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.