Zhizhen Zhang vs. Arthur Gea prediction, odds, picks for ATP China Open 2026
Get Zhang vs Gea predictions for ATP China Open.

Zhizhen Zhang will square off with Arthur Gea in the round of 32 at the ATP Beijing event on Wednesday.
Dimers' proven tennis model projects Arthur Gea as the most likely winner in this Zhang vs. Gea prediction.
This article offers a closer look at Wednesday's match, including best bets and picks.
Zhang vs. Gea prediction and odds
Our leading predictive model gives Gea a 75% chance of beating Zhang at the ATP China Open tournament.
Latest betting odds
We have sourced the most up-to-date betting odds in America for this match, which are listed here:
- Moneyline: Arthur Gea -344, Zhizhen Zhang +280
All odds are correct at the time of publication and are subject to change.
Model picks and best bet
After matching our predictions against the best current odds, we can reveal our picks for each of the major markets in this matchup. Plus, find our best bet for the game below:
- Moneyline: Zhang @ +280 via DraftKings
- First Set: Gea @ -130 via DraftKings
According to our best tennis bets, the top play to make on this match is Zhizhen Zhang to win the first set.
These picks are based on our probabilities matched against the implied probabilities from the current available odds.
Game info & first serve details
The ATP China Open match between Zhizhen Zhang and Arthur Gea is scheduled to start on Wednesday at 5:00 AM ET.
- Who: Zhizhen Zhang vs. Arthur Gea
- Date: Wednesday, September 30, 2026
- Approx. Time: 5:00 AM ET/2:00AM PT
- Tournament: ATP Beijing, China Men's Singles 2026
- Round: Round of 32
All dates and times in this article are United States Eastern Time unless otherwise noted.
Conclusion
We predict Arthur Gea, with a 75% win probability, will likely beat Zhizhen Zhang at the ATP China Open tournament.
Additional tools and information for betting safely can be found in our Responsible Gambling hub.
This article's predictions and probabilities are generated by Dimers' proprietary Tennis prediction model. Our editorial team reviews the resulting content for accuracy and presentation before publication.



