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Bartunkova vs Sabalenka Prediction & More at WTA China Open, H2H, Picks

Thomas
Thomas
October 3, 20262 min read

We've got three picks from the China Open third round today in Beijing. Sabalenka is the obvious headline, but the Shnaider-Alexandrova match is the one I'm most curious about. Let's get into it.

WTA China Open Predictions

Nikola Bartunkova vs Aryna Sabalenka

Head-to-head: Bartunkova 0-1 Sabalenka

Bartunkova is a solid player and her ranking at 34 is no fluke, but this is about as tough a draw as you can get. Sabalenka is one of the best hard-court players in the world and she's been in dominant form all season, and the one previous meeting between them went to Sabalenka without much drama.

Hard to see this being competitive at all.

Prediction: Sabalenka in 2

Ekaterina Alexandrova vs Diana Shnaider

Head-to-head: Alexandrova 2-1 Shnaider

This one's genuinely interesting. Alexandrova actually leads the head-to-head and she's no pushover on hard courts, but Shnaider has improved a lot over the past year and the rankings gap between them is closing fast. The tricky part is that Alexandrova's game, flat and low over the net, has historically given Shnaider some problems with her more upward-swinging ball-striking.

That said, I feel like Shnaider's power eventually becomes too much to handle over three sets, and she flips this H2H today.

Prediction: Shnaider in 3

Sara Bejlek vs Naomi Osaka

Head-to-head: First meeting

Osaka has had a complicated return from her extended break but she's been finding her level again on hard courts, and Beijing feels like a tournament where she can put a good run together. Bejlek at 29 in the world is no easy match, but hard courts are where Osaka is most dangerous and her serving and groundstroke pace should be too much here, all things considered.

Prediction: Osaka in 2

For full predictions on ATP and WTA matches today, check out Uppset.

Credit Image: © Leonardo Ramirez/eyepix via ZUMA Press Wire

Thomas

Thomas

Data-driven tennis analysis from the team behind Uppset.

@getuppset

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