Weather and Climate in Football Predictions — How Temperature, Altitude, and Humidity Shape Match Outcomes

July 13, 2026 · 11 min read

Every prediction model accounts for team strength, form, and home advantage. Almost none account for the weather. Yet a 30°C afternoon in Houston, a rain-slicked pitch in Seattle, or a dry 2,200-meter evening in Mexico City can swing a match result as decisively as any tactical adjustment. This guide shows you how to build weather data into your football prediction models — and why ignoring it leaves accuracy on the table.

Why Weather Belongs in Every Prediction Model

Football analytics has become extraordinarily sophisticated. We have expected goals (xG), expected threat (xT), Elo ratings, Poisson models, and neural networks. We track 30+ on-ball events per match. We simulate entire tournaments with Monte Carlo methods. Yet the weather — one of the few truly exogenous variables in a football match — remains largely absent from most prediction frameworks.

This is a blind spot with measurable consequences. A 2019 study published in the International Journal of Biometeorology analyzed over 13,000 Bundesliga matches and found that temperature, humidity, and altitude each had statistically significant effects on match outcomes. Goals scored per match dropped by approximately 0.15 when temperatures exceeded 30°C. Sprint distances fell by 2–3% for every degree above 25°C. These are not marginal effects — they are the difference between a model that predicts 52% accuracy and one that predicts 55%.

The 2026 FIFA World Cup makes this particularly relevant. With matches played across three countries — the United States, Mexico, and Canada — from June 11 to July 19, the tournament spans an enormous range of climates. A group stage match in Vancouver (18°C, coastal humidity) and a knockout match in Houston (35°C, subtropical humidity) are fundamentally different sporting events. Your prediction model should treat them that way.

Temperature — The Most Studied Weather Variable

Temperature is the weather variable with the strongest evidence base. The mechanism is straightforward: heat increases physiological stress, reduces aerobic capacity, and accelerates fatigue. Players cover less ground, sprint less frequently, and make more errors in the final 30 minutes of a match.

The Key Thresholds

Research from the 2014 FIFA World Cup in Brazil established a clear framework. FIFA introduced cooling breaks — the first in World Cup history — when the Wet Bulb Globe Temperature (WBGT) exceeded 32°C (90°F). The first such break occurred during the Netherlands vs Mexico Round of 16 match in Fortaleza, where the temperature was 32°C with 68% humidity. The match was ultimately decided by a late goal in the 88th minute — a pattern consistent with heat-affected matches where defensive concentration drops late.

The critical temperature thresholds for prediction modeling are:

• Below 15°C (59°F): Optimal performance range. Players can maintain high-intensity efforts for 90 minutes. Goals per match are at their highest. No meaningful weather adjustment needed.
• 15–25°C (59–77°F): Comfortable range. Minor effects on hydration and fatigue. A slight reduction in second-half goals (approximately 3–5% fewer goals after the 70th minute compared to cooler conditions).
• 25–30°C (77–86°F): Performance degradation begins. Sprint distances drop 2–3% per degree. Teams that rely on high pressing see their pressing intensity fall by 10–15% in the second half. Favor technically disciplined teams over physically dominant ones.
• Above 30°C (86°F): Significant impact. Total distance covered drops 5–8%. Cooling breaks interrupt momentum. Goals per match decline by 0.1–0.2. Teams from cooler climates (Scandinavia, northern Europe) are disproportionately affected compared to teams from tropical or subtropical regions.

Applying Temperature Data to Predictions

The practical approach is to build a temperature adjustment factor into your model. Here is a simplified version:

1. Check the forecast temperature for kick-off time (not the daily high — a 3 PM local kick-off in Phoenix in June will be far hotter than a 7 PM kick-off).
2. Calculate the temperature differential between the match venue and each team's home climate. A team from Riyadh will handle 35°C in Houston far better than a team from Stockholm.
3. Apply a scoring adjustment: reduce expected total goals by 0.05–0.15 for matches above 28°C, with the reduction increasing as temperature rises.
4. Adjust individual player predictions: attacking midfielders and wingers (high-intensity positions) are more affected than centre-backs or goalkeepers.

The2026 World Cup provides a live laboratory for this. Group stage matches in Houston (average June high: 33°C) and Dallas (34°C) will be played in significantly different conditions than matches in Seattle (20°C) or Vancouver (19°C). A model that ignores this treats a match in Houston's NRG Stadium the same as a match in Seattle's Lumen Field — a mistake that could easily swing your prediction by 5–8 percentage points.

Humidity — The Invisible Performance Killer

Temperature alone does not tell the full story. A dry 30°C in Guadalajara is a very different sporting environment from a humid 30°C in Miami. Humidity affects the body's ability to cool itself through sweat evaporation. When the air is already saturated with moisture, sweat drips off the body without evaporating — providing no cooling effect.

The metric to track is the Wet Bulb Globe Temperature (WBGT), which combines temperature, humidity, wind speed, and solar radiation into a single number. FIFA uses WBGT to trigger cooling breaks. For prediction purposes, you can approximate WBGT using temperature and relative humidity alone:

WBGT ≈ 0.567 × Temperature + 0.393 × Vapor Pressure + 3.94

When WBGT exceeds 28°C, even fit professional athletes begin to show measurable declines in high-intensity running. When it exceeds 32°C, the effect is severe enough that FIFA mandates stoppages.

For prediction modeling, the practical thresholds are:

• Relative humidity below 40%: Minimal impact, even at higher temperatures. Dry heat is uncomfortable but manageable for elite athletes.
• Relative humidity 40–60%: Moderate impact when combined with temperatures above 25°C. Expect a 3–5% reduction in second-half sprint output.
• Relative humidity above 60%: Significant compounding effect with heat. At 30°C and 68% humidity (the conditions for that Netherlands vs Mexico cooling break in 2014), expect meaningful reductions in both total goals and second-half intensity.

Miami is the venue to watch at the 2026 World Cup. Average June humidity in Miami hovers around 75%, with afternoon temperatures around 32°C. Hard Rock Stadium will host multiple knockout matches in conditions that rival the worst of the 2014 Brazil tournament. Any team with a high-pressing style will need to adjust — and your model should account for it.

Altitude — The Forgotten Variable

Altitude is the weather variable most prediction models completely ignore — and it matters enormously. At higher elevations, the air is thinner. This has two direct effects on football: the ball travels farther (aerodynamic drag is reduced), and players tire faster (less oxygen per breath).

The Estadio Azteca in Mexico City, which hosts matches at the 2026 World Cup, sits at an elevation of 2,241 meters (7,352 feet) above sea level. At this altitude, the ball travels approximately 5% farther than at sea level. Goalkeepers must adjust their positioning for long-range shots and crosses. Free kicks and corners behave differently. A ball struck with the same force that curls just over the bar at sea level in London will sail into the stands in Mexico City.

The physiological effects are equally significant. At 2,200 meters, the partial pressure of oxygen is roughly 20% lower than at sea level. Players acclimatized to altitude (like those who play domestically in Mexico City, Bogotá, or Quito) have a genuine physiological advantage over visitors. Research on CONMEBOL World Cup qualifying matches has shown that home advantage is significantly amplified at altitude — Bolivia's home record at the Estadio Hernando Siles in La Paz (3,637 meters / 11,933 feet) is dramatically better than their away record, far exceeding the typical home advantage seen in European qualifying.

FIFA's Altitude Ban and Its Reversal

The altitude question became so contentious that FIFA briefly banned international matches above 2,500 meters in 2007 — a decision widely seen as targeting Bolivia and Ecuador, who play home matches at extreme altitude. The ban was overturned after protests from South American football associations, but the debate highlighted a genuine analytical question: how much of a team's home advantage at altitude is due to crowd support, familiarity, and travel fatigue, and how much is due to the physiological effects of thin air?

For the 2026 World Cup, the altitude factor is concentrated in Mexico City. Guadalajara (1,566 meters / 5,138 feet) also has a meaningful altitude effect, though less extreme. The other venues are all at or near sea level. This creates an asymmetry worth modeling: teams drawn to play in Mexico City will face a genuine altitude challenge, while teams playing in coastal US venues will not.

The prediction adjustment for altitude is straightforward. At venues above 1,500 meters:

• Increase expected goals by 0.05–0.10 (the ball travels farther, benefiting long-range shooters and set-piece takers).
• Amplify home advantage by 5–8 percentage points if the home team is acclimatized (Mexican clubs playing in Mexico City, for example).
• Reduce expected distance covered by visiting teams by 3–5%, particularly in the second half.
• Factor in acclimatization time: teams arriving 2–3 days before the match will perform worse than those who arrive a week early. Tournament scheduling matters.

Rain, Wind, and Pitch Conditions

Rain and wind receive less research attention than temperature and altitude, but their effects are real and quantifiable.

Rain

A wet pitch changes the game in several ways. The ball moves faster along the surface, benefiting teams that play quick, direct passes. It skids off the turf unpredictably, increasing the likelihood of defensive errors. Goalkeepers struggle with shots that bounce or skip off the wet surface. The net effect on scoring is modest — research suggests rain reduces total goals by approximately 5–10% — but the distribution of goals changes. More goals come from defensive errors and set pieces, fewer from intricate build-up play.

For prediction purposes, rain is most relevant in specific venues. Seattle and Vancouver are the rainiest host cities at the 2026 World Cup. If a match in either city coincides with a rain forecast, adjust your model slightly: reduce expected goals from open play, increase the probability of goals from set pieces and defensive errors, and favor the team with the more direct style.

Wind

Wind is the hardest weather variable to model because its effects are asymmetric — a team playing into the wind in the first half faces a different challenge than the team playing into it in the second half. Strong crosswinds (above 30 km/h) make long passing less accurate and increase the unpredictability of aerial duels. Headwinds reduce the effective range of long balls and shots. Tailwinds increase them.

The practical prediction impact of wind is smaller than temperature or altitude, but it is not zero. In exposed open-air stadiums (most 2026 venues are open-air), wind speeds above 25 km/h can reduce total goals by 0.05–0.10 and favor the team that plays on the ground rather than in the air.

Pitch Surface

The 2026 World Cup has introduced an interesting pitch variable. FIFA commissioned a University of Tennessee–Michigan State University research team to install natural grass surfaces in all venues, many of which had permanent artificial turf. Depending on the venue's climate, the turf used is either a hybrid of 84% Kentucky bluegrass and 16% perennial ryegrass (for cooler temperatures) or Bermuda grass (for warmer temperatures). These surfaces behave differently — Bermuda grass is denser and produces a faster, lower bounce, while bluegrass-ryegrass hybrids offer more grip and a slightly higher bounce. Teams accustomed to one surface may find the other disorienting.

Building a Weather-Adjusted Prediction Model

Integrating weather data into your prediction model does not require a complete rebuild. Here is a practical four-step framework:

Step 1: Source the Data

For match-day weather forecasts, use a reliable weather API (Open-Meteo is free and covers all 2026 venues). Pull temperature, humidity, wind speed, and precipitation probability for the kick-off time at the match venue. For altitude, use the venue's known elevation — this is a static variable that does not change.

Step 2: Calculate Adjustment Factors

Create a multi-factor weather score for each match. A simple weighted formula:

Weather Impact = (Temperature Factor × 0.4) + (Humidity Factor × 0.25) + (Altitude Factor × 0.25) + (Rain Factor × 0.1)

Each factor is scaled from 0 (no impact) to 1 (maximum impact). The weights reflect the relative research evidence for each variable's effect on match outcomes.

Step 3: Adjust Team Strength

Not all teams are equally affected by weather. Teams from tropical climates (Brazil, Nigeria, Saudi Arabia) are better adapted to heat and humidity. Teams from high-altitude cities (Mexico City, Bogotá, Quito) have a physiological advantage at altitude. Teams from northern Europe (Sweden, Norway, Iceland) perform worse in extreme heat. Build a "climate affinity" factor for each team based on their domestic league conditions.

Step 4: Apply to Your Base Model

Multiply your base expected goals prediction by (1 + Weather Impact) or (1 − Weather Impact) depending on whether the conditions favor higher or lower scoring. For match outcome predictions, adjust the win probability by 2–8 percentage points depending on the severity of the weather differential between the two teams.

Case Studies — Weather Deciding Tournaments

History is full of examples where weather shaped tournament outcomes:

• 2014 World Cup, Brazil: The introduction of cooling breaks acknowledged what the data showed — heat was altering match outcomes. The Netherlands vs Mexico Round of 16 match (32°C, 68% humidity) saw the first cooling break in World Cup history. Mexico, leading 1-0, conceded two late goals as defensive concentration collapsed in the heat.
• 2022 World Cup, Qatar: FIFA moved the entire tournament to November–December to avoid Qatar's extreme summer heat, where temperatures routinely exceed 40°C. This was the most dramatic acknowledgment in football history that weather is not a background variable — it is a structural one.
• CONMEBOL Qualifying, Bolivia: Bolivia's home record at the Estadio Hernando Siles (3,637m altitude) is one of the most extreme home advantages in world football. Brazil, Argentina, and Colombia have all lost there despite being dramatically superior teams on paper. The altitude effect is so pronounced that it essentially creates a "home win" probability boost of 15–20 percentage points — larger than any other single factor in football prediction.
• 2026 World Cup, group stage: Weather delays affected multiple matches, including Mexico vs Ecuador and Mexico vs England, both delayed by an hour due to adverse weather conditions. These disruptions altered team preparation and warm-up routines — factors that rarely appear in prediction models but matter in practice.

The2026 World Cup Weather Map

The 2026 tournament presents a unique weather challenge because of its geographic spread. Here is a venue-by-venue weather profile for the knockout stage:

• Mexico City (Estadio Azteca): 2,241m altitude, June average 22°C with afternoon thunderstorms. The altitude is the defining factor — expect reduced oxygen, faster ball movement, and amplified home advantage for acclimatized teams.
• Houston (NRG Stadium): Indoor with climate control, but the outdoor environment is brutal — 33°C average with 75% humidity. Players arriving from the hotel and warming up outdoors will carry heat stress into the match.
• Miami (Hard Rock Stadium): Open-air, 32°C, 70%+ humidity. The most challenging outdoor venue at the tournament for players from temperate climates. Afternoon kick-offs will be punishing.
• Dallas (AT&T Stadium): Indoor with climate control. Weather-neutral venue — the indoor environment eliminates temperature and humidity as factors.
• Atlanta (Mercedes-Benz Stadium): Indoor with climate control. Like Dallas, weather is not a factor.
• Seattle (Lumen Field): Open-air, 20°C average, moderate humidity. Comfortable conditions. The Pacific Northwest climate is ideal for football — no weather adjustment needed.
• Vancouver (BC Place): Indoor with retractable roof. Weather-neutral when closed; mild and comfortable (19°C) when open.
• MetLife Stadium (New Jersey): Open-air, 28°C average in July, moderate humidity. Slightly warm but not extreme. The final venue is weather-neutral for prediction purposes.

Key Takeaways

• Temperature is the strongest weather variable — above 25°C, expect measurable reductions in sprint output, total distance covered, and goals scored. Above 30°C, the effect is significant enough to swing predictions by 5–8 percentage points.
• Humidity compounds heat — a humid 30°C is far worse than a dry 30°C. Track WBGT or approximate it from temperature and relative humidity. Miami and Houston are the high-risk venues at the 2026 World Cup.
• Altitude is the most underused variable — Mexico City's 2,241m elevation changes ball physics and player physiology. Build an altitude adjustment into any model that includes matches at the Estadio Azteca.
• Indoor venues eliminate weather — Dallas, Atlanta, Houston (NRG), and Vancouver (when closed) are climate-controlled. Do not apply weather adjustments to matches in these stadiums.
• Climate affinity matters — teams from tropical or high-altitude climates have built-in advantages in extreme conditions. A Brazilian team playing in Miami's heat is better prepared than a Scandinavian team.
• Source real forecast data — use a weather API to pull match-day conditions, not monthly averages. A freak cold front in Houston or an unseasonable heat wave in Seattle can change everything.