# South Africa vs Korea Republic > World Cup · Kickoff Thu 25 Jun 2026, 01:00 UTC · [Canonical HTML](https://betsprinter.com/fixtures/36995) **Status:** Finished **Final score:** South Africa 1–0 Korea Republic ## Model verdict - **South Africa win:** 68% - **Draw:** 19% - **Korea Republic win:** 12% - **Source:** model ## Pre-match deep dive ### Home edge and Elo advantage shape a clear model lean ## The stage This World Cup fixture kicks off on Thu 25 Jun 2026 at 01:00 UTC, a narrow window where results will immediately reshape group math and immediate trajectories[^fact-1]. The fixture sits under tournament pressure rather than a long league slog; the timetable and stakes concentrate margins for error. ## Form & momentum Model output gives a decisive lean to the hosts: Home 68% / Draw 19% / Away 12%, with the model reporting high confidence and a 49 percentage-point gap to the runner-up outcome[^fact-2]. That probabilistic picture is reinforced by an Elo differential of +88 points for the hosts once home advantage is applied, a structural rating gap that usually maps to a clear on-paper edge[^fact-3]. Recent performances suggest the contest won’t be a simple walkover. South Africa’s last two competitive appearances read as D-L in the sequences listed and they are averaging 0.50 points per game with 0.50 goals scored and 1.50 conceded per match across those two outings[^fact-4]. Korea Republic’s last two are L-W — listed as W-D-L in order — and their short-run numbers show 1.50 points per game with 1.00 goals scored and 1.00 conceded per match[^fact-5]. Those lines imply Korea are producing more attacking output and a tighter defensive return in the narrow sample, but the model’s probabilities still favour the home side decisively[^fact-2][^fact-3]. ## Personnel Teboho Mokoena is the in-form focal point cited for South Africa: 1 goal, 0 assists in his last two appearances, and an average match rating of 7.51 across that same window[^fact-6]. For Korea Republic, In-beom Hwang carries the recent attacking influence: 1 goal and 1 assist in his last two appearances, with an average rating of 7.61[^fact-7]. Those two are the only individual form metrics supplied and therefore form the best available gauges for immediate influence on the contest[^fact-6][^fact-7]. No explicit absences or suspensions are listed in the supplied facts, so any selection gaps cannot be quantified from these inputs and are excluded from the assessment below. ## Where the model sees value The model’s headline probabilities (Home 68% / Draw 19% / Away 12%) are the primary signal for where edges might exist against market pricing, given the model’s stated high confidence and the 49-point gap to the runner-up outcome[^fact-2]. The underlying Elo edge of +88 points, with home advantage applied, is the secondary structural justification for that lean[^fact-3]. Three markets were analysed against the model, establishing the cross-checks used in the desk’s view[^fact-8]. Because the supplied facts do not include market odds, the exact pricing comparison cannot be printed here; the quantitative case is therefore expressed in model probabilities and rating differentials alone[^fact-2][^fact-3][^fact-8]. In practical terms, the model’s 68% home probability and +88 Elo gap create a large theoretical gap that will be the basis for any disagreement with market prices once odds are available[^fact-2][^fact-3]. The short-form match data on goals and points per game — South Africa 0.50 PPG and 0.50 goals scored / 1.50 conceded; Korea 1.50 PPG and 1.00 goals scored / 1.00 conceded — offers a secondary layer to judge expected match tempo and margin, with Korea showing marginally better short-term attacking balance while still being the underdog to the host on ratings and model output[^fact-4][^fact-5]. ## Verdict The model leans clearly to the home side, assigning 68% to a home win and underpinning that with an +88-point Elo advantage after home adjustment; short-form numbers give Korea slightly better attacking returns in two matches, but not enough to overturn the structural edge in the model’s view[^fact-2][^fact-3][^fact-5][^fact-4]. Markets were compared across three lines for this assessment, but odds were not supplied in the facts provided[^fact-8]. ### Cited facts [^fact-1]: **Kickoff** — Thu 25 Jun 2026, 01:00 UTC — World Cup [^fact-2]: **Model verdict** — Home 68% / Draw 19% / Away 12% (source: model; confidence high, 49 pp gap to runner-up). [^fact-3]: **Elo edge** — RSA vs KOR — Elo differential +88 points (with home advantage applied). [^fact-4]: **RSA recent form** — DL last 2: 0-1-1 (W-D-L), 0.50 PPG, 0.50 goals scored / 1.50 conceded per match. [^fact-5]: **KOR recent form** — LW last 2: 1-0-1 (W-D-L), 1.50 PPG, 1.00 goals scored / 1.00 conceded per match. [^fact-6]: **RSA in-form player** — Teboho Mokoena — 1 goals, 0 assists in last 2 appearances, avg rating 7.51. [^fact-7]: **KOR in-form player** — In-beom Hwang  — 1 goals, 1 assists in last 2 appearances, avg rating 7.61. [^fact-8]: **Markets analysed** — 3 market(s) compared against the model. --- Methodology: . Canonical HTML: .