Consistent bounce, medium-fast: neutral conditions, no style favored.
Strong heat: warm air speeds the ball up and physical wear tells in long matches.
Dry air: the ball travels normally.
Some wind: makes baseline control harder.
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: 1843 vs 1746 — favorite by rating
›ATP qualifying / early round · 298 matches in the favorite's track record
›Elo estimate (not the ATP factor model): qualifying draws have no clean main-tour history
!Qualifying/soft context: Elo estimate only — read the round context (already-through, lucky loser, dead rubber) from the dossier; it is not a proven edge.
The model makes G. Mpetshi Perricard the favorite with a 64% win probability, against T. Svajda's 36% — a solid favorite, though Svajda keeps real chances. Converted to odds, that probability is worth about @1.57; the offered odds are around @1.51 (a 66% implied), virtually the same as what the market prices in.
Several factors explain the number: 1843 vs 1746 — favorite by rating; atp qualifying / early round · 298 matches in the favorite's track record; qualifying draws have no clean main-tour history.
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 36 out of every 100 times Svajda 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: Qualifying/soft context: Elo estimate only — read the round context (already-through, lucky loser, dead rubber) from the dossier; it is not a proven edge.. This is informational analysis, not a betting recommendation. 18+ · play responsibly.