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# Barbora Krejcikova faces a model gap
- URL: https://courtshark.ai/blog/barbora-krejcikova-anna-lena-friedsam-model-gap/
- Published: 2026-09-21T15:01:28.000Z
- Updated: 2026-10-05T21:01:01.000Z
- Description: Why Court Shark gives Anna Lena Friedsam a stronger chance against Barbora Krejcikova than the consensus view does.
- Author: Court Shark Desk
- Tags: match-preview, Barbora Krejcikova, Anna Lena Friedsam, SINGAPORE, Hard, WTA

In this analysis

1. [Key takeaway](#key-takeaway)
2. [A wider match than the consensus](#s-a-wider-match-than-the-consensus)
3. [Recent work points in opposite directions](#s-recent-work-points-in-opposite-directions)
4. [No past meeting to settle the question](#s-no-past-meeting-to-settle-the-question)
5. [What the model says](#s-what-the-model-says)
6. [Explore this matchup](#explore)
7. [Methodology and sources](#methodology)

Key takeaway

Barbora Krejcikova enters as the clear favorite against Anna Lena Friedsam in New York. Court Shark’s model agrees on the favorite, but gives Friedsam more room than the market consensus does.

## A wider match than the consensus

On the hard court in New York, the first return will quickly test the gap around this match. [Barbora Krejcikova](https://courtshark.ai/players/barbora-krejcikova-10206252) arrives as the established player, while [Anna Lena Friedsam](https://courtshark.ai/players/anna-lena-friedsam-10204431) brings a more immediate run of hard-court matches. The difference between their rankings is plain. Krejcikova is No. 12 and Friedsam is No. 90, a separation that explains why this is framed as a clear favorite against an outsider.

That gap matters. The model does not dispute Krejcikova’s position, her higher career peak, or the larger body of results behind her ranking. It simply sees a match with more uncertainty than the public view suggests, particularly because one player has been away from competition while the other has played recently. A 17 point distance separates the model from the consensus on Krejcikova. That is large enough to shift the discussion from whether she should be favored to how secure that advantage really is.

This is still a best-of-three match, where a strong start can change the feel of a favorite’s first outing. Friedsam does not need a long history against Krejcikova to make the contest awkward. She needs to establish clean patterns early, hold her nerve in return games, and make the higher-ranked player play enough points after a break. Krejcikova’s task is simpler on paper. She must make the ranking gap visible from the opening exchanges.

### Model win probability

Article snapshot, Sep 21, 2026 · Not live

The Court Shark model's chance for each player to win this match.

__Model win probability by player__
| Player             | Win probability | Recent form (last 10) |
| ------------------ | --------------- | --------------------- |
| Barbora Krejcikova | 71%             | 6-4                   |
| Anna Lena Friedsam | 29%             | 4-6                   |

Source: Court Shark model and match data.View data table

## Recent work points in opposite directions

Krejcikova’s recent record gives both sides of the argument. She has gone 6-4 across her last ten matches, which is a useful base but not a dominant one for a player at her standing. Her hard-court record over that same span is also 6-4\. Those figures show familiarity with the surface, yet they do not erase the four-match losing run she carries into Tuesday.

The pause matters. Krejcikova has had 22 days since her last match and no appearances in the previous fortnight. Time away can allow a player to reset physically and sharpen preparation, but competition rhythm is different from practice. Her first service games should offer an early read. A few loose returns or rushed decisions would give Friedsam a reason to settle rather than chase the match.

Friedsam’s overall recent record is less imposing, but her hard-court sample is stronger. She has gone 7-3 on the surface across her last ten, a contrast with her broader record. She also comes in on a two-match winning run. That does not put her on equal footing with Krejcikova, though it does make her current setting more favorable than the ranking alone implies.

She played one day ago and has had two matches in the past fortnight. Fresh competition can help her judge the ball and manage pressure points from the start. It can also leave less recovery time, especially if the previous outing was demanding. The available form points are clear enough: Friedsam owns the sharper hard-court run, while Krejcikova owns the much stronger standing.

## No past meeting to settle the question

There is no history. The head-to-head stands at 0-0, so neither player can draw on a prior match against the other in this setting. That removes an easy reference point for the matchup. There is no previous scoreline showing whether Krejcikova can impose her preferred patterns quickly or whether Friedsam has found ways to make her uncomfortable.

The absence of a meeting can matter most in the opening set, when players learn the pace of the other’s ball and the shape of each return game. Krejcikova has reached No. 2 in her career, evidence of a level Friedsam has not matched. Friedsam’s best ranking is 45\. Career highs are not a forecast for one afternoon, but they describe the greater ceiling that Krejcikova has shown over time.

Age offers little separation here. Krejcikova is 30 and Friedsam is 32, so this is not a straightforward contrast between an inexperienced player and a veteran. Both should understand how to manage a match that develops unevenly. The first player to establish a reliable service rhythm may have an outsized advantage, because neither has a direct memory of this opponent’s pressure patterns.

Krejcikova should expect resistance rather than assume that ranking will solve the contest. Friedsam should treat the lack of history as an opening to make her own terms early. Her recent surface results give her a reasonable basis for that approach. The model’s disagreement begins there: the underdog has enough current hard-court evidence and enough unknown matchup space to be treated as a live opponent.

## What the model says

The model gives Krejcikova a 71% chance and Friedsam a 29% chance. That remains a firm advantage for the higher-ranked Czech player. It is not a projection of an automatic win. Friedsam’s better recent hard-court record and Krejcikova’s extended competitive pause leave room for the match to become more complicated than the rankings suggest.

The market consensus has Krejcikova at 88% and Friedsam at 12%. It moved toward Krejcikova from 86% before the match. The direction is understandable given the ranking difference, the career peak, and Friedsam’s lower overall recent record. Court Shark’s view is narrower on Krejcikova because the current surface form and match activity point the other way.

The disagreement is about degree. Both views identify Krejcikova as the player more likely to advance, and neither requires Friedsam to be the better player over a long stretch. The question is whether this particular hard-court meeting asks enough of Krejcikova after her break to keep the contest close. A fast, composed opening from the favorite would support the consensus; a steady Friedsam start would make the model’s caution feel more relevant.

The winner would move into the next round with either a confirmation of Krejcikova’s standing or a significant hard-court result for Friedsam.

## Explore this matchup

Compare both players side by side: recent results, head-to-head and the model.

### Player comparison

Article snapshot, Sep 21, 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               | Barbora Krejcikova | Anna Lena Friedsam |
| --------------------- | ------------------ | ------------------ |
| Wins, last 10 matches | 60% (6-4)          | 40% (4-6)          |
| Wins, last 10 on hard | 60% (6-4)          | 70% (7-3)          |
| Model win probability | 71%                | 29%                |

Source: Court Shark model and match data.View data table

### Model and market on Barbora Krejcikova

Article snapshot, Sep 21, 2026 · Not live

Barbora Krejcikova's chance as the market consensus priced it from the opening line, next to the Court Shark model.

__Barbora Krejcikova's chance to win__
| Reading           | Barbora Krejcikova | Anna Lena Friedsam |
| ----------------- | ------------------ | ------------------ |
| Opening consensus | 86%                | 14%                |
| Latest consensus  | 88%                | 12%                |
| Court Shark model | 71%                | 29%                |

Source: Court Shark model and pre-match market consensus.View data table

[Explore this matchup](https://courtshark.ai/h2h?p1=10204431&p2=10206252)

Court Shark win probabilities are statistical estimates, not advice. No outcome is guaranteed.

Match context

Players

Barbora Krejcikova  
vs Anna Lena Friedsam

Event

SINGAPORE

Surface

Hard

Date

Tuesday, September 22

Article snapshot · Not live

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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=10204431&p2=10206252)
- [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/)