Cheetahs v Scarlets
Set a scoreline and see where it sits among the 5,000 simulated runs behind this fixture. Ratings as at 2026-10-19.
CheetahsScarletsThe steppers skip 1, 2 and 4. Points come in fives, threes and twos with a conversion needing a try in front of it, so no match has ever finished on any of them.
18.5% of runs finished in this bin — within 2 points either way.
50 points is around the 52nd percentile of the total — the middle run scored 49.
Those two figures are separate readings and are not multiplied together. The simulation draws the gap and the amount of scoring independently, so one bar of the histogram covers many scorelines and the payload holds nothing about which of them came up with which gap.
That is the model’s own average scoreline. Nothing here scores any of it. The model’s gap is an average over both winners and yours is one afternoon, so the two are different kinds of number rather than one being right. How the model works.
Why Cheetahs are favoured
How the model arrives at the chance of Cheetahs winning. It starts at an even 50%, and each line below adds to that or takes from it, in percentage points (pp): a bar to the right helps Cheetahs, a bar to the left helps Scarlets.
Cheetahs 1270, Scarlets 1273 — 4 points, favouring Scarlets
Cheetahs are at home
Recent results, past meetings between the two sides, and days of rest. It has a limit, so it can nudge the rating but never overrule it.
Cheetahs and Scarlets have no fixture on the published schedule, so there is no ground, no kick-off forecast and no team sheet to put beside this. The prediction itself does not need any of them.
6 meetings — Cheetahs 1, Scarlets 5. Last 2023: Scarlets 20–17 Cheetahs.
None of this reaches the model. Weather is not a feature, team selection is not a feature and the record is already inside both ratings, so the probability above does not move because any of it changed.
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