Counter-Strike Match Outcome Predictor

Simulating Best-of-Three Series with Machine Learning

Machine Learning Esports Analytics Python Simulation

Executive Summary

Counter-Strike 2 (CS2) is one of the most-played esports titles, with over a million concurrent players each month and a competitive scene built around best-of-three (Bo3) map series. This project builds a machine learning model that predicts match outcomes from team performance metrics and map-specific history, then simulates each map in a Bo3 series to estimate a team's overall probability of winning the series — rather than treating the match as a single coin flip.

Core Contribution

Modeled team strength at the map level instead of the match level, since a team's odds shift depending on which maps are in play — then ran series-level simulations to turn per-map win probabilities into a full Bo3 forecast.

Series Format

Best-of-3

Map-by-map simulation

Feature Set

Form + Map History

Team performance metrics

Output

Win Probability

Per-map and series-level

What It Does

Map-Level, Not Match-Level

Team strength in CS2 isn't uniform across the map pool — a team that dominates on one map can be well below average on another. The model captures this by evaluating team performance metrics and historical results at the individual map level, rather than producing one overall rating per team.

Series Simulation

Once each map has a modeled win probability for a given matchup, the Bo3 series is simulated map by map to estimate the probability of each possible series outcome — turning individual map forecasts into a single series-level prediction.

Methodology Overview

The project follows a structured pipeline from raw match data to series-level forecasts:

  1. Data Collection — team and player performance metrics, plus map-by-map match history
  2. Feature Engineering — recent form, head-to-head results, and map-specific win rates
  3. Model Development — a classification model estimating each team's win probability for a given map
  4. Series Simulation — per-map probabilities combined to simulate full Bo3 outcomes
  5. Validation — simulated outcomes compared against actual series results

Feature Categories

  • Team Form — recent match results and performance trend
  • Map History — team-specific win rates on each map in the active pool
  • Head-to-Head — historical results between the two teams, where available

Simulation Logic

Each map in the series is treated as an independent probabilistic event based on the model's output, and the Bo3 outcome space (2-0 or 2-1 in either direction) is simulated to produce a series-level win probability for each team.

Business Applications

Map-aware match prediction has a few natural uses in the esports ecosystem:

Broadcast Insight

Pre-match win probability graphics that account for the specific map pool in play.

Team Scouting

Identify a team's map-pool strengths and weaknesses ahead of a series.

Fan Engagement

Give fans a data-driven view of series odds beyond raw team rankings.

Strategic Insight

Because map pool matters as much as overall team strength, forecasts that ignore map-level performance miss a big part of what actually decides a Bo3 series.

Conclusion & Future Work

Simulating a series map by map, rather than predicting a single match-level winner, produces a forecast that better reflects how Bo3 series actually play out.