RallyIQ

Game plan

Su Wei Hsieh v Elena Vesnina

Every number combines what Su Wei Hsieh does well with what Elena Vesnina allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Su Wei Hsieh wins, best of 3 71%90%: 36%–92% · best of 5: 75%
Serve points won 57.7% / 53.6% Su / Elena · tour 56.3%
Strengths only, no similarity priors 71%serve 57.4% / 53.3%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Su Wei Hsieh's record against Elena Vesnina's tactical lookalikes and in their charted head-to-heads (lookalikes: +7.2 on serve, −8.8 on return vs expectation (119 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

CareerSuElena
Direction choice+0.33 ±0.09
better than 96%
+0.07 ±0.20
better than 68%
Shot selection−0.59 ±0.14
better than 9%
+0.03 ±0.21
better than 47%
Execution+0.24 ±0.61
better than 70%
−0.91 ±0.99
better than 21%

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.

Su Wei Hsieh serving

Deuce court

1st serveNowSu winsv ElenaMatchupOptimal
Wide48%66%63%63.5%±8.763% ▲
Body20%54%55%52.0%±12.15% ▼
T32%60%75%67.6%±9.932%

Optimal v Elena Vesnina: +0.9±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +5.1 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowSu winsv ElenaMatchupOptimal
Wide45%66%65%66.2%±9.158% ▲
Body20%60%55%58.0%±12.15% ▼
T35%61%67%63.3%±10.137% ▲

Optimal v Elena Vesnina: +0.4±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.7 per 100 first serves in before the returner adjusts.

Elena Vesnina serving

Deuce court

1st serveNowElena winsv SuMatchupOptimal
Wide40%60%66%60.4%±9.440%
Body22%52%49%43.4%±11.77% ▼
T38%64%72%69.1%±8.853% ▲

Optimal v Su Wei Hsieh: +1.4±1.1 per 100 first serves (faults included) over the current mix. Serving T every time would read +9.1 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowElena winsv SuMatchupOptimal
Wide48%61%66%60.6%±9.548%
Body27%50%59%52.6%±11.912% ▼
T25%58%68%61.3%±11.040% ▲

Optimal v Su Wei Hsieh: +0.7±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +2.7 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.

Su Wei Hsieh returning

1st serve to the forehand

ReturnNowTourOwnv ElenaValue
FH slice through the middle27%−6.7+0.5±0.0−6.2±1.7
FH crosscourt23%+5.3+4.5+1.6+11.3±4.2
FH through the middle22%+4.2+0.1−0.8+3.5±3.1
FH slice crosscourt18%−6.6−1.1±0.0−7.8±2.0
FH down the line5%+1.5+2.1+4.4+8.1±4.4

Lean FH crosscourt: +11.2±3.4 per 100 returns v the current mix (386 returns charted)

1st serve to the backhand

ReturnNowTourOwnv ElenaValue
BH through the middle47%+6.0+2.9+2.0+11.0±2.8
BH crosscourt35%+7.7+0.9+1.9+10.6±3.5
BH down the line9%+2.2+2.4−0.1+4.5±3.8
BH slice down the line3%−12.5−1.5−1.6−15.6±2.5
BH slice through the middle3%−6.2−0.9−0.1−7.2±2.2

Lean BH through the middle: +2.7±2.0 per 100 returns v the current mix (338 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv ElenaValue
FH crosscourt52%+0.5+1.5+1.0+3.1±4.3
FH through the middle23%−3.2+0.6+2.1−0.5±2.9
FH down the line15%−0.6+4.9+5.3+9.6±4.6
FH slice through the middle10%−15.2±0.0±0.0−15.2±1.3

Lean FH crosscourt: +1.7±2.3 per 100 returns v the current mix (88 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv ElenaValue
BH crosscourt47%+1.5+0.4+0.3+2.1±3.5
BH through the middle30%−2.6+0.7+0.7−1.2±2.7
BH down the line23%−0.5+0.8+0.3+0.6±4.7

Lean BH crosscourt: +1.3±2.3 per 100 returns v the current mix (207 returns charted, inside the 90% margin)

Elena Vesnina returning

1st serve to the forehand

ReturnNowTourOwnv SuValue
FH crosscourt39%+5.3+4.1+1.3+10.7±4.1
FH through the middle35%+4.2+1.0+0.7+5.8±2.7
FH down the line13%+1.5−2.1+2.5+1.9±4.1
FH slice through the middle8%−6.7−0.2+0.5−6.4±1.9
FH slice crosscourt5%−6.6−0.5+0.6−6.5±2.0

Lean FH crosscourt: +5.1±2.8 per 100 returns v the current mix (98 returns charted)

1st serve to the backhand

ReturnNowTourOwnv SuValue
BH through the middle35%+6.0+1.1+1.8+8.9±2.5
BH crosscourt33%+7.7−1.3±0.0+6.4±3.4
BH down the line16%+2.2−2.8+3.9+3.3±4.3
BH slice through the middle11%−6.2−0.1−2.5−8.8±2.3
BH slice crosscourt6%−4.2−1.0−0.8−6.0±2.3

Lean BH through the middle: +4.4±2.1 per 100 returns v the current mix (103 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv SuValue
BH crosscourt48%+1.5+0.4+1.8+3.7±3.4
BH through the middle30%−2.6−1.8+0.2−4.2±2.6
BH down the line21%−0.5−3.0+1.3−2.3±4.8

Lean BH crosscourt: +3.7±2.2 per 100 returns v the current mix (66 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.

Su Wei Hsieh

Favour

ShotEdgeOwnTheirs
BH to the middle · return+4.2±2.5+2.0+2.3
FH to their forehand · rally+3.3±3.0+0.1+3.2
BH to their backhand · return+3.3±3.3+1.1+2.2
FH to the middle · return+1.6±2.9+1.0+0.5
FH to their backhand · rally+1.6±3.7+2.2−0.6
FH to their forehand · serve +1+1.5±3.9+0.6+1.0

Avoid

ShotEdgeOwnTheirs
BH to their backhand · rally−1.3±2.9−1.0−0.2
BH to the middle · rally+1.2±2.3+0.3+0.9
FH to the middle · rally+1.3±2.5+0.6+0.7
FH to their forehand · serve +1+1.5±3.9+0.6+1.0
FH to their backhand · rally+1.6±3.7+2.2−0.6

Elena Vesnina

Favour

ShotEdgeOwnTheirs
FH to their forehand · return+6.4±4.1+4.2+2.2
BH to their backhand · return +1+1.7±3.5+1.0+0.7
BH to the middle · return+1.7±2.3−0.4+2.1
FH to their forehand · rally+1.4±3.0+2.4−1.0
BH to their backhand · return+1.1±3.1+0.1+1.0
FH to their forehand · return +1+1.0±3.8+0.7+0.2

Avoid

ShotEdgeOwnTheirs
FH to their forehand · serve +1−7.4±3.8−6.2−1.2
FH to their backhand · rally−6.4±3.6−5.6−0.9
BH to the middle · serve +1−3.1±2.6−2.6−0.4
BH to their forehand · rally−3.0±4.3−2.7−0.3
FH to the middle · rally−1.1±2.4−2.3+1.2

Against Elena Vesnina-like opponents

Su Wei Hsieh vMatchesServe pts wonReturn pts won
All charted opponents–55.2%42.1%
Players most similar to Elena Vesnina1 57.6%28.3%

Similar by tactical fingerprint: Iga Swiatek, Magda Linette, Sorana Cirstea, Varvara Gracheva, Olivia Gadecki, Veronika Kudermetova, Anhelina Kalinina, Anett Kontaveit, Anna Lena Friedsam. When two players have rarely met, their records against these lookalikes fill the gap.