RallyIQ

ATP · Right-handed · 66 charted matches · 2016–2026

Frances Tiafoe

Archetype: Ad-court T server · Two-fisted driver

Against an average opponent

Serve points won 65.8% ±2.6 raw 64.2% · tour 63.4% · 4,006 points
Return points won 37.7% ±2.7 raw 33.4% · tour 36.6% · 4,020 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 +0.06 ±0.06 better than 72% of ATP · raw +0.06
Shot selection +0.14 ±0.09 better than 69% of ATP · raw +0.14
Execution −0.26 ±0.34 better than 54% of ATP · raw −0.30
Tactical adaptability +0.11 first serves toward what's working, set to set · 44 matches
Adaptation speed +0.08 same, every two to three service games · per 100 first serves
Long-rally execution −0.27 ±0.56 shot 9 on v own earlier rally shots · 2,253 shots · better than 40% of ATP
Points left on the table 2.38 per 100 shots vs best direction · lower than 75% 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 19,409 shots.

Shot expected value

The share of points Frances Tiafoe 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 · 1,166 shots

OptionUsedWin %Tour
BH crosscourt 50% 47.6%±3.3 47.6%
BH through the middle 20% 46.6%±5.1 43.7%
BH down the line 11% 49.2%±6.6 46.4%
BH slice crosscourt 8% 35.0%±7.4 42.5%
BH slice through the middle 3% 28.6%±9.9 35.1%
FH inside-out 2% 58.4%±12.8 51.8%
BH slice down the line 2% 32.7%±12.5 37.1%
BH drop shot crosscourt 1% 53.6%±13.7 46.4%

Rally, shots 5–8: drive to your middle

position worth 51% to the average player · 956 shots

OptionUsedWin %Tour
FH crosscourt 24% 48.0%±5.2 52.7%
FH down the line 15% 44.6%±6.4 51.5%
BH crosscourt 15% 44.0%±6.4 49.1%
FH through the middle 14% 47.1%±6.6 47.0%
BH through the middle 13% 48.2%±6.8 46.8%
BH down the line 6% 45.3%±9.1 48.3%
BH slice crosscourt 3% 51.8%±11.7 47.0%
BH slice through the middle 2% 48.7%±12.5 44.8%

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

position worth 44% to the average player · 713 shots

OptionUsedWin %Tour
FH crosscourt 46% 47.5%±4.4 46.6%
FH through the middle 22% 40.4%±6.1 41.5%
FH down the line 19% 41.7%±6.5 44.7%
FH slice through the middle 5% 30.7%±10.2 24.5%
FH slice crosscourt 4% 28.9%±10.7 30.9%
FH slice down the line 2% 38.2%±13.1 25.6%
FH down the line + approach 1% 72.9%±13.4 69.3%

Long rally, 9+: drive to your backhand side

position worth 46% to the average player · 658 shots

OptionUsedWin %Tour
BH crosscourt 44% 45.1%±4.6 48.0%
BH through the middle 16% 41.1%±7.2 43.8%
BH down the line 13% 49.8%±8.0 46.5%
BH slice crosscourt 9% 47.4%±9.1 42.1%
BH slice through the middle 5% 35.2%±10.7 35.1%
BH drop shot crosscourt 3% 43.0%±12.4 47.4%
FH inside-out 2% 41.5%±13.7 52.6%

Serve under pressure

Pressure predictability index +4 How much less varied Frances Tiafoe's first-serve direction gets on break points. Positive means easier to read. Based on 317 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 50% 56% 72% / 73%
Body 8% 4% 63% / 63%
T 42% 39% 79% / 75%

2,007 normal · 89 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 38% 54% ▲ 76% / 73%
Body 10% 9% 60% / 63%
T 52% 37% ▼ 70% / 72%

1,681 normal · 228 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
Wide50% 65.7%±2.4 n=1,051 56% ▲
Body8% 61.7%±5.8 n=162 0% ▼
T42% 65.3%±2.6 n=883 44% ▲

Consistent with an optimal mix (p = 0.49).
Optimal mix: +0.4 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide40% 65.0%±2.8 n=758 53% ▲
Body10% 56.1%±5.6 n=186 0% ▼
T51% 63.0%±2.5 n=965 47% ▼

Off equilibrium (p = 0.031): serve wide more. Gap 1.9 points per 100 first serves.
Optimal mix: +0.7 per 100 first serves.

Exploitability 0.56 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: +1.2±2.9 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. (1,541 repeats, 2,372 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 131 33% −4.0±6.1
1stAd courtT 487 24% −3.8±3.1
1stAd courtWide 528 22% −4.8±2.9
1stDeuce courtBody 142 35% −1.8±6.0
1stDeuce courtT 540 24% −1.3±2.9
1stDeuce courtWide 675 25% −1.8±2.7
2ndAd courtBody 315 47% −1.9±4.4
2ndAd courtT 153 56% +6.3±6.0
2ndAd courtWide 296 51% +2.4±4.6
2ndDeuce courtBody 324 48% −1.1±4.4
2ndDeuce courtT 279 45% −4.7±4.7
2ndDeuce courtWide 147 38% −10.5±6.0

Signature patterns

Recurring sequences that win more than Frances Tiafoe'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 2.5% · won 65% · −2.6±7.1 vs own baseline
  2. T serve (ad court) → FH crosscourt used 3.5% · won 64% · −4.0±6.3 vs own baseline
  3. Wide serve (deuce court) → FH down the line used 2.8% · won 62% · −5.6±7.0 vs own baseline
  4. T serve (deuce court) → FH down the line used 2.1% · won 60% · −7.7±7.8 vs own baseline
  5. Wide serve (ad court) → FH crosscourt used 2.3% · won 60% · −7.9±7.6 vs own baseline

Return

  1. vs wide serve (ad court) → BH crosscourt, deep used 2.3% · won 54% · +15.7±8.5 vs own baseline
  2. vs T serve (deuce court) → BH through the middle, deep used 2.5% · won 47% · +9.4±8.2 vs own baseline
  3. vs wide serve (ad court) → BH crosscourt, mid used 3.8% · won 45% · +7.5±7.0 vs own baseline
  4. vs body serve (ad court) → BH crosscourt, mid used 2.2% · won 48% · +9.7±8.7 vs own baseline
  5. vs wide serve (deuce court) → FH through the middle, deep used 2.7% · won 45% · +6.7±7.9 vs own baseline

Rally, consecutive own shots

  1. BH crosscourt → FH through the middle used 1.8% · won 51% · +5.6±8.3 vs own baseline
  2. BH through the middle → BH through the middle used 1.5% · won 51% · +5.8±8.9 vs own baseline
  3. FH crosscourt → BH crosscourt used 3.3% · won 49% · +3.6±6.6 vs own baseline
  4. BH through the middle → BH crosscourt used 2.6% · won 49% · +3.8±7.3 vs own baseline
  5. FH down the line → FH crosscourt used 1.6% · won 50% · +4.1±8.6 vs own baseline

Discovered sequences

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

  1. BH crosscourt → BH slice through the middle → FH crosscourt used 0.5% · won 61% · +12.8±9.7 vs own baseline · +11.2 vs tour on the same sequence
  2. FH down the line → BH through the middle → FH crosscourt used 0.5% · won 59% · +10.6±9.3 vs own baseline · +9.4 vs tour on the same sequence Disrupted by Taylor Fritz (2/7), Hubert Hurkacz (6/6)
  3. BH down the line → FH through the middle → FH down the line used 0.2% · won 61% · +13.3±12.1 vs own baseline · +25.0 vs tour on the same sequence
  4. FH crosscourt → FH through the middle → BH through the middle used 0.3% · won 57% · +8.9±10.7 vs own baseline · +16.3 vs tour on the same sequence
  5. BH crosscourt → BH slice crosscourt → BH crosscourt used 0.3% · won 57% · +9.4±11.1 vs own baseline · +13.6 vs tour on the same sequence Disrupted by Grigor Dimitrov (6/8)
  6. FH crosscourt → FH slice through the middle → FH down the line + approach used 0.2% · won 60% · +12.0±12.7 vs own baseline · +5.6 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

BH to their forehand · return +1+2.2151
Body 2nd serve · deuce court+2.2239
Wide 2nd serve · ad court+1.6472
BH to the middle · serve +1+1.5275
BH to their backhand · rally+0.91,278

Most exposed to

Wide 2nd serve · deuce court−3.4140
FH to their forehand · return +1−2.9293
FH to their backhand · serve +1−2.7489
FH to their forehand · serve +1−2.2476
FH to the middle · return−2.2762

Best-equipped opponents

Active players whose shot mix lines up best against these weaknesses, by structural compatibility per 100 rally shots: Rafael Nadal +2.32, Miomir Kecmanovic +2.06, Roberto Bautista Agut +1.84, Casper Ruud +1.79, Jack Draper +1.77

Favourable matchups

Miomir Kecmanovic +0.94, Fabian Marozsan +0.89, Pedro Martinez +0.85, Roberto Carballes Baena +0.74, Roberto Bautista Agut +0.71

Active players who are best at the shot in the top weakness: Andrey Rublev, Brandon Nakashima, Jiri Lehecka, Giovanni Mpetshi Perricard, Nick Kyrgios

Tactical fingerprint

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

T serves · ad51%
Drop shots / shot2.8%
Wide serves · deuce50%
Deep returns33%
Point-ending shots24.3%
Unforced errors / shot10.0%
Points at net11%
Avg rally length3.9
BH down the line19%
Through the middle23%
Serve & volley3%
FH down the line29%
Chipped returns12%
Backhand slice16%
T serves · deuce42%
Run-around forehands14%
Forehand share51%
1st serve in58%
Wide serves · ad40%

Plays most like

  1. Otto Virtanen 2022–2025 plan v
  2. Matteo Arnaldi 2022–2025 plan v
  3. Alexander Shevchenko 2023–2026 plan v
  4. Tomas Berdych 2005–2019 plan v
  5. Gregoire Barrere 2016–2024 plan v
  6. Lucas Pouille 2015–2024 plan v
  7. Fabio Fognini 2012–2025 plan v
  8. Alexandre Muller 2023–2025 plan v

Closest from another era

  1. Yevgeny Kafelnikov 1994–2002
  2. Thomas Johansson 1996–2005
  3. Thomas Enqvist 1993–2001

Charted matches