03/09/2025
Hey everyone,
I ran 1,000,000+ simulations for TI14 Swiss, full predictions, probabilities & dark horse outcomes.
I’ve been working on a massive prediction project for The International 2025 (TI14) Swiss stage. Instead of just going off vibes or recent match results, I combined multiple approaches:
Monte Carlo simulations (1M+ runs)
Performance data (June–August 2025)
Elo + Glicko2 models
Expert heuristics
Upset potential
After crunching everything, I generated a summary card, plus raw data files with probabilities for every team (who’s more likely to 4-0, 4-1, drop to elimination, etc).
EDIT: Quick clarifications: the intro paragraph was AI-generated just for readability, since I don’t post on Reddit often, i just said to chat: please make me a body and title for my monte carlo predictions, but all simulations, probability matrices, and calculations were done with real match data and code. The Monte Carlo Swiss sims assign each team a probability distribution over final records (rows sum to 100%), and the “final picks” table is a single illustrative run, which is why Team Nemesis can show 2-3 despite their highest probability being 0-4 or 1-4. Early fast outputs used independent per-team estimates, so column sums may not match slot counts, but proper joint simulations enforce exact slot counts. Bottom-tier rows sometimes looked like “5 teams in bottom 3” due to showing all low-probability teams; the bracket itself respects the elimination slots. Expert heuristics and upset potential incorporate recent scrims, roster volatility, and H2H tendencies on top of Elo/MMR. Group constraints in rounds 1–4 slightly shift early probabilities, explaining SEA teams like Nemesis being more likely 1-4 than 0-4. For Tobi standing in for Tundra, the model uses base team Elo plus player delta plus a chemistry effect accounting for role fit, hero similarity, prep time, and overlap, producing a small Elo/MMR drag (~–10) that modestly lowers BO3 win chance; simulations capture variance across events.
u/Tight-Fold-7766