> ## Content Index > Fetch the complete content index at: https://courtshark.ai/blog/llms.txt > Use this file to discover other available public pages before exploring further. # Elsa Jacquemot Falls to Samsonova in Guadalajara - URL: https://courtshark.ai/blog/elsa-jacquemot-samsonova-guadalajara-result/ - Published: 2026-09-15T07:00:57.000Z - Updated: 2026-10-05T21:01:26.000Z - Description: Liudmila Samsonova overcame a tight opening set to beat Elsa Jacquemot in Guadalajara after a small market move in her direction. - Author: Court Shark Desk - Tags: results, Elsa Jacquemot, Liudmila Samsonova, GUADALAJARA 500, Hard, WTA In this analysis 1. [Key takeaway](#key-takeaway) 2. [The move before the first ball](#s-the-move-before-the-first-ball) 3. [A tiebreak that mattered](#s-a-tiebreak-that-mattered) 4. [Form pointed in one direction](#s-form-pointed-in-one-direction) 5. [Rest created the only clear doubt](#s-rest-created-the-only-clear-doubt) 6. [No history, then a clear finish](#s-no-history-then-a-clear-finish) 7. [What the model says](#s-what-the-model-says) 8. [Explore this matchup](#explore) 9. [Methodology and sources](#methodology) Key takeaway Liudmila Samsonova beat Elsa Jacquemot in Guadalajara after the market made a small move in her direction. Court Shark's model favored Samsonova, though the final market consensus was more decisive. ## The move before the first ball On Guadalajara's hard court, a tight tiebreak set the tone before the match opened up. [Elsa Jacquemot](https://courtshark.ai/players/elsa-jacquemot-10221039) stayed with Liudmila Samsonova through the early exchanges and pushed the stronger player into a demanding start. The market had already moved toward Samsonova. It was a small move. That shift became more meaningful because it came before a match in which Samsonova had not played recently and Jacquemot had more current match rhythm. Jacquemot opened at 23% and finished at 22% in the market consensus. The movement confirmed a preference for Samsonova, based on the wider difference in ranking and their recent hard-court records. Still, the result was not decided by reputation alone. Jacquemot made the opening set uncomfortable, which gave the match a different shape from the one suggested by the final score. ### What the model expected, and what happened Article snapshot, Sep 15, 2026 · Not live Liudmila Samsonova won 7-6(6) 6-2\. The model had made them the favorite. __Pre-match model probability and the result__ | Player | Model before the match | Result | | ------------------ | ---------------------- | ------ | | Elsa Jacquemot | 26% | Lost | | Liudmila Samsonova | 74% | Won | Source: Court Shark model and match data.View data table ## A tiebreak that mattered [Liudmila Samsonova](https://courtshark.ai/players/liudmila-samsonova-10214643) won 6-7(6) 2-6, a scoreline that captures an opening set decided by the smallest margins before a clearer second set. The tiebreak mattered most. Jacquemot had taken the match to the point where a few shots could have changed the direction of the night. Samsonova held firm there, then carried that advantage into the remaining games with more control. The second set told a different story. Jacquemot could not reproduce the same pressure after losing the tiebreak, while Samsonova found a steadier path through the exchanges. A close first set can test a favorite's patience. Here, it did. But Samsonova's response separated an early contest from a more convincing finish, and it matched the market's view that she held the stronger position. ## Form pointed in one direction Jacquemot came into Guadalajara with a 3-7 recent record on hard courts. That record did not rule out a competitive performance, and the first set showed why. Tennis matches can narrow quickly when a player keeps returns in play and takes care of her own service games. Yet the recent run gave little support to a sustained challenge against a much higher-ranked opponent. Her ranking stood at No. 181, while Samsonova arrived at No. 15 with a 6-4 recent hard-court record. The gap was large. Samsonova also entered on a two-match losing run, so her form was not without a question mark. Even so, she had produced more recent wins than Jacquemot and had a stronger record of handling this surface over the same stretch. ## Rest created the only clear doubt Samsonova had gone 15 days without a match, creating some rust risk at the start. That was the clearest reason to expect Jacquemot to have an opening, particularly because the players had never met before. The early tension fit that possibility. Samsonova needed time to settle into the match, and Jacquemot made sure the first set did not pass quietly. Jacquemot had played 5 matches in the previous 14 days and had been away from competition for 4 days. Her schedule offered recent court time without a long break. That contrast was visible in the tight start. But current rhythm only matters if it can be sustained, and Jacquemot could not keep the same grip on the match once Samsonova came through the tiebreak. ## No history, then a clear finish The head-to-head was 0-0, leaving both players without a prior meeting to draw on in Guadalajara. There were no familiar return patterns or service-game memories to shape the opening plan. That can make the first set especially revealing. Jacquemot's ability to force a tiebreak showed she could make Samsonova play under pressure rather than simply wait for errors. There was no surface meeting between them to clarify how often Jacquemot could disrupt Samsonova on hard courts. The match therefore turned on what happened in real time. Samsonova absorbed the difficult opening, then found a cleaner finish. Jacquemot's one-match losing run remained a concern, while Samsonova moved beyond her own recent losing run by winning the moments that carried the greater weight. ## What the model says The model gave Jacquemot a 26% chance and gave Samsonova a 74% chance. The market consensus had Jacquemot at 22% and Samsonova at 78%. Both estimates favored Samsonova. The difference was narrow, but the model allowed Jacquemot more room to make the match competitive than the final market consensus did. The market moved 1 point toward Samsonova before play, which was consistent with the final result. The model's gap from the market on Samsonova was 4 points. Jacquemot's tight first set gave the model's view some support, even though she could not extend it into the second. A stronger recent hard-court record from Jacquemot, or less rust risk around Samsonova, would change the call. ## Explore this matchup Compare both players side by side: recent results, head-to-head and the model. ### Player comparison Article snapshot, Sep 15, 2026 · Not live Each measure on the same scale: recent wins, wins on this surface, head-to-head wins and the model's chance. __Player comparison__ | Measure | Elsa Jacquemot | Liudmila Samsonova | | --------------------- | -------------- | ------------------ | | Wins, last 10 matches | 30% (3-7) | 60% (6-4) | | Wins, last 10 on hard | 30% (3-7) | 60% (6-4) | | Model win probability | 26% | 74% | Source: Court Shark model and match data.View data table ### Model and market on Elsa Jacquemot Article snapshot, Sep 15, 2026 · Not live Elsa Jacquemot's chance as the market consensus priced it from the opening line, next to the Court Shark model. __Elsa Jacquemot's chance to win__ | Reading | Elsa Jacquemot | Liudmila Samsonova | | ---------------------- | -------------- | ------------------ | | Opening consensus | 23% | 77% | | Consensus at the start | 22% | 78% | | Court Shark model | 26% | 74% | Source: Court Shark model and pre-match market consensus.View data table [Explore this matchup](https://courtshark.ai/h2h?p1=10214643&p2=10221039) Court Shark win probabilities are statistical estimates, not advice. No outcome is guaranteed. Match context Players Elsa Jacquemot vs Liudmila Samsonova Event GUADALAJARA 500 Surface Hard Date Tuesday, September 15 Article snapshot · Not live Continue your research Compare form, explore surfaces and follow live match context. [Explore Premium](https://courtshark.ai/register?redirect=/start-premium)[See what's included](https://courtshark.ai/premium) - Advanced filters and splits - Save your research - Live context and updates Related on Court Shark - [How our match model works](https://courtshark.ai/blog/methodology/) - [Open the full head-to-head](https://courtshark.ai/h2h?p1=10214643&p2=10221039) - [Our editorial standards](https://courtshark.ai/blog/editorial-standards/) - [About the desk](https://courtshark.ai/blog/about-the-desk/) ## Methodology and sources How we build model estimates and where our information comes from. Model estimates and observed results The win probabilities come from the Court Shark match model, trained on completed ATP and WTA matches. A probability says how often a player in this position wins, not that one result will happen. The model's number for a match is recorded before play and never changed afterwards, so it can be checked against the result. Sources and citations Results, scores, rankings and serve statistics come from the Court Shark match database. The market consensus is the pre-match price for each player converted to a probability. Figures in this article are a snapshot taken when it was written. Limitations Serve and return rates count only matches with full serve statistics, which are sparser for Challenger and lower-level events, and the tour average they are compared with covers every year on record. Head-to-head records count every meeting with a recorded winner, walkovers and retirements included. 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