RallyIQ

Game plan

Rafael Nadal v Taylor Fritz

Every number combines what Rafael Nadal does well with what Taylor Fritz allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Rafael Nadal wins, best of 3 74%90%: 54%–88% · best of 5: 79%
Serve points won 68.1% / 62.8% Rafael / Taylor · tour 63.4%
Strengths only, no similarity priors 68%serve 67.7% / 64.0%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Rafael Nadal's record against Taylor Fritz's tactical lookalikes and in their charted head-to-heads (lookalikes: +3.2 on serve, +4.4 on return vs expectation (1645 points); head-to-head: −7.8 on serve, −5.4 on return vs expectation (281 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

CareerRafaelTaylor
Direction choice+0.02 ±0.03
better than 62%
+0.03 ±0.04
better than 64%
Shot selection+0.38 ±0.05
better than 85%
−0.11 ±0.06
better than 38%
Execution+1.32 ±0.10
better than 99%
+0.66 ±0.27
better than 88%
Points left on the table2.66 ±0.04
lower than 43%
2.35 ±0.06
lower than 78%

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: Rafael Nadal +1.69, Taylor Fritz −0.29. 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.

Rafael Nadal serving

Deuce court

1st serveNowRafael winsv TaylorMatchupOptimal
Wide31%75%74%75.6%±2.144% ▲
Body18%64%63%64.5%±4.55% ▼
T51%69%77%72.0%±2.251%

Optimal v Taylor Fritz: +1.2±0.5 per 100 first serves (faults included) over the current mix. Serving wide every time would read +3.9 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowRafael winsv TaylorMatchupOptimal
Wide54%71%76%75.1%±2.167% ▲
Body18%67%63%66.9%±4.75% ▼
T28%75%74%76.5%±2.128%

Optimal v Taylor Fritz: +0.8±0.5 per 100 first serves (faults included) over the current mix. Serving T every time would read +2.5 per 100 first serves in before the returner adjusts.

Taylor Fritz serving

Deuce court

1st serveNowTaylor winsv RafaelMatchupOptimal
Wide48%76%66%70.3%±2.138% ▼
Body3%66%58%60.6%±7.40% ▼
T49%82%74%81.5%±1.962% ▲

Optimal v Rafael Nadal: +0.4±0.3 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 serveNowTaylor winsv RafaelMatchupOptimal
Wide52%80%71%79.0%±2.065% ▲
Body4%62%57%55.5%±7.20% ▼
T45%76%66%70.4%±2.335% ▼

Optimal v Rafael Nadal: +0.4±0.3 per 100 first serves (faults included) over the current mix. 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.

Rafael Nadal returning

1st serve to the forehand

ReturnNowTourOwnv TaylorValue
FH through the middle46%+4.3+0.4−0.6+4.0±1.1
FH crosscourt31%+5.5+2.4−0.9+7.1±2.3
FH down the line16%+1.7−0.8−0.6+0.2±2.6
FH slice through the middle3%−4.2+0.1±0.0−4.1±1.7
FH slice crosscourt2%−4.3+7.5+1.3+4.4±2.7
BH through the middle0%+6.6+0.4+0.6+7.6±2.1

Lean BH through the middle: +3.5±2.3 per 100 returns v the current mix (5711 returns charted)

1st serve to the backhand

ReturnNowTourOwnv TaylorValue
BH through the middle38%+6.4−0.2+0.6+6.8±1.1
BH crosscourt19%+8.8−1.2−4.5+3.2±2.2
BH slice through the middle17%−4.2+2.1−0.3−2.4±1.3
BH down the line15%+4.2+3.3−0.5+7.1±2.5
BH slice down the line7%−8.4+5.7−1.8−4.6±2.3
FH through the middle1%+7.7+2.5−0.6+9.6±2.0

Lean FH through the middle: +6.1±2.2 per 100 returns v the current mix (9039 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv TaylorValue
FH crosscourt48%−0.2+0.7−1.6−1.1±2.5
FH through the middle33%−3.5−2.6+0.4−5.7±1.4
FH down the line17%−1.8+3.8−1.1+1.0±3.3
BH inside-out1%+0.9+0.3−0.9+0.3±3.0
FH slice through the middle1%−12.7−0.2+0.6−12.2±2.1

Lean FH down the line: +3.4±3.0 per 100 returns v the current mix (2037 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv TaylorValue
BH through the middle35%−2.9+2.0+0.8−0.1±0.9
BH crosscourt28%+1.0−1.0−0.4−0.4±1.9
BH down the line17%−0.2+1.8−0.9+0.7±2.3
FH through the middle9%−2.9+1.2+0.4−1.3±1.3
FH inside-in8%+0.6+1.5−1.6+0.5±2.9

Lean BH down the line: +0.9±2.0 per 100 returns v the current mix (5854 returns charted, inside the 90% margin)

Taylor Fritz returning

1st serve to the forehand

ReturnNowTourOwnv RafaelValue
FH through the middle46%+4.3+1.3−0.9+4.6±1.0
FH down the line22%+1.7+0.4+0.1+2.1±2.3
FH slice through the middle12%−4.2+0.1±0.0−4.1±1.6
FH crosscourt10%+5.5+1.8+1.7+9.0±2.5
FH slice down the line7%−4.3+0.4−3.6−7.5±2.9

Lean FH crosscourt: +6.5±2.4 per 100 returns v the current mix (2456 returns charted)

1st serve to the backhand

ReturnNowTourOwnv RafaelValue
BH through the middle47%+6.4+2.1+0.7+9.2±1.0
BH crosscourt29%+8.8−1.8+4.3+11.2±1.6
BH down the line9%+4.2+1.7−1.0+4.9±2.8
BH slice through the middle9%−4.2−2.5+1.9−4.8±1.6
BH slice crosscourt3%+0.5−3.9+3.6+0.2±2.5

Lean BH crosscourt: +4.1±1.3 per 100 returns v the current mix (2022 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv RafaelValue
FH through the middle51%−3.5−0.7−0.4−4.6±1.5
FH down the line25%−1.8−3.4+0.4−4.8±3.2
FH crosscourt14%−0.2+1.8−0.7+0.8±3.2
FH slice through the middle4%−12.7−1.6+0.1−14.2±2.4
FH slice down the line3%−9.4+1.8−1.2−8.8±2.5

Lean FH crosscourt: +5.2±3.0 per 100 returns v the current mix (707 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv RafaelValue
BH through the middle42%−2.9+0.8+0.1−2.0±1.1
BH crosscourt34%+1.0−0.8−0.2±0.0±1.6
BH down the line13%−0.2−1.3−1.3−2.8±3.3
FH through the middle6%−2.9+0.2−0.4−3.1±1.8
FH inside-in3%+0.6−0.5−0.7−0.7±3.4

Lean BH crosscourt: +1.5±1.2 per 100 returns v the current mix (1417 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.

Rafael Nadal

Favour

ShotEdgeOwnTheirs
FH volley to the middle · rally+10.3±5.4+8.0+2.2
FH drop shot to their backhand · rally+10.2±4.9+7.1+3.1
BH volley to their forehand · rally+9.3±4.6+11.2−1.9
Smash to their backhand · rally+7.7±3.9+2.8+4.9
FH slice to their backhand · return+7.5±2.8+5.7+1.8
FH volley to their backhand · serve +1+6.9±5.3+5.5+1.4

Avoid

ShotEdgeOwnTheirs
FH slice to their forehand · rally−3.4±3.2−4.6+1.1
FH slice to their forehand · return +1−2.7±3.9−2.9+0.2
FH to their forehand · return−2.2±2.1−1.2−1.1
BH slice to their backhand · serve +1−2.2±2.4−1.1−1.1
BH slice to their backhand · return−1.7±1.9−0.6−1.1

Taylor Fritz

Favour

ShotEdgeOwnTheirs
BH to the middle · return+3.7±0.7+1.7+2.0
BH to their forehand · return+3.5±2.0+0.4+3.1
FH to their backhand · return +1+2.8±1.8+0.9+1.8
BH to their forehand · rally+2.1±1.3+1.3+0.8
FH slice to their backhand · return+1.8±2.4+0.2+1.6
FH to their backhand · serve +1+1.7±1.2+1.5+0.3

Avoid

ShotEdgeOwnTheirs
BH volley to their backhand · rally−10.7±4.4−5.5−5.2
FH volley to their forehand · rally−9.5±4.0−2.5−7.0
BH slice to their backhand · rally−7.5±1.6−3.7−3.8
FH slice to their forehand · rally−6.4±3.4−2.2−4.3
BH slice to their backhand · return−6.1±2.4−3.8−2.3

Against Taylor Fritz-like opponents

Rafael Nadal vMatchesServe pts wonReturn pts won
All charted opponents–66.0%40.1%
Players most similar to Taylor Fritz11 69.4%44.5%
Taylor Fritz (charted head-to-head)2 59.9%30.6%

Similar by tactical fingerprint: Jannik Sinner, Gael Monfils, Karen Khachanov, Marcos Giron, Alexander Shevchenko, Rinky Hijikata, Miomir Kecmanovic, Arthur Cazaux, Borna Coric, Andreas Seppi. When two players have rarely met, their records against these lookalikes fill the gap.

Charted head-to-head