday 6
1
Matchdays 1 to 5Current-season signal builds
2
Quality checksInputs and model outputs are validated
3
Matchday 6First forecasts are published

Why not predict from day one?

Our match forecasts combine neural networks, current-season match data and team ratings. A simple Elo-only estimate can run immediately, but it is not the model we validate and publish. We do not currently have a cold-start neural-network variant that meets the same performance bar, so we prefer a transparent wait to a lower-confidence forecast.

Get the first forecasts on Telegram

Our free Telegram channel posts every day's predictions once the models go live. Join now and the first Serie A forecasts reach you on day one, without checking back here.

View season simulations Join Telegram Channel How we validate models

Season simulations remain available because they use a separate ratings and Monte Carlo engine. The date above is an estimate and can move if fixtures or data availability change.

Share this page

What's Inside Each Match Analysis

Every published forecast gets its own in-depth match analysis page, linked from its card as soon as the round is out. Each page provides insights you won't find elsewhere:

AI-Generated Match Preview

Data-driven match analysis with prediction summary, team form assessment, and key tactical insights.

Under/Over 2.5 Goals Analysis

Detailed probability breakdown for goals predictions based on team scoring patterns and defensive records.

Shots Predictions

Expected shots derived from neural network probability distributions, not simple averages.

Corners Predictions

Neural network-based corner kick predictions with probability context for more informed analysis.

Goal Spread Predictions

Expected goal difference (home - away) with full probability distributions for handicap analysis.

Learn About Our Methodology

AI-Powered Serie A Predictions

Serie A is Italy's top-flight football league, known for its tactical depth and defensive traditions. Our AI models analyze last 10 seasons of Serie A data, processing approximately 700 features including Expected Goals (xG), shots made and conceded, and recent team form to generate accurate match predictions with full probability context.

Our proprietary neural network models are trained on 10 seasons of Serie A data and leverage approximately 700 features including latest teams performance, Games metrics , Expected Goals.

Match Winner
Under/Over 2.5
Shots
Goal No Goal
Corners
Goal Spread

What sets us apart: Unlike competitors who provide simple averages for shots and corners, our neural network models generate expected values derived from probability distributions. This means our predictions capture the full range of likely outcomes, providing deeper analytical insight than basic statistical averages.

Our models incorporate recent team form, home/away performance differentials, Expected Goals (xG), and match-level metrics such as shots made and conceded, corners, fouls, and possession patterns to deliver comprehensive match predictions.

Historical Serie A KPIs

Explore how key Serie A metrics have evolved over the last 10 seasons. These trends inform our AI models and provide context for current predictions.

42%
Home Wins %
31%
Away Wins %
26%
Draws %
52%
Over 2.5 Goals %
55%
Both Teams Score %
2.7
Avg Goals per Match
24.5
Avg Shots per Match
10.1
Avg Corners per Match

Match Results Trend

Serie A match results trend chart showing home wins, away wins, and draws percentages across seasons

Goals & Over 2.5 Trend

Serie A goals trend chart showing average goals scored and over 2.5 percentage across seasons

Match Metrics Trend

Serie A match metrics chart showing average shots and corners per match across seasons

2026-2027 Serie A Teams

Atalanta
Bologna
Cagliari
Como
Cremonese
Fiorentina
Genoa
Inter
Juventus
Lazio
Lecce
Milan
Napoli
Parma
Pisa
Roma
Sassuolo
Torino
Udinese
Verona

Serie A Power Rankings

Two rating models, one table. ELO measures accumulated season strength. Glicko-2 adds a confidence layer, flagging teams whose form is inconsistent. The Rank Δ column shows where both systems disagree most. Updated after every matchday.

View Power Rankings

Cross-League Prediction Hub

Compare predictions across all twelve leagues in one view. Sort matches by over/under 2.5 probability, goal spread, corners, or draw likelihood.

O/U 2.5 Spread Corners BTTS Draws

Prediction Transparency

We believe in full transparency. All our historical predictions and their outcomes are publicly available. Track our prediction accuracy and model performance over time:

Current Season Performance 2025-2026 Season Performance Model Calibration

Get Daily Serie A Predictions

If you like our content and want to receive daily predictions, subscribe to our Telegram channel and receive daily AI-driven Serie A predictions directly on your phone.

Join Our Telegram Channel

Download Serie A Probability Data

Every match analysis includes downloadable probability distributions in JSON and CSV format. Access full PDFs, CDFs, and match prediction data for your own analysis.

Open any match analysis above and download structured data with a free account.

Learn How to Use Our Data

Frequently Asked Questions

We are the only sports analytics platform providing neural network-based probability predictions for Serie A matches that go beyond simple averages. Our models generate expected values from deep learning with full probability context for shots and corners predictions, giving you deeper insight into the range of likely outcomes.

Our neural networks process approximately 700 features including Expected Goals (xG), shots made and conceded, recent team form, and historical match data spanning 10 Serie A seasons. The models incorporate both team-level and match-level metrics.

We provide predictions for match winner (home, draw, away with probabilities), under/over 2.5 goals, shots predictions, and goal/no goal predictions. Each prediction type uses a dedicated neural network model optimized for that specific task.

We believe in full transparency. All our historical predictions and outcomes are publicly available on our performance tracking page, where you can verify our accuracy across different prediction types and seasons.

A probability distribution shows the likelihood of each possible outcome, not just a single prediction. For example, for shots predictions, instead of just saying 'expected 12 shots,' our models provide the expected value derived from neural network probability distributions, giving you deeper insight than simple averages. Learn more about probability distributions.

We provide two types of charts in each match analysis. The probability distribution chart shows you the likelihood of each possible outcome—the peak indicates the most likely result, and the spread shows how certain the prediction is. The CDF (Cumulative Distribution Function) chart is perfect for understanding over/under scenarios—it shows the probability of outcomes being less than or equal to any value. Read our complete guide to understanding probability charts.

Our predictions are generated on average 2-3 days before each matchday. We process the latest match results, team form data, and performance metrics to ensure predictions reflect the most current information available.

Serie A consists of 20 teams competing in a round-robin format, with each team playing 38 matches per season (19 home and 19 away). The bottom three teams are relegated to Serie B at the end of each season.

Expected Goals (xG) is a metric that measures the quality of scoring chances by assigning a probability to each shot based on factors like distance, angle, and assist type. Our models use xG as one of the key features to assess team attacking quality beyond just goals scored, providing more accurate predictions.