RallyIQ

Game plan

Lulu Sun v Serena Williams

Every number combines what Lulu Sun does well with what Serena Williams 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 6%90%: 1%–27% · best of 5: 3%
Serve points won 53.3% / 65.2% Lulu / Serena · tour 55.0%
Strengths only, no similarity priors 6%serve 53.3% / 65.2%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Serena Williams's tactical lookalikes and in their charted head-to-heads. 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

CareerLuluSerena
Direction choice−0.04 ±0.13
better than 48%
+0.34 ±0.04
better than 97%
Shot selection+0.31 ±0.35
better than 79%
+0.47 ±0.05
better than 93%
Execution−0.39 ±1.24
better than 39%
−0.04 ±0.27
better than 59%
Points left on the table2.91 ±0.34
lower than 16%
2.20 ±0.07
lower than 94%

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 SerenaMatchupOptimal
Wide30%67%65%65.6%±9.046% ▲
Body24%53%53%48.3%±9.29% ▼
T45%69%66%66.4%±7.645%

Optimal v Serena Williams: +1.3±1.0 per 100 first serves (faults included) over the current mix. Serving T every time would read +4.7 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowLulu winsv SerenaMatchupOptimal
Wide42%73%66%73.4%±7.143% ▲
Body16%62%57%63.0%±10.51% ▼
T42%84%61%82.3%±7.056% ▲

Optimal v Serena Williams: +0.6±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.7 per 100 first serves in before the returner adjusts.

Serena Williams serving

Deuce court

1st serveNowSerena winsv LuluMatchupOptimal
Wide50%70%70%74.3%±5.665% ▲
Body8%62%53%57.9%±9.80% ▼
T42%79%71%81.6%±6.335% ▼

Optimal v Lulu Sun: +1.0±0.8 per 100 first serves (faults included) over the current mix. Serving T every time would read +5.5 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowSerena winsv LuluMatchupOptimal
Wide45%75%71%79.4%±6.060% ▲
Body6%60%58%61.4%±10.10% ▼
T49%70%65%70.1%±6.640% ▼

Optimal v Lulu Sun: +0.6±0.8 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +5.7 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 SerenaValue
FH through the middle59%+4.2−0.2−1.3+2.6±2.4
FH crosscourt26%+5.3+0.9−1.4+4.8±3.5
FH down the line15%+1.5−3.0−4.0−5.4±3.4

Lean FH crosscourt: +2.8±3.0 per 100 returns v the current mix (95 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv SerenaValue
BH through the middle46%+6.0−1.6−1.0+3.4±2.2
BH crosscourt27%+7.7+2.1−4.1+5.7±3.2
BH slice through the middle12%−6.2±0.0−0.2−6.5±2.2
BH slice crosscourt8%−4.2−3.5−1.2−8.8±2.8
BH down the line7%+2.2−0.5−0.2+1.5±3.6

Lean BH crosscourt: +4.0±2.6 per 100 returns v the current mix (214 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv SerenaValue
BH crosscourt48%+1.5−1.3−2.1−1.9±3.1
BH through the middle46%−2.6−0.1−0.3−2.9±2.1
FH through the middle6%−2.7−0.5−0.9−4.0±1.5

Lean BH crosscourt: +0.6±1.9 per 100 returns v the current mix (87 returns charted, inside the 90% margin)

Serena Williams returning

1st serve to the forehand

ReturnNowTourOwnv LuluValue
FH through the middle49%+4.2−2.6−0.1+1.4±2.4
FH crosscourt36%+5.3−1.6−2.7+1.0±3.4
FH down the line11%+1.5−0.4−2.8−1.7±4.0
BH through the middle1%+5.2−3.4+1.2+3.0±2.8
FH slice through the middle1%−6.7−0.7−1.5−8.9±2.2

Lean BH through the middle: +2.2±3.3 per 100 returns v the current mix (2166 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv LuluValue
BH through the middle48%+6.0+0.3+1.2+7.5±2.2
BH crosscourt33%+7.7+1.3−1.4+7.7±2.8
BH down the line12%+2.2+1.1−5.2−2.0±4.2
BH slice through the middle3%−6.2−4.5+1.3−9.4±2.4
BH slice crosscourt2%−4.2−2.8+2.1−4.8±2.5

Lean BH crosscourt: +2.2±2.2 per 100 returns v the current mix (1939 returns charted, inside the 90% margin)

2nd serve to the forehand

ReturnNowTourOwnv LuluValue
FH crosscourt43%+0.5−0.5−1.6−1.6±3.3
FH through the middle37%−3.2−0.1+2.4−0.8±2.7
FH down the line18%−0.6+5.3−7.2−2.5±4.9
BH inside-in1%+2.0−1.4+2.5+3.0±3.5
BH through the middle1%−1.0−0.2−0.1−1.3±2.2

Lean FH through the middle: +0.6±2.4 per 100 returns v the current mix (750 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv LuluValue
BH crosscourt51%+1.5+0.9+2.5+4.8±3.0
BH through the middle30%−2.6−1.8−0.1−4.5±2.4
BH down the line17%−0.5+3.4−2.3+0.5±4.7
FH inside-in1%+0.7+0.7−1.6−0.2±3.5
FH inside-out1%+1.4+0.2−7.2−5.7±3.6

Lean BH crosscourt: +3.7±1.8 per 100 returns v the current mix (954 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 clay. Each player's clay record is shrunk toward their all-surface one, so a thin sample on it moves the numbers only a little.

Lulu Sun

Favour

ShotEdgeOwnTheirs
FH to their forehand · rally+2.3±4.4+0.5+1.8
FH to their forehand · serve +1+2.2±3.8+3.2−0.9
FH to the middle · rally+0.8±3.1+1.0−0.2
BH to their forehand · return+0.4±4.4+2.3−1.9
FH to their backhand · return +1−0.3±4.2+0.8−1.2
FH to the middle · serve +1−0.5±2.6−1.4+0.9

Avoid

ShotEdgeOwnTheirs
BH to their forehand · rally−2.6±4.2−1.0−1.6
FH to their backhand · rally−2.5±4.2−2.1−0.5
FH to the middle · return−2.3±3.0−1.6−0.7
BH to the middle · rally−1.8±2.8−3.8+2.0
BH to the middle · return−1.6±2.9−0.4−1.2

Serena Williams

Favour

ShotEdgeOwnTheirs
BH to their forehand · return+5.8±4.3+5.1+0.8
FH to their forehand · rally+5.3±4.1+0.1+5.2
FH to their backhand · return +1+5.2±4.2+1.7+3.5
FH to their forehand · serve +1+3.3±3.9±0.0+3.3
FH to their backhand · serve +1+3.0±4.6+2.0+1.0
BH to the middle · rally+1.0±2.8+1.1−0.1

Avoid

ShotEdgeOwnTheirs
FH to the middle · serve +1−3.1±2.9−2.4−0.6
FH to the middle · return−1.9±3.2−2.5+0.5
BH to their forehand · rally−0.9±4.1−1.1+0.3
FH to their backhand · rally−0.8±4.4+0.2−1.0
FH to the middle · rally±0.0±3.1+0.5−0.6

Against Serena Williams-like opponents

Lulu Sun vMatchesServe pts wonReturn pts won
All charted opponents–62.3%39.1%

Similar by tactical fingerprint: Naomi Osaka, Karolina Pliskova, Ekaterina Alexandrova, Sorana Cirstea, Elena Gabriela Ruse, Veronika Kudermetova, Anett Kontaveit, Daniela Hantuchova, Jelena Dokic, Lindsay Davenport. When two players have rarely met, their records against these lookalikes fill the gap.