J J Wolf

Against an average opponent

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

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 2,625 shots.

Shot expected value

The share of points J J Wolf 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.

Serve under pressure

Pressure predictability index −3 How much less varied J J Wolf's first-serve direction gets on break points. Positive means easier to read. Based on 55 break-point first 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.

Exploitability 0.45 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±5.0 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. (199 repeats, 311 switches.)

Return by serve direction

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

Signature patterns

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

Discovered sequences

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

1. BH crosscourt → BH crosscourt → BH crosscourt used 1.5% · won 42% · −1.7±12.1 vs own baseline · −8.4 vs tour on the same sequence Disrupted by Carlos Alcaraz (3/10)
2. BH crosscourt → BH through the middle → FH crosscourt used 1.1% · won 35% · −9.0±12.5 vs own baseline · −33.1 vs tour on the same sequence Disrupted by Mikael Ymer (2/9)

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.

Tactical fingerprint

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

Charted matches

• Carlos Alcaraz v J J Wolf L Roland Garros R128 · Clay · 26 May 2024
• Ugo Humbert v J J Wolf L Shanghai Masters R16 · Hard · 11 Oct 2023
• J J Wolf v Hubert Hurkacz W Rome Masters R64 · Clay · 13 May 2023
• Mikael Ymer v J J Wolf W Florence SF · Hard · 15 Oct 2022
• J J Wolf v Nick Kyrgios L US Open R32 · Hard · 3 Sep 2022
• J J Wolf v Dominic Thiem L Winston Salem R64 · Hard · 22 Aug 2022
• J J Wolf v Gijs Brouwer L Houston R16 · Clay · 6 Apr 2022
• J J Wolf v Sergiy Stakhovsky W Australian Open Q1 · Hard · 11 Jan 2022