K League 1 Predictions — Model Probabilities

Research, not instructions — the model states a probability, never a selection.
MatchKickoff (UK)Model 1X2Most likely score
Jeonbuk Hyundai Motors crestJeonbuk Hyundai Motors v FC SeoulFC Seoul crest Sat 12 Sept, 08:30
Bucheon FC 1995 crestBucheon FC 1995 v Jeju United FCJeju United FC crest Sat 12 Sept, 08:30
Ulsan Hyundai FC crestUlsan Hyundai FC v Incheon UnitedIncheon United crest Sat 12 Sept, 08:30
Daejeon Citizen crestDaejeon Citizen v Pohang SteelersPohang Steelers crest Sat 12 Sept, 11:00
Sangju Sangmu FC crestSangju Sangmu FC v Gangwon FCGangwon FC crest Sun 13 Sept, 08:30
Gwangju FC crestGwangju FC v FC AnyangFC Anyang crest Sun 13 Sept, 11:00
Read the full picture. Every row links to the fixture's prediction view: expected goals, fair odds, price movement and the grounded team-news brief. The track record grades every published call in the open.

Frequently asked questions

How are these k league 1 predictions made?
A Dixon-Coles + ELO statistical model, trained continuously on a growing results archive, produces expected goals per team and win/draw/win probabilities for every fixture. The model is then priced against the real odds market — the market always owns the final number.
Are these betting tips?
No — they are model probabilities published as research. Probabilities carry uncertainty and no outcome is certain; judge the process on the public track record, which grades every published call against the closing line.
How do I know the model is any good?
The track record page publishes calibration (predicted vs observed frequencies), closing-line value on every call, and a held-out walk-forward backtest — process metrics first, never performance claims.
Prices captured in the scheduled market snapshot, 11 September at 08:02 UK time. · engine slate assembled 13 h ago · this page fetched the feed just now. Every price shown is a real captured bookmaker price with its own capture time.