Kurumi Nara

Against an average opponent

Serve and return points won, refitted against every opponent at once so a record built on weak or strong opposition is put on the same scale. Career, all surfaces, with a 90% margin. Raw is the plain share of points won.

Value per 100 shots

Points gained per 100 shots compared with an average tour player in the same position, adjusted for the strength of the opponents faced, with a 90% margin (shots clustered by match). Raw is before the opponent adjustment. Built on 2,262 shots.

Shot expected value

The share of points Kurumi Nara goes on to win after each option in the positions they face most often, shrunk toward tour average when the sample is small. Showing the 8 most-used options; teal marks the best one with at least 30 shots.

Serve under pressure

Pressure predictability index +2 How much less varied Kurumi Nara's first-serve direction gets on break points. Positive means easier to read. Based on 49 break-point first serves.

Is the serve mix in equilibrium?

Game theory says a well-mixed server wins equally often with every direction they use. If one direction wins more, it's underused and points are being left behind. This is the minimax test Walker and Wooders ran on Wimbledon finals, applied to every charted first serve. Win rates include faults. "Optimal" allows for returners reading a habit. A direction loses 0.19 points per 100 serves for every 10 points of habitual usage, measured from WTA servers whose mix drifted between matches. Shifts stay within the range servers' habits actually vary, the only range that response was measured over.

Return by serve direction

Return points won against each serve direction, compared with the tour average.

Signature patterns

Recurring sequences that win more than Kurumi Nara's own baseline, ranked by edge weighted by how often they're used.

Discovered sequences

Mined from every me → opponent → me run of three shots, with no templates. Ranked by how much more often Kurumi Nara wins the point once the sequence happens, weighted by how often it happens. Think of them as chess openings.

1. FH down the line → BH crosscourt → BH crosscourt used 1.7% · won 55% · +5.1±11.8 vs own baseline · +12.5 vs tour on the same sequence Disrupted by Agnieszka Radwanska (3/6), Johanna Larsson (7/8)
2. FH crosscourt → FH crosscourt → FH down the line used 1.5% · won 55% · +4.3±12.2 vs own baseline · +12.3 vs tour on the same sequence Disrupted by Johanna Larsson (6/6)
3. BH crosscourt → BH crosscourt → BH through the middle used 1.2% · won 55% · +4.8±12.8 vs own baseline · +18.6 vs tour on the same sequence Disrupted by Agnieszka Radwanska (3/6)
4. FH through the middle → FH crosscourt → FH crosscourt used 1.1% · won 51% · +1.2±13.0 vs own baseline · +6.6 vs tour on the same sequence
5. BH through the middle → FH crosscourt → FH through the middle used 1.1% · won 51% · +1.2±13.0 vs own baseline · +14.3 vs tour on the same sequence Disrupted by Johanna Larsson (5/7)
6. BH through the middle → FH crosscourt → FH crosscourt used 1.2% · won 50% · −0.1±12.9 vs own baseline · +5.5 vs tour on the same sequence

Strengths and vulnerabilities

Value per 100 shots compared with the average player hitting (strengths) or facing (vulnerabilities) the same shot. Only shot types seen at least 120 times.

Charted matches

• Rebecca Marino v Kurumi Nara L ITF Osaka QF · Hard · 13 Apr 2018
• Ana Ivanovic v Kurumi Nara L Roland Garros R64 · Clay · 26 May 2016
• Agnieszka Radwanska v Kurumi Nara L Australian Open R128 · Hard · 20 Jan 2015
• Kurumi Nara v Johanna Larsson W Hobart R16 · Hard · 13 Jan 2015
• Kurumi Nara v Klara Koukalova W Hobart R32 · Hard · 11 Jan 2015
• Agnieszka Radwanska v Kurumi Nara L Cincinnati R32 · Hard · 13 Aug 2014