Win Probability Calculator

Enter two player ratings to see the statistical probability of each outcome - win, draw, or loss. Based on the Elo expected score formula with draw probability estimates derived from historical master-level data.

Professor Archer says: Statistics are interesting, but remember - any single game can go either way. I have seen 1200-rated players beat 1800s when they play with clarity and purpose. The probability just tells you what tends to happen over many games. Your job is to play the best moves on the board in front of you, regardless of the numbers.

Features

  • Visual win/draw/loss probability bar
  • Based on Elo expected score formula
  • Historical draw probability estimates
  • Compare any two ratings from 100 to 3000
  • See how rating gaps affect outcomes

Frequently Asked Questions

How accurate are chess win probability predictions?

Win probability estimates based on Elo ratings are statistically accurate over large numbers of games. However, individual games are unpredictable. Factors like preparation, time control, psychological state, and playing style all influence the actual outcome. Use these probabilities as a general guide, not a guarantee.

How is draw probability estimated?

Draw probability is estimated using the formula P_draw = 0.5 * e^(-|R1 - R2| / 200), which models the observation that games between equally-rated players are more likely to end in draws than games with large rating gaps. This approximation is based on statistical analysis of professional chess games.

Does the rating difference tell you everything about a matchup?

No. Rating difference gives you the statistical expectation, but it does not account for playing style matchups, opening preparation, time control preferences, or current form. Some players consistently perform better against certain styles of play, regardless of the rating gap.

About Old School Chess

Professor Archer - A chess coach grounded in classical literature, built to teach adult beginners with patience and clarity. Developed with research and AI. Human-reviewed.

Learn more about Professor Archer