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# Khachanov vs Machac Prediction: Almaty Open 2026 Round of 16
- URL: https://courtshark.ai/blog/khachanov-vs-machac-almaty-open-2026-prediction/
- Published: 2026-10-21T21:40:53.000Z
- Updated: 2026-10-21T21:40:53.000Z
- Description: Khachanov's Almaty title matters, but his hard-court serving advantage makes the stronger case against Machac.
- Author: Court Shark Desk
- Tags: match-preview, Tomas Machac, Karen Khachanov, Almaty Open 2026, Hard, ATP

In this analysis

1. [Key takeaway](#key-takeaway)
2. [Khachanov has won here before](#s-khachanov-has-won-here-before)
3. [The serving gap gives the history substance](#s-the-serving-gap-gives-the-history-substance)
4. [Close sets offer Machac a route](#s-close-sets-offer-machac-a-route)
5. [The prediction](#s-the-prediction)
6. [Explore this matchup](#explore)
7. [Methodology and sources](#methodology)

Key takeaway

Khachanov's Almaty title supports the pick, but his stronger hard-court service record matters more against Machac. Close sets give Machac his clearest route to an upset.

Karen Khachanov's Almaty Open record strengthens his case against Tomas Machac in the round of 16, though his serving advantage is more persuasive. Court Shark's model gives Khachanov a 71% chance.

## Khachanov has won here before

The winner when [Tomas Machac](https://courtshark.ai/players/tomas-machac-207830) meets [Karen Khachanov](https://courtshark.ai/players/karen-khachanov-111575) in Thursday's Almaty Open round of 16 reaches the quarterfinals. Khachanov's tournament record supports his position as favorite, but it is evidence of past success in these conditions rather than a decisive argument against this opponent. He won a title here. His earlier main-draw record stood at 5-1 across three seasons, a meaningful contrast with Machac's much shorter history at the event.

Machac's previous appearance produced a 1-1 record and no title, so this meeting puts a former champion against a player with little established tournament history. Both won their opening matches this week. That makes Khachanov's experience relevant to the next step in the draw, although those earlier results alone cannot establish how the court will affect this particular matchup.

### Two years of form

Article snapshot, Oct 21, 2026 · Not live

Each player's pre-match Elo rating since Oct 23, 2024\. Latest: Karen Khachanov 1980 (Oct 13, 2026), Tomas Machac 1900 (Oct 9, 2026).

__Pre-match Elo rating over the last two years__
| Player          | Start of window     | Two-year peak      | Latest              |
| --------------- | ------------------- | ------------------ | ------------------- |
| Tomas Machac    | 2003 (Oct 23, 2024) | 2049 (Mar 7, 2025) | 1900 (Oct 9, 2026)  |
| Karen Khachanov | 1970 (Oct 23, 2024) | 2073 (Aug 7, 2025) | 1980 (Oct 13, 2026) |

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

## The serving gap gives the history substance

The stronger reason to favor Khachanov comes from his service record on hard courts over the past 52 weeks, rather than from his trophy alone. He held 87% of his service games. That is a substantial base for a favorite, because it leaves an opponent fewer opportunities to turn a competitive set into a lead. Machac held 79% of his service games over the same window, making protection of his own serve a more pressing concern.

Recent results point the same way. Khachanov went 7-3 in his latest hard-court matches, while Machac went 5-5, so the tournament history is supported by a current difference in results. This is not simply a choice between a former champion and a visitor. The broader surface evidence gives a tennis reason to expect Khachanov's comfort here to matter against Machac, rather than treating familiarity itself as an advantage.

## Close sets offer Machac a route

Machac's clearest opening is to keep the sets close enough that Khachanov's stronger service record does not produce a comfortable advantage on the scoreboard. Khachanov broke in 21% of his hard-court return games during the same window. The tour benchmark was 23%. His return was less imposing. If Machac protects serve more effectively than his recent record suggests, Khachanov may have to settle sets without the cushion of an early break.

Machac also won 83 of 142 career tiebreaks, which gives him a credible route if neither player creates much separation on return. Those are career results, not a forecast for this court. Their only previous meeting ended in Khachanov's favor at the Australian Open, but it included two tiebreaks and a set won by Machac. That single encounter supports the favorite without establishing that this opponent is easy for him to beat.

## The prediction

Court Shark's model gives Khachanov a 71% win probability, compared with the market consensus of 67%. Both favor Khachanov. The difference is modest, and the case for him is stronger when his tournament success is considered alongside his hard-court serving advantage rather than on its own.

Court Shark's AI analyst, which reads the matchup without the market, also picks Khachanov, agreeing with the model on the strength of his serve and recent results. The model gives a Khachanov straight-sets win a 49% chance. Its median match length is 23 games, suggesting that a win without dropping a set need not mean an especially short match.

The middle half of projected outcomes runs from 19 to 30 games. Machac's career tiebreak record makes Khachanov's ability to create return chances especially important if the sets stay level.

### Ways to win

Article snapshot, Oct 21, 2026 · Not live

Every way the match can finish. The likeliest single result is Karen Khachanov in straight sets (49%).

__Chance of each final set score__
| Result                   | Chance |
| ------------------------ | ------ |
| Tomas Machac wins 2-0    | 15%    |
| Tomas Machac wins 2-1    | 14%    |
| Karen Khachanov wins 2-1 | 22%    |
| Karen Khachanov wins 2-0 | 49%    |

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

## Explore this matchup

Compare both players side by side: recent results, surfaces, serve and return.

### Player comparison

Article snapshot, Oct 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               | Tomas Machac | Karen Khachanov |
| --------------------- | ------------ | --------------- |
| Wins, last 10 matches | 50% (5-5)    | 70% (7-3)       |
| Wins, last 10 on hard | 50% (5-5)    | 70% (7-3)       |
| Head-to-head wins     | 0% (0-1)     | 100% (1-0)      |
| Model win probability | 29%          | 71%             |

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

### Recent form

Article snapshot, Oct 21, 2026 · Not live

Each player's most recent completed matches, up to 10, oldest on the left.

__Recent results by player, oldest first__
| Player          | Date         | Result | Opponent              | Score                  | Event            |
| --------------- | ------------ | ------ | --------------------- | ---------------------- | ---------------- |
| Tomas Machac    | Aug 30, 2026 | L      | Toby Samuel           | 2-6 3-6 2-6            | US Open          |
| Tomas Machac    | Sep 28, 2026 | W      | Yannick Hanfmann      | 7-6(7) 6-7(6) 6-4      | Beijing          |
| Tomas Machac    | Sep 29, 2026 | L      | Arthur Gea            | 3-6 4-6                | Beijing          |
| Tomas Machac    | Oct 1, 2026  | L      | Alex Molcan           | 3-6 2-6                | Beijing          |
| Tomas Machac    | Oct 7, 2026  | W      | Zhizhen Zhang         | 1-6 7-6(3) 6-2         | Shanghai Masters |
| Tomas Machac    | Oct 9, 2026  | L      | Alexander Bublik      | 4-6 6-7(4)             | Shanghai Masters |
| Tomas Machac    | Oct 13, 2026 | W      | Alexandre Muller      | 6-3 6-4                | Jinan Challenger |
| Tomas Machac    | Oct 15, 2026 | W      | Aleksandar Vukic      | 6-2 6-1                | Jinan Challenger |
| Tomas Machac    | Oct 16, 2026 | L      | Liam Draxl            | 4-6 6-3 4-6            | Jinan Challenger |
| Tomas Machac    | Oct 21, 2026 | W      | Zangar Nurlanuly      | 6-2 6-0                | ATP Almaty       |
| Karen Khachanov | Sep 7, 2026  | W      | Learner Tien          | 7-5 4-6 6-7(6) 6-1 6-4 | US Open          |
| Karen Khachanov | Sep 9, 2026  | W      | Alexander Blockx      | 6-2 7-5 3-2 RET        | US Open          |
| Karen Khachanov | Sep 11, 2026 | L      | Alexander Zverev      | 3-6 6-7(7) 6-7(6)      | US Open          |
| Karen Khachanov | Sep 30, 2026 | W      | Felix Auger Aliassime | 6-3 6-3                | Beijing          |
| Karen Khachanov | Oct 2, 2026  | W      | Alex Molcan           | 7-5 6-3                | Beijing          |
| Karen Khachanov | Oct 4, 2026  | L      | Hubert Hurkacz        | 6-7(2) 2-6             | Beijing          |
| Karen Khachanov | Oct 9, 2026  | W      | Arthur Fery           | 7-6(6) 6-7(5) 7-6(2)   | Shanghai Masters |
| Karen Khachanov | Oct 11, 2026 | W      | Adrian Mannarino      | 6-2 6-4                | Shanghai Masters |
| Karen Khachanov | Oct 13, 2026 | L      | Alex De Minaur        | 3-6 4-6                | Shanghai Masters |
| Karen Khachanov | Oct 20, 2026 | W      | Rei Sakamoto          | 6-2 6-4                | ATP Almaty       |

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

### Head-to-head by surface

Article snapshot, Oct 21, 2026 · Not live

Tomas Machac and Karen Khachanov have met once. Karen Khachanov leads 1-0.

__Head-to-head record, Tomas Machac first__
| Surface      | Tomas Machac | Karen Khachanov |
| ------------ | ------------ | --------------- |
| All surfaces | 0            | 1               |
| Hard         | 0            | 1               |

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

### Win rate by surface

Article snapshot, Oct 21, 2026 · Not live

Share of matches won on each surface across each player's last 50 matches with full statistics.

__Win rate by surface__
| Surface | Tomas Machac | Karen Khachanov     |
| ------- | ------------ | ------------------- |
| Hard    | 46% (16-19)  | 50% (18-18)         |
| Clay    | 50% (5-5)    | 50% (5-5)           |
| Grass   | 60% (3-2)    | Too few matches (4) |

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

### Serve and return on hard courts

Article snapshot, Oct 21, 2026 · Not live

How often each player held serve and broke serve on hard over their last 50 matches with full statistics, against the tour's all-years average on the surface.

__Hold and break rates on hard__
| Measure             | Tomas Machac | Karen Khachanov | Tour average, all years |
| ------------------- | ------------ | --------------- | ----------------------- |
| Service games held  | 80%          | 87%             | 77%                     |
| Return games broken | 19%          | 20%             | 23%                     |
| Matches in sample   | 35           | 36              |                         |

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

### Model and market on Tomas Machac

Article snapshot, Oct 21, 2026 · Not live

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

__Tomas Machac's chance to win__
| Reading           | Tomas Machac | Karen Khachanov |
| ----------------- | ------------ | --------------- |
| Opening consensus | 33%          | 67%             |
| Latest consensus  | 33%          | 67%             |
| Court Shark model | 29%          | 71%             |

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

### Model win probability

Article snapshot, Oct 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) |
| --------------- | --------------- | --------------------- |
| Tomas Machac    | 29%             | 5-5                   |
| Karen Khachanov | 71%             | 7-3                   |

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

### How long it could go

Article snapshot, Oct 21, 2026 · Not live

A 60% chance the match lasts 19 to 30 games; 23 is the middle of the range.

__Chance of each total number of games in the match__
| Total games | Chance |
| ----------- | ------ |
| 13          | 0.2%   |
| 14          | 0.6%   |
| 15          | 1.8%   |
| 16          | 3.1%   |
| 17          | 5.9%   |
| 18          | 6.8%   |
| 19          | 8.4%   |
| 20          | 8.1%   |
| 21          | 5.4%   |
| 22          | 9.3%   |
| 23          | 7.6%   |
| 24          | 0.8%   |
| 25          | 3.2%   |
| 26          | 5.4%   |
| 27          | 2.2%   |
| 28          | 2.8%   |
| 29          | 3.1%   |
| 30          | 3.2%   |
| 31          | 4.3%   |
| 32          | 4.6%   |
| 33          | 2.9%   |
| 34          | 2.1%   |
| 35          | 3.5%   |
| 36          | 2.5%   |
| 37          | 0.2%   |
| 38          | 0.8%   |
| 39          | 1.0%   |

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

### Service games held: where they stand

Article snapshot, Oct 21, 2026 · Not live

Each dot is one of the ATP top 100 with enough matches in the last 52 weeks. Tomas Machac is 81st of 94; Karen Khachanov is 15th of 94.

__Service games held, last 52 weeks, against the ATP top 100__
| Player                     | Service games held | Place      |
| -------------------------- | ------------------ | ---------- |
| Tomas Machac               | 76.3%              | 81st of 94 |
| Karen Khachanov            | 86.7%              | 15th of 94 |
| Field median               | 81.7%              |            |
| Field best (Jannik Sinner) | 94.6%              | 1st        |

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

[Explore this matchup](https://courtshark.ai/h2h?p1=111575&p2=207830)

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

Our forecast

Court Shark's model

Karen Khachanov 71% to win

Match shape

Karen Khachanov in straight sets: 49%

Deciding set: 36%

Total games: median 23, middle half 19 to 30

AI analyst

Picks Karen Khachanov (agrees with the model)

Estimates recorded before play, not certainties.

Keep exploring

[Follow this match on Court Shark](https://courtshark.ai/matches?file=bdl%5F4724844)[Create a free account to keep it](https://courtshark.ai/register?redirect=%2Fmatches%3Ffile%3Dbdl%5F4724844)

Match context

Players

Tomas Machac  
vs Karen Khachanov

Event

Almaty Open

Surface

Hard

Round

Round of 16

Date

Thursday, October 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=111575&p2=207830)
- [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 works on the Tennis Desk 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/)