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Национальная Студенческая Лига Киберспорта Dota 2 · BO3
result vs prediction
СФУloss 0:2
series probability
51%49%
probability at match start · final 0:2
СевГУwin 2:0
The prediction was locked in using data as of the match start: hero winrates, form and head-to-head were computed only from games before it began. This is how the model's honest accuracy is verified.
Teams' go-to heroes · 6 months
Who plays what and how often they win. "Signature" — from 6 games with a 60%+ winrate: such a pick statistically raises the win chance.

СФУ

No games played over 6 months.

СевГУ

No games played over 6 months.
Patch meta · 3 months
The most picked heroes of the pro scene and their winrate. Picking a meta hero adds points to the team in the model.
Tusk
Tusk
506 picks · 685 bans
52%
Clockwerk
Clockwerk
430 picks · 488 bans
53%
Pangolier
Pangolier
370 picks · 533 bans
49%
Luna
Luna
356 picks · 457 bans
55%
Dark Willow
Dark Willow
351 picks · 180 bans
50%
Dragon Knight
Dragon Knight
329 picks · 480 bans
52%
Enchantress
Enchantress
326 picks · 634 bans
53%
Batrider
Batrider
317 picks · 328 bans
48%
Prediction components
Form (series winrate)60% vs 71%
Hero pool strength50% СФУ
Head-to-head0 – 0
Model total51% СФУ
Prediction by game vs result
Probabilities were locked in using statistics as of the match start: the draft was assessed by hero comfort, counter-picks and late-game potential. We compare against the actual result.
Game 1prediction missed
СФУ 51%СевГУ 49%
The model gave 51% on СФУ — the game was taken by СевГУ.
Game 2prediction missed
СФУ 51%СевГУ 49%
The model gave 51% on СФУ — the game was taken by СевГУ.
Model check result
Model favoriteСФУ · 51%
Series resultСевГУ 0:2
Series predictionmissed
Accuracy by game0 of 2