RallyIQ

ATP · Right-handed · 46 charted matches · 2012–2024

Robin Haase

Archetype: Rallies through the middle · Crosscourt backhand

Against an average opponent

Serve points won 64.0% ±2.6 raw 62.9% · tour 63.4% · 3,730 points
Return points won 36.2% ±2.6 raw 34.9% · tour 36.6% · 3,715 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 −0.13 ±0.05 better than 23% of ATP · raw −0.12
Shot selection −0.56 ±0.12 better than 10% of ATP · raw −0.54
Execution −0.60 ±0.33 better than 41% of ATP · raw −0.31
Tactical adaptability +0.20 first serves toward what's working, set to set · 46 matches
Adaptation speed +0.12 same, every two to three service games · per 100 first serves
Long-rally execution −0.32 ±0.58 shot 9 on v own earlier rally shots · 2,224 shots · better than 35% of ATP
Points left on the table 2.82 per 100 shots vs best direction · lower than 29% 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 19,104 shots.

Shot expected value

The share of points Robin Haase 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 · 1,280 shots

OptionUsedWin %Tour
BH through the middle 22% 40.0%±4.6 43.7%
BH slice crosscourt 22% 43.4%±4.7 42.5%
BH crosscourt 18% 40.2%±5.1 47.6%
BH slice through the middle 10% 40.3%±6.7 35.1%
BH down the line 8% 43.0%±7.5 46.4%
FH inside-out 8% 52.9%±7.6 51.8%
FH inside-in 4% 54.3%±9.9 54.7%
BH slice down the line 3% 30.2%±9.7 37.1%

Rally, shots 5–8: drive to your middle

position worth 51% to the average player · 820 shots

OptionUsedWin %Tour
FH down the line 26% 47.6%±5.4 51.5%
FH through the middle 20% 51.8%±6.1 47.0%
FH crosscourt 14% 50.4%±7.0 52.7%
BH through the middle 12% 46.4%±7.6 46.8%
BH crosscourt 7% 48.4%±9.6 49.1%
BH down the line 6% 51.6%±9.8 48.3%
BH slice through the middle 5% 32.1%±10.0 44.8%
BH slice crosscourt 4% 47.8%±11.7 47.0%

Long rally, 9+: drive to your backhand side

position worth 46% to the average player · 753 shots

OptionUsedWin %Tour
BH slice crosscourt 29% 42.3%±5.2 42.1%
BH through the middle 19% 42.5%±6.4 43.8%
BH crosscourt 17% 43.3%±6.7 48.0%
BH slice through the middle 11% 39.8%±7.9 35.1%
BH down the line 7% 40.9%±9.4 46.5%
FH inside-out 5% 44.8%±10.8 52.6%
FH inside-in 4% 52.9%±12.0 54.3%
BH slice down the line 2% 32.0%±12.4 35.8%

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

position worth 44% to the average player · 707 shots

OptionUsedWin %Tour
FH through the middle 31% 45.5%±5.3 41.5%
FH crosscourt 30% 43.7%±5.4 46.6%
FH down the line 21% 45.3%±6.3 44.7%
FH slice through the middle 7% 19.6%±7.7 24.5%
FH slice crosscourt 6% 26.4%±9.0 30.9%
FH down the line + approach 2% 77.0%±12.4 69.3%

Serve under pressure

Pressure predictability index +12 How much less varied Robin Haase's first-serve direction gets on break points. Positive means easier to read. Based on 337 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 42% 59% ▲ 73% / 73%
Body 7% 4% 58% / 63%
T 51% 37% ▼ 76% / 75%

1,857 normal · 76 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 56% 63% 76% / 73%
Body 8% 3% 57% / 63%
T 37% 33% 71% / 72%

1,522 normal · 261 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
Wide43% 62.6%±2.7 n=828 37% ▼
Body7% 52.3%±6.5 n=131 0% ▼
T50% 63.2%±2.5 n=974 63% ▲

Off equilibrium (p = 0.013): serve T more. Gap 1.0 points per 100 first serves.
Optimal mix: +0.5 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide57% 64.2%±2.4 n=1,015 59% ▲
Body7% 56.7%±6.6 n=124 0% ▼
T36% 64.3%±3.0 n=644 41% ▲

Consistent with an optimal mix (p = 0.10).
Optimal mix: +0.4 per 100 first serves.

Exploitability 0.46 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: +0.6±3.0 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. (1,797 repeats, 1,827 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 119 36% −0.7±6.5
1stAd courtT 371 27% −1.0±3.6
1stAd courtWide 567 25% −1.7±2.9
1stDeuce courtBody 172 36% −1.1±5.5
1stDeuce courtT 528 24% −1.3±3.0
1stDeuce courtWide 519 28% +0.3±3.1
2ndAd courtBody 243 52% +2.6±5.0
2ndAd courtT 85 49% −0.8±7.7
2ndAd courtWide 373 44% −4.6±4.1
2ndDeuce courtBody 297 49% −0.2±4.5
2ndDeuce courtT 279 48% −1.8±4.7
2ndDeuce courtWide 140 46% −2.5±6.3

Signature patterns

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

Serve → +1

  1. Wide serve (ad court) → FH crosscourt used 2.3% · won 66% · −0.7±7.7 vs own baseline
  2. Wide serve (ad court) → FH down the line used 3.0% · won 65% · −2.0±7.0 vs own baseline
  3. Wide serve (deuce court) → FH down the line used 2.7% · won 61% · −5.8±7.4 vs own baseline
  4. T serve (ad court) → FH down the line used 2.8% · won 58% · −8.6±7.4 vs own baseline
  5. T serve (deuce court) → FH crosscourt used 2.2% · won 53% · −14.1±8.3 vs own baseline

Return

  1. vs wide serve (deuce court) → FH through the middle, deep used 2.5% · won 49% · +12.6±8.2 vs own baseline
  2. vs wide serve (ad court) → BH through the middle, deep used 3.0% · won 45% · +8.4±7.6 vs own baseline
  3. vs T serve (deuce court) → BH through the middle, mid used 3.8% · won 42% · +5.9±6.9 vs own baseline
  4. vs wide serve (deuce court) → FH through the middle, mid used 2.9% · won 42% · +5.6±7.6 vs own baseline
  5. vs body serve (deuce court) → FH through the middle, mid used 2.8% · won 41% · +4.9±7.7 vs own baseline

Rally, consecutive own shots

  1. FH down the line → FH down the line used 2.2% · won 53% · +9.3±7.8 vs own baseline
  2. FH down the line → FH crosscourt used 1.4% · won 55% · +11.2±9.1 vs own baseline
  3. FH crosscourt → FH down the line used 1.9% · won 53% · +8.7±8.3 vs own baseline
  4. FH down the line → FH inside-out used 1.8% · won 53% · +8.8±8.4 vs own baseline
  5. FH through the middle → FH crosscourt used 2.2% · won 50% · +6.1±7.9 vs own baseline

Discovered sequences

Mined from every me → opponent → me run of three shots, with no templates. Ranked by how much more often Robin Haase 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 → FH inside-out used 0.7% · won 56% · +9.1±8.6 vs own baseline · +7.0 vs tour on the same sequence Disrupted by Jaume Munar (7/9)
  2. FH crosscourt → FH through the middle → FH through the middle used 0.3% · won 58% · +11.6±10.8 vs own baseline · +19.3 vs tour on the same sequence
  3. T serve → FH through the middle return, short → FH down the line used 0.2% · won 61% · +14.4±12.4 vs own baseline · +23.5 vs tour on the same sequence
  4. T serve → BH through the middle return, mid → FH through the middle used 0.2% · won 62% · +15.0±12.7 vs own baseline · +38.1 vs tour on the same sequence
  5. FH inside-out → BH through the middle → FH down the line used 0.2% · won 58% · +10.9±11.7 vs own baseline · +16.9 vs tour on the same sequence
  6. FH crosscourt → FH crosscourt → FH down the line used 0.6% · won 53% · +6.6±9.2 vs own baseline · +9.6 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 backhand · return +1+2.1304
FH to the middle · rally+1.8702
FH to their backhand · rally+1.81,117
FH to the middle · return +1+1.6276
BH slice to their backhand · return +1+1.4190

Most exposed to

T 2nd serve · deuce court−2.0279
FH to their backhand · return +1−1.7279
FH to their backhand · serve +1−1.5679
BH slice to their backhand · return +1−1.2137
FH to their forehand · serve +1−1.1607

Best-equipped opponents

Active players whose shot mix lines up best against these weaknesses, by structural compatibility per 100 rally shots: Rafael Nadal +1.26, Miomir Kecmanovic +0.96, Jack Draper +0.78, Casper Ruud +0.78, Pedro Martinez +0.77

Favourable matchups

Fabian Marozsan +1.25, Miomir Kecmanovic +1.17, Roberto Bautista Agut +1.17, Roberto Carballes Baena +1.16, Pedro Martinez +1.11

Active players who are best at the shot in the top weakness: Giovanni Mpetshi Perricard, Reilly Opelka, Joao Fonseca, Brandon Nakashima, Matteo Berrettini

Tactical fingerprint

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

Through the middle32%
Backhand slice43%
Drop shots / shot3.2%
FH down the line36%
Wide serves · ad57%
Run-around forehands27%
T serves · deuce50%
Chipped returns22%
Forehand share56%
Deep returns30%
BH down the line22%
Avg rally length4.1
Wide serves · deuce43%
Points at net10%
Serve & volley4%
T serves · ad36%
Point-ending shots21.5%
Unforced errors / shot8.6%
1st serve in58%

Plays most like

  1. Marton Fucsovics 2018–2026 plan v
  2. Lorenzo Musetti 2019–2026 plan v
  3. Flavio Cobolli 2022–2026 plan v
  4. Dominic Thiem 2011–2024 plan v
  5. Pedro Martinez 2019–2026 plan v
  6. Philipp Kohlschreiber 2008–2019 plan v
  7. Arthur Cazaux 2020–2025 plan v
  8. Malek Jaziri 2015–2023 plan v

Closest from another era

  1. Sebastien Grosjean 1999–2005
  2. Andrei Pavel 1999–2006
  3. Carlos Moya 1997–2007

Charted matches