EPL Masters I. Play-In · BO3
result vs predictionKKWwin 2:0
series probability
13%87%
probability at match start · final 2:0
TSTeam Syntaxloss 0:2
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.
KW
No games played over 6 months.
Team Syntax
Tusk
Stoic · 5 games
Axeoften banned
emptiness · 3 games
Beastmasteroften banned
emptiness · 2 games
Invokeroften banned
Jeezy · 2 games
Largooften banned
Jeezy · 2 games
Phoenix
Jeezy · 2 games
Snapfireoften banned
emptiness · 2 games
Warlock
Stoic · 2 games
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.
Hoodwink
498 picks · 359 bans
Rubick
398 picks · 240 bans
Tiny
343 picks · 353 bans
Clockwerk
337 picks · 632 bans
Windranger
321 picks · 233 bans
Pangolier
303 picks · 405 bans
Bane
291 picks · 276 bans
Storm Spirit
287 picks · 306 bans
Prediction components
Form (series winrate)40% vs 75%
Hero pool strength56% Team Syntax
Head-to-head0 – 0
Model total87% Team Syntax
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
KW 13%Team Syntax 87%
The model gave 87% on Team Syntax — the game was taken by KW.
Game 2prediction missed
KW 13%Team Syntax 87%
The model gave 87% on Team Syntax — the game was taken by KW.
Model check result
Model favoriteTeam Syntax · 87%
Series resultKW 2:0
Series predictionmissed
Accuracy by game0 of 2