RallyIQ

ATP · Right-handed · 46 charted matches · 2017–2020

Karim Mohamed Maamoun

Archetype: Rallies through the middle · Crosscourt backhand

Against an average opponent

Serve points won 60.6% ±2.9 raw 58.5% · tour 63.4% · 3,238 points
Return points won 36.2% ±2.8 raw 38.0% · tour 36.6% · 3,186 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 −0.08 ±0.10 better than 37% of ATP · raw −0.07
Shot selection −0.54 ±0.09 better than 11% of ATP · raw −0.51
Execution −1.83 ±0.36 better than 5% of ATP · raw −1.36
Tactical adaptability +0.03 first serves toward what's working, set to set · 46 matches
Adaptation speed −0.09 same, every two to three service games · per 100 first serves
Long-rally execution −0.24 ±0.54 shot 9 on v own earlier rally shots · 2,493 shots · better than 46% of ATP
Points left on the table 2.38 per 100 shots vs best direction · lower than 76% 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 17,735 shots.

Shot expected value

The share of points Karim Mohamed Maamoun 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 · 1,193 shots

OptionUsedWin %Tour
BH crosscourt 46% 48.1%±3.5 47.6%
BH through the middle 25% 43.4%±4.5 43.7%
BH slice crosscourt 7% 40.8%±7.7 42.5%
BH down the line 6% 45.3%±8.7 46.4%
BH slice through the middle 5% 36.0%±9.1 35.1%
FH inside-out 3% 61.4%±10.7 51.8%
FH inside-in 3% 55.2%±10.9 54.7%
BH drop shot crosscourt 3% 54.2%±11.2 46.4%

Rally, shots 5–8: drive to your middle

position worth 51% to the average player · 1,090 shots

OptionUsedWin %Tour
FH through the middle 26% 45.0%±4.7 47.0%
FH crosscourt 20% 56.5%±5.3 52.7%
FH down the line 19% 56.5%±5.4 51.5%
BH through the middle 17% 49.7%±5.8 46.8%
BH crosscourt 11% 54.6%±6.9 49.1%
BH down the line 2% 50.4%±12.5 48.3%
FH down the line + approach 1% 64.2%±13.1 70.5%
FH drop shot crosscourt 1% 62.9%±13.4 55.1%

Serve +1: mid-depth return to your middle

position worth 54% to the average player · 937 shots

OptionUsedWin %Tour
FH crosscourt 24% 56.9%±5.2 56.0%
FH through the middle 23% 46.8%±5.4 47.3%
FH down the line 18% 53.5%±5.9 53.4%
BH crosscourt 15% 48.4%±6.5 49.5%
BH through the middle 15% 46.8%±6.5 47.3%
BH down the line 2% 50.3%±12.7 50.6%
FH crosscourt + approach 1% 71.6%±12.9 73.2%

Long rally, 9+: drive to your backhand side

position worth 46% to the average player · 762 shots

OptionUsedWin %Tour
BH crosscourt 47% 45.1%±4.2 48.0%
BH through the middle 26% 42.3%±5.5 43.8%
BH slice crosscourt 8% 30.2%±8.4 42.1%
BH slice through the middle 7% 36.7%±9.4 35.1%
BH down the line 5% 48.8%±10.8 46.5%
BH drop shot crosscourt 2% 47.4%±13.2 47.4%
FH inside-out 2% 50.1%±13.9 52.6%
FH inside-in 2% 52.7%±14.5 54.3%

Serve under pressure

Pressure predictability index +5 How much less varied Karim Mohamed Maamoun's first-serve direction gets on break points. Positive means easier to read. Based on 348 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 36% 51% ▲ 68% / 73%
Body 9% 8% 69% / 63%
T 54% 41% ▼ 64% / 75%

1,595 normal · 80 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 50% 46% 64% / 73%
Body 8% 5% 63% / 63%
T 42% 49% 67% / 72%

1,272 normal · 268 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
Wide37% 60.4%±3.2 n=619 49% ▲
Body9% 62.2%±5.8 n=157 10%
T54% 56.4%±2.7 n=899 41% ▼

Consistent with an optimal mix (p = 0.11).
Optimal mix: +0.4 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide50% 57.9%±2.9 n=765 37% ▼
Body7% 60.7%±6.7 n=113 7%
T43% 58.6%±3.1 n=662 56% ▲

Consistent with an optimal mix (p = 0.77).
Optimal mix: +0.2 per 100 first serves.

Exploitability 0.32 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: +1.5±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. (1,014 repeats, 2,109 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 63 40% +2.9±8.4
1stAd courtT 331 29% +1.2±3.9
1stAd courtWide 526 28% +0.9±3.1
1stDeuce courtBody 130 38% +0.8±6.3
1stDeuce courtT 379 28% +2.9±3.7
1stDeuce courtWide 464 26% −1.2±3.2
2ndAd courtBody 131 55% +5.8±6.4
2ndAd courtT 69 48% −1.0±8.3
2ndAd courtWide 389 50% +1.9±4.0
2ndDeuce courtBody 337 52% +3.2±4.3
2ndDeuce courtT 170 49% −0.7±5.8
2ndDeuce courtWide 174 53% +5.1±5.7

Signature patterns

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

Serve → +1

  1. Wide serve (ad court) → FH crosscourt used 3.8% · won 64% · +1.7±6.6 vs own baseline
  2. Wide serve (deuce court) → FH crosscourt used 2.3% · won 62% · +0.1±8.1 vs own baseline
  3. T serve (ad court) → FH through the middle used 2.1% · won 59% · −3.1±8.4 vs own baseline
  4. Wide serve (deuce court) → BH crosscourt used 2.0% · won 58% · −3.9±8.7 vs own baseline
  5. Wide serve (ad court) → FH down the line used 2.4% · won 58% · −3.8±8.1 vs own baseline

Return

  1. vs body serve (deuce court) → FH through the middle, mid used 2.5% · won 58% · +19.1±8.5 vs own baseline
  2. vs wide serve (ad court) → BH crosscourt, short used 5.4% · won 49% · +9.8±6.4 vs own baseline
  3. vs wide serve (deuce court) → FH crosscourt, mid used 2.6% · won 52% · +13.3±8.5 vs own baseline
  4. vs wide serve (ad court) → BH crosscourt, mid used 8.2% · won 46% · +7.3±5.3 vs own baseline
  5. vs wide serve (deuce court) → FH through the middle, mid used 3.3% · won 47% · +8.4±7.8 vs own baseline

Rally, consecutive own shots

  1. FH down the line → FH crosscourt used 2.3% · won 62% · +13.7±7.3 vs own baseline
  2. FH crosscourt → BH crosscourt used 2.8% · won 55% · +7.3±6.9 vs own baseline
  3. BH crosscourt → FH crosscourt used 3.5% · won 54% · +5.7±6.3 vs own baseline
  4. FH through the middle → FH down the line used 1.4% · won 55% · +7.1±8.9 vs own baseline
  5. FH crosscourt → BH through the middle used 1.5% · won 54% · +6.2±8.7 vs own baseline

Discovered sequences

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

  1. Wide serve → BH through the middle return, mid → FH crosscourt used 0.7% · won 66% · +17.2±8.0 vs own baseline · +13.1 vs tour on the same sequence Disrupted by Adam Pavlasek (5/6)
  2. FH down the line → BH through the middle → FH crosscourt used 0.6% · won 63% · +14.4±9.0 vs own baseline · +15.2 vs tour on the same sequence Disrupted by Nikola Milojevic (3/6), Sanjar Fayziev (5/6)
  3. FH down the line → BH slice through the middle → FH down the line used 0.2% · won 67% · +18.4±11.5 vs own baseline · +31.2 vs tour on the same sequence
  4. BH through the middle → FH down the line → BH crosscourt used 0.9% · won 55% · +6.9±7.7 vs own baseline · +10.8 vs tour on the same sequence Disrupted by Andreas Haider Maurer (3/9), Nikola Milojevic (5/9)
  5. FH through the middle → FH through the middle → FH down the line used 0.3% · won 61% · +12.1±11.0 vs own baseline · +21.6 vs tour on the same sequence Disrupted by Nikola Milojevic (4/6)
  6. FH down the line → BH slice through the middle → FH crosscourt used 0.3% · won 61% · +12.5±11.2 vs own baseline · +10.1 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 their forehand · return+2.7129
Body 2nd serve · deuce court+0.1265
Body 1st serve · deuce court−0.1157
FH to the middle · serve +1−0.5379
BH to the middle · return−0.5770

Most exposed to

BH slice to the middle · return−2.0132
BH to the middle · return−1.8982
FH to their backhand · return−0.5180
BH to their forehand · rally−0.4370
Body 1st serve · deuce court−0.4184

Active players who are best at the shot in the top weakness: Rafael Nadal, Andy Murray, Richard Gasquet, Nick Kyrgios, Giovanni Mpetshi Perricard

Tactical fingerprint

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

Through the middle31%
T serves · deuce54%
Drop shots / shot2.8%
Avg rally length4.4
Unforced errors / shot11.7%
T serves · ad43%
1st serve in62%
Wide serves · ad50%
Run-around forehands18%
Point-ending shots22.8%
Forehand share52%
Backhand slice16%
Chipped returns11%
Serve & volley0%
Points at net8%
FH down the line27%
Wide serves · deuce37%
Deep returns18%
BH down the line9%

Plays most like

  1. Andreas Seppi 2008–2021 plan v
  2. Gael Monfils 2005–2026 plan v
  3. Mackenzie Mcdonald 2015–2025 plan v
  4. Guido Pella 2014–2021 plan v
  5. Yannick Hanfmann 2021–2025 plan v
  6. Luca Nardi 2022–2025 plan v
  7. Jannik Sinner 2013–2026 plan v
  8. Jesper De Jong 2021–2026 plan v

Closest from another era

  1. Andrei Pavel 1999–2006
  2. Marat Safin 1998–2009
  3. Nicolas Kiefer 1999–2008

Charted matches