RallyIQ

Game plan

Andrei Pavel v Paolo Lorenzi

Every number combines what Andrei Pavel does well with what Paolo Lorenzi allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Andrei Pavel wins, best of 3 78%90%: 49%–94% · best of 5: 83%
Serve points won 67.9% / 61.7% Andrei / Paolo · tour 63.8%
Strengths only, no similarity priors 78%serve 67.9% / 61.7%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Andrei Pavel's record against Paolo Lorenzi'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

CareerAndreiPaolo
Direction choice−0.18 ±0.09
better than 16%
−0.14 ±0.21
better than 20%
Shot selection+0.06 ±0.31
better than 60%
−0.03 ±0.20
better than 49%
Execution+0.16 ±0.60
better than 75%
+0.01 ±0.79
better than 68%
Points left on the table3.10 ±0.37
lower than 15%
2.74 ±0.31
lower than 37%

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.

Andrei Pavel serving

Deuce court

1st serveNowAndrei winsv PaoloMatchupOptimal
Wide43%71%76%74.0%±8.537% ▼
Body8%67%71%74.9%±12.50% ▼
T49%79%78%82.2%±7.363% ▲

Optimal v Paolo Lorenzi: +0.5±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +4.1 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowAndrei winsv PaoloMatchupOptimal
Wide53%65%80%73.3%±9.245% ▼
Body4%62%58%57.1%±16.70% ▼
T42%60%76%64.8%±10.155% ▲

Optimal v Paolo Lorenzi: +0.3±0.8 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.3 per 100 first serves in before the returner adjusts.

Paolo Lorenzi serving

Deuce court

1st serveNowPaolo winsv AndreiMatchupOptimal
Wide38%71%79%78.0%±8.334% ▼
Body9%58%68%63.7%±16.60% ▼
T53%80%78%82.8%±7.066% ▲

Optimal v Andrei Pavel: +0.7±0.9 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 serveNowPaolo winsv AndreiMatchupOptimal
Wide54%71%75%74.0%±8.952% ▼
Body12%70%64%70.8%±14.50% ▼
T35%75%73%76.0%±9.048% ▲

Optimal v Andrei Pavel: +0.5±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +1.7 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.

Andrei Pavel returning

1st serve to the forehand

ReturnNowTourOwnv PaoloValue
FH through the middle56%+4.3−2.0+0.2+2.5±3.0
FH down the line24%+1.7−1.5+2.5+2.7±4.5
FH crosscourt20%+5.5+1.0+2.3+8.9±4.3

Lean FH crosscourt: +5.0±4.0 per 100 returns v the current mix (144 returns charted)

1st serve to the backhand

ReturnNowTourOwnv PaoloValue
BH slice through the middle40%−4.2+1.1+0.9−2.2±2.5
BH through the middle22%+6.4−1.2+1.0+6.2±2.8
BH crosscourt13%+8.8−0.1−0.6+8.0±3.4
BH slice crosscourt11%+0.5+1.1+1.3+2.9±2.7
BH slice down the line9%−8.4−0.3−2.5−11.2±2.6

Lean BH crosscourt: +7.0±3.2 per 100 returns v the current mix (141 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv PaoloValue
FH through the middle50%−3.5+1.3−0.5−2.6±2.8
FH crosscourt33%−0.2+1.1−2.5−1.7±4.0
FH down the line17%−1.8−0.3−1.8−3.9±4.0

Lean FH crosscourt: +0.9±3.1 per 100 returns v the current mix (46 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv PaoloValue
BH crosscourt45%+1.0+1.7−0.4+2.3±3.3
BH through the middle31%−2.9−0.3+0.1−3.0±2.5
BH down the line13%−0.2−1.2−1.4−2.8±4.6
BH slice crosscourt11%−4.0+0.2+0.3−3.5±2.0

Lean BH crosscourt: +3.0±2.1 per 100 returns v the current mix (116 returns charted)

Paolo Lorenzi returning

1st serve to the forehand

ReturnNowTourOwnv AndreiValue
FH through the middle43%+4.3+0.8+0.1+5.1±3.0
FH slice through the middle18%−4.2+1.0±0.0−3.2±1.6
FH crosscourt15%+5.5+1.0+2.6+9.1±4.3
FH down the line13%+1.7+1.9−0.7+2.9±4.4
FH slice down the line6%−4.3±0.0±0.0−4.3±1.7

Lean FH crosscourt: +6.0±4.0 per 100 returns v the current mix (190 returns charted)

1st serve to the backhand

ReturnNowTourOwnv AndreiValue
BH through the middle39%+6.4+1.4+1.2+8.9±2.9
BH crosscourt23%+8.8+0.4−0.7+8.5±3.5
BH slice through the middle20%−4.2−0.1+0.6−3.7±2.5
BH slice crosscourt10%+0.5+0.4−0.3+0.6±2.7
BH down the line4%+4.2−1.2−1.9+1.1±3.4

Lean BH through the middle: +4.5±2.0 per 100 returns v the current mix (140 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv AndreiValue
BH crosscourt36%+1.0+4.8−0.4+5.4±3.3
BH through the middle36%−2.9+2.1+0.9+0.1±2.5
FH through the middle10%−2.9−0.1+0.1−2.9±2.2
FH inside-out8%+1.0+0.8+0.2+2.0±3.1
BH down the line5%−0.2±0.0+2.2+2.0±3.6

Lean BH crosscourt: +3.5±2.3 per 100 returns v the current mix (113 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.

Andrei Pavel

Favour

ShotEdgeOwnTheirs
FH to their backhand · rally+3.5±4.4+0.6+2.9
BH to their backhand · rally+1.8±3.6−0.4+2.1
BH to their backhand · serve +1+1.5±5.1+3.3−1.8
BH to the middle · rally+1.4±3.3±0.0+1.4
FH to their forehand · serve +1+0.4±6.0−3.6+4.0
FH to their forehand · rally+0.3±4.3−3.0+3.3

Avoid

ShotEdgeOwnTheirs
BH to the middle · return−1.2±3.7−2.3+1.1
BH to their backhand · return−0.6±4.9+0.6−1.1
FH to their backhand · serve +1−0.4±5.5−4.5+4.0
FH to the middle · rally−0.4±3.2−1.2+0.8
FH to the middle · return±0.0±3.9+0.1−0.1

Paolo Lorenzi

Favour

ShotEdgeOwnTheirs
FH to their backhand · serve +1+7.0±5.6+1.1+5.9
BH to the middle · return+3.5±3.8+0.6+2.8
BH to their backhand · return+2.8±5.0+3.4−0.5
BH to the middle · rally+2.4±3.3+2.1+0.3
FH to their forehand · rally+1.2±4.3+0.5+0.7
FH to their forehand · serve +1+1.1±6.2−0.9+2.1

Avoid

ShotEdgeOwnTheirs
FH to their backhand · rally−1.8±4.8+1.2−3.0
BH to their backhand · rally−0.3±3.4+1.4−1.7
BH to their forehand · rally+0.5±6.6+4.6−4.1
FH to the middle · rally+0.7±3.3+2.1−1.5
FH to the middle · return+0.8±3.7+0.5+0.2

Against Paolo Lorenzi-like opponents

Andrei Pavel vMatchesServe pts wonReturn pts won
All charted opponents–62.2%32.9%

Similar by tactical fingerprint: Jannik Sinner, Flavio Cobolli, Gael Monfils, Marton Fucsovics, Arthur Cazaux, Mattia Bellucci, Dominik Koepfer, Janko Tipsarevic, Marcos Baghdatis. When two players have rarely met, their records against these lookalikes fill the gap.