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 Daejeon CitizenDaejeon Citizen crest Sat 31 Oct, 05:00 — —
Gwangju FC crestGwangju FC v Sangju Sangmu FCSangju Sangmu FC crest Sat 31 Oct, 07:30 — —
Pohang Steelers crestPohang Steelers v Jeju United FCJeju United FC crest Sun 1 Nov, 05:00 — —
Ulsan Hyundai FC crestUlsan Hyundai FC v Gangwon FCGangwon FC crest Sun 1 Nov, 05:00 — —
FC Seoul crestFC Seoul v Incheon UnitedIncheon United crest Sun 1 Nov, 07:30 — —
FC Anyang crestFC Anyang v Bucheon FC 1995Bucheon FC 1995 crest Sun 1 Nov, 07:30 — —
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, 27 October at 08:04 UK time. · engine slate assembled 1 h ago · this page fetched the feed just now. Every price shown is a real captured bookmaker price with its own capture time.