RallyIQ

ATP · Left-handed · 9 charted matches · 2019–2019

Kevin King

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

Against an average opponent

Serve points won 61.5% ±3.7 raw 61.2% · tour 63.4% · 717 points
Return points won 36.2% ±3.6 raw 36.7% · tour 36.6% · 697 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 −0.05 ±0.05 better than 44% of ATP · raw −0.04
Shot selection +0.49 ±0.15 better than 93% of ATP · raw +0.53
Execution −2.29 ±1.18 better than 1% of ATP · raw −1.78
Points left on the table 3.19 per 100 shots vs best direction · lower than 12% 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 3,257 shots.

Shot expected value

The share of points Kevin King 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 · 164 shots

OptionUsedWin %Tour
BH crosscourt 37% 46.3%±9.1 47.6%
BH through the middle 21% 34.7%±10.7 43.7%
BH down the line 14% 51.8%±12.5 46.4%
BH slice crosscourt 9% 44.3%±13.8 42.5%
FH inside-out 9% 45.2%±14.0 51.8%

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

position worth 44% to the average player · 146 shots

OptionUsedWin %Tour
FH crosscourt 50% 39.0%±8.3 46.6%
FH through the middle 25% 49.6%±10.9 41.5%
FH down the line 19% 41.6%±11.7 44.7%

Return +1: drive to your backhand side

position worth 44% to the average player · 121 shots

OptionUsedWin %Tour
BH crosscourt 40% 44.0%±9.8 46.9%
BH through the middle 26% 45.4%±11.4 43.0%
BH down the line 20% 42.8%±12.3 44.2%

Long rally, 9+: drive to your forehand side

position worth 44% to the average player · 89 shots

OptionUsedWin %Tour
FH crosscourt 53% 42.4%±9.9 46.9%
FH down the line 31% 43.7%±11.8 44.8%
FH through the middle 16% 36.1%±13.6 41.4%

Serve under pressure

Pressure predictability index −11 How much less varied Kevin King's first-serve direction gets on break points. Positive means easier to read. Based on 68 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 43% 41% 81% / 73%
Body 11% 24% ▲ 60% / 63%
T 46% 35% ▼ 72% / 75%

358 normal · 17 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 54% 47% 75% / 73%
Body 9% 18% ▲ 65% / 63%
T 37% 35% 74% / 72%

291 normal · 51 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
Wide43% 64.9%±5.7 n=161 56% ▲
Body12% 54.9%±9.5 n=44 0% ▼
T45% 58.5%±5.7 n=170 44% ▼

Consistent with an optimal mix (p = 0.14).
Optimal mix: +0.8 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide53% 62.1%±5.5 n=180 66% ▲
Body11% 63.6%±9.7 n=36 0% ▼
T37% 61.1%±6.4 n=126 34% ▼

Consistent with an optimal mix (p = 0.91).
Optimal mix: +0.4 per 100 first serves.

Exploitability 0.58 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: −0.9±5.7 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. (237 repeats, 462 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 12 36% −1.0±12.2
1stAd courtT 103 24% −3.7±6.1
1stAd courtWide 75 24% −3.3±6.9
1stDeuce courtBody 23 38% +1.0±11.0
1stDeuce courtT 87 33% +7.9±7.1
1stDeuce courtWide 105 24% −3.4±6.0
2ndAd courtBody 66 50% +0.4±8.4
2ndAd courtT 49 49% −0.2±9.3
2ndAd courtWide 28 42% −6.1±10.7
2ndDeuce courtBody 66 56% +6.9±8.3
2ndDeuce courtT 35 44% −5.2±10.1
2ndDeuce courtWide 48 53% +5.0±9.3

Signature patterns

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

Serve → +1

  1. Not enough data

Return

  1. vs T serve (ad court) → BH through the middle, mid used 10.6% · won 31% · −1.5±10.8 vs own baseline
  2. vs wide serve (deuce court) → BH through the middle used 16.2% · won 22% · −10.2±8.8 vs own baseline

Rally, consecutive own shots

  1. FH crosscourt → FH crosscourt used 12.4% · won 40% · +2.0±9.7 vs own baseline
  2. FH crosscourt → FH down the line used 9.7% · won 35% · −2.8±10.3 vs own baseline

Discovered sequences

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

  1. FH crosscourt → BH crosscourt → FH crosscourt used 2.3% · won 42% · −2.3±10.9 vs own baseline · −8.4 vs tour on the same sequence Disrupted by Mitchell Krueger (1/6), Cem Ilkel (3/8)
  2. FH crosscourt → BH crosscourt → FH down the line used 1.8% · won 41% · −3.3±11.6 vs own baseline · −11.4 vs tour on the same sequence Disrupted by Mitchell Krueger (2/6)

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 backhand · rally+2.0215
Wide 1st serve · ad court+1.6180
T 1st serve · ad court+1.0126
T 1st serve · deuce court+0.1170
Wide 1st serve · deuce court−0.9161

Most exposed to

BH to their forehand · rally−2.5145
FH to their forehand · rally−0.4144
FH to their backhand · rally+0.6237
T 1st serve · deuce court+1.0155
Wide 1st serve · ad court+1.3139

Active players who are best at the shot in the top weakness: Juncheng Shang, Pedro Martinez, Learner Tien, Miomir Kecmanovic, Nishesh Basavareddy

Tactical fingerprint

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

BH down the line30%
Forehand share59%
Points at net17%
Serve & volley22%
Point-ending shots26.9%
Wide serves · ad53%
Chipped returns19%
1st serve in62%
T serves · deuce45%
Deep returns27%
Run-around forehands18%
Wide serves · deuce43%
FH down the line30%
Backhand slice18%
T serves · ad37%
Unforced errors / shot8.6%
Avg rally length3.5
Through the middle21%
Drop shots / shot0.3%

Plays most like

  1. Alexei Popyrin 2019–2026 plan v
  2. Jo Wilfried Tsonga 2007–2022 plan v
  3. Fernando Verdasco 2005–2022 plan v
  4. Hubert Hurkacz 2018–2026 plan v
  5. Roger Federer 1998–2021 plan v
  6. Kyle Edmund 2016–2023 plan v
  7. Joao Fonseca 2024–2026 plan v
  8. James Blake 2002–2011 plan v

Closest from another era

  1. Carlos Moya 1997–2007
  2. Andrei Pavel 1999–2006
  3. Marcelo Rios 1995–2001

Charted matches