RallyIQ

Game plan

Lulu Sun v Taylor Townsend

Every number combines what Lulu Sun does well with what Taylor Townsend allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Lulu Sun wins, best of 3 39%90%: 14%–69% · best of 5: 36%
Serve points won 61.2% / 63.5% Lulu / Taylor · tour 56.4%
Strengths only, no similarity priors 41%serve 61.5% / 63.2%

Each player's serve and return strength is fitted against every opponent they were charted against, so a record built on weak opponents counts for less. At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers. The result is then nudged by Lulu Sun's record against Taylor Townsend's tactical lookalikes and in their charted head-to-heads (lookalikes: −10.1 on serve, −8.5 on return vs expectation (97 points)). A game-by-game Markov chain turns point odds into match odds; the 90% range covers the uncertainty in the two strengths, not the nudges. Charted matches lean toward big events, so treat this as a scouting estimate, not a betting line.

Head to head, per 100 shots

CareerLuluTaylor
Direction choice−0.04 ±0.13
better than 48%
+0.03 ±0.07
better than 60%
Shot selection+0.31 ±0.35
better than 79%
+0.29 ±0.38
better than 78%
Execution−0.39 ±1.24
better than 39%
−0.66 ±0.65
better than 28%
Points left on the table2.91 ±0.34
lower than 16%
3.14 ±0.14
lower than 6%

Each player's career against an average tour player in the same position, adjusted for opponent strength, with a 90% margin (shots clustered by match). Points left on the table is the gap to the best-value direction for the same stroke, so lower is better. Percentiles are within each player's own tour. A side is highlighted only when the gap is larger than the margin on the difference.

Serve plan

The share of points the server wins when a first serve lands in that direction (hover a rate for its 90% interval; ± is the 90% margin). "Matchup" combines the server's rate with how this returner handles that serve. "Optimal" is the mix that wins most against this returner once they start reading a habit, at the response measured across the tour, and only within the range servers' habits actually vary. The gain over the current mix is how exploitable that mix is.

Lulu Sun serving

Deuce court

1st serveNowLulu winsv TaylorMatchupOptimal
Wide30%67%67%68.2%±9.746% ▲
Body24%53%54%49.5%±11.69% ▼
T45%69%73%73.5%±8.745%

Optimal v Taylor Townsend: +1.2±1.2 per 100 first serves (faults included) over the current mix. Serving T every time would read +7.5 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowLulu winsv TaylorMatchupOptimal
Wide42%73%70%76.6%±8.141%
Body16%62%64%69.7%±11.61% ▼
T42%84%64%84.0%±7.058% ▲

Optimal v Taylor Townsend: +1.2±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +5.4 per 100 first serves in before the returner adjusts.

Taylor Townsend serving

Deuce court

1st serveNowTaylor winsv LuluMatchupOptimal
Wide36%64%70%68.7%±8.151% ▲
Body24%55%53%50.4%±11.18% ▼
T41%65%71%68.0%±10.141%

Optimal v Lulu Sun: +1.3±1.2 per 100 first serves (faults included) over the current mix. Serving wide every time would read +4.6 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowTaylor winsv LuluMatchupOptimal
Wide44%69%71%73.8%±8.259% ▲
Body16%58%58%60.0%±11.81% ▼
T40%62%65%62.0%±9.540%

Optimal v Lulu Sun: +1.0±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +6.9 per 100 first serves in before the returner adjusts.

Return plan

Value of each return, in points per 100 returns against an average return of the same serve (direction, court, surface): the tour's result with that return, the returner's own edge with it, and what this server gives up when it comes back to that side. Returns with no charted direction are left out, so values compare with each other rather than with zero. Depth isn't a choice here: missed returns have no depth. Serve quality isn't charted, so a block through the middle partly reflects the serve that forced it.

Lulu Sun returning

1st serve to the forehand

ReturnNowTourOwnv TaylorValue
FH through the middle59%+4.2−0.2−1.3+2.7±3.0
FH crosscourt26%+5.3+0.9+2.3+8.5±4.3
FH down the line15%+1.5−3.0±0.0−1.4±4.3

Lean FH crosscourt: +4.9±3.6 per 100 returns v the current mix (95 returns charted)

1st serve to the backhand

ReturnNowTourOwnv TaylorValue
BH through the middle46%+6.0−1.6−1.1+3.3±2.6
BH crosscourt27%+7.7+2.1−0.2+9.6±3.7
BH slice through the middle12%−6.2±0.0−1.2−7.4±2.4
BH slice crosscourt8%−4.2−3.5−0.9−8.6±2.5
BH down the line7%+2.2−0.5+0.9+2.6±4.3

Lean BH crosscourt: +6.9±3.0 per 100 returns v the current mix (214 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv TaylorValue
BH crosscourt48%+1.5−1.3+1.2+1.4±3.4
BH through the middle46%−2.6−0.1−0.3−2.9±2.7
FH through the middle6%−2.7−0.5+0.4−2.7±2.1

Lean BH crosscourt: +2.2±2.2 per 100 returns v the current mix (87 returns charted)

Taylor Townsend returning

1st serve to the forehand

ReturnNowTourOwnv LuluValue
FH through the middle31%+4.2−1.2−0.1+2.9±3.2
FH slice through the middle25%−6.7−0.2−1.5−8.5±2.4
FH crosscourt19%+5.3+1.9−2.8+4.4±4.2
FH slice crosscourt16%−6.6+2.4±0.0−4.2±1.9
FH down the line8%+1.5−2.5−2.7−3.6±4.4

Lean FH crosscourt: +5.8±3.6 per 100 returns v the current mix (190 returns charted)

1st serve to the backhand

ReturnNowTourOwnv LuluValue
BH through the middle30%+6.0−0.2+1.2+7.0±2.8
BH slice through the middle21%−6.2+2.4+1.3−2.5±2.4
BH crosscourt20%+7.7−3.2−5.2−0.7±3.6
BH down the line14%+2.2±0.0−1.4+0.8±4.6
BH slice crosscourt9%−4.2+0.1−1.5−5.6±2.6

Lean BH through the middle: +6.5±2.3 per 100 returns v the current mix (385 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv LuluValue
FH crosscourt49%+0.5+2.0−7.2−4.6±4.2
FH through the middle34%−3.2−0.1+2.4−0.9±2.9
FH down the line17%−0.6−1.2−1.6−3.4±3.8

Lean FH crosscourt: −1.5±2.5 per 100 returns v the current mix (41 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv LuluValue
BH through the middle47%−2.6−0.4−0.1−3.1±2.8
BH crosscourt37%+1.5−3.4−2.3−4.2±3.6
BH down the line11%−0.5+0.2+2.5+2.1±4.9
FH through the middle6%−2.7−1.0+2.4−1.3±2.3

Lean BH down the line: +5.0±4.8 per 100 returns v the current mix (150 returns charted)

Rally plan

Edge, in points per 100 shots: the hitter's skill with the shot (own) plus how much the receiver usually gives up against it (theirs), both measured against the tour average on hard. Each player's hard record is shrunk toward their all-surface one, so a thin sample on it moves the numbers only a little.

Lulu Sun

Favour

ShotEdgeOwnTheirs
BH to their forehand · return+4.3±5.9+4.0+0.3
BH to their forehand · rally+3.3±5.9+0.2+3.1
FH to their backhand · rally+2.5±5.0+1.9+0.6
FH to their forehand · serve +1+1.9±5.6+3.0−1.1
FH to their forehand · rally−0.5±5.2+3.1−3.6
FH to the middle · rally−0.8±3.6+0.8−1.6

Avoid

ShotEdgeOwnTheirs
BH to the middle · rally−7.5±3.5−4.8−2.7
FH to the middle · return−6.3±3.7−4.1−2.2
BH to the middle · return−1.5±3.2−0.1−1.3
FH to the middle · serve +1−1.1±4.0−1.8+0.7
FH to their backhand · serve +1−1.1±5.5−0.2−0.9

Taylor Townsend

Favour

ShotEdgeOwnTheirs
BH to their forehand · return+3.2±6.0±0.0+3.2
FH to their backhand · return +1+1.8±5.9−4.0+5.8
FH to their forehand · rally+1.6±5.2−1.1+2.7
FH to their backhand · rally−0.3±5.1−1.1+0.9
FH to their backhand · serve +1−0.3±5.6−0.7+0.4
FH to the middle · serve +1−0.7±4.0−1.3+0.6

Avoid

ShotEdgeOwnTheirs
BH to the middle · rally−4.7±3.4−3.4−1.3
BH to their backhand · rally−3.6±5.2−0.9−2.7
FH to the middle · rally−2.9±3.8−1.8−1.1
FH to the middle · return−1.8±4.0−1.2−0.5
FH to their forehand · serve +1−1.5±5.7−5.6+4.2

Against Taylor Townsend-like opponents

Lulu Sun vMatchesServe pts wonReturn pts won
All charted opponents–62.3%39.1%
Players most similar to Taylor Townsend1 48.9%30.2%

Similar by tactical fingerprint: Karolina Muchova, Bianca Andreescu, Barbora Krejcikova, Anastasia Pavlyuchenkova, Olivia Gadecki, Xiyu Wang, Caroline Garcia, Barbora Strycova, Anna Lena Friedsam, Johanna Konta. When two players have rarely met, their records against these lookalikes fill the gap.