Game plan

Fernando Gonzalez v Taylor Fritz

Every number combines what Fernando Gonzalez 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

Fernando Gonzalez wins, best of 3 33%90%: 23%–44% · best of 5: 29%

Serve points won 65.5% / 69.2% Fernando / Taylor · tour 63.8%

Strengths only, no similarity priors 33%serve 65.5% / 69.2%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

Career	Fernando	Taylor
Direction choice	+0.17 ±0.06 better than 91%	+0.03 ±0.04 better than 64%
Shot selection	−0.36 ±0.12 better than 19%	−0.11 ±0.06 better than 38%
Execution	+0.08 ±0.42 better than 72%	+0.66 ±0.27 better than 88%
Points left on the table	2.62 ±0.11 lower than 47%	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.

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.

Fernando Gonzalez serving

Deuce court

1st serve	Now	Fernando wins	v Taylor	Matchup	Optimal
Wide	43%	71%	74%	71.8%±3.3	40% ▼
Body	10%	64%	63%	64.4%±6.7	0% ▼
T	47%	81%	77%	82.6%±2.7	60% ▲

Optimal v Taylor Fritz: +0.9±0.6 per 100 first serves (faults included) over the current mix. Serving T every time would read +6.5 per 100 first serves in before the returner adjusts.

Ad court

1st serve	Now	Fernando wins	v Taylor	Matchup	Optimal
Wide	56%	71%	76%	74.7%±3.1	69% ▲
Body	5%	63%	63%	63.3%±8.9	0% ▼
T	39%	73%	74%	74.8%±3.5	31% ▼

Optimal v Taylor Fritz: +0.3±0.5 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +0.6 per 100 first serves in before the returner adjusts.

Taylor Fritz serving

Deuce court

1st serve	Now	Taylor wins	v Fernando	Matchup	Optimal
Wide	48%	76%	76%	79.0%±3.0	61% ▲
Body	3%	66%	68%	70.3%±7.9	0% ▼
T	49%	82%	70%	78.6%±2.9	39% ▼

Optimal v Fernando Gonzalez: +0.6±0.4 per 100 first serves (faults included) over the current mix. Serving wide every time would read +0.5 per 100 first serves in before the returner adjusts.

Ad court

1st serve	Now	Taylor wins	v Fernando	Matchup	Optimal
Wide	52%	80%	73%	80.5%±2.7	42% ▼
Body	4%	62%	66%	64.7%±9.0	0% ▼
T	45%	76%	75%	78.3%±3.2	58% ▲

Optimal v Fernando Gonzalez: +0.5±0.5 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.

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.

Fernando Gonzalez returning

1st serve to the forehand

Return	Now	Tour	Own	v Taylor	Value
FH through the middle	29%	+4.3	−1.9	−0.6	+1.7±1.9
FH crosscourt	22%	+5.5	−5.3	−0.6	−0.4±3.3
FH down the line	19%	+1.7	−0.3	−0.9	+0.5±3.5
FH slice through the middle	17%	−4.2	+1.4	±0.0	−2.8±1.8
FH slice down the line	8%	−4.3	+3.0	+1.3	±0.0±3.1

Lean FH through the middle: +1.8±1.7 per 100 returns v the current mix (852 returns charted)

1st serve to the backhand

Return	Now	Tour	Own	v Taylor	Value
BH slice through the middle	26%	−4.2	+4.9	−0.3	+0.5±1.6
BH through the middle	23%	+6.4	−0.4	+0.6	+6.6±1.8
BH slice crosscourt	17%	+0.5	+3.9	−1.8	+2.5±2.4
BH down the line	14%	+4.2	−2.8	−4.5	−3.0±3.7
BH crosscourt	12%	+8.8	−1.2	−0.5	+7.1±2.8

Lean BH crosscourt: +4.8±2.6 per 100 returns v the current mix (1298 returns charted)

2nd serve to the forehand

Return	Now	Tour	Own	v Taylor	Value
FH crosscourt	35%	−0.2	−1.4	−1.1	−2.7±3.7
FH through the middle	28%	−3.5	−1.6	+0.4	−4.7±2.2
FH down the line	18%	−1.8	+3.1	−1.6	−0.3±4.1
FH slice down the line	10%	−9.4	+0.5	+1.1	−7.8±2.8
FH slice through the middle	7%	−12.7	+2.1	+0.6	−9.9±2.3

Lean FH down the line: +3.8±3.6 per 100 returns v the current mix (314 returns charted)

2nd serve to the backhand

Return	Now	Tour	Own	v Taylor	Value
BH slice crosscourt	19%	−4.0	+0.2	−0.1	−4.0±2.4
BH through the middle	17%	−2.9	+0.7	+0.8	−1.3±1.8
FH inside-out	15%	+1.0	+3.7	−1.6	+3.1±3.0
FH through the middle	12%	−2.9	−2.5	+0.4	−5.0±1.8
BH down the line	11%	−0.2	−4.5	−0.4	−5.1±4.3
FH inside-in	5%	+0.6	+5.0	−1.1	+4.5±3.9

Lean FH inside-in: +7.4±3.8 per 100 returns v the current mix (822 returns charted)

Taylor Fritz returning

1st serve to the forehand

Return	Now	Tour	Own	v Fernando	Value
FH through the middle	46%	+4.3	+1.3	+0.7	+6.2±1.6
FH down the line	22%	+1.7	+0.4	−1.0	+1.1±3.1
FH slice through the middle	12%	−4.2	+0.1	+2.4	−1.7±2.0
FH crosscourt	10%	+5.5	+1.8	+1.2	+8.6±3.4
FH slice down the line	7%	−4.3	+0.4	+0.4	−3.5±2.9

Lean FH crosscourt: +5.1±3.3 per 100 returns v the current mix (2456 returns charted)

1st serve to the backhand

Return	Now	Tour	Own	v Fernando	Value
BH through the middle	47%	+6.4	+2.1	±0.0	+8.5±1.6
BH crosscourt	29%	+8.8	−1.8	+0.3	+7.2±2.2
BH down the line	9%	+4.2	+1.7	+1.9	+7.9±3.9
BH slice through the middle	9%	−4.2	−2.5	+2.3	−4.4±2.0
BH slice crosscourt	3%	+0.5	−3.9	±0.0	−3.4±2.8

Lean BH through the middle: +2.6±1.1 per 100 returns v the current mix (2022 returns charted)

2nd serve to the forehand

Return	Now	Tour	Own	v Fernando	Value
FH through the middle	51%	−3.5	−0.7	+1.1	−3.1±2.0
FH down the line	25%	−1.8	−3.4	+1.3	−3.9±3.8
FH crosscourt	14%	−0.2	+1.8	−0.7	+0.8±4.1
FH slice through the middle	4%	−12.7	−1.6	+1.2	−13.1±2.2
FH slice down the line	3%	−9.4	+1.8	±0.0	−7.6±2.1

Lean FH crosscourt: +4.1±3.8 per 100 returns v the current mix (707 returns charted)

2nd serve to the backhand

Return	Now	Tour	Own	v Fernando	Value
BH through the middle	42%	−2.9	+0.8	+1.4	−0.7±1.5
BH crosscourt	34%	+1.0	−0.8	−0.7	−0.5±2.1
BH down the line	13%	−0.2	−1.3	−2.6	−4.1±4.5
FH through the middle	6%	−2.9	+0.2	+1.1	−1.5±2.1
FH inside-in	3%	+0.6	−0.5	−0.7	−0.7±4.2

Lean BH crosscourt: +0.7±1.6 per 100 returns v the current mix (1417 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.

Fernando Gonzalez

Favour

Shot	Edge	Own	Theirs
BH slice to their forehand · rally	+4.9±3.7	+2.1	+2.7
BH slice to their forehand · return	+4.5±4.6	+3.1	+1.4
BH slice to the middle · return	+4.3±1.9	+4.3	+0.1
FH slice to their backhand · return	+3.6±3.9	+1.6	+2.0
BH to their backhand · rally	+3.2±1.9	+2.0	+1.2
BH slice to their backhand · rally	+3.2±1.5	+2.9	+0.3

Avoid

Shot	Edge	Own	Theirs
BH to their forehand · return	−6.2±3.8	−4.2	−2.0
BH to their forehand · serve +1	−4.8±5.0	−2.8	−2.0
FH to the middle · return	−3.5±1.8	−2.0	−1.5
FH to their forehand · return	−3.4±3.6	−2.0	−1.5
FH volley to their backhand · rally	−3.4±7.8	+2.6	−6.0

Taylor Fritz

Favour

Shot	Edge	Own	Theirs
BH to their forehand · serve +1	+4.7±4.7	−0.1	+4.8
FH to their backhand · serve +1	+3.9±2.2	+2.0	+1.9
Smash to their forehand · rally	+3.5±6.0	+1.1	+2.4
BH to their forehand · return +1	+3.2±5.0	+0.6	+2.7
BH to the middle · return	+2.9±1.5	+2.1	+0.8
BH to their forehand · return	+2.7±4.2	+0.5	+2.1

Avoid

Shot	Edge	Own	Theirs
BH volley to their forehand · rally	−12.2±7.9	−7.9	−4.3
BH volley to their backhand · rally	−10.8±7.7	−7.7	−3.1
BH slice to their forehand · rally	−10.3±4.6	−4.4	−5.9
BH slice to their backhand · return	−6.7±3.5	−5.8	−0.9
FH volley to their backhand · rally	−6.6±8.1	−2.8	−3.7

Against Taylor Fritz-like opponents

Fernando Gonzalez v	Matches	Serve pts won	Return pts won
All charted opponents	–	63.6%	34.8%

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.