RallyIQ

Game plan

Sebastian Ofner v Jared Donaldson

Every number combines what Sebastian Ofner does well with what Jared Donaldson allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Sebastian Ofner wins, best of 3 31%90%: 7%–69% · best of 5: 27%
Serve points won 61.0% / 65.0% Sebastian / Jared · tour 63.8%
Strengths only, no similarity priors 31%serve 61.0% / 65.0%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerSebastianJared
Direction choice+0.19 ±0.11
better than 94%
+0.02 ±0.10
better than 63%
Shot selection+0.10 ±0.16
better than 65%
−0.01 ±0.23
better than 52%
Execution−1.43 ±0.81
better than 12%
+1.08 ±0.69
better than 97%

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.

Sebastian Ofner serving

Deuce court

1st serveNowSebastian winsv JaredMatchupOptimal
Wide46%70%73%70.6%±9.659% ▲
Body7%58%61%55.9%±17.60% ▼
T46%71%70%65.7%±11.141% ▼

Optimal v Jared Donaldson: +0.3±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +3.3 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowSebastian winsv JaredMatchupOptimal
Wide54%68%74%69.4%±10.467% ▲
Body4%70%63%70.1%±16.00% ▼
T42%68%67%62.2%±11.633% ▼

Optimal v Jared Donaldson: +0.1±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving body every time would read +3.7 per 100 first serves in before the returner adjusts.

Jared Donaldson serving

Deuce court

1st serveNowJared winsv SebastianMatchupOptimal
Wide38%80%77%83.1%±7.731% ▼
Body6%65%63%65.1%±17.30% ▼
T56%75%74%73.8%±9.469% ▲

Optimal v Sebastian Ofner: +0.5±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +6.2 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowJared winsv SebastianMatchupOptimal
Wide51%70%74%71.5%±10.564% ▲
Body6%61%57%54.8%±18.20% ▼
T43%67%67%61.1%±11.636% ▼

Optimal v Sebastian Ofner: +0.6±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +5.5 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.

Sebastian Ofner returning

1st serve to the forehand

ReturnNowTourOwnv JaredValue
FH through the middle44%+4.3−6.4+5.0+2.9±3.1
FH down the line36%+1.7−5.0+3.8+0.5±4.4
FH crosscourt16%+5.5−3.1+2.5+4.9±4.0
FH slice through the middle4%−4.2+0.4±0.0−3.8±1.0

Lean FH crosscourt: +2.8±3.9 per 100 returns v the current mix (154 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv JaredValue
BH through the middle43%+6.4−4.2+4.1+6.3±2.9
BH crosscourt36%+8.8−0.6+2.7+10.8±3.3
BH down the line12%+4.2−4.0+3.6+3.8±3.9
BH slice through the middle5%−4.2−0.7+1.9−3.0±1.9
BH slice crosscourt4%+0.5−1.0+1.6+1.1±2.2

Lean BH crosscourt: +3.9±2.5 per 100 returns v the current mix (139 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv JaredValue
FH through the middle44%−3.5−1.1+2.4−2.2±2.8
FH crosscourt29%−0.2+0.2−0.9−0.9±3.8
FH down the line27%−1.8−0.1+3.4+1.5±4.2

Lean FH through the middle: −1.4±2.2 per 100 returns v the current mix (45 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv JaredValue
BH through the middle52%−2.9−2.6+2.3−3.2±2.6
BH crosscourt38%+1.0±0.0−0.2+0.8±3.2
BH down the line10%−0.2+2.6+1.9+4.3±4.3

Lean BH crosscourt: +1.7±2.4 per 100 returns v the current mix (97 returns charted, inside the 90% margin)

Jared Donaldson returning

1st serve to the forehand

ReturnNowTourOwnv SebastianValue
FH through the middle50%+4.3+3.6−0.6+7.3±3.1
FH down the line26%+1.7+5.5−1.2+6.0±4.3
FH crosscourt12%+5.5+2.9+0.8+9.3±4.0
FH slice through the middle8%−4.2+0.7−1.9−5.4±1.7
FH slice down the line4%−4.3+0.3−0.4−4.4±1.8

Lean FH through the middle: +1.6±2.0 per 100 returns v the current mix (117 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv SebastianValue
BH through the middle49%+6.4+3.2+1.3+10.9±2.8
BH crosscourt27%+8.8+3.0−2.9+8.9±3.5
BH down the line24%+4.2+2.7−3.9+3.0±4.3

Lean BH through the middle: +2.5±2.0 per 100 returns v the current mix (94 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv SebastianValue
BH through the middle51%−2.9+2.1+0.6−0.3±2.5
BH crosscourt32%+1.0+0.7+0.9+2.6±3.1
BH down the line17%−0.2+2.0+0.1+1.9±3.9

Lean BH crosscourt: +1.6±2.5 per 100 returns v the current mix (53 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.

Sebastian Ofner

Favour

ShotEdgeOwnTheirs
FH to their backhand · rally+4.0±4.6+1.3+2.7
FH to the middle · return+2.9±4.2−4.5+7.5
BH to the middle · return+1.6±3.7−3.8+5.4
FH to their forehand · return +1+0.4±6.4−1.6+2.0
FH to their backhand · serve +1−2.7±5.6−1.2−1.5
BH to their backhand · rally−2.9±3.9+0.8−3.7

Avoid

ShotEdgeOwnTheirs
FH to the middle · rally−6.6±3.7−5.6−1.0
FH to their forehand · serve +1−4.0±6.0−1.6−2.4
FH to their forehand · rally−3.5±4.5+1.2−4.7
BH to the middle · rally−3.2±3.6−3.6+0.4
BH to their backhand · rally−2.9±3.9+0.8−3.7

Jared Donaldson

Favour

ShotEdgeOwnTheirs
BH to the middle · return+10.2±3.8+6.7+3.5
FH to the middle · return+8.6±4.1+6.7+1.9
FH to their forehand · rally+5.2±4.6+0.9+4.2
FH to the middle · rally+3.3±3.7+2.2+1.1
BH to the middle · rally+1.7±3.4−0.3+1.9
FH to their backhand · serve +1+0.5±5.7+0.1+0.5

Avoid

ShotEdgeOwnTheirs
FH to their backhand · rally−3.8±4.8+0.3−4.0
BH to their backhand · rally−3.3±3.8+0.1−3.5
FH to their forehand · serve +1+0.2±5.8+1.5−1.3
FH to their backhand · serve +1+0.5±5.7+0.1+0.5
BH to the middle · rally+1.7±3.4−0.3+1.9

Against Jared Donaldson-like opponents

Sebastian Ofner vMatchesServe pts wonReturn pts won
All charted opponents–59.3%35.0%

Similar by tactical fingerprint: Sebastian Korda, Jack Draper, Borna Coric, David Goffin, Kyle Edmund, Peter Gojowczyk, Janko Tipsarevic, Nicolas Almagro, Marat Safin. When two players have rarely met, their records against these lookalikes fill the gap.