RallyIQ

Game plan

Laura Pigossi v Tatjana Maria

Every number combines what Laura Pigossi does well with what Tatjana Maria allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Laura Pigossi wins, best of 3 33%90%: 7%–72% · best of 5: 29%
Serve points won 57.2% / 60.6% Laura / Tatjana · tour 58.1%
Strengths only, no similarity priors 34%serve 57.1% / 60.3%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Laura Pigossi's record against Tatjana Maria's tactical lookalikes and in their charted head-to-heads (head-to-head: +1.4 on serve, −5.3 on return vs expectation (188 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

CareerLauraTatjana
Direction choice−0.29 ±0.23
better than 8%
+0.17 ±0.11
better than 82%
Shot selection+0.32 ±0.33
better than 81%
−4.80 ±0.55
better than 0%
Execution−0.70 ±2.24
better than 27%
+1.80 ±0.44
better than 98%

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.

Laura Pigossi serving

Deuce court

1st serveNowLaura winsv TatjanaMatchupOptimal
Wide38%63%61%57.3%±10.938%
Body44%53%61%56.6%±10.529% ▼
T18%61%66%59.0%±12.333% ▲

Optimal v Tatjana Maria: +0.5±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +1.8 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowLaura winsv TatjanaMatchupOptimal
Wide27%62%58%54.3%±11.542% ▲
Body45%59%58%60.8%±10.630% ▼
T28%66%56%58.0%±12.228%

Optimal v Tatjana Maria: +0.1±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving body every time would read +2.5 per 100 first serves in before the returner adjusts.

Tatjana Maria serving

Deuce court

1st serveNowTatjana winsv LauraMatchupOptimal
Wide38%70%65%69.1%±10.254% ▲
Body14%60%56%58.9%±14.30% ▼
T47%75%66%73.3%±8.946% ▼

Optimal v Laura Pigossi: +1.0±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +3.6 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowTatjana winsv LauraMatchupOptimal
Wide24%74%56%66.3%±11.439% ▲
Body11%54%54%52.0%±15.20% ▼
T65%68%67%70.4%±10.461% ▼

Optimal v Laura Pigossi: +0.8±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +3.0 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.

Laura Pigossi returning

1st serve to the forehand

ReturnNowTourOwnv TatjanaValue
FH through the middle52%+4.2+0.3−0.1+4.3±2.8
FH crosscourt36%+5.3+3.2+0.4+8.9±4.1
FH down the line11%+1.5−1.8−6.0−6.3±3.7

Lean FH crosscourt: +4.1±3.0 per 100 returns v the current mix (44 returns charted)

1st serve to the backhand

ReturnNowTourOwnv TatjanaValue
BH through the middle52%+6.0−0.6−2.0+3.4±2.9
BH crosscourt35%+7.7+2.9−2.5+8.1±3.6
BH down the line12%+2.2−1.9−6.2−5.9±4.1

Lean BH crosscourt: +4.2±2.8 per 100 returns v the current mix (82 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv TatjanaValue
BH crosscourt28%+1.5+0.2±0.0+1.7±3.3
BH through the middle28%−2.6−0.6+2.2−1.0±2.5
FH through the middle19%−2.7±0.0+0.4−2.3±2.2
FH inside-out15%+1.4−1.3−0.1±0.0±3.6
FH inside-in11%+0.7−0.4+3.2+3.5±3.8

Tatjana Maria returning

1st serve to the forehand

ReturnNowTourOwnv LauraValue
FH slice through the middle44%−6.7+3.5−0.8−4.1±2.2
FH slice crosscourt19%−6.6+4.2+1.7−0.8±2.5
FH through the middle14%+4.2−3.2−0.1+0.8±3.2
FH crosscourt11%+5.3+0.7−0.7+5.4±4.2
FH slice down the line9%−10.5+3.7±0.0−6.8±2.3

Lean FH crosscourt: +7.2±3.9 per 100 returns v the current mix (254 returns charted)

1st serve to the backhand

ReturnNowTourOwnv LauraValue
BH slice through the middle46%−6.2+4.6+0.4−1.3±2.3
BH slice crosscourt31%−4.2+3.2±0.0−1.0±2.8
BH slice down the line9%−12.5+3.9±0.0−8.6±2.5
BH through the middle8%+6.0−1.0+0.1+5.1±2.9
BH crosscourt4%+7.7+0.9+0.1+8.8±3.4

Lean BH crosscourt: +9.6±3.5 per 100 returns v the current mix (356 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv LauraValue
FH slice through the middle33%−15.2+3.0−0.1−12.3±2.1
FH crosscourt25%+0.5−0.4−3.2−3.1±3.7
FH through the middle16%−3.2−0.3−0.7−4.1±2.3
FH slice crosscourt14%−14.9+1.4±0.0−13.5±1.6
FH slice down the line12%−14.1+1.2±0.0−12.9±1.9

Lean FH crosscourt: +5.8±2.9 per 100 returns v the current mix (76 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv LauraValue
BH slice through the middle34%−11.7+1.2−0.6−11.1±2.0
BH slice crosscourt30%−7.5+0.2+0.4−6.9±2.4
BH through the middle7%−2.6−0.5−0.4−3.5±2.1
BH slice down the line7%−10.6+4.5±0.0−6.1±2.6
BH down the line6%−0.5−1.3−0.9−2.7±3.9

Lean BH through the middle: +3.4±2.2 per 100 returns v the current mix (202 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 grass. Each player's grass record is shrunk toward their all-surface one, so a thin sample on it moves the numbers only a little.

Laura Pigossi

Favour

ShotEdgeOwnTheirs
BH to their backhand · return+1.1±4.6+3.3−2.2
FH to the middle · return+0.2±3.6+0.9−0.7
BH to the middle · return−0.1±3.4+0.1−0.2
BH to the middle · rally−1.6±3.1+1.4−3.0
FH to the middle · rally−3.2±3.1−0.4−2.8
FH to their forehand · serve +1−3.8±4.8−0.3−3.5

Avoid

ShotEdgeOwnTheirs
BH to their backhand · rally−7.6±3.7−1.1−6.5
FH to their backhand · serve +1−6.3±5.0+0.8−7.0
FH to their forehand · rally−5.9±4.1−0.9−5.0
FH to their backhand · rally−5.1±4.4−2.0−3.1
FH to their forehand · serve +1−3.8±4.8−0.3−3.5

Tatjana Maria

Favour

ShotEdgeOwnTheirs
BH slice to their backhand · rally+4.8±3.1+3.9+1.0
FH to the middle · rally+2.6±2.7+0.5+2.1
BH slice to the middle · rally+1.3±2.7+1.7−0.5
FH to their forehand · rally+0.2±4.3+1.2−1.0
FH to their backhand · rally−3.8±4.0−0.9−2.9
FH to the middle · return−5.0±3.7−5.4+0.4

Avoid

ShotEdgeOwnTheirs
FH to the middle · return−5.0±3.7−5.4+0.4
FH to their backhand · rally−3.8±4.0−0.9−2.9
FH to their forehand · rally+0.2±4.3+1.2−1.0
BH slice to the middle · rally+1.3±2.7+1.7−0.5
FH to the middle · rally+2.6±2.7+0.5+2.1

Against Tatjana Maria-like opponents

Laura Pigossi vMatchesServe pts wonReturn pts won
All charted opponents–55.3%45.2%
Tatjana Maria (charted head-to-head)1 57.3%35.9%

Similar by tactical fingerprint: Karolina Muchova, Viktorija Golubic, Diane Parry, Alison Van Uytvanck, Kirsten Flipkens, Ashleigh Barty, Francesca Schiavone, Roberta Vinci, Justine Henin, Amelie Mauresmo. When two players have rarely met, their records against these lookalikes fill the gap.

Charted head-to-head