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Lin Zhu vs Diana Shnaider Prediction - WTA Ningbo 2025

Lin Zhu will sqaure off with Diana Shnaider in the quarter-finals at the 2025 WTA Ningbo tournament on Friday.

This article features Stats Insider's free betting tips for the Zhu vs Shnaider match, plus the latest betting odds in Australia.

Who Will Win?

Utilising advanced machine learning and data, Stats Insider has simulated the outcome of Friday's Zhu-Shnaider women's singles match 10,000 times.

Our proven predictive analytics model currently gives Shnaider a 69% chance of winning against Zhu at the WTA Ningbo tournament.

Zhu vs Shnaider: Odds

The current betting odds in Australia for Friday's WTA Ningbo match between Zhu and Shnaider are listed below:

  • Head to Head: Zhu $3.20, Shnaider $1.36
  • First Set: Zhu $2.62, Shnaider $1.50

Odds are correct at the time of publication and subject to change.

Looking at the latest head-to-head odds, TAB currently has Zhu at $3.20 and Shnaider at $1.36.

TAB currently has odds for Zhu to win the first set at $2.62 and odds for Shnaider to win the first set at $1.50.

Zhu vs Shnaider: Best Bets

  • Head to Head: Zhu at $3.20 with TAB
  • First Set: Shnaider at $1.90 with TAB

Stats Insider's Lin Zhu vs Diana Shnaider predictions are based on complex simulations and gambling experience to bring you the best possible advice 24/7/365.

Although our predictive analytics model suggests that Shnaider is more likely to win the match, taking Zhu to win is the best option due to the edge we discovered when comparing our data-led probabilities to the odds that are currently being offered.

Taking advantage of the edges published here at Stats Insider is one of the keys to being a profitable sports bettor over the long run.

And while Shnaider is more likely to win the first set on this occassion, our recommended bet of Shnaider ($1.90) is based on the expectation of that happening, per our model, and the betting odds currently available.

Zhu vs Shnaider: Prediction

Stats Insider provides full coverage of the Lin Zhu vs Diana Shnaider match at the WTA Ningbo tournament, including data-driven predictions and the best betting tips.

Our model updates regularly, so refresh this article for the latest betting analysis before the Zhu-Shnaider match at the WTA Ningbo tournament.

As always, see Stats Insider's Best Bets for betting tips on every tennis match, plus predictions for a wide range of other sports.

Zhu vs Shnaider: WTA Ningbo Essential Details

The 2025 WTA Ningbo match between Lin Zhu and Diana Shnaider is scheduled to begin at 11:00pm AEDT.

  • Match: Lin Zhu vs Diana Shnaider
  • Date: Friday 17 October 2025
  • Approx. Time: 11:00pm AEDT
  • Event: WTA Ningbo, China Women's Singles 2025
  • Round: Quarter-finals

All dates and times mentioned in this article are in Australian Eastern Daylight Time (AEDT), unless otherwise stated.

Conclusion

Our Lin Zhu vs Diana Shnaider predictions are the result of 10,000 data-driven simulations of the game, all curated by our team of expert data scientists and analysts. We use the latest in predictive analytics technology and machine learning to ensure our tennis tips are trustworthy and reliable, giving you the confidence to make informed decisions.

If you choose to use our tennis predictions for betting purposes, it's important that you gamble responsibly and manage your finances effectively. For free and confidential support, call 1800 858 858 or visit gamblinghelponline.org.au.

More on Tennis

Stats Insider is your home for betting on tennis in Australia, with the latest tennis betting news, tips for every tennis tournament, and our in-house approach to accurately ranking the world's top 100 men's and women's players.

Stats Insider

Stats Insider, Australia's leading predictive analytics website, offers Australian sports fans innovative tools and content to enhance their enjoyment of major sporting events both domestically and internationally. Our goal is to transform the sports fan experience by providing readily accessible, data-driven content for sports enthusiasts like us.

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