RallyIQ

Game plan

Roberto Bautista Agut v Jaume Munar

Every number combines what Roberto Bautista Agut 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

Roberto Bautista Agut wins, best of 3 88%90%: 78%–94% · best of 5: 93%
Serve points won 68.1% / 58.5% Roberto / Jaume · tour 63.8%
Strengths only, no similarity priors 79%serve 66.1% / 59.7%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Roberto Bautista Agut's record against Jaume Munar's tactical lookalikes and in their charted head-to-heads (lookalikes: +5.1 on serve, +2.9 on return vs expectation (1928 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

CareerRobertoJaume
Direction choice+0.03 ±0.06
better than 65%
−0.01 ±0.08
better than 53%
Shot selection+0.08 ±0.08
better than 62%
−0.31 ±0.21
better than 22%
Execution+1.23 ±0.28
better than 98%
+0.60 ±0.36
better than 86%
Points left on the table2.30 ±0.09
lower than 85%
2.55 ±0.11
lower than 55%

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.

Structural compatibility

Expected edge per 100 rally shots from style alone: Roberto Bautista Agut +1.62, Jaume Munar +2.03. Each player's shot mix weighted by their own skill with each shot and by how much the other gives up against it. This is why some rankings gaps don't hold in a given matchup.

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.

Roberto Bautista Agut serving

Deuce court

1st serveNowRoberto winsv JaumeMatchupOptimal
Wide45%70%72%69.2%±4.158% ▲
Body9%64%54%54.8%±8.90% ▼
T47%72%71%68.6%±4.542% ▼

Optimal v Jaume Munar: +0.7±0.6 per 100 first serves (faults included) over the current mix. Serving wide every time would read +1.5 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowRoberto winsv JaumeMatchupOptimal
Wide56%70%70%66.5%±4.251% ▼
Body9%61%63%60.9%±9.40% ▼
T36%72%69%69.4%±4.949% ▲

Optimal v Jaume Munar: +0.7±0.7 per 100 first serves (faults included) over the current mix. Serving T every time would read +2.3 per 100 first serves in before the returner adjusts.

Jaume Munar serving

Deuce court

1st serveNowJaume winsv RobertoMatchupOptimal
Wide55%68%74%68.7%±3.968% ▲
Body10%65%60%62.4%±8.70% ▼
T35%73%76%73.9%±4.632% ▼

Optimal v Roberto Bautista Agut: +0.6±0.6 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +3.9 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowJaume winsv RobertoMatchupOptimal
Wide51%68%77%72.3%±4.164% ▲
Body14%64%53%54.7%±9.21% ▼
T35%66%72%66.0%±5.035%

Optimal v Roberto Bautista Agut: +0.7±0.8 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.7 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.

Roberto Bautista Agut returning

1st serve to the forehand

ReturnNowTourOwnv JaumeValue
FH through the middle47%+4.3+2.4+1.2+8.0±1.8
FH down the line19%+1.7+2.4+0.3+4.4±3.7
FH crosscourt15%+5.5+1.8+2.3+9.6±3.6
FH slice through the middle13%−4.2+0.4−0.5−4.3±2.2
FH slice crosscourt3%−4.3+0.3−0.7−4.7±2.8

Lean FH crosscourt: +4.4±3.3 per 100 returns v the current mix (1224 returns charted)

1st serve to the backhand

ReturnNowTourOwnv JaumeValue
BH through the middle42%+6.4+2.7−0.8+8.4±1.8
BH crosscourt20%+8.8+2.4+0.3+11.5±2.8
BH slice through the middle17%−4.2−0.1−1.1−5.4±2.3
BH down the line11%+4.2+4.9+1.6+10.7±4.2
BH slice crosscourt6%+0.5−1.2−1.9−2.6±2.9

Lean BH crosscourt: +6.0±2.4 per 100 returns v the current mix (1218 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv JaumeValue
FH through the middle48%−3.5+0.4+1.1−2.0±2.3
FH crosscourt24%−0.2+0.7+0.3+0.8±4.1
FH down the line24%−1.8+0.9−0.2−1.1±4.4
FH slice through the middle4%−12.7±0.0±0.0−12.7±1.3

Lean FH crosscourt: +2.3±3.5 per 100 returns v the current mix (289 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv JaumeValue
BH through the middle48%−2.9+0.6−1.6−3.9±1.9
BH crosscourt33%+1.0+1.7−1.1+1.6±2.5
BH down the line12%−0.2+0.8+3.3+3.9±4.8
FH through the middle3%−2.9−0.4+1.1−2.2±2.1
FH inside-out2%+1.0±0.0−0.2+0.8±3.3

Lean BH down the line: +4.9±4.3 per 100 returns v the current mix (961 returns charted)

Jaume Munar returning

1st serve to the forehand

ReturnNowTourOwnv RobertoValue
FH through the middle44%+4.3−0.4+2.6+6.4±1.9
FH slice through the middle22%−4.2+0.7+2.1−1.5±2.0
FH down the line13%+1.7−0.2+4.3+5.8±3.8
FH crosscourt11%+5.5+2.3−1.2+6.6±3.7
FH slice down the line7%−4.3+1.8−0.1−2.6±3.2

Lean FH crosscourt: +2.9±3.5 per 100 returns v the current mix (701 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv RobertoValue
BH through the middle42%+6.4+2.3+1.2+9.9±1.7
BH crosscourt27%+8.8+1.2+2.2+12.2±2.5
BH slice through the middle13%−4.2−0.1+3.1−1.1±2.1
BH down the line10%+4.2+1.9+1.3+7.5±4.3
BH slice crosscourt6%+0.5−1.6+1.1±0.0±2.9

Lean BH crosscourt: +4.3±2.0 per 100 returns v the current mix (760 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv RobertoValue
FH through the middle44%−3.5+2.5−0.4−1.3±2.5
FH crosscourt25%−0.2+0.4−1.4−1.3±4.2
FH down the line21%−1.8+0.8+1.5+0.6±4.4
FH slice through the middle6%−12.7+0.8±0.0−11.9±1.3
FH slice down the line4%−9.4+0.6−0.1−8.9±1.9

Lean FH down the line: +2.4±3.8 per 100 returns v the current mix (170 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv RobertoValue
BH through the middle47%−2.9−0.7+2.3−1.2±1.8
BH crosscourt29%+1.0+1.3+0.2+2.5±2.5
BH down the line13%−0.2+2.2+2.5+4.5±4.7
FH through the middle6%−2.9−1.1−0.4−4.4±2.1
FH inside-in3%+0.6−0.1−1.4−0.9±3.9

Lean BH down the line: +4.0±4.2 per 100 returns v the current mix (456 returns charted, inside the 90% margin)

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.

Roberto Bautista Agut

Favour

ShotEdgeOwnTheirs
BH to their forehand · return+7.0±5.0+3.7+3.3
FH to their forehand · rally+5.2±1.7+3.1+2.1
FH to the middle · serve +1+5.1±2.8+3.3+1.8
FH to their forehand · return+4.1±4.3+2.4+1.8
FH to the middle · return+4.1±1.9+2.4+1.7
BH to their forehand · rally+4.0±3.6+2.4+1.6

Avoid

ShotEdgeOwnTheirs
BH slice to their backhand · return +1−4.3±3.5−0.5−3.8
Smash to their backhand · rally−3.5±6.9−2.2−1.4
FH to their backhand · return +1−3.1±3.8−1.6−1.5
Smash to their forehand · rally−2.4±6.4−1.7−0.7
FH slice to the middle · rally−2.3±4.0−1.3−1.0

Jaume Munar

Favour

ShotEdgeOwnTheirs
BH to their backhand · return+5.9±2.7+3.9+2.0
FH to their forehand · return +1+5.7±3.8+4.1+1.6
FH to their backhand · return+5.5±4.2+2.2+3.3
BH to the middle · return +1+5.0±2.4+4.3+0.7
BH to their forehand · return+4.9±4.9+0.7+4.3
BH to the middle · return+4.5±1.7+1.1+3.5

Avoid

ShotEdgeOwnTheirs
FH slice to their forehand · rally−4.1±5.8−2.6−1.5
FH to their forehand · return−1.9±4.9−0.4−1.5
BH slice to the middle · return +1−1.7±3.2+1.2−2.9
FH to their backhand · return +1−0.9±4.1+0.8−1.8
BH slice to the middle · rally−0.6±2.2−0.3−0.4

Against Jaume Munar-like opponents

Roberto Bautista Agut vMatchesServe pts wonReturn pts won
All charted opponents–64.6%35.0%
Players most similar to Jaume Munar11 66.4%37.9%

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.