Season projections, match predictions, and betting odds for 15 leagues and tournaments, updated nightly.
Purely mathematical simulations. Why is this different from betting odds?
Scores through Apr 06, 2026, at 10:00 p.m. UTC | Forecasts & Odds updated daily
| Avg. Simulated Season | End-of-Season Probabilities | |||||||
|---|---|---|---|---|---|---|---|---|
| Team | Pts | Goal Diff |
Every Position
↓ 20th1st ↓
|
Relegation | Conference League | Europa League | Champions League | Win League |
|
FC Barcelona
76 pts
|
92.5 | +59 |
|
0% | 0% | 0% | 100% | 93% |
|
Real Madrid
69 pts
|
84.8 | +43 |
|
0% | 0% | 0% | 100% | 7% |
|
Atlético
57 pts
|
71.5 | +25 |
|
0% | <1% | <1% | >99% | 0% |
|
Villarreal
58 pts
|
70.6 | +21 |
|
0% | <1% | <1% | >99% | 0% |
|
Real Betis
45 pts
|
56.5 | +7 |
|
0% | 27% | 43% | <1% | 0% |
|
Celta
44 pts
|
55.7 | +8 |
|
0% | 31% | 33% | <1% | 0% |
|
Real Sociedad
41 pts
|
52.8 | +2 |
|
0% | 19% | 13% | <1% | 0% |
|
Getafe CF
41 pts
|
51.7 | -4 |
|
0% | 12% | 6% | <1% | 0% |
|
CA Osasuna
38 pts
|
48.5 | -1 |
|
<1% | 4% | 2% | 0% | 0% |
|
Bilbao
38 pts
|
48.3 | -12 |
|
<1% | 3% | 1% | 0% | 0% |
|
Espanyol
38 pts
|
46.8 | -11 |
|
<1% | 1% | <1% | 0% | 0% |
|
Girona FC
37 pts
|
46.5 | -14 |
|
1% | <1% | <1% | 0% | 0% |
|
Rayo Vallecano
35 pts
|
45.8 | -6 |
|
1% | <1% | <1% | 0% | 0% |
|
Valencia CF
35 pts
|
44.6 | -12 |
|
4% | <1% | <1% | 0% | 0% |
|
RCD Mallorca
31 pts
|
41.6 | -12 |
|
15% | <1% | 0% | 0% | 0% |
|
Alavés
32 pts
|
40.9 | -14 |
|
20% | 0% | 0% | 0% | 0% |
|
Sevilla FC
31 pts
|
39.0 | -17 |
|
38% | 0% | 0% | 0% | 0% |
|
Elche CF
29 pts
|
37.8 | -12 |
|
48% | 0% | 0% | 0% | 0% |
|
Levante UD
26 pts
|
35.1 | -18 |
|
76% | 0% | 0% | 0% | 0% |
|
Real Oviedo
24 pts
|
32.0 | -31 |
|
95% | 0% | 0% | 0% | 0% |
FootySim uses a custom Monte Carlo simulation engine to project match results and the final outcome of the season.
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:
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.
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: