RallyIQ

ATP · Right-handed · 10 charted matches · 2011–2019

Marcel Granollers

Archetype: Grinder · Ad-court T server

Against an average opponent

Serve points won 62.1% ±3.7 raw 59.7% · tour 63.4% · 632 points
Return points won 38.3% ±3.7 raw 35.8% · tour 36.6% · 654 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 −0.15 ±0.22 better than 20% of ATP · raw −0.14
Shot selection −0.52 ±0.36 better than 11% of ATP · raw −0.51
Execution −0.26 ±0.54 better than 55% of ATP · raw −0.05
Tactical adaptability −0.06 first serves toward what's working, set to set · 10 matches
Adaptation speed +0.01 same, every two to three service games · per 100 first serves
Points left on the table 2.75 per 100 shots vs best direction · lower than 35% 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 3,305 shots.

Shot expected value

The share of points Marcel Granollers 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 · 174 shots

OptionUsedWin %Tour
BH crosscourt 41% 46.7%±8.6 47.6%
BH through the middle 36% 47.9%±9.0 43.7%
BH slice crosscourt 10% 46.0%±13.3 42.5%
BH slice through the middle 7% 43.8%±14.4 35.1%
BH down the line 6% 40.9%±14.8 46.4%

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

position worth 44% to the average player · 124 shots

OptionUsedWin %Tour
FH crosscourt 49% 36.2%±8.8 46.6%
FH through the middle 22% 41.1%±11.8 41.5%
FH down the line 14% 53.9%±13.5 44.7%

Rally, shots 5–8: drive to your middle

position worth 51% to the average player · 119 shots

OptionUsedWin %Tour
FH crosscourt 26% 42.2%±11.4 52.7%
BH through the middle 21% 49.7%±12.3 46.8%
FH through the middle 19% 47.4%±12.5 47.0%
BH crosscourt 15% 44.3%±13.3 49.1%
FH down the line 14% 49.5%±13.5 51.5%

Long rally, 9+: drive to your backhand side

position worth 46% to the average player · 110 shots

OptionUsedWin %Tour
BH crosscourt 43% 50.2%±10.0 48.0%
BH through the middle 24% 49.5%±12.1 43.8%
BH slice crosscourt 14% 32.6%±13.0 42.1%
BH slice through the middle 10% 42.0%±14.6 35.1%

Serve under pressure

Pressure predictability index −6 How much less varied Marcel Granollers's first-serve direction gets on break points. Positive means easier to read. Based on 70 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 49% 56% 69% / 73%
Body 17% 13% 66% / 63%
T 35% 31% 74% / 75%

307 normal · 16 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 28% 35% 77% / 73%
Body 16% 24% 56% / 63%
T 55% 41% ▼ 72% / 72%

244 normal · 54 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
Wide49% 61.9%±5.8 n=159 50%
Body16% 58.2%±8.9 n=53 3% ▼
T34% 62.4%±6.7 n=111 47% ▲

Consistent with an optimal mix (p = 0.68).
Optimal mix: +0.7 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide30% 60.5%±7.4 n=88 29%
Body18% 50.8%±9.0 n=53 5% ▼
T53% 61.1%±5.9 n=157 66% ▲

Consistent with an optimal mix (p = 0.09).
Optimal mix: +0.8 per 100 first serves.

Exploitability 0.74 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: +0.4±8.4 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. (235 repeats, 366 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 30 37% −0.2±10.2
1stAd courtT 76 30% +1.6±7.3
1stAd courtWide 81 34% +7.2±7.4
1stDeuce courtBody 27 33% −3.4±10.3
1stDeuce courtT 95 24% −1.5±6.2
1stDeuce courtWide 91 27% +0.2±6.7
2ndAd courtBody 50 45% −4.6±9.1
2ndAd courtT 23 51% +1.3±11.3
2ndAd courtWide 49 45% −3.4±9.2
2ndDeuce courtBody 63 47% −2.0±8.5
2ndDeuce courtT 37 51% +0.9±10.0
2ndDeuce courtWide 21 50% +1.8±11.5

Signature patterns

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

Serve → +1

  1. Body serve (ad court) → FH crosscourt used 6.2% · won 57% · −10.0±11.4 vs own baseline

Return

  1. vs wide serve (ad court) → BH through the middle, mid used 13.0% · won 37% · −2.8±10.9 vs own baseline

Rally, consecutive own shots

  1. BH through the middle → BH crosscourt used 5.8% · won 52% · +6.7±11.1 vs own baseline
  2. BH crosscourt → BH crosscourt used 4.7% · won 51% · +5.9±11.7 vs own baseline
  3. FH crosscourt → BH crosscourt used 6.3% · won 50% · +4.8±10.9 vs own baseline
  4. BH crosscourt → FH crosscourt used 5.1% · won 45% · ±0.0±11.4 vs own baseline
  5. FH through the middle → BH crosscourt used 4.7% · won 45% · −0.1±11.7 vs own baseline

Discovered sequences

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

  1. BH crosscourt → BH crosscourt → BH crosscourt used 1.3% · won 51% · +4.9±12.1 vs own baseline · +8.8 vs tour on the same sequence Disrupted by Ivan Dodig (3/6)
  2. BH crosscourt → BH crosscourt → BH through the middle used 1.0% · won 49% · +2.6±13.0 vs own baseline · +8.1 vs tour on the same sequence
  3. FH crosscourt → FH down the line → BH crosscourt used 1.1% · won 47% · +0.3±12.7 vs own baseline · −1.8 vs tour on the same sequence
  4. FH crosscourt → FH through the middle → BH crosscourt used 1.0% · won 46% · +0.1±13.0 vs own baseline · −3.5 vs tour on the same sequence Disrupted by Novak Djokovic (2/8)
  5. BH through the middle → FH crosscourt → FH crosscourt used 1.5% · won 44% · −2.3±11.7 vs own baseline · −3.3 vs tour on the same sequence Disrupted by Novak Djokovic (2/8), Ivan Dodig (2/7)
  6. FH crosscourt → FH crosscourt → FH crosscourt used 1.3% · won 43% · −3.8±12.0 vs own baseline · −10.7 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

BH to the middle · rally±0.0135
FH to their forehand · rally−0.1189
BH to their backhand · rally−0.3187
Wide 1st serve · deuce court−1.1159
BH to the middle · return−1.4174

Most exposed to

FH to their forehand · rally−1.0178
FH to the middle · return−0.6151
BH to their backhand · rally−0.6125
FH to their backhand · rally−0.3216
T 1st serve · deuce court−0.2152

Active players who are best at the shot in the top weakness: Roberto Carballes Baena, Roberto Bautista Agut, Miomir Kecmanovic, Pedro Martinez, Camilo Ugo Carabelli

Tactical fingerprint

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

T serves · ad53%
Through the middle33%
Backhand slice35%
Wide serves · deuce49%
Avg rally length4.2
Deep returns30%
Serve & volley15%
Points at net13%
Chipped returns19%
Drop shots / shot1.2%
1st serve in58%
FH down the line26%
Point-ending shots18.5%
Unforced errors / shot7.2%
Run-around forehands3%
T serves · deuce34%
BH down the line10%
Forehand share43%
Wide serves · ad30%

Plays most like

  1. Hyeon Chung 2015–2019 plan v
  2. Diego Schwartzman 2015–2025 plan v
  3. Botic Van De Zandschulp 2019–2026 plan v
  4. Alexandre Muller 2023–2025 plan v
  5. Gilles Simon 2008–2022 plan v
  6. Frances Tiafoe 2016–2026 plan v
  7. Alex De Minaur 2016–2026 plan v
  8. Daniil Medvedev 2017–2026 plan v

Closest from another era

  1. Aaron Krickstein 1989–1995
  2. Bjorn Borg 1974–1991
  3. Brad Gilbert 1987–1994

Charted matches