RallyIQ

ATP · Right-handed · 40 charted matches · 2020–2026

Brandon Nakashima

Archetype: Rallies through the middle · Crosscourt backhand

Against an average opponent

Serve points won 64.9% ±2.5 raw 65.5% · tour 63.4% · 3,535 points
Return points won 36.3% ±2.6 raw 32.8% · tour 36.6% · 3,488 points

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

Direction choice −0.01 ±0.07 better than 54% of ATP · raw −0.01
Shot selection −0.11 ±0.14 better than 38% of ATP · raw −0.10
Execution +0.79 ±0.43 better than 91% of ATP · raw +0.93
Tactical adaptability +0.12 first serves toward what's working, set to set · 40 matches
Adaptation speed +0.14 same, every two to three service games · per 100 first serves
Long-rally execution −0.67 ±0.53 shot 9 on v own earlier rally shots · 2,290 shots · better than 7% of ATP
Points left on the table 2.50 per 100 shots vs best direction · lower than 60% of ATP

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 17,220 shots.

Shot expected value

The share of points Brandon Nakashima 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.

Rally, shots 5–8: drive to your backhand side

position worth 46% to the average player · 1,180 shots

OptionUsedWin %Tour
BH crosscourt 39% 45.1%±3.7 47.6%
BH through the middle 27% 40.0%±4.4 43.7%
BH slice crosscourt 10% 41.1%±6.8 42.5%
BH down the line 9% 43.3%±7.3 46.4%
BH slice through the middle 6% 30.7%±8.1 35.1%
FH inside-out 4% 36.1%±10.0 51.8%
BH slice down the line 2% 41.3%±11.8 37.1%
FH inside-in 2% 58.4%±12.7 54.7%

Rally, shots 5–8: drive to your middle

position worth 51% to the average player · 885 shots

OptionUsedWin %Tour
FH crosscourt 24% 46.3%±5.4 52.7%
FH down the line 21% 46.2%±5.7 51.5%
FH through the middle 17% 43.1%±6.3 47.0%
BH through the middle 16% 48.7%±6.5 46.8%
BH crosscourt 13% 43.5%±7.1 49.1%
BH down the line 3% 46.2%±11.7 48.3%
FH down the line + approach 2% 78.1%±11.3 70.5%
BH slice crosscourt 2% 45.6%±13.7 47.0%

Rally, shots 5–8: drive to your forehand side

position worth 44% to the average player · 771 shots

OptionUsedWin %Tour
FH crosscourt 36% 47.4%±4.8 46.6%
FH through the middle 27% 40.9%±5.4 41.5%
FH down the line 25% 43.8%±5.6 44.7%
FH slice through the middle 8% 30.3%±8.5 24.5%
FH slice down the line 2% 22.6%±11.5 25.6%
FH slice crosscourt 2% 29.1%±12.6 30.9%

Long rally, 9+: drive to your backhand side

position worth 46% to the average player · 699 shots

OptionUsedWin %Tour
BH crosscourt 33% 38.8%±5.0 48.0%
BH through the middle 26% 41.8%±5.7 43.8%
BH down the line 14% 49.4%±7.6 46.5%
BH slice crosscourt 12% 41.0%±8.1 42.1%
BH slice through the middle 7% 34.9%±9.7 35.1%
BH slice down the line 4% 38.1%±11.9 35.8%
FH inside-out 2% 36.6%±13.0 52.6%
FH inside-in 2% 48.2%±13.9 54.3%

Serve under pressure

Pressure predictability index +6 How much less varied Brandon Nakashima's first-serve direction gets on break points. Positive means easier to read. Based on 263 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 52% 56% 73% / 73%
Body 5% 2% 64% / 63%
T 43% 43% 78% / 75%

1,792 normal · 61 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 54% 53% 72% / 73%
Body 9% 6% 69% / 63%
T 37% 40% 72% / 72%

1,470 normal · 202 break-point 1st 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. No measurable response (−0.05 ± 0.15 points per 100 serves for every 10 points of habitual usage), measured from ATP servers whose mix drifted between matches. Shifts stay within the range servers' habits actually vary, the only range that response was measured over.

Deuce court

1st serveUsagePoints wonOptimal
Wide52% 64.3%±2.5 n=968 44% ▼
Body5% 64.2%±7.2 n=90 0% ▼
T43% 66.0%±2.7 n=795 56% ▲

Consistent with an optimal mix (p = 0.49).
Optimal mix: +0.3 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide54% 66.2%±2.5 n=902 67% ▲
Body9% 64.8%±5.9 n=149 0% ▼
T37% 66.0%±3.1 n=621 33% ▼

Consistent with an optimal mix (p = 0.92).
Optimal mix: +0.3 per 100 first serves.

Exploitability 0.31 points per 100 first serves What the optimal mix would win over the current one, both courts. More exploitable than 100% of ATP servers. Tested on matches they weren't fitted on, ATP mixes picked this way win 0.33 per 100 first serves on average.

Repeating the previous direction to the same court: −0.1±3.6 points per 100 against switching. Negative means returners read repeats. Tour-wide, repeating costs women about 0.4 points per 100 and costs men nothing, so men's returners don't measurably anticipate direction. (1,293 repeats, 2,152 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 126 33% −4.2±6.2
1stAd courtT 504 26% −2.3±3.1
1stAd courtWide 439 25% −2.2±3.3
1stDeuce courtBody 103 34% −2.9±6.7
1stDeuce courtT 503 21% −3.6±2.9
1stDeuce courtWide 564 26% −1.4±3.0
2ndAd courtBody 225 47% −2.4±5.1
2ndAd courtT 143 48% −0.8±6.3
2ndAd courtWide 219 50% +2.0±5.2
2ndDeuce courtBody 255 45% −3.9±4.8
2ndDeuce courtT 250 47% −2.9±4.9
2ndDeuce courtWide 153 42% −5.8±6.0

Signature patterns

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

Serve → +1

  1. T serve (ad court) → FH crosscourt used 2.3% · won 59% · −8.6±7.9 vs own baseline
  2. Wide serve (ad court) → FH crosscourt used 4.3% · won 59% · −8.5±6.2 vs own baseline
  3. T serve (deuce court) → FH crosscourt used 2.8% · won 56% · −11.5±7.4 vs own baseline
  4. T serve (deuce court) → FH down the line used 2.9% · won 56% · −12.1±7.3 vs own baseline
  5. Wide serve (ad court) → BH crosscourt used 2.8% · won 53% · −14.9±7.5 vs own baseline

Return

  1. vs T serve (deuce court) → BH through the middle, deep used 3.4% · won 53% · +15.3±7.7 vs own baseline
  2. vs wide serve (deuce court) → FH through the middle, deep used 3.6% · won 52% · +14.8±7.5 vs own baseline
  3. vs T serve (ad court) → FH through the middle, deep used 2.5% · won 53% · +14.9±8.6 vs own baseline
  4. vs wide serve (ad court) → BH crosscourt, deep used 3.2% · won 50% · +12.2±7.8 vs own baseline
  5. vs wide serve (ad court) → BH through the middle, deep used 2.3% · won 47% · +9.8±8.7 vs own baseline

Rally, consecutive own shots

  1. FH crosscourt → FH down the line used 5.2% · won 51% · +6.1±5.3 vs own baseline
  2. FH crosscourt → BH crosscourt used 1.8% · won 53% · +7.8±8.1 vs own baseline
  3. FH down the line → FH crosscourt used 1.7% · won 53% · +8.0±8.3 vs own baseline
  4. BH through the middle → FH crosscourt used 4.2% · won 50% · +4.3±5.8 vs own baseline
  5. BH crosscourt → BH slice crosscourt used 1.1% · won 51% · +6.3±9.6 vs own baseline

Discovered sequences

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

  1. FH crosscourt → FH through the middle → FH through the middle used 0.6% · won 55% · +7.8±9.3 vs own baseline · +10.0 vs tour on the same sequence Disrupted by Mackenzie Mcdonald (3/7), Jordan Thompson (4/8)
  2. BH through the middle → BH through the middle → FH down the line used 0.3% · won 57% · +10.2±11.3 vs own baseline · +17.2 vs tour on the same sequence
  3. Wide serve → FH through the middle return, mid → FH down the line + approach used 0.2% · won 59% · +11.9±12.5 vs own baseline · +0.8 vs tour on the same sequence
  4. T serve → FH through the middle return, deep → FH down the line used 0.3% · won 57% · +9.6±11.6 vs own baseline · +20.7 vs tour on the same sequence
  5. Body serve → BH crosscourt return, mid → BH crosscourt used 0.2% · won 57% · +9.5±12.6 vs own baseline · +21.4 vs tour on the same sequence
  6. Wide serve → BH through the middle return, mid → FH down the line used 0.2% · won 57% · +9.9±12.9 vs own baseline · +16.0 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.

Hurts opponents most with

FH to their forehand · serve +1+4.0532
FH to their backhand · serve +1+3.2592
FH to the middle · return +1+3.2170
T 1st serve · deuce court+3.2768
Wide 2nd serve · deuce court+3.2214

Most exposed to

FH to their forehand · serve +1−4.7459
FH to their forehand · return−3.1281
FH to their backhand · return +1−2.4190
BH to the middle · serve +1−2.2213
FH to the middle · return +1−2.2177

Best-equipped opponents

Active players whose shot mix lines up best against these weaknesses, by structural compatibility per 100 rally shots: Rafael Nadal +2.43, Miomir Kecmanovic +2.37, Nishesh Basavareddy +2.16, Casper Ruud +2.10, Roberto Bautista Agut +2.06

Favourable matchups

Miomir Kecmanovic +1.82, Fabian Marozsan +1.75, Pedro Martinez +1.66, Roberto Carballes Baena +1.63, Roberto Bautista Agut +1.63

Active players who are best at the shot in the top weakness: Luciano Darderi, Milos Raonic, Francisco Cerundolo, Rinky Hijikata, Casper Ruud

Tactical fingerprint

Each bar shows how far a style trait is from the ATP average, in standard deviations.

Deep returns40%
Wide serves · deuce52%
1st serve in66%
Through the middle28%
Wide serves · ad54%
Avg rally length4.2
Forehand share55%
FH down the line31%
Backhand slice22%
Run-around forehands17%
Chipped returns13%
T serves · ad37%
Points at net10%
T serves · deuce43%
Serve & volley2%
Drop shots / shot1.1%
Point-ending shots20.4%
Unforced errors / shot8.4%
BH down the line15%

Plays most like

  1. Karen Khachanov 2015–2026 plan v
  2. Marcos Giron 2018–2026 plan v
  3. Rinky Hijikata 2023–2026 plan v
  4. Jaume Munar 2018–2026 plan v
  5. Roberto Bautista Agut 2013–2026 plan v
  6. Casper Ruud 2017–2026 plan v
  7. Gregoire Barrere 2016–2024 plan v
  8. Taylor Fritz 2016–2026 plan v

Closest from another era

  1. Guillermo Coria 2002–2005
  2. Nikolay Davydenko 2002–2014
  3. Magnus Norman 2000–2001

Charted matches