RallyIQ

Game plan

Paolo Lorenzi v Brandon Nakashima

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

Forecast

Paolo Lorenzi wins, best of 3 21%90%: 6%–48% · best of 5: 15%
Serve points won 63.3% / 70.1% Paolo / Brandon · tour 65.7%
Strengths only, no similarity priors 21%serve 63.3% / 70.1%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerPaoloBrandon
Direction choice−0.14 ±0.21
better than 20%
−0.01 ±0.07
better than 54%
Shot selection−0.03 ±0.20
better than 49%
−0.11 ±0.14
better than 38%
Execution+0.01 ±0.79
better than 68%
+0.79 ±0.43
better than 91%
Points left on the table2.74 ±0.31
lower than 37%
2.50 ±0.10
lower than 60%

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.

Paolo Lorenzi serving

Deuce court

1st serveNowPaolo winsv BrandonMatchupOptimal
Wide38%71%74%72.7%±7.334% ▼
Body9%58%66%61.5%±13.00% ▼
T53%80%79%83.2%±5.266% ▲

Optimal v Brandon Nakashima: +1.0±0.8 per 100 first serves (faults included) over the current mix. Serving T every time would read +6.0 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowPaolo winsv BrandonMatchupOptimal
Wide54%71%75%73.7%±6.852% ▼
Body12%70%67%73.6%±10.50% ▼
T35%75%74%77.4%±6.948% ▲

Optimal v Brandon Nakashima: +0.5±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +2.4 per 100 first serves in before the returner adjusts.

Brandon Nakashima serving

Deuce court

1st serveNowBrandon winsv PaoloMatchupOptimal
Wide52%73%76%75.6%±6.244% ▼
Body5%64%71%71.9%±11.10% ▼
T43%78%78%80.8%±6.356% ▲

Optimal v Paolo Lorenzi: +0.2±0.7 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +3.2 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowBrandon winsv PaoloMatchupOptimal
Wide54%72%80%79.3%±6.550% ▼
Body9%69%58%65.3%±11.90% ▼
T37%72%76%75.7%±6.650% ▲

Optimal v Paolo Lorenzi: +0.6±0.8 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.6 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.

Paolo Lorenzi returning

1st serve to the forehand

ReturnNowTourOwnv BrandonValue
FH through the middle43%+4.3+0.8+1.7+6.7±2.5
FH slice through the middle18%−4.2+1.0−0.4−3.6±2.3
FH crosscourt15%+5.5+1.0+3.4+9.9±4.0
FH down the line13%+1.7+1.9−1.2+2.4±4.1
FH slice down the line6%−4.3±0.0+0.5−3.8±2.8

Lean FH crosscourt: +6.1±3.6 per 100 returns v the current mix (190 returns charted)

1st serve to the backhand

ReturnNowTourOwnv BrandonValue
BH through the middle39%+6.4+1.4−0.3+7.5±2.5
BH crosscourt23%+8.8+0.4+0.4+9.6±3.2
BH slice through the middle20%−4.2−0.1+0.4−3.9±2.4
BH slice crosscourt10%+0.5+0.4+2.1+3.1±2.8
BH down the line4%+4.2−1.2−0.3+2.7±3.6

Lean BH crosscourt: +5.3±2.7 per 100 returns v the current mix (140 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv BrandonValue
BH crosscourt36%+1.0+4.8−0.2+5.7±2.9
BH through the middle36%−2.9+2.1+2.3+1.5±2.2
FH through the middle10%−2.9−0.1−1.0−4.0±1.9
FH inside-out8%+1.0+0.8+1.7+3.5±3.2
BH down the line5%−0.2±0.0+1.4+1.2±3.8

Lean BH crosscourt: +3.1±2.1 per 100 returns v the current mix (113 returns charted)

Brandon Nakashima returning

1st serve to the forehand

ReturnNowTourOwnv PaoloValue
FH through the middle48%+4.3−1.1+0.2+3.3±2.6
FH down the line19%+1.7−1.5+2.5+2.6±4.2
FH slice through the middle14%−4.2+1.0−1.2−4.3±2.1
FH crosscourt11%+5.5+0.6+2.3+8.4±4.3
FH slice down the line4%−4.3+0.9+0.7−2.8±2.8

Lean FH crosscourt: +6.3±4.1 per 100 returns v the current mix (771 returns charted)

1st serve to the backhand

ReturnNowTourOwnv PaoloValue
BH through the middle38%+6.4+0.2+1.0+7.6±2.5
BH crosscourt29%+8.8+1.2−0.6+9.4±3.3
BH slice through the middle12%−4.2+0.1+0.9−3.1±2.5
BH down the line10%+4.2−1.3−1.0+1.9±4.4
BH slice crosscourt8%+0.5−3.1+1.3−1.3±2.9

Lean BH crosscourt: +4.4±2.6 per 100 returns v the current mix (648 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv PaoloValue
FH through the middle62%−3.5−1.0−0.5−4.9±2.7
FH crosscourt21%−0.2+3.2−2.5+0.5±4.4
FH down the line17%−1.8−0.7−1.8−4.2±4.6

Lean FH crosscourt: +4.2±3.9 per 100 returns v the current mix (228 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv PaoloValue
BH through the middle50%−2.9−0.6+0.1−3.4±2.3
BH crosscourt34%+1.0+1.9−0.4+2.5±3.1
BH down the line11%−0.2+0.7−1.4−1.0±5.0
BH slice through the middle4%−11.3−1.2±0.0−12.5±1.5
BH slice crosscourt2%−4.0−0.1+0.3−3.8±1.8

Lean BH crosscourt: +4.0±2.4 per 100 returns v the current mix (387 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.

Paolo Lorenzi

Favour

ShotEdgeOwnTheirs
FH to the middle · rally+5.1±3.2+1.6+3.6
BH to the middle · return+5.0±3.5+2.1+2.9
BH to their backhand · rally+4.7±3.8+1.3+3.3
FH to their forehand · serve +1+4.4±5.8−1.6+6.1
BH to the middle · serve +1+3.8±3.8+2.9+1.0
BH to their backhand · return+3.6±5.1+3.6±0.0

Avoid

ShotEdgeOwnTheirs
FH to their backhand · rally−2.9±4.7−1.0−1.9
BH to their forehand · rally−1.7±6.1−0.2−1.5
FH to their forehand · rally−1.7±4.7−2.1+0.3
FH to the middle · return−0.9±3.7−1.6+0.7
BH to their backhand · serve +1−0.4±4.9−0.8+0.4

Brandon Nakashima

Favour

ShotEdgeOwnTheirs
FH to their backhand · serve +1+11.0±5.1+5.8+5.2
FH to their forehand · serve +1+9.1±5.3+5.5+3.6
BH to their backhand · serve +1+6.4±4.9+2.7+3.7
FH to their forehand · rally+5.1±4.5+2.7+2.3
FH to their backhand · rally+4.3±4.5−1.7+6.1
FH to their forehand · return +1+4.0±6.4+2.0+2.0

Avoid

ShotEdgeOwnTheirs
BH to their forehand · rally−6.1±6.0−4.3−1.8
BH to their backhand · return−2.1±5.1−0.5−1.6
FH to their backhand · return−0.6±6.0−4.8+4.2
BH to the middle · rally−0.6±3.0−2.1+1.5
FH to their forehand · return+0.3±6.8+1.0−0.7

Against Brandon Nakashima-like opponents

Paolo Lorenzi vMatchesServe pts wonReturn pts won
All charted opponents–62.7%32.0%

Similar by tactical fingerprint: Casper Ruud, Karen Khachanov, Marcos Giron, Jaume Munar, Roberto Bautista Agut, Pablo Carreno Busta, Taylor Fritz, Rinky Hijikata, Gregoire Barrere, Tennys Sandgren. When two players have rarely met, their records against these lookalikes fill the gap.