K. Singh vs N. Waldner — prediction
Consistent bounce, medium-fast: neutral conditions, no style favored.
Strong heat: warm air speeds the ball up and physical wear tells in long matches.
Very dry air: the ball travels faster.
Light wind: no noticeable effect.
Surface feeds the model (surface specialization is one of its factors). Weather and altitude are context we publish for you — they do NOT move the probability.
›Tour Elo: 1643 vs 1374 — favorite by rating
›ITF tier · 265 matches in the favorite's track record
›Elo estimate (not the ATP factor model): these are softer, less-analyzed markets
!Soft market: the value edge in Challenger/ITF is NOT proven live — treat it as an estimate, not an opportunity.
The model makes K. Singh the favorite with a 83% win probability, against N. Waldner's 17% — a conviction read: the model sees the match clearly leaning one way. Converted to odds, that probability is worth about @1.21; the offered odds are around @1.17 (a 85% implied), virtually the same as what the market prices in.
Several factors explain the number: 1643 vs 1374 — favorite by rating; itf tier · 265 matches in the favorite's track record; these are softer, less-analyzed markets.
Read it with caution. This number does NOT come from the ATP factor model, but from the tour's real Elo: it ranks who is the favorite well, but it is a coarser estimate. Being the favorite is not being the winner: roughly 17 out of every 100 times Waldner wins. And above all, in Challenger/ITF the value edge is not proven live, so treat it as a reference, not an opportunity. Watch out for: Soft market: the value edge in Challenger/ITF is NOT proven live — treat it as an estimate, not an opportunity.. This is informational analysis, not a betting recommendation. 18+ · play responsibly.