Weekly digest · Top-tier CS2 matches · Week 23 · May 25 – May 31, 2026
This week (Week 23 · May 25 – May 31, 2026) we analyzed 23 top-tier CS2 matches across 24 prediction models. SQUAD dominated with 60.9% accuracy (14/23 correct), ahead of MIND at 50.0%. SQUAD achieved a Brier Score of 0.2732 and Log Loss of 0.8257.
🏆 Model of the Week: SQUAD
SQUAD edged out the competition this week, though accuracy across all models was moderate.
SQUAD showed decent performance with 60.9% accuracy across 23 predictions (14 correct, 9 wrong).
📊 Full Rankings This Week
Complete model performance for the week. The leader is highlighted.
🎮 SQUAD Predictions This Week
14/23 correct predictions on top-tier matches.
| MATCH | PREDICTION | RESULT |
|---|---|---|
| FOKUS 2-1 Nemiga | 90% FOKUS | CORRECT |
| Gentle Mates 0-2 Nemesis | 81% Nemesis | CORRECT |
| TDK 2-1 OG | 71% OG | WRONG |
| 100 Thieves 1-2 Nemesis | 67% Nemesis | CORRECT |
| magic 1-2 Ninjas in Pyjamas | 72% Ninjas in | CORRECT |
| FOKUS 2-1 fnatic | 77% FOKUS | CORRECT |
| Gentle Mates 2-0 GenOne | 89% Gentle Mat | CORRECT |
| Ninjas in Pyjamas 2-0 FaZe | 67% Ninjas in | CORRECT |
| Tricked 0-2 Nemiga | 89% Nemiga | CORRECT |
| Gentle Mates 2-1 Eternal Fire | 79% Gentle Mat | CORRECT |
| FaZe 2-0 Sharks | 70% Sharks | WRONG |
| 9z 1-2 FaZe | 68% FaZe | CORRECT |
| Nemesis 2-3 TDK | 88% Nemesis | WRONG |
| magic 2-1 FaZe | 56% FaZe | WRONG |
| HEROIC 1-2 9z | 72% HEROIC | WRONG |
| 3DMAX 1-2 Alliance | 93% 3DMAX | WRONG |
| 9z 1-2 Sharks | 83% Sharks | CORRECT |
| Eternal Fire 2-1 fnatic | 77% fnatic | WRONG |
| FaZe 2-0 Alliance | 61% Alliance | WRONG |
| Gentle Mates 2-0 ALGO | 73% Gentle Mat | CORRECT |
| HEROIC 0-2 Ninjas in Pyjamas | 64% Ninjas in | CORRECT |
| 3DMAX 0-2 magic | 96% 3DMAX | WRONG |
| fnatic 2-0 Rune Eaters | 64% fnatic | CORRECT |
🤔 Why every model struggled this week
60.9% as the best model is the lowest weekly leader we've ever recorded on cs2predict.gg — normal weeks see the top model land between 75% and 85%. The drop wasn't a data glitch (we verified every match against HLTV directly). It was a real upset wave driven by an unusually packed tournament calendar.
Three top-tier events ran in parallel this stretch:
- CS Asia Championships 2026
- IEM Atlanta 2026
- PGL Astana 2026
Plus Major qualifiers leading into Cologne. Top organisations split rosters, tested experimental lineups, and showed visible signs of late-season fatigue. The result: 7 matches where every single one of 18–22 active models was wrong.
The 7 matches that broke the week
| Match | Tournament | What happened |
|---|---|---|
| MongolZ vs B8 | CS Asia Championships | B8 won 2-1 (huge upset) |
| MIBR vs PARIVISION | CS Asia Championships | MIBR ran out 2-0 |
| NaVi vs Vitality | IEM Atlanta | NaVi took it 2-1 |
| B8 vs FUT | IEM Atlanta | B8 won 2-0 again |
| PARIVISION vs G2 | PGL Astana | G2 needed 3 maps |
| PARIVISION vs 9z | PGL Astana | 9z swept 2-0 |
| Aurora vs HEROIC | PGL Astana | HEROIC took it 2-0 |
The pattern: B8 (Ukrainian Tier-2) and PARIVISION (rising Russian roster) were involved in 5 of 7 of these matches. B8 ran the Cinderella story — taking down both MongolZ AND FUT in the same week.
Why SQUAD held up better
When ELO-based, rank-based, and form-based models all converge on the same prediction and are all wrong, the only model that can stand out is one looking at something different. SQUAD does exactly that: it weights individual player ratings rather than team-level rank.
For a week dominated by:
- Rosters with new players (player ratings hadn't caught up to team rank)
- Substitute lineups (team rank was misleading)
- Tier-2 squads with strong individuals punching above their rank
SQUAD's player-first approach happened to fit. 60.9% isn't great absolutely — it's just that team-based models did far worse, because their signals were polluted by mid-tournament roster changes.
What this means going forward
CS2's 2026 calendar is more packed than ever. When 3+ tournaments overlap, expect lower model agreement, more upsets in matches where rosters changed mid-week, and player-based models (SQUAD, WR10) gaining a temporary edge.
We're not adjusting any model weights based on a single week — that would be classic over-fitting. But it's a useful reminder: models are statistical, not magical. Stable meta produces high accuracy. Chaotic weeks produce chaotic predictions.
Major Cologne Stage 1 kicks off this week (June 2). It'll be interesting to see how models behave once the scene refocuses on one big event.