RallyIQ

ATP · Right-handed · 50 charted matches · 2021–2026

Sebastian Baez

Archetype: Grinder · Ad-court T server

Against an average opponent

Serve points won 60.4% ±2.8 raw 57.8% · tour 63.4% · 3,469 points
Return points won 40.1% ±2.8 raw 39.7% · tour 36.6% · 3,649 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 34% of ATP · raw −0.08
Shot selection +0.02 ±0.14 better than 54% of ATP · raw +0.03
Execution −0.04 ±0.36 better than 66% of ATP · raw +0.05
Tactical adaptability −0.04 first serves toward what's working, set to set · 48 matches
Adaptation speed −0.01 same, every two to three service games · per 100 first serves
Long-rally execution −0.13 ±0.54 shot 9 on v own earlier rally shots · 3,029 shots · better than 60% of ATP
Points left on the table 2.58 per 100 shots vs best direction · lower than 51% 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 19,482 shots.

Shot expected value

The share of points Sebastian Baez 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 · 1,800 shots

OptionUsedWin %Tour
BH crosscourt 41% 50.1%±3.0 47.6%
BH through the middle 21% 43.0%±4.1 43.7%
BH down the line 9% 42.8%±6.1 46.4%
FH inside-out 7% 53.9%±6.7 51.8%
FH inside-in 7% 62.4%±6.7 54.7%
BH slice crosscourt 4% 41.2%±8.5 42.5%
BH slice through the middle 4% 38.2%±8.5 35.1%
FH through the middle 3% 44.2%±9.9 45.2%

Long rally, 9+: drive to your backhand side

position worth 46% to the average player · 1,124 shots

OptionUsedWin %Tour
BH crosscourt 42% 45.8%±3.7 48.0%
BH through the middle 19% 40.6%±5.3 43.8%
BH down the line 12% 39.1%±6.6 46.5%
BH slice crosscourt 6% 37.3%±8.5 42.1%
FH inside-in 6% 53.4%±9.0 54.3%
FH inside-out 5% 44.7%±9.4 52.6%
BH slice down the line 3% 33.0%±10.2 35.8%
BH drop shot down the line 3% 51.9%±11.1 47.8%

Rally, shots 5–8: drive to your middle

position worth 51% to the average player · 1,007 shots

OptionUsedWin %Tour
FH crosscourt 28% 54.5%±4.7 52.7%
FH down the line 24% 52.4%±5.1 51.5%
FH through the middle 17% 48.9%±6.0 47.0%
BH through the middle 12% 38.2%±6.6 46.8%
BH crosscourt 8% 50.3%±8.2 49.1%
BH down the line 3% 45.3%±11.6 48.3%
BH slice through the middle 2% 40.8%±12.2 44.8%
FH down the line + approach 2% 65.3%±12.4 70.5%

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

position worth 44% to the average player · 992 shots

OptionUsedWin %Tour
FH crosscourt 41% 54.2%±4.0 46.6%
FH through the middle 27% 45.6%±4.8 41.5%
FH down the line 21% 48.6%±5.5 44.7%
FH slice through the middle 6% 28.3%±8.2 24.5%
FH slice crosscourt 2% 31.2%±12.2 30.9%
FH slice down the line 1% 24.6%±12.3 25.6%
FH down the line + approach 1% 73.7%±13.0 69.3%

Serve under pressure

Pressure predictability index +7 How much less varied Sebastian Baez's first-serve direction gets on break points. Positive means easier to read. Based on 394 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 45% 53% ▲ 66% / 73%
Body 22% 26% 61% / 63%
T 33% 21% ▼ 59% / 75%

1,691 normal · 107 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 31% 26% 60% / 73%
Body 20% 15% 62% / 63%
T 49% 58% ▲ 64% / 72%

1,360 normal · 287 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
Wide46% 60.3%±2.8 n=820 59% ▲
Body22% 57.6%±3.9 n=394 21%
T32% 55.7%±3.3 n=584 20% ▼

Consistent with an optimal mix (p = 0.18).
Optimal mix: +0.4 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide30% 56.4%±3.5 n=500 30%
Body19% 55.9%±4.4 n=311 6% ▼
T51% 58.6%±2.8 n=836 64% ▲

Consistent with an optimal mix (p = 0.54).
Optimal mix: +0.3 per 100 first serves.

Exploitability 0.35 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±2.8 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. (1,126 repeats, 2,219 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 121 44% +6.8±6.6
1stAd courtT 407 27% −1.4±3.5
1stAd courtWide 561 30% +3.3±3.1
1stDeuce courtBody 150 39% +2.7±6.0
1stDeuce courtT 513 27% +1.7±3.1
1stDeuce courtWide 536 32% +5.0±3.2
2ndAd courtBody 157 52% +2.4±6.0
2ndAd courtT 97 53% +3.3±7.3
2ndAd courtWide 399 52% +3.3±4.0
2ndDeuce courtBody 215 59% +10.1±5.2
2ndDeuce courtT 288 52% +2.5±4.6
2ndDeuce courtWide 181 58% +9.5±5.6

Signature patterns

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

Serve → +1

  1. Wide serve (deuce court) → FH crosscourt used 3.0% · won 62% · +1.5±7.1 vs own baseline
  2. T serve (ad court) → FH down the line used 3.6% · won 60% · −0.7±6.7 vs own baseline
  3. Wide serve (deuce court) → FH down the line used 4.4% · won 59% · −1.0±6.1 vs own baseline
  4. T serve (ad court) → FH crosscourt used 3.9% · won 58% · −2.4±6.5 vs own baseline
  5. T serve (deuce court) → FH crosscourt used 4.2% · won 56% · −4.0±6.3 vs own baseline

Return

  1. vs wide serve (ad court) → BH through the middle, deep used 2.0% · won 61% · +17.6±8.8 vs own baseline
  2. vs wide serve (deuce court) → FH through the middle, deep used 2.1% · won 54% · +10.2±8.9 vs own baseline
  3. vs wide serve (ad court) → BH crosscourt, short used 2.3% · won 51% · +7.8±8.7 vs own baseline
  4. vs wide serve (ad court) → BH crosscourt, mid used 2.9% · won 50% · +6.6±8.0 vs own baseline
  5. vs wide serve (deuce court) → FH through the middle, mid used 2.4% · won 49% · +5.5±8.5 vs own baseline

Rally, consecutive own shots

  1. BH crosscourt → FH crosscourt used 2.7% · won 58% · +9.5±6.3 vs own baseline
  2. FH crosscourt → FH crosscourt used 3.8% · won 55% · +7.0±5.5 vs own baseline
  3. FH crosscourt → BH crosscourt used 3.2% · won 55% · +7.1±5.9 vs own baseline
  4. FH down the line → FH crosscourt used 1.5% · won 57% · +8.5±8.0 vs own baseline
  5. BH crosscourt → FH down the line used 1.6% · won 55% · +6.8±7.8 vs own baseline

Discovered sequences

Mined from every me → opponent → me run of three shots, with no templates. Ranked by how much more often Sebastian Baez 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 crosscourt used 1.5% · won 56% · +6.7±6.0 vs own baseline · +7.8 vs tour on the same sequence Disrupted by Lorenzo Musetti (1/6), Carlos Alcaraz (1/6)
  2. BH crosscourt → BH through the middle → FH crosscourt used 0.5% · won 59% · +10.4±8.9 vs own baseline · +10.7 vs tour on the same sequence Disrupted by Francisco Cerundolo (4/6)
  3. FH crosscourt → FH slice through the middle → FH crosscourt used 0.2% · won 64% · +15.2±11.9 vs own baseline · +18.6 vs tour on the same sequence
  4. Wide serve → FH crosscourt return, short → FH down the line used 0.2% · won 65% · +15.9±12.1 vs own baseline · +33.0 vs tour on the same sequence Disrupted by Alexander Zverev (5/6)
  5. Wide serve → BH through the middle return → FH crosscourt used 0.1% · won 64% · +15.1±12.3 vs own baseline · +26.7 vs tour on the same sequence Disrupted by Thiago Monteiro (6/8)
  6. T serve → FH through the middle return, short → FH down the line used 0.2% · won 61% · +12.5±11.8 vs own baseline · +17.4 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 · return +1+4.6271
BH to their backhand · return +1+2.6359
FH to the middle · return +1+2.0262
FH to their forehand · rally+2.01,431
FH slice to the middle · rally+1.7128

Most exposed to

FH to their forehand · return−5.5297
BH to their forehand · return−3.4165
BH to the middle · return−2.7747
FH to their backhand · return−2.6320
FH to the middle · return−2.5727

Best-equipped opponents

Active players whose shot mix lines up best against these weaknesses, by structural compatibility per 100 rally shots: Rafael Nadal +1.91, Miomir Kecmanovic +1.72, Jack Draper +1.66, Nishesh Basavareddy +1.58, Casper Ruud +1.53

Favourable matchups

Miomir Kecmanovic +2.03, Fabian Marozsan +1.84, Roberto Carballes Baena +1.82, Pedro Martinez +1.78, Roberto Bautista Agut +1.76

Active players who are best at the shot in the top weakness: Tomas Machac, Andrey Rublev, Alexei Popyrin, Daniil Medvedev, Novak Djokovic

Tactical fingerprint

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

1st serve in72%
T serves · ad51%
Avg rally length4.8
Run-around forehands27%
Drop shots / shot2.5%
Forehand share56%
Through the middle27%
Wide serves · deuce46%
Deep returns28%
Backhand slice16%
Unforced errors / shot9.1%
Chipped returns11%
Serve & volley1%
FH down the line28%
Point-ending shots20.0%
Points at net7%
BH down the line14%
T serves · deuce32%
Wide serves · ad30%

Plays most like

  1. Diego Schwartzman 2015–2025 plan v
  2. David Ferrer 2003–2019 plan v
  3. Tomas Martin Etcheverry 2022–2026 plan v
  4. Pablo Carreno Busta 2015–2026 plan v
  5. Alejandro Davidovich Fokina 2019–2025 plan v
  6. Botic Van De Zandschulp 2019–2026 plan v
  7. Emil Ruusuvuori 2021–2024 plan v
  8. Alejandro Tabilo 2021–2026 plan v

Closest from another era

  1. Andy Roddick 2001–2012
  2. Guillermo Canas 2001–2007
  3. Guillermo Coria 2002–2005

Charted matches