In a Nutshell
Rain, wind, snow, and extreme temperatures affect teams differently. A possession-based team that relies on precise passing may struggle in heavy rain, while a direct team that plays long balls may barely notice. This signal compares how well each team has historically performed in the forecasted weather conditions, whether their playing style is vulnerable to those conditions, and whether their climate familiarity gives them an edge. It only activates when weather is expected to be impactful.
Why It Matters
Weather affects teams differently based on playing style and geographic adaptation. Possession-based teams are more vulnerable to rain and wind that disrupts passing accuracy. Teams from warmer climates may struggle in cold, snowy conditions. Historical data reveals consistent performance patterns in adverse weather that provide genuine predictive signal.
How It's Calculated
Data Sources
Weather forecasts and historical archives from Open-Meteo. Weather is classified into 6 condition groups: Clear, Cloudy, Rain, Snow, Storm, and Fog. Historical team points-per-game data is matched to past weather conditions. Competition region determines climate familiarity.
Key Formula
Looks at three things for each team: how they have historically performed in similar weather conditions (the main factor), whether their playing style is vulnerable to the forecasted conditions (for example, possession teams struggle more in heavy rain), and whether their home region prepares them for this type of weather. The gap between the two teams’ adaptation scores determines which side has the edge.
Normalization
Each factor contributes to an overall adaptation score per team. The final impact is kept modest and is scaled by how fresh the weather forecast is. A forecast from hours ago carries full weight, while one from days ago is treated with more caution.
Confidence Impact
Base uncertainty is 0.30. Teams with fewer than 5 condition-specific matches are flagged with +0.15 per team. Forecast freshness degrades signal reliability: <4 hours = full confidence, 4-12 hours = 0.85, 12-24 hours = 0.60, >24 hours = 0.30. Forecast distance also scales impact: 0-2 days = full, 3-5 days = 0.85–0.75, 6-10 days = 0.65–0.45, beyond 14 days the signal is excluded entirely.
Example
See how this signal played out in actual matches.
Liverpool vs Tottenham
Premier League — Dec 2024
What happened: Liverpool's metrics were consistently superior across this signal, aligning with the eventual 6-3 result. The signal correctly identified a significant imbalance.
View this match on xGoalMan City vs Everton
Premier League — Dec 2024
What happened: Both sides showed similar metrics for this signal — neither had a meaningful edge. The match ended 1-1, consistent with the balanced assessment.
View this match on xGoalAston Villa vs Arsenal
Premier League — Dec 2025
What happened: The signal leaned slightly toward Villa, but Arsenal's strengths in other signals offset this. This illustrates why no single signal tells the full story — context from all signals matters.
View this match on xGoalCommon Misconceptions
Myth: "Weather barely affects professional football"
Reality: Research shows consistent performance patterns in adverse weather. Possession-based teams are measurably more vulnerable to rain and wind. The signal only activates when weather severity exceeds normal conditions, filtering out irrelevant clear-sky matches.
Myth: "This signal is just about rain"
Reality: The signal covers 6 weather condition groups including snow, extreme heat (>30°C), extreme cold (<2°C), fog, storm, and high wind (>25 km/h). Each condition has specific vulnerability assessments based on team playing style.
Myth: "A forecast 10 days out is reliable enough"
Reality: The signal explicitly degrades with forecast distance. At 10 days, the forecast reliability factor is only 0.45, meaning the impact is more than halved. Beyond 14 days, the signal is excluded entirely because weather forecasts are too unreliable.
Advanced Insights
What This Signal Cannot Tell You
- No single signal determines match outcome
- All signals carry uncertainty that widens probability ranges
- Data freshness affects confidence in the assessment
- Context matters: signals interact with each other and with match circumstances
The Bottom Line
Measures which team better adapts to forecasted weather conditions using three factors: historical performance in similar conditions (PPG delta, 60%), tactical vulnerability to weather (25%), and climate familiarity (15%). Use it alongside other signals for a balanced view of the match.
Related Signals
This signal works alongside others to build a complete picture.
Key Terms
Understand the terminology used in this signal.