RallyIQ

Game plan

Nikola Milojevic v Jaume Munar

Every number combines what Nikola Milojevic does well with what Jaume Munar allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Nikola Milojevic wins, best of 3 77%90%: 37%–96% · best of 5: 82%
Serve points won 62.8% / 57.1% Nikola / Jaume · tour 63.8%
Strengths only, no similarity priors 77%serve 62.8% / 57.1%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerNikolaJaume
Direction choice−0.10 ±0.23
better than 31%
−0.01 ±0.08
better than 53%
Shot selection−0.14 ±0.18
better than 34%
−0.31 ±0.21
better than 22%
Execution−1.32 ±0.82
better than 15%
+0.60 ±0.36
better than 86%

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.

Nikola Milojevic serving

Deuce court

1st serveNowNikola winsv JaumeMatchupOptimal
Wide49%74%72%73.4%±8.562% ▲
Body9%58%54%48.5%±14.30% ▼
T41%69%71%65.3%±10.438% ▼

Optimal v Jaume Munar: +1.0±0.8 per 100 first serves (faults included) over the current mix. Serving wide every time would read +5.7 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowNikola winsv JaumeMatchupOptimal
Wide52%67%70%63.9%±9.265% ▲
Body11%53%63%53.8%±15.00% ▼
T37%65%69%61.7%±10.935% ▼

Optimal v Jaume Munar: +0.5±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.0 per 100 first serves in before the returner adjusts.

Jaume Munar serving

Deuce court

1st serveNowJaume winsv NikolaMatchupOptimal
Wide55%68%62%56.4%±9.558% ▲
Body10%65%62%64.7%±14.020% ▲
T35%73%60%57.7%±10.022% ▼

Optimal v Nikola Milojevic: +0.2±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving body every time would read +7.0 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowJaume winsv NikolaMatchupOptimal
Wide51%68%64%58.1%±10.251%
Body14%64%66%67.6%±12.827% ▲
T35%66%65%58.2%±10.022% ▼

Optimal v Nikola Milojevic: +0.3±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving body every time would read +8.1 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.

Nikola Milojevic returning

1st serve to the forehand

ReturnNowTourOwnv JaumeValue
FH through the middle66%+4.3+1.8+1.2+7.3±2.5
FH down the line16%+1.7+1.1+0.3+3.1±3.9
FH crosscourt13%+5.5−1.6+2.3+6.3±3.6
FH slice through the middle5%−4.2−1.1−0.5−5.8±1.9

Lean FH through the middle: +1.5±1.2 per 100 returns v the current mix (104 returns charted)

1st serve to the backhand

ReturnNowTourOwnv JaumeValue
BH through the middle56%+6.4−0.7−0.8+4.9±2.5
BH crosscourt29%+8.8+1.5+0.3+10.6±3.1
BH down the line9%+4.2+0.2+1.6+6.0±3.8
BH slice through the middle7%−4.2−0.1−1.1−5.4±2.1

Lean BH crosscourt: +4.7±2.6 per 100 returns v the current mix (90 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv JaumeValue
BH through the middle63%−2.9−0.4−1.6−4.9±2.4
BH crosscourt25%+1.0−0.5−1.1−0.5±2.8
BH down the line12%−0.2−0.9+3.3+2.2±4.3

Lean BH crosscourt: +2.4±2.6 per 100 returns v the current mix (67 returns charted, inside the 90% margin)

Jaume Munar returning

1st serve to the forehand

ReturnNowTourOwnv NikolaValue
FH through the middle44%+4.3−0.4−1.8+2.1±2.7
FH slice through the middle22%−4.2+0.7−0.5−4.0±1.9
FH down the line13%+1.7−0.2±0.0+1.4±4.0
FH crosscourt11%+5.5+2.3−0.4+7.4±4.1
FH slice down the line7%−4.3+1.8±0.0−2.4±2.3

Lean FH crosscourt: +6.7±3.9 per 100 returns v the current mix (701 returns charted)

1st serve to the backhand

ReturnNowTourOwnv NikolaValue
BH through the middle42%+6.4+2.3−0.4+8.3±2.5
BH crosscourt27%+8.8+1.2+0.8+10.8±3.1
BH slice through the middle13%−4.2−0.1−0.2−4.4±2.0
BH down the line10%+4.2+1.9+1.7+7.8±4.1
BH slice crosscourt6%+0.5−1.6±0.0−1.1±2.2

Lean BH crosscourt: +4.4±2.5 per 100 returns v the current mix (760 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv NikolaValue
FH through the middle44%−3.5+2.5−0.6−1.5±2.6
FH crosscourt25%−0.2+0.4−1.2−1.1±3.7
FH down the line21%−1.8+0.8±0.0−1.0±3.3
FH slice through the middle6%−12.7+0.8±0.0−11.9±1.3
FH slice down the line4%−9.4+0.6±0.0−8.8±1.4

Lean FH down the line: +1.3±3.0 per 100 returns v the current mix (170 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv NikolaValue
BH through the middle47%−2.9−0.7−1.0−4.6±2.3
BH crosscourt29%+1.0+1.3+0.1+2.4±3.1
BH down the line13%−0.2+2.2±0.0+1.9±3.5
FH through the middle6%−2.9−1.1−0.6−4.6±2.2
FH inside-in3%+0.6−0.1−1.2−0.8±3.3

Lean BH crosscourt: +3.9±2.5 per 100 returns v the current mix (456 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.

Nikola Milojevic

Favour

ShotEdgeOwnTheirs
FH to the middle · return+3.7±2.5+1.9+1.7
BH to their backhand · rally+3.6±3.0+3.1+0.4
FH to their forehand · rally+1.9±4.0−0.2+2.1
FH to the middle · rally−0.5±2.3−2.6+2.1
BH to the middle · return−1.1±3.0−1.3+0.2
FH to their backhand · rally−1.6±3.9−2.7+1.1

Avoid

ShotEdgeOwnTheirs
BH to the middle · rally−2.3±2.1−1.9−0.4
FH to their backhand · rally−1.6±3.9−2.7+1.1
BH to the middle · return−1.1±3.0−1.3+0.2
FH to the middle · rally−0.5±2.3−2.6+2.1
FH to their forehand · rally+1.9±4.0−0.2+2.1

Jaume Munar

Favour

ShotEdgeOwnTheirs
BH to their backhand · rally+3.8±2.5+2.5+1.3
BH to the middle · rally+0.9±2.0+1.8−0.9
BH to the middle · return+0.5±2.4+1.1−0.6
FH to the middle · rally−0.5±2.7+1.7−2.1
FH to their forehand · rally−4.2±4.1+0.4−4.6
FH to their backhand · serve +1−4.3±4.6−1.8−2.5

Avoid

ShotEdgeOwnTheirs
FH to their backhand · rally−4.7±4.0+0.6−5.3
FH to their backhand · serve +1−4.3±4.6−1.8−2.5
FH to their forehand · rally−4.2±4.1+0.4−4.6
FH to the middle · rally−0.5±2.7+1.7−2.1
BH to the middle · return+0.5±2.4+1.1−0.6

Against Jaume Munar-like opponents

Nikola Milojevic vMatchesServe pts wonReturn pts won
All charted opponents–59.8%48.4%

Similar by tactical fingerprint: Brandon Nakashima, Novak Djokovic, Jenson Brooksby, Karen Khachanov, Pablo Carreno Busta, Andy Murray, Bernabe Zapata Miralles, Mikhail Kukushkin, Carlos Berlocq, Guillermo Coria. When two players have rarely met, their records against these lookalikes fill the gap.