Terminal-style voxel portrait of Emma Raducanu

wta

Player Profile

Emma Raducanu

Public WTA coverage-qualified profile built from charted matches. Start with the summary, then follow one reliable trait into the wider tour.

Reliability CStyle signal 43 / 100First-strike aggressor

Scouting Summary

This is a provisional public read for Emma Raducanu. Only 20 charted matches sit in this window, so the read is still directional. Emma Raducanu profiles as a first-strike aggressor after opponent-strength weighting, uncertainty checks, and sample normalization. Emma Raducanu keeps service points short more often than the adjusted peer baseline (+0.4 pts vs adjusted baseline). Emma Raducanu goes forward less often than the stronger front-court profiles in the adjusted sample (-1.8 pts vs adjusted baseline).

What stands out

Short service points

59.0%

61th

+0.4 pts vs baseline

Forehand attack rate

29.9%

39th

-1.7 pts vs baseline

Serve variety

98 / 100

24th

-1.3 pts vs baseline

What opponents can target

Net approach rate

6.0%

12th

-1.8 pts vs baseline

Deep return rate

32.3%

23th

-1.1 pts vs baseline

Similar Profiles

Where to go next.

These are the closest style matches in the same tour and season view.

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47 matches · Right-handed

83% match

Metric match

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Clara Tauson

23 matches · Right-handed

74% match

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Amanda Anisimova

37 matches · Right-handed

73% match

First-strike aggressor

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Coco Gauff

20 matches · Right-handed

69% match

Metric match

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Detailed Metrics

Follow the profile across eight public traits.

The top line stays simple. Expand any card when you want the raw sample, uncertainty, baseline, rank, and nearby names in the leaderboard.

Profile Shape

A quick read of the full profile.

Emma Raducanu

Read the page like a scout

Start with one question, not all eight.

Start here

Short service points

59.0% · 61st percentile

Start here

Forehand attack rate

29.9% · 39th percentile

Start here

Serve variety

98 / 100 · 24th percentile

Deep return rate

32.3%

Outlier lowHigh confidence

23rd percentile#25 of 32

Higher values usually mean the return is buying time, pinning the server back, and moving the point off easy first-ball offense.

90% interval 29.9-34.7 · nEff 999.6

See leaderboard

Show details

Adjusted value: 32.3%

Raw value: 31.6%

Baseline: 33.4%

Vs baseline: -1.1 pts

Confidence: High confidence

90% interval: 29.9-34.7

Effective sample size: 999.6

Sample: 1006 returnable returns · weighted 1102

Threshold: 300+ returnable returns

Nearby names

#23 Daria KasatkinaJust above · 10 matches33.1%#24 Ashlyn KruegerJust above · 10 matches32.8%#26 Suzan LamensJust below · 11 matches32.1%#27 Linda NoskovaJust below · 16 matches31.9%

Forehand attack rate

29.9%

Below medianHigh confidence

39th percentile#20 of 32

Higher values point to a forehand that steps on the point instead of simply recycling crosscourt.

90% interval 28.0-31.9 · nEff 1451.3

See leaderboard

Show details

Adjusted value: 29.9%

Raw value: 29.2%

Baseline: 31.6%

Vs baseline: -1.7 pts

Confidence: High confidence

90% interval: 28.0-31.9

Effective sample size: 1451.3

Sample: 1461 charted forehand directions · weighted 1599

Threshold: 500+ charted forehand directions

Nearby names

#18 Emma NavarroJust above · 9 matches30.2%#19 Linda NoskovaJust above · 16 matches30.1%#21 Magda LinetteJust below · 18 matches29.5%#22 Naomi OsakaJust below · 8 matches29.4%

Serve variety

98 / 100

Outlier lowHigh confidence

24th percentile#27 of 35

Higher values usually mean the first-ball read is harder because serve patterns are not concentrated in one lane.

90% interval 97.6-98.7 · nEff 1273.5

See leaderboard

Show details

Adjusted value: 98 / 100

Raw value: 97 / 100

Baseline: 99 / 100

Vs baseline: -1.3 pts

Confidence: High confidence

90% interval: 97.6-98.7

Effective sample size: 1273.5

Sample: 1282 charted serves · weighted 1413

Threshold: 300+ charted serves

Nearby names

#25 Anna KalinskayaJust above · 13 matches99 / 100 #26 Amanda AnisimovaJust above · 37 matches98 / 100 #28 Clara TausonJust below · 23 matches98 / 100 #29 Linda NoskovaJust below · 16 matches98 / 100

Net approach rate

6.0%

Outlier lowHigh confidence

12th percentile#31 of 35

Higher values point to a profile that uses the front court as a recurring pressure tool rather than an emergency option.

90% interval 5.2-6.8 · nEff 2561.0

See leaderboard

Show details

Adjusted value: 6.0%

Raw value: 5.5%

Baseline: 7.8%

Vs baseline: -1.8 pts

Confidence: High confidence

90% interval: 5.2-6.8

Effective sample size: 2561.0

Sample: 143 net points across 2578 total points · weighted 2838

Threshold: 30+ net points and 600+ total points

Nearby names

#29 Iga SwiatekJust above · 47 matches6.7%#30 Amanda AnisimovaJust above · 37 matches6.4%#32 Daria KasatkinaJust below · 10 matches5.9%#33 Ekaterina AlexandrovaJust below · 18 matches5.9%

Net conversion

66.2%

Below medianLow confidence

33rd percentile#28 of 41

Higher values point to cleaner front-court execution once the approach or transition decision is made.

90% interval 59.7-72.8 · nEff 142.2

See leaderboard

Show details

Adjusted value: 66.2%

Raw value: 65.7%

Baseline: 68.6%

Vs baseline: -2.4 pts

Confidence: Low confidence

90% interval: 59.7-72.8

Effective sample size: 142.2

Sample: 143 net points · weighted 157

Threshold: 30+ net points

Nearby names

No nearby leaderboard context is available here.

Break-point resistance

57.5%

MiddleLow confidence

65th percentile#16 of 44

Higher values point to a service profile that stays stable when the score tightens.

90% interval 50.7-64.3 · nEff 142.9

See leaderboard

Show details

Adjusted value: 57.5%

Raw value: 57.6%

Baseline: 57.0%

Vs baseline: +0.6 pts

Confidence: Low confidence

90% interval: 50.7-64.3

Effective sample size: 142.9

Sample: 144 break points faced on serve · weighted 162

Threshold: 20+ break points faced on serve

Nearby names

No nearby leaderboard context is available here.

Break-point attack

47.6%

MiddleLow confidence

61st percentile#17 of 42

Higher values point to a return profile that leans forward at the moment the service game can swing.

90% interval 40.3-54.8 · nEff 127.4

See leaderboard

Show details

Adjusted value: 47.6%

Raw value: 48.4%

Baseline: 46.8%

Vs baseline: +0.8 pts

Confidence: Low confidence

90% interval: 40.3-54.8

Effective sample size: 127.4

Sample: 128 break points on return · weighted 137

Threshold: 20+ break points on return

Nearby names

No nearby leaderboard context is available here.

Short service points

59.0%

MiddleHigh confidence

61st percentile#13 of 32

Higher values point to service games that stay short and front-footed. Lower values usually mean a profile that lives in longer points.

90% interval 56.8-61.2 · nEff 1355.8

See leaderboard

Show details

Adjusted value: 59.0%

Raw value: 59.0%

Baseline: 58.6%

Vs baseline: +0.4 pts

Confidence: High confidence

90% interval: 56.8-61.2

Effective sample size: 1355.8

Sample: 1360 charted service points · weighted 1487

Threshold: 300+ charted service points

Nearby names

#11 Linda NoskovaJust above · 16 matches59.7%#12 Ashlyn KruegerJust above · 10 matches59.3%#14 Ekaterina AlexandrovaJust below · 18 matches58.9%#15 Magda LinetteJust below · 18 matches58.6%

By Surface

Surface splits with enough charted volume.

Surface cards only show up when the selected season window has at least 10 charted matches on that surface.

Surface

Hard

16 matches

16 charted hard matches. The surface profile leans most clearly into bp attack (+3.5 pts vs adjusted baseline), while forehand intent is softer (-2.4 pts vs adjusted baseline).

Break-point attack: 50.3%Short service points: 59.8%Net conversion: 69.1%

16 charted matches on hard.

Methodology

Need the formulas under the profile?

The methodology page explains thresholds, weighting, uncertainty, reliability grades, and why some players or surfaces disappear when the sample gets too thin.

Open Methodology

Private workflow

Want this turned onto a real match?

Tour Radar is the public proof layer. Private work starts when your team brings a live coaching question and sends team footage, tracking exports, or official reports or exports it can share.

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wta

Player Profile

Emma Raducanu

Public WTA coverage-qualified profile built from charted matches. Start with the summary, then follow one reliable trait into the wider tour.

Reliability CStyle signal 43 / 100First-strike aggressor

Scouting Summary

What stands out

Short service points

59.0%

61th

+0.4 pts vs baseline

Forehand attack rate

29.9%

39th

-1.7 pts vs baseline

Serve variety

98 / 100

24th

-1.3 pts vs baseline

What opponents can target

Net approach rate

6.0%

12th

-1.8 pts vs baseline

Deep return rate

32.3%

23th

-1.1 pts vs baseline

Similar Profiles

Where to go next.

These are the closest style matches in the same tour and season view.

wta

Iga Swiatek

47 matches · Right-handed

83% match

Metric match

Read profile

wta

Clara Tauson

23 matches · Right-handed

74% match

Metric match

Read profile

wta

Amanda Anisimova

37 matches · Right-handed

73% match

First-strike aggressor

Read profile

wta

Coco Gauff

20 matches · Right-handed

69% match

Metric match

Read profile

Detailed Metrics

Follow the profile across eight public traits.

The top line stays simple. Expand any card when you want the raw sample, uncertainty, baseline, rank, and nearby names in the leaderboard.

Profile Shape

A quick read of the full profile.

Emma Raducanu

Read the page like a scout

Start with one question, not all eight.

Start here

Short service points

59.0% · 61st percentile

Start here

Forehand attack rate

29.9% · 39th percentile

Start here

Serve variety

98 / 100 · 24th percentile

Deep return rate

32.3%

Outlier lowHigh confidence

23rd percentile#25 of 32

Higher values usually mean the return is buying time, pinning the server back, and moving the point off easy first-ball offense.

90% interval 29.9-34.7 · nEff 999.6

See leaderboard

Show details

Adjusted value: 32.3%

Raw value: 31.6%

Baseline: 33.4%

Vs baseline: -1.1 pts

Confidence: High confidence

90% interval: 29.9-34.7

Effective sample size: 999.6

Sample: 1006 returnable returns · weighted 1102

Threshold: 300+ returnable returns

Nearby names

#23 Daria KasatkinaJust above · 10 matches33.1%#24 Ashlyn KruegerJust above · 10 matches32.8%#26 Suzan LamensJust below · 11 matches32.1%#27 Linda NoskovaJust below · 16 matches31.9%

Forehand attack rate

29.9%

Below medianHigh confidence

39th percentile#20 of 32

Higher values point to a forehand that steps on the point instead of simply recycling crosscourt.

90% interval 28.0-31.9 · nEff 1451.3

See leaderboard

Show details

Adjusted value: 29.9%

Raw value: 29.2%

Baseline: 31.6%

Vs baseline: -1.7 pts

Confidence: High confidence

90% interval: 28.0-31.9

Effective sample size: 1451.3

Sample: 1461 charted forehand directions · weighted 1599

Threshold: 500+ charted forehand directions

Nearby names

#18 Emma NavarroJust above · 9 matches30.2%#19 Linda NoskovaJust above · 16 matches30.1%#21 Magda LinetteJust below · 18 matches29.5%#22 Naomi OsakaJust below · 8 matches29.4%

Serve variety

98 / 100

Outlier lowHigh confidence

24th percentile#27 of 35

Higher values usually mean the first-ball read is harder because serve patterns are not concentrated in one lane.

90% interval 97.6-98.7 · nEff 1273.5

See leaderboard

Show details

Adjusted value: 98 / 100

Raw value: 97 / 100

Baseline: 99 / 100

Vs baseline: -1.3 pts

Confidence: High confidence

90% interval: 97.6-98.7

Effective sample size: 1273.5

Sample: 1282 charted serves · weighted 1413

Threshold: 300+ charted serves

Nearby names

Net approach rate

6.0%

Outlier lowHigh confidence

12th percentile#31 of 35

Higher values point to a profile that uses the front court as a recurring pressure tool rather than an emergency option.

90% interval 5.2-6.8 · nEff 2561.0

See leaderboard

Show details

Adjusted value: 6.0%

Raw value: 5.5%

Baseline: 7.8%

Vs baseline: -1.8 pts

Confidence: High confidence

90% interval: 5.2-6.8

Effective sample size: 2561.0

Sample: 143 net points across 2578 total points · weighted 2838

Threshold: 30+ net points and 600+ total points

Nearby names

#29 Iga SwiatekJust above · 47 matches6.7%#30 Amanda AnisimovaJust above · 37 matches6.4%#32 Daria KasatkinaJust below · 10 matches5.9%#33 Ekaterina AlexandrovaJust below · 18 matches5.9%

Net conversion

66.2%

Below medianLow confidence

33rd percentile#28 of 41

Higher values point to cleaner front-court execution once the approach or transition decision is made.

90% interval 59.7-72.8 · nEff 142.2

See leaderboard

Show details

Adjusted value: 66.2%

Raw value: 65.7%

Baseline: 68.6%

Vs baseline: -2.4 pts

Confidence: Low confidence

90% interval: 59.7-72.8

Effective sample size: 142.2

Sample: 143 net points · weighted 157

Threshold: 30+ net points

Nearby names

No nearby leaderboard context is available here.

Break-point resistance

57.5%

MiddleLow confidence

65th percentile#16 of 44

Higher values point to a service profile that stays stable when the score tightens.

90% interval 50.7-64.3 · nEff 142.9

See leaderboard

Show details

Adjusted value: 57.5%

Raw value: 57.6%

Baseline: 57.0%

Vs baseline: +0.6 pts

Confidence: Low confidence

90% interval: 50.7-64.3

Effective sample size: 142.9

Sample: 144 break points faced on serve · weighted 162

Threshold: 20+ break points faced on serve

Nearby names

No nearby leaderboard context is available here.

Break-point attack

47.6%

MiddleLow confidence

61st percentile#17 of 42

Higher values point to a return profile that leans forward at the moment the service game can swing.

90% interval 40.3-54.8 · nEff 127.4

See leaderboard

Show details

Adjusted value: 47.6%

Raw value: 48.4%

Baseline: 46.8%

Vs baseline: +0.8 pts

Confidence: Low confidence

90% interval: 40.3-54.8

Effective sample size: 127.4

Sample: 128 break points on return · weighted 137

Threshold: 20+ break points on return

Nearby names

No nearby leaderboard context is available here.

Short service points

59.0%

MiddleHigh confidence

61st percentile#13 of 32

Higher values point to service games that stay short and front-footed. Lower values usually mean a profile that lives in longer points.

90% interval 56.8-61.2 · nEff 1355.8

See leaderboard

Show details

Adjusted value: 59.0%

Raw value: 59.0%

Baseline: 58.6%

Vs baseline: +0.4 pts

Confidence: High confidence

90% interval: 56.8-61.2

Effective sample size: 1355.8

Sample: 1360 charted service points · weighted 1487

Threshold: 300+ charted service points

Nearby names

#11 Linda NoskovaJust above · 16 matches59.7%#12 Ashlyn KruegerJust above · 10 matches59.3%#14 Ekaterina AlexandrovaJust below · 18 matches58.9%#15 Magda LinetteJust below · 18 matches58.6%

By Surface

Surface splits with enough charted volume.

Surface cards only show up when the selected season window has at least 10 charted matches on that surface.

Surface

Hard

16 matches

16 charted hard matches. The surface profile leans most clearly into bp attack (+3.5 pts vs adjusted baseline), while forehand intent is softer (-2.4 pts vs adjusted baseline).

Break-point attack: 50.3%Short service points: 59.8%Net conversion: 69.1%

16 charted matches on hard.

Methodology

Need the formulas under the profile?

The methodology page explains thresholds, weighting, uncertainty, reliability grades, and why some players or surfaces disappear when the sample gets too thin.

Open Methodology

Private workflow

Want this turned onto a real match?

Tour Radar is the public proof layer. Private work starts when your team brings a live coaching question and sends team footage, tracking exports, or official reports or exports it can share.

Email Surge Open Contact Paths