STOCHASTIC RATING ENGINE // COMPETITIVE ANALYTICS

CS2 & Dota 2 Elo & Glicko-2 Match Predictor — True Win Probability

Calculate exact map and series win probabilities between competitive esports teams using dual Elo and Glicko-2 rating models. Factors in rating deviation (RD), series length (Bo1/Bo3/Bo5), and dynamic K-factors.

TEAM PARAMETERS MARK V PROBABILISTIC MODEL

TEAM A METRICS FAVORITE

Rating 1650

Rating Deviation (RD) 50

TEAM B METRICS UNDERDOG

Rating 1520

Rating Deviation (RD) 75

Match Format

K-Factor Rigidity

Logistic Win Curve & Delta Operating Point

Delta: +130 Elo

PROJECTED OUTCOMES LIVE CALC

SERIES WIN PROBABILITY

69.2% 30.8%

TEAM A MAP WIN PROBABILITY

63.4% 36.6%

Team A Fair Odds 1.45 -222

Team B Fair Odds 3.25 +225

RATING ADJUSTMENT ON WIN

If Team A Wins: +8.8 pts

If Team A Loses: -15.2 pts

Frequently Answered Questions

Why does Glicko-2 outperform classical Elo in esports? ▼

Classical Elo assumes static player skill and constant uncertainty. Glicko-2 introduces Rating Deviation (RD), which expands during roster changes and tournament breaks, shrinking win probability confidence and preventing overconfident betting lines.

How does series format (Bo1 vs Bo3 vs Bo5) amplify skill differentials? ▼

In a Bo1, variance is high because a single pistol cascade can decide the map. In a Bo3, the stronger team has multiple chances to recover, compounding their probability: P(Bo3) = P(A)^2 + 2*P(A)^2*(1-P(A)). A 65% Bo1 favorite becomes a 71.8% Bo3 favorite and an 76.5% Bo5 favorite.

How should traders exploit discrepancies between Elo fair odds and bookmaker lines? ▼

When your Glicko-2 model calculates 68% probability (1.47 fair odds) and the bookmaker lists 1.62, the expected value is positive: EV = (0.68 * 1.62) - 1 = +10.16%. Use Quarter-Kelly staking to size positions while buffering against map pool variance.