What Are Predictions on CS2PREDICT?

CS2PREDICT lets you test your CS2 knowledge by predicting match outcomes. Use your account balance to place predictions on upcoming matches and earn payouts when you're right.

How It Works

1. Find a match — browse upcoming matches on the main page. Look for matches with the "PREDICT" buttons visible.
2. Choose your team — click PREDICT Team A or PREDICT Team B
3. Set your amount — enter how much you want to predict (min $1). Free tier: up to $50. Pro tier: up to $500.
4. Confirm — your prediction is locked in and your balance is deducted
5. Wait for results — when the match ends, payouts are calculated automatically

The Prediction Pool

All predictions on a match go into a shared pool. Below each match you'll see:

• Pool: $X — total amount predicted by all users
• X vs Y — how the pool is split between the two teams
• Odds (x1.5, x2.3, etc.) — your potential payout multiplier

How Odds Work

Odds are calculated from the pool distribution, not from AI predictions:

• If 80% of the pool is on Team A, Team A pays x1.25 (low payout, popular pick)
• Team B would pay x5.0 (high payout, underdog pick)
• The less popular the pick, the higher the potential payout

Odds update in real-time as more users place predictions.

Payouts

When the match completes:

• Correct prediction — you receive your stake multiplied by the odds. Example: $10 at x2.0 odds = $20 payout ($10 profit).
• Wrong prediction — you lose your stake. It goes to the winners' pool.
• Payouts are processed automatically within minutes of the match result.

Prediction Locking

Predictions lock when the match starts (goes LIVE). You cannot change or cancel a prediction after it's locked. Plan ahead — place your predictions before match start.

Your Balance

Check your balance in the top-right corner of the site. To add funds:

1. Go to Balance page
2. Send USDT (TRC-20) to the provided wallet address
3. Deposits are detected automatically (checked every 5 minutes)

Subscription Tiers

See Subscription page for current pricing.

Tips

• Check model consensus before predicting — when most AI models agree, the outcome is more likely
• Watch the odds — sometimes the crowd is wrong, and the underdog pays well
• Start small — learn the system with $1-5 predictions before going bigger
• Diversify — don't put everything on one match

Where to See Your Predictions

Your active predictions appear on each match card (below the pool info). Past results are visible on the Results page — check which of your predictions were correct.

How to Use Match Predictions

Every upcoming match on CS2PREDICT shows predictions from multiple AI models. This guide explains how to read the match page and make the most of the data.

The Match Card

Each match card on the main page shows:

• Teams — with logos, HLTV rankings, and clickable names leading to team profiles
• Lineups — 5 players per team with photos, each clickable to their player profile
• Time — match start time (converted to your local timezone)
• Event — tournament name with star rating (more stars = bigger event)
• Predictions — model outputs shown as percentage cards on the right

Reading the Prediction Cards

Each small card represents one AI model's prediction:

• Top label — model name (e.g., ORACL, APEX, TIER)
• Percentage — how confident the model is in the predicted winner (always shown as the higher side, e.g., 67%)
• Bottom label — which team the model predicts to win
• Lock icon — model requires Basic or Pro subscription

Color Coding

Prediction percentages are color-coded:

• Green (70%+) — high confidence, the model sees a clear advantage
• Yellow (60-70%) — moderate confidence, edge exists but upset possible
• Red/Gray (50-60%) — low confidence, close match

Predictions between 48-52% are hidden (shown as 50/50) because they carry no useful signal.

The Consensus Bar

Below the team names, a colored bar shows the overall consensus across all models:

• If the bar is mostly on one side (e.g., 70/30), most models agree — stronger signal
• If it's close to 50/50, models disagree — uncertain match, proceed with caution

Match Detail Page

Click any match to see the full detail page with:

• All model predictions — not just the top 15
• Model accuracy badges — how accurate each model has been historically
• Player lineups — clickable to individual player profiles with tactical radars
• Team links — clickable to team pages with roster analysis and match history
• Wager pool — community predictions with odds

After the Match

Once a match completes:

• The result appears on the Results page
• Each prediction is scored — correct (green check) or wrong (red cross)
• Model accuracy on the leaderboard updates immediately
• ELO ratings recalculate, improving future predictions

Tips for Best Results

1. Follow consensus — when 15+ models agree, they're usually right
2. Check the leaderboard — not all models are equal, some are consistently better
3. Watch for model disagreement — if TIER says 75% Team A but MIND says 60% Team B, the match is genuinely uncertain
4. Consider the tier — we only show predictions for Top & Major matches where accuracy is highest
5. Look at player profiles — check the tactical radar to understand each team's playstyle

Overview

CS2PREDICT uses multiple independent AI models to predict the outcome of professional CS2 matches. Each model analyzes different aspects of the game — from pure skill ratings to psychological momentum to team chemistry. No single model is perfect, but together they provide a comprehensive view of each matchup.

How predictions are generated

When a new match appears on HLTV, our system automatically:

1. Identifies both teams and their current rosters (5 players each)
2. Loads player history — recent match stats, ratings, form trends from our database
3. Computes 50+ features per match — ELO ratings, momentum, tilt, head-to-head record, roster stability, strength of schedule, and more
4. Runs all models — each model outputs a probability (e.g., "Team A has 67% chance to win")
5. Displays results — predictions refresh every 5 minutes as new data arrives

Model categories

What happens after a match

Results are checked every 10 minutes. When a match completes:

• Each model's prediction is scored as correct or incorrect
• ELO ratings update based on the result (winners gain, losers drop)
• Model accuracy on the leaderboard updates in real-time
• All upcoming matches are re-predicted with updated ratings

Prediction tiers

We classify matches into tiers based on team rankings and tournament importance:

• Top — matches between ranked teams at major events. Best prediction accuracy.
• Major — major qualifier and playoff matches.
• Other — lower-tier matches. Shown as schedule only, without predictions, because accuracy drops to ~50%.

Data sources

All data comes from HLTV.org — the authoritative source for professional CS2 statistics. We collect match results, player box scores, team rankings, and tactical profiles (Rating 3.0 radar). Data refreshes every 5 minutes for matches and daily for player profiles.

The Tactical Radar

Each player profile on CS2PREDICT shows a tactical radar with 7 metrics scored from 0 to 100. These come from HLTV's Rating 3.0 system and represent the player's style and strengths over the past 3 months.

Metrics explained

Reading team compositions

A well-rounded team typically has:

• 1-2 high Firepower players (star fraggers)
• 1 high Entrying player (entry fragger)
• 1 high Sniping player (AWPer)
• 1-2 high Utility players (support/IGL)

Teams with balanced radar across all players tend to be more consistent. Teams with one extremely high firepower player and low stats elsewhere are "star-dependent" — if the star underperforms, the team collapses.

Example

See a live player profile: ZywOo — one of the highest-rated players in CS2 history. Notice his extreme Firepower and Sniping scores.

Data freshness

Tactical profiles update daily at 02:00 UTC. The metrics reflect the last 3 months of competitive play on HLTV.

What is the Model Leaderboard?

The Model Leaderboard ranks all AI models by their prediction accuracy on Top-tier matches. It's a live scoreboard — accuracy updates after every completed match.

Color coding

Models are colored on a gradient from green (best accuracy) to red (worst). The color updates dynamically as accuracy changes — if a model goes on a hot streak, it moves up and turns greener.

Tier filters

• Top — accuracy on top-tier matches only (most reliable signal)
• Major — accuracy on major event matches
• All — combined Top + Major accuracy

Calibrating models

New models show a pulsing orange dot and "calibrating" status until they have at least 10 predictions. This prevents ranking models based on too few data points.

Which model should I follow?

No single model is always right. The best strategy is to look at consensus — when most models agree, the prediction is usually reliable. When models disagree sharply, the match is genuinely uncertain.

Check the leaderboard regularly — the best model this week might not be the best next week. Form matters for models too.

What is Brier Score?

Brier Score measures how good probability predictions are, not just whether the winner was guessed correctly. It ranges from 0 (perfect) to 1 (worst possible).

Formula: Average of (predicted probability - actual outcome)² across all predictions.

Why accuracy alone is misleading

Consider two models predicting the same 10 matches:

• Model A: Always predicts 51% for the winner. Accuracy: 100%. But useless — barely more confident than a coin flip.
• Model B: Predicts 80% for 9 winners, 60% for 1 loser. Accuracy: 90%. Much more useful — when it's confident, it's right.

Brier Score captures this: Model B has a much better (lower) Brier Score because its confident predictions are correct.

What's a good Brier Score?

Sharp predictions

We also track "Sharp" — accuracy specifically when the model is highly confident (80%+). A model with 85% Sharp score means when it's very confident, it's right 85% of the time. This is what matters for high-stakes decisions.

What does 65% mean?

When a model predicts Team A at 65%, it means that in similar situations historically, Team A won about 65 out of 100 times. It's not a guarantee — it's a probability based on patterns the model has learned.

High confidence vs low confidence

80%+ — The model is very confident. One team has a clear advantage in skill, form, or matchup. These predictions are correct about 75-85% of the time.

60-70% — Moderate confidence. The favored team has an edge but upsets happen regularly. About 65% accuracy.

50-55% — Coin flip. The model sees no clear advantage. We hide these predictions (shown as 50/50) because they carry no useful signal.

Multiple models disagree — what now?

If ELO says 70% Team A but MIND says 60% Team B, the matchup is genuinely uncertain. Different models weigh different factors:

• ELO models — pure skill rating based on past results
• MIND — psychological factors (tilt, momentum, recovery)
• SQUAD — individual player form and chemistry
• SOS — strength of schedule (who they've beaten recently)

When models disagree, the match is likely to be close.

Why we only predict Top & Major tier

Our models achieve strong accuracy on top-tier matches where we have rich data about both teams. For lower-tier matches with unknown teams, predictions drop to ~50% — no better than a coin flip. We only show predictions where they add real value.

How matches are classified

Every match on CS2PREDICT is automatically classified into one of three tiers based on team rankings and event importance:

Top tier

Matches between top-30 ranked teams at significant events (BLAST, ESL, PGL, IEM). This is where our models perform best. Both teams are well-known with extensive historical data.

Major tier

Major championship qualifiers and playoff matches. Even if teams aren't in the top 30, the event importance is high. Good data availability, reliable predictions.

Other tier

Lower-tier events, regional qualifiers, open qualifiers. Teams often have limited data — small match history, unstable rosters, no HLTV ranking. Our models achieve only ~50% accuracy here (coin flip level), so we show these matches as schedule only, without predictions.

Why this matters

Showing bad predictions is worse than showing no predictions. A user who follows a 50% accurate model will lose trust quickly. By focusing on Top and Major tiers, we ensure every prediction we show has genuine analytical value behind it.