RallyIQ

ATP · Right-handed · 10 charted matches · 2012–2019

Lukas Rosol

Archetype: High-risk · Runs around the backhand

Against an average opponent

Serve points won 64.0% ±3.0 raw 60.1% · tour 63.4% · 877 points
Return points won 35.2% ±3.1 raw 29.9% · tour 36.6% · 816 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 −0.27 ±0.17 better than 7% of ATP · raw −0.28
Shot selection −0.18 ±0.18 better than 31% of ATP · raw −0.20
Execution −1.65 ±0.79 better than 7% of ATP · raw −1.89
Tactical adaptability −0.14 first serves toward what's working, set to set · 10 matches
Adaptation speed +0.03 same, every two to three service games · per 100 first serves
Points left on the table 2.97 per 100 shots vs best direction · lower than 21% 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 4,032 shots.

Shot expected value

The share of points Lukas Rosol 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 · 196 shots

OptionUsedWin %Tour
BH crosscourt 37% 52.7%±8.6 47.6%
BH through the middle 27% 42.7%±9.6 43.7%
BH down the line 12% 33.2%±11.8 46.4%
BH slice crosscourt 7% 45.6%±14.1 42.5%
BH slice through the middle 6% 34.5%±13.8 35.1%

Rally, shots 5–8: drive to your middle

position worth 51% to the average player · 166 shots

OptionUsedWin %Tour
FH crosscourt 26% 40.5%±10.2 52.7%
FH down the line 23% 54.0%±10.8 51.5%
FH through the middle 20% 45.2%±11.1 47.0%
BH crosscourt 17% 43.4%±11.8 49.1%
BH through the middle 14% 45.0%±12.5 46.8%

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

position worth 44% to the average player · 129 shots

OptionUsedWin %Tour
FH crosscourt 45% 46.6%±9.3 46.6%
FH down the line 26% 43.3%±11.2 44.7%
FH through the middle 24% 32.0%±10.7 41.5%

Return +1: drive to your backhand side

position worth 44% to the average player · 103 shots

OptionUsedWin %Tour
BH crosscourt 36% 30.5%±10.0 46.9%
BH through the middle 21% 44.3%±12.6 43.0%
BH slice through the middle 13% 22.8%±12.0 32.6%
BH slice crosscourt 12% 41.2%±14.3 40.9%

Serve under pressure

Pressure predictability index +2 How much less varied Lukas Rosol's first-serve direction gets on break points. Positive means easier to read. Based on 74 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 49% 58% ▲ 66% / 73%
Body 19% 11% ▼ 63% / 63%
T 32% 32% 78% / 75%

435 normal · 19 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 42% 45% 75% / 73%
Body 19% 24% 53% / 63%
T 39% 31% ▼ 68% / 72%

360 normal · 55 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% 61.7%±5.0 n=224 50%
Body18% 59.9%±7.6 n=83 5% ▼
T32% 63.5%±6.0 n=147 45% ▲

Consistent with an optimal mix (p = 0.76).
Optimal mix: +0.7 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide42% 64.5%±5.5 n=175 55% ▲
Body20% 51.6%±7.8 n=81 6% ▼
T38% 54.2%±6.0 n=159 39%

Off equilibrium (p = 0.014): serve wide more. Gap 6.5 points per 100 first serves.
Optimal mix: +1.0 per 100 first serves.

Exploitability 0.84 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: +6.0±4.9 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. (312 repeats, 537 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 50 34% −3.1±8.7
1stAd courtT 87 29% +0.5±6.9
1stAd courtWide 113 22% −4.7±5.7
1stDeuce courtBody 66 33% −3.4±7.9
1stDeuce courtT 119 23% −1.9±5.7
1stDeuce courtWide 93 20% −7.5±5.9
2ndAd courtBody 75 46% −3.8±8.0
2ndAd courtT 17 38% −11.4±11.6
2ndAd courtWide 44 40% −8.5±9.4
2ndDeuce courtBody 92 46% −2.5±7.4
2ndDeuce courtT 33 51% +1.0±10.4
2ndDeuce courtWide 22 37% −10.7±11.0

Signature patterns

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

Serve → +1

  1. T serve (ad court) → FH down the line used 3.6% · won 74% · +6.0±10.2 vs own baseline
  2. Body serve (deuce court) → FH down the line used 3.6% · won 66% · −1.7±10.9 vs own baseline
  3. Body serve (ad court) → FH down the line used 3.9% · won 63% · −4.1±10.8 vs own baseline
  4. Body serve (deuce court) → FH crosscourt used 3.2% · won 63% · −5.0±11.3 vs own baseline
  5. T serve (ad court) → FH crosscourt used 4.1% · won 62% · −5.3±10.8 vs own baseline

Return

  1. vs body serve (ad court) → BH crosscourt used 11.2% · won 37% · +6.7±10.4 vs own baseline
  2. vs wide serve (ad court) → BH direction unknown, deep used 9.5% · won 32% · +1.9±10.6 vs own baseline

Rally, consecutive own shots

  1. FH crosscourt → FH down the line used 7.3% · won 50% · +5.1±10.7 vs own baseline
  2. BH through the middle → FH crosscourt used 7.1% · won 46% · +0.8±10.8 vs own baseline
  3. BH crosscourt → BH crosscourt used 8.6% · won 44% · −0.4±10.1 vs own baseline
  4. FH through the middle → BH crosscourt used 5.3% · won 44% · −0.8±11.5 vs own baseline
  5. BH crosscourt → FH down the line used 5.1% · won 43% · −1.9±11.6 vs own baseline

Discovered sequences

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

  1. FH crosscourt → FH crosscourt → FH down the line used 0.9% · won 46% · +0.6±13.0 vs own baseline · −0.1 vs tour on the same sequence Disrupted by Grigor Dimitrov (3/6)
  2. BH crosscourt → BH crosscourt → BH crosscourt used 1.0% · won 45% · −0.2±12.5 vs own baseline · −3.9 vs tour on the same sequence Disrupted by Guillermo Garcia Lopez (4/10)

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

T 1st serve · deuce court+0.6147
FH to their forehand · serve +1−0.1143
Wide 1st serve · ad court−0.2175
FH to their forehand · rally−0.8190
Wide 1st serve · deuce court−1.3224

Most exposed to

FH to their backhand · rally−2.4193
FH to their backhand · serve +1−1.5131
Wide 1st serve · ad court−1.4175
T 1st serve · deuce court−1.1181
FH to their forehand · rally−1.1130

Active players who are best at the shot in the top weakness: Adrian Andreev, Alex Molcan, Rafael Nadal, Hugo Gaston, Daniel Evans

Tactical fingerprint

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

Unforced errors / shot13.7%
Deep returns37%
Point-ending shots30.8%
Forehand share59%
Wide serves · deuce49%
Through the middle27%
FH down the line33%
Drop shots / shot2.0%
Run-around forehands20%
Backhand slice21%
T serves · ad38%
1st serve in60%
Serve & volley3%
Points at net9%
Chipped returns11%
Avg rally length3.6
BH down the line17%
Wide serves · ad42%
T serves · deuce32%

Plays most like

  1. Roman Safiullin 2017–2025 plan v
  2. Zizou Bergs 2021–2026 plan v
  3. Alexander Shevchenko 2023–2026 plan v
  4. Ben Shelton 2021–2026 plan v
  5. Gregoire Barrere 2016–2024 plan v
  6. Holger Rune 2019–2025 plan v
  7. Arthur Fils 2023–2026 plan v
  8. Arthur Rinderknech 2021–2026 plan v

Closest from another era

  1. Magnus Norman 2000–2001
  2. Thomas Johansson 1996–2005
  3. Yevgeny Kafelnikov 1994–2002

Charted matches