FootySim Football Predictions

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

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

Final Season Results

Ligue 1

Ligue 1 2020/2021

Paris Saint-Germain vs LOSC Lille

Match Results & 10,000x Simulated Statistics

Kickoff: 2021-04-03 15:00 UTC (FT)
Pre-Match Model Forecast Incorrect Prediction
58%
26%
16%
Analytical Summary This result registered as a statistical upset from the model's perspective. The simulation initially leaned away from this outcome, driven by the 155-point Elo gap between the squads at kickoff.

Top Predicted Scores

1 - 1
12.1%
2 - 0
11.0%
1 - 0
10.6%
2 - 1
8.9%
0 - 0
7.1%
3 - 0
6.9%
3 - 1
6.2%
HISTORICAL CLOSING ODDS
Market Status: Archived
Historical betting odds are not available for this specific fixture.
In the meantime, check odds and stats for upcoming Ligue 1 matches, see the current season Ligue 1 forecast, or try our Ligue 1 simulator.

*Market data powered by The Odds API. 18+ Only. Please gamble responsibly. Help: check-dein-spiel.de / begambleaware.org / ncpgambling.org

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