The field for the 52nd edition of the 2023 WTA Finals is set, with eight of the world’s best players heading to Cancun for the year-end championships.
The stakes are high, with up to 1,500 ranking points and over $3 million on the line, as all eight competitors look to end their year on a high.
Ahead of the finals, the Opta supercomputer has been hard at work and has calculated its group stage, tournament and ranking predictions.
Let’s dive in.
WTA Finals Predictions: The Quick Hits
- Iga Swiatek has the highest chance of winning the tournament (20%).
- Aryna Sabalenka and Swiatek will (once again) fight for the world No. 1 ranking. The supercomputer gives Sabalenka an 85% chance of keeping her spot to end the year as No. 1.
- Sabalenka and Elena Rybakina are expected to progress from the Bacalar group.
This year is the second time the WTA Finals will take place in Mexico after the 2021 edition saw the players compete in Guadalajara with Garbiñe Muguruza taking home the trophy.
Six of the eight participants in this year’s tournament played in last year’s WTA Finals, with Rybakina and Marketa Vondrousova – who won Wimbledon this year – the newcomers.
The format of the tournament is very simple. The eight players are divided into two round-robin groups of four. The top two players in each group advance to the semi-finals.
Group Stage Predictions
In the Bacalar group, Sabalenka is the supercomputer’s big favourite to finish top, winning the group in 33% of simulations. Rybakina and Jessica Pegula are expected to closely compete for second place in the group, with Rybakina given a very slight edge in the model’s predictions (27% vs 26% likelihood).
In the Chetumal group, Swiatek and Gauff are the two clear favourites to get through the group stage with a 62% and 60% likelihood of making the semi-finals respectively. If Coco Gauff does indeed qualify for the last four, she would become the youngest player to reach the singles semi-finals at the WTA Finals since Caroline Wozniacki in 2009 (who was beaten in the semis by Serena Williams).
Tournament Predictions
Let’s look beyond the group stage and towards the knockouts.
Swiatek is the supercomputer’s favourite to make the final (36%) and to go on and win the whole tournament. Her 20% chance of glory is closely followed by Gauff (18%) and Sabalenka (17%).
While Gauff is looking to become the youngest finalist since Maria Sharapova in 2004 (who won the title that year), Sabalenka could become just the third player in the last 10 years to reach back-to-back singles finals at the WTA Finals after Elina Svitolina (2018-2019) and Serena Williams (2013-2014). Gauff has a 33% chance of making the final, while the current world No. 1 has a 32% probability of doing that.
Sakkari, the No. 8 seed has the lowest probability of making the final (14%) and winning the tournament (5%). In fact, since the current round-robin format was reintroduced in 2003, the No. 8 seed has never won the WTA Finals.
Ranking Predictions
Finally, we simulated what each player’s ranking would be at the end of the tournament based on their expected results. For example, if a player was to win the tournament while going undefeated in the group stage, they’d win 1,500 points.
Let’s take a look at the predicted rankings.
Despite not being predicted to make it past the group stage (35% likelihood), Sakkari is the only player in the tournament expected to increase her ranking come the end of the Finals, given a 78% probability of ending the year as world No. 8. If she achieved that, she’d leapfrog Karolina Muchova who was unable to participate in this tournament due to injury. Everyone else competing at this event is expected to maintain their current world ranking.
The No. 1 spot will once again be on the line for Sabalenka and Swiatek, who have had a remarkable rivalry this season. This time, however, Sabalenka will be defending the top position with Swiatek looking to dethrone her. Sabalenka, who took the No. 1 spot in September after reaching the US Open final, is expected to keep her place at the top of the tour (85% likelihood).
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