FootySim Football Predictions

Season projections, standings, match predictions, and betting odds for 15 leagues and tournaments, updated nightly.

Purely mathematical simulations. Why is this different from betting odds?

Scores through Nov 06, 2026, at 10:45 p.m. UTC | Forecasts & Odds updated daily

Serie A

Avg. Simulated Season End-of-Season Probabilities
Team Pts Goal Diff
Every Position
↓ 20th1st ↓
Relegation Conference League Europa League Champions League Win League
Inter 25 pts
80.0 +39 0% 2% 3% 94% 56%
AS Roma 24 pts
72.3 +33 0% 6% 10% 74% 18%
Como 23 pts
67.8 +20 <1% 11% 13% 55% 8%
Lazio 28 pts
67.1 +17 <1% 11% 13% 51% 8%
Milan 21 pts
65.4 +18 <1% 12% 14% 44% 5%
Juventus 17 pts
62.2 +14 <1% 14% 13% 29% 3%
Napoli 14 pts
61.9 +19 <1% 13% 13% 28% 2%
Atalanta 11 pts
55.6 +6 1% 9% 7% 10% <1%
Bologna 14 pts
54.1 +4 2% 8% 6% 8% <1%
47.9 -5 6% 3% 2% 1% 0%
Torino 13 pts
46.3 -9 10% 2% 1% 1% <1%
46.3 -7 11% 3% 1% 1% <1%
Genoa 13 pts
45.8 -12 12% 2% 1% <1% <1%
Cagliari 13 pts
45.2 -15 14% 2% 1% <1% 0%
41.5 -17 26% <1% <1% <1% <1%
Parma 8 pts
39.9 -16 33% <1% <1% <1% <1%
Udinese 5 pts
38.7 -17 37% <1% <1% <1% 0%
Lecce 9 pts
37.5 -25 43% <1% <1% <1% 0%
Venezia 6 pts
36.9 -22 46% <1% <1% <1% 0%
Monza 7 pts
34.5 -23 59% <1% <1% <1% 0%
Champions League Europa League Conference League Relegation
Rank Team Pts Pld Goals GD
1 Lazio 28 10 24:8 +16
2 Inter 25 10 26:13 +13
3 AS Roma 24 10 28:10 +18
4 Como 23 10 18:8 +10
5 Milan 21 10 21:12 +9
6 Frosinone 20 10 24:14 +10
7 Juventus 17 10 13:10 +3
8 Napoli 14 10 17:13 +4
9 Bologna 14 10 14:13 +1
10 Torino 13 10 14:16 -2
11 Genoa 13 10 10:14 -4
12 Cagliari 13 10 9:15 -6
13 Atalanta 11 10 16:20 -4
14 Lecce 9 10 11:21 -10
15 Parma 8 10 8:15 -7
16 Fiorentina 8 10 15:23 -8
17 Sassuolo 8 10 16:26 -10
18 Monza 7 10 17:24 -7
19 Venezia 6 11 8:21 -13
20 Udinese 5 11 12:25 -13

Season Timeline

Probability evolution over time

A statistical line chart showing the evolution of Serie A probabilities for Inter and others based on 10,000 Monte Carlo simulations.

* Mathematical projections reflect the engine's state at the time of daily simulation. Probabilities may lag real-world match results by up to 48 hours due to upstream data processing.

How this works

FootySim uses a custom Monte Carlo simulation engine to project match results and the final outcome of the season.

Analytical Comparison

Betting markets are influenced by public sentiment and financial liability. This model is strictly performance-based. By focusing solely on Elo ratings and xG distributions, this method provides a pure statistical perspective, fueled by on-pitch results rather than media sentiment or betting volume.

Note on Statistical Variance:

  • Regional Rating Bubbles: Elo is a relative system. In global tournaments, teams from isolated confederations may occasionally display "inflated" ratings if they haven't faced top-tier international opposition recently.
  • The "Pure Data" Trade-off: By ignoring "soft data" (injuries, lineup news, or tactical shifts), this model remains objective but may lag behind real-time squad changes that haven't yet manifested in a final scoreline.

1. Team Power (Elo Ratings): Every team is assigned a power rating based on the Elo system. This rating reflects their current real-world strength based on historical results, opponent quality, and recent form.

2. Match Probabilities & xG: For every unplayed fixture, the engine compares the Elo ratings of the two competing teams. This difference dictates the win probabilities, which are then converted into an Expected Goals (xG) metric for each team, anchored to a real-world average of 2.77 goals per match.

3. Scoreline Generation: The engine feeds these xG values into independent Poisson distributions to generate a realistic final scoreline. It also applies a Dixon-Coles adjustment—a statistical modifier that accounts for late-game human psychology (like "parking the bus" or pushing for a late equalizer) to ensure mathematically accurate draw rates.

4. Dynamic Tournament Momentum: The simulation is path-dependent. As the engine simulates through the schedule, teams dynamically gain or lose Elo points after every simulated match. A team that goes on a giant-killing run in the group stage becomes mathematically stronger before the knockout rounds.

5. The 10,000 Simulations: The engine plays out the remainder of the tournament 10,000 times. Every match is decided by a random number generator weighted by these dynamic metrics. It then tallies up where each team finished across all 10,000 simulated universes to generate the final percentage chances and match probabilities shown across the site.

Interactive Engine

Want to run your own "what-if" scenarios using the exact engine behind these forecasts? Head over to FootySim.io to time-travel through matchdays and simulate alternate realities ⚽

Data Sources: This engine is powered by these incredible community resources:

• eloratings.net: For national team ratings
• clubelo.com: For club elo ratings
• fixturedownload.com: For schedules and results