Finished Match Overview Eternal Fire vs RBLS in CS 2

CS2

Circuito Retake Season 12

Eternal Fire

2:109/17

RBLS

8Map 113

13Map 29

13Map 34

Winio Prediction

Before draft

66%

Prediction: Eternal Fire

34%

Match breakdown

1 of 2 correct

Our predictions vs actual result

Match winnercorrect
Total mapswrong

Prediction factors

49 factors analysed · key ones shown

Winio prediction

Eternal Fire to win

66%34%

Average roster strength

1776

1528

Form over 10 matches

50%

40%

Time with current roster

5 days

131 days

Strongest player

1864

1620

Model accuracy

Full archive

Form & Head-to-Head

Last five matches and head-to-head over a year.

Rosters & Form

Our rating for every player, powered by the Winio algorithm

player form over the last 10 maps, regardless of team

Additional predictions

Total maps

55.0%

Prediction: 2 maps

45.0%

2 maps3 maps

Pistol rounds

Round 1 (1st half)

56.6%

Prediction: Eternal Fire

43.4%

Eternal FireRBLS

Round 13 (2nd half)

53.7%

Prediction: Eternal Fire

46.3%

Eternal FireRBLS

Eternal Fire vs RBLS — CS2 Match Analysis, September 17 2026

Explore a detailed breakdown of the Eternal Fire vs RBLS match at Circuito Retake Season 12. Winio's analytical model processed every phase of the game, identified the decisive factors, and measured how closely the pre-match prediction matched the actual result.

CS2 Post-match Analysis: What's Inside

Final result and prediction accuracy. Compare the pre-match win probability with the actual outcome and see which factors proved decisive.
Key moments that changed the game. A breakdown of the critical events that shaped the result: turning points, map control shifts, and individual performances.
Full match statistics. Detailed metrics for both teams and every player: rating, ADR, KAST, map efficiency, and other indicators that explain the final score.
Comparison with previous meetings. See how this match fits into the teams' head-to-head history and which patterns repeated.

Use finished match data to understand how Winio's model performs over time. Each completed match adds to the prediction history available at the Ended Matches section.