> ## 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. # Marta Kostyuk's Guadalajara model gap - URL: https://courtshark.ai/blog/marta-kostyuk-guadalajara-model-gap/ - Published: 2026-09-18T07:01:34.000Z - Updated: 2026-10-05T21:01:15.000Z - Description: Liudmila Samsonova's win over Marta Kostyuk showed why the model saw more uncertainty than the market consensus. - Author: Court Shark Desk - Tags: results, Marta Kostyuk, Liudmila Samsonova, GUADALAJARA 500, Hard, WTA In this analysis 1. [Key takeaway](#key-takeaway) 2. [A lead that did not hold](#s-a-lead-that-did-not-hold) 3. [Why the market went further](#s-why-the-market-went-further) 4. [Samsonova's familiar matchup](#s-samsonova-s-familiar-matchup) 5. [Form favored Kostyuk, narrowly](#s-form-favored-kostyuk-narrowly) 6. [The swing across three sets](#s-the-swing-across-three-sets) 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 Marta Kostyuk in Guadalajara after the model gave Kostyuk a narrower edge than the market consensus. The three-set result reflected a matchup history and competitive range that left Samsonova with a meaningful route through. ## A lead that did not hold On the Guadalajara hard court, [Marta Kostyuk](https://courtshark.ai/players/marta-kostyuk-10216146) took the opening set from [Liudmila Samsonova](https://courtshark.ai/players/liudmila-samsonova-10214643). The early advantage gave Kostyuk a firm hold on the match. Samsonova stayed composed. She recovered to win 6-4 2-6 5-7 in the first qualifying round, turning a strong start by Kostyuk into a long and difficult finish. Kostyuk was No. 39, while Samsonova was No. 15\. The rankings suggested a capable opponent on each side of the net, even with Kostyuk entering as the favored player. The match remained close. Kostyuk had enough control to win a set, but Samsonova found a steadier path through the later stages when the contest demanded repeated holds and clean decisions. ### What the model expected, and what happened Article snapshot, Sep 18, 2026 · Not live Liudmila Samsonova won 4-6 6-2 7-5\. The model had made the other player the favorite. __Pre-match model probability and the result__ | Player | Model before the match | Result | | ------------------ | ---------------------- | ------ | | Marta Kostyuk | 64% | Lost | | Liudmila Samsonova | 36% | Won | Source: Court Shark model and match data.View data table ## Why the market went further The market consensus placed Kostyuk at 77%, a much stronger view than the model's assessment. Its opening view had been 72%. The movement went toward Kostyuk before play began, reflecting confidence in her recent results and her place as the expected winner. That confidence grew before the first ball. The model still favored Kostyuk, but it left room for Samsonova to overturn the match. That difference shaped the disagreement. A first-set lead can change the tone of a match, yet it cannot settle a contest against a player with Samsonova's ranking and a favorable history in this pairing. The gap between the model and market was 13 points. The result did not undo Kostyuk's form, but it showed that the wider market margin left too little space for Samsonova's chances. ## Samsonova's familiar matchup Samsonova arrived with a 3-0 head-to-head record against Kostyuk. She also held a 2-0 advantage on hard courts. Those earlier meetings could not decide the Guadalajara result, but they gave Samsonova a clear reference point against an opponent receiving stronger support before the match. The history favored her. Their latest meeting came in Washington in 2023, where Samsonova won 6-4 6-2\. That was a cleaner result than the contest in Guadalajara. Kostyuk changed the pattern by claiming the opening set, then pushed the match into a deciding set. Still, Samsonova again found an answer when the pressure increased. The past meetings offered no certainty, yet they showed that Kostyuk had not previously solved this particular opponent. ## Form favored Kostyuk, narrowly Kostyuk had won 7 of her last 10 matches before Guadalajara. Her hard-court record over that stretch was also 7-3\. That was a better recent return than Samsonova's and helped explain why both the model and market preferred her. She came in active. Samsonova's recent record was 6-4 overall and 6-4 on hard courts. She was on a two-match winning run. The difference between the players was real, though it was not vast enough to remove Samsonova from the contest. Both had played recently, and neither arrived after a long absence. Kostyuk had played two matches in the previous 14 days, while Samsonova had done the same, leaving little reason to expect either player to lack match rhythm. ## The swing across three sets Kostyuk's opening set made the market's preference easy to understand. She won it 6-4 and put herself in position to close the match quickly. Samsonova did not allow that shape to last. The second set finished 2-6, and the contest had a different center of gravity before the final set began. The decider ended 5-7\. It was a close finish rather than a one-sided drop after the opener. Kostyuk showed enough quality to take a set and keep the match deep, while Samsonova had time to reset her level after the initial setback. That is where the model's more restrained view made sense. In a best-of-three match, a player who can recover through the middle set can force the favorite to win the tightest points late. ## What the model says The model gave Kostyuk a 64% chance and Samsonova a 36% chance. It preferred Kostyuk, while still describing Samsonova as a credible winner. That was a more cautious reading than the market consensus. The completed score showed why that caution fit this matchup, particularly after Samsonova's earlier success against Kostyuk. The market consensus had Kostyuk at 77% and Samsonova at 23%. It had moved toward Kostyuk from its opening view, increasing the separation before the match. Samsonova now moves into the next round in Guadalajara after a demanding three-set recovery. She carries forward a win over an opponent she had beaten in all of their earlier meetings. ## Explore this matchup Compare both players side by side: recent results, head-to-head and the model. ### Player comparison Article snapshot, Sep 18, 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 | Marta Kostyuk | Liudmila Samsonova | | --------------------- | ------------- | ------------------ | | Wins, last 10 matches | 70% (7-3) | 60% (6-4) | | Wins, last 10 on hard | 70% (7-3) | 60% (6-4) | | Head-to-head wins | 0% (0-3) | 100% (3-0) | | Model win probability | 64% | 36% | Source: Court Shark model and match data.View data table ### Head-to-head by surface Article snapshot, Sep 18, 2026 · Not live Marta Kostyuk and Liudmila Samsonova have met 3 times. Liudmila Samsonova leads 3-0. __Head-to-head record, Marta Kostyuk first__ | Surface | Marta Kostyuk | Liudmila Samsonova | | ------------ | ------------- | ------------------ | | All surfaces | 0 | 3 | | Hard | 0 | 2 | Source: Court Shark model and match data.View data table ### Model and market on Marta Kostyuk Article snapshot, Sep 18, 2026 · Not live Marta Kostyuk's chance as the market consensus priced it from the opening line, next to the Court Shark model. __Marta Kostyuk's chance to win__ | Reading | Marta Kostyuk | Liudmila Samsonova | | ---------------------- | ------------- | ------------------ | | Opening consensus | 72% | 28% | | Consensus at the start | 77% | 23% | | Court Shark model | 64% | 36% | Source: Court Shark model and pre-match market consensus.View data table [Explore this matchup](https://courtshark.ai/h2h?p1=10214643&p2=10216146) Court Shark win probabilities are statistical estimates, not advice. No outcome is guaranteed. Match context Players Marta Kostyuk vs Liudmila Samsonova Event GUADALAJARA 500 Surface Hard Round First qualifying round Date Thursday, September 17 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=10216146) - [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. Charts and tables are a snapshot and do not update after publication. About the desk Who writes the Journal and how an article is put together. [Read about the desk](https://courtshark.ai/blog/about-the-desk/) Editorial standards What we publish, what we check and how we decide what is fit to print. [Read our standards](https://courtshark.ai/blog/editorial-standards/) Corrections Spotted an error? Write to hello@courtshark.ai and we will correct it and note the change. [Read the corrections policy](https://courtshark.ai/blog/editorial-standards/)