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DSPick the outright winner of the match. Pays the implied probability less the bookmaker's margin.
Bet on the margin of sets, total sets played, or whether the match reaches a tiebreak.
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Pick the exact set scoreline (e.g. 2-1, 2-0, 1-2) at the end of the match. Highest payout on the long-shots.
Odds via Cloudbet · auto-refresh every 30s · For informational purposes only. Not financial or betting advice.
Yoshihito Nishioka faces Dalibor Svrcina in the round of 64 of ATP Shanghai, contested on hard. The CHALLENGER matchup is scheduled for TOMORROW 1:10 AM EDT, with pre-match odds via Cloudbet.
ATP Shanghai is a WTA 1000 event on hard — one tier below the Grand Slams. Most top-50 players enter, and ranking points reward deep runs heavily, so favourites typically take it seriously even in early rounds.
Yoshihito Nishioka (Japan) enters this match unranked or outside the public tour rankings. Hard-court tennis is the best-sampled surface on tour, so form here is the truest signal of overall level.
Dalibor Svrcina (Czech Republic) enters this match unranked or outside the public tour rankings. Hard-court tennis is the best-sampled surface on tour, so form here is the truest signal of overall level.
Cloudbet currently prices Svrcina as the favourite at roughly 68% implied to win, against Nishioka at the long end.
How to think about this matchup: hard-court tennis is the most predictable surface, so books price tightly. Look for value in total-games or set-handicap markets when one player has a clear edge in serve hold or return-of-serve metrics, rather than the moneyline where the margin is already baked in.
Auto-generated from match metadata + live Cloudbet markets · refreshed each request
Hard courts reward all-court games and balanced power. Bounce is medium-high and consistent; rallies fall between clay and grass in length. Most ATP/WTA Masters and the US Open / Australian Open are played here, so hard-court form is the most-sampled surface in the year. Both serve and return strength matter, and the books price tightly because the data is rich.
