Game plan
Kaia Kanepi v Ekaterina Alexandrova
Every number combines what Kaia Kanepi does well with what Ekaterina Alexandrova allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective
Forecast
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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Kaia Kanepi's record against Ekaterina Alexandrova's tactical lookalikes and in their charted head-to-heads (lookalikes: +2.2 on serve, +7.1 on return vs expectation (237 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
| Career | Kaia | Ekaterina |
|---|---|---|
| Direction choice | −0.07 ±0.12 better than 41% | +0.16 ±0.04 better than 81% |
| Shot selection | −0.07 ±0.17 better than 38% | +0.22 ±0.06 better than 70% |
| Execution | −0.92 ±0.56 better than 20% | −0.41 ±0.36 better than 38% |
| Points left on the table | 2.82 ±0.16 lower than 22% | 2.21 ±0.06 lower than 94% |
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.
Kaia Kanepi serving
Deuce court
| 1st serve | Now | Kaia wins | v Ekaterina | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 55% | 70% | 67% | 70.7%±6.5 | 70% ▲ |
| Body | 10% | 53% | 59% | 54.0%±11.0 | 0% ▼ |
| T | 35% | 68% | 68% | 67.5%±7.8 | 30% ▼ |
Optimal v Ekaterina Alexandrova: +0.5±0.8 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.8 per 100 first serves in before the returner adjusts.
Ad court
| 1st serve | Now | Kaia wins | v Ekaterina | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 50% | 67% | 64% | 66.0%±7.2 | 45% ▼ |
| Body | 9% | 64% | 56% | 64.1%±11.2 | 0% ▼ |
| T | 40% | 69% | 66% | 70.9%±7.4 | 55% ▲ |
Optimal v Ekaterina Alexandrova: +0.3±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +3.1 per 100 first serves in before the returner adjusts.
Ekaterina Alexandrova serving
Deuce court
| 1st serve | Now | Ekaterina wins | v Kaia | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 44% | 70% | 77% | 80.6%±6.2 | 44% |
| Body | 21% | 59% | 68% | 69.6%±8.1 | 6% ▼ |
| T | 35% | 75% | 70% | 76.2%±6.6 | 50% ▲ |
Optimal v Kaia Kanepi: +0.2±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +3.8 per 100 first serves in before the returner adjusts.
Ad court
| 1st serve | Now | Ekaterina wins | v Kaia | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 46% | 73% | 77% | 82.7%±5.5 | 61% ▲ |
| Body | 15% | 59% | 64% | 66.7%±9.4 | 15% |
| T | 39% | 63% | 61% | 59.5%±8.8 | 24% ▼ |
Optimal v Kaia Kanepi: +1.2±0.9 per 100 first serves (faults included) over the current mix. Serving wide every time would read +11.4 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.
Kaia Kanepi returning
1st serve to the forehand
| Return | Now | Tour | Own | v Ekaterina | Value |
|---|---|---|---|---|---|
| FH through the middle | 38% | +4.2 | −1.5 | +0.8 | +3.4±2.6 |
| FH slice through the middle | 18% | −6.7 | +0.4 | +1.7 | −4.6±2.3 |
| FH crosscourt | 16% | +5.3 | +2.3 | +1.1 | +8.7±3.8 |
| FH down the line | 14% | +1.5 | −3.2 | −2.4 | −4.1±3.9 |
| FH slice down the line | 7% | −10.5 | ±0.0 | −0.3 | −10.8±2.9 |
Lean FH crosscourt: +8.4±3.4 per 100 returns v the current mix (104 returns charted)
1st serve to the backhand
| Return | Now | Tour | Own | v Ekaterina | Value |
|---|---|---|---|---|---|
| BH through the middle | 45% | +6.0 | +0.5 | −0.1 | +6.5±2.4 |
| BH crosscourt | 23% | +7.7 | −1.3 | −1.4 | +5.0±3.1 |
| BH down the line | 15% | +2.2 | +1.4 | −1.1 | +2.5±4.1 |
| BH slice crosscourt | 10% | −4.2 | −0.7 | −1.8 | −6.7±2.8 |
| BH slice through the middle | 6% | −6.2 | −1.6 | −0.9 | −8.8±2.1 |
Lean BH through the middle: +3.3±1.7 per 100 returns v the current mix (125 returns charted)
2nd serve to the backhand
| Return | Now | Tour | Own | v Ekaterina | Value |
|---|---|---|---|---|---|
| BH through the middle | 49% | −2.6 | −1.3 | −1.5 | −5.4±2.3 |
| BH crosscourt | 33% | +1.5 | +0.7 | +1.7 | +4.0±3.0 |
| BH down the line | 18% | −0.5 | −2.1 | −2.0 | −4.6±4.5 |
Lean BH crosscourt: +6.1±2.4 per 100 returns v the current mix (76 returns charted)
Ekaterina Alexandrova returning
1st serve to the forehand
| Return | Now | Tour | Own | v Kaia | Value |
|---|---|---|---|---|---|
| FH through the middle | 43% | +4.2 | −3.0 | −2.2 | −1.1±2.6 |
| FH crosscourt | 37% | +5.3 | −1.1 | −1.8 | +2.4±3.7 |
| FH down the line | 14% | +1.5 | +1.5 | −0.2 | +2.8±4.4 |
| FH slice crosscourt | 3% | −6.6 | −5.3 | ±0.0 | −11.9±1.9 |
| FH slice through the middle | 3% | −6.7 | −0.8 | −0.5 | −8.0±2.2 |
Lean FH down the line: +2.6±4.2 per 100 returns v the current mix (854 returns charted, inside the 90% margin)
1st serve to the backhand
| Return | Now | Tour | Own | v Kaia | Value |
|---|---|---|---|---|---|
| BH through the middle | 53% | +6.0 | −1.4 | −3.0 | +1.6±2.4 |
| BH crosscourt | 23% | +7.7 | −2.4 | +1.3 | +6.6±3.3 |
| BH down the line | 16% | +2.2 | −1.1 | +1.4 | +2.5±4.2 |
| BH slice through the middle | 5% | −6.2 | −1.0 | −2.0 | −9.2±2.5 |
| BH slice crosscourt | 3% | −4.2 | −4.6 | −0.3 | −9.0±2.5 |
Lean BH crosscourt: +4.6±2.9 per 100 returns v the current mix (1000 returns charted)
2nd serve to the forehand
| Return | Now | Tour | Own | v Kaia | Value |
|---|---|---|---|---|---|
| FH crosscourt | 42% | +0.5 | −2.3 | −1.5 | −3.3±4.2 |
| FH through the middle | 35% | −3.2 | +0.7 | +0.8 | −1.7±3.1 |
| FH down the line | 21% | −0.6 | +0.8 | +0.5 | +0.8±4.8 |
| FH slice crosscourt | 2% | −14.9 | −1.1 | ±0.0 | −15.9±1.2 |
Lean FH down the line: +2.9±4.3 per 100 returns v the current mix (243 returns charted, inside the 90% margin)
2nd serve to the backhand
| Return | Now | Tour | Own | v Kaia | Value |
|---|---|---|---|---|---|
| BH crosscourt | 43% | +1.5 | +0.2 | −1.3 | +0.4±3.2 |
| BH through the middle | 38% | −2.6 | −1.3 | +1.1 | −2.8±2.5 |
| BH down the line | 19% | −0.5 | −1.2 | −2.6 | −4.3±5.0 |
Lean BH crosscourt: +2.1±2.3 per 100 returns v the current mix (557 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.
Kaia Kanepi
Favour
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| BH to their forehand · rally | +3.1±5.2 | +3.8 | −0.7 |
| BH to their backhand · return | +2.8±4.0 | +2.3 | +0.5 |
| FH to the middle · rally | +1.0±2.6 | +0.9 | +0.1 |
| BH to their backhand · serve +1 | −0.2±4.4 | −0.7 | +0.6 |
| BH to the middle · serve +1 | −0.2±3.0 | −1.7 | +1.5 |
| BH to their backhand · rally | −0.8±3.6 | −0.6 | −0.2 |
Avoid
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| FH to their backhand · rally | −5.1±4.0 | −6.0 | +0.9 |
| BH to the middle · return | −4.8±2.5 | −3.0 | −1.8 |
| FH to their forehand · rally | −3.9±3.6 | −3.7 | −0.2 |
| FH to their forehand · return +1 | −2.6±4.8 | −1.4 | −1.2 |
| FH to the middle · serve +1 | −1.3±3.1 | −2.1 | +0.8 |
Ekaterina Alexandrova
Favour
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| FH to their backhand · rally | +8.0±4.3 | +0.3 | +7.8 |
| BH to their backhand · serve +1 | +3.1±4.4 | +0.4 | +2.7 |
| FH to the middle · rally | +2.0±2.7 | +0.3 | +1.7 |
| FH to their forehand · rally | +1.6±3.3 | +0.1 | +1.5 |
| BH to the middle · rally | +0.6±2.6 | −0.3 | +0.8 |
| BH to their backhand · rally | +0.1±3.4 | +0.3 | −0.2 |
Avoid
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| FH to their forehand · return +1 | −6.3±4.7 | −0.1 | −6.2 |
| FH to their forehand · return | −6.2±5.2 | −1.7 | −4.5 |
| FH to the middle · serve +1 | −6.1±3.4 | −4.8 | −1.3 |
| BH to the middle · return | −3.8±2.5 | −1.1 | −2.7 |
| FH to the middle · return | −3.7±3.1 | −2.1 | −1.6 |
Against Ekaterina Alexandrova-like opponents
| Kaia Kanepi v | Matches | Serve pts won | Return pts won |
|---|---|---|---|
| All charted opponents | – | 58.2% | 39.2% |
| Players most similar to Ekaterina Alexandrova | 1 | 54.0% | 43.4% |
Similar by tactical fingerprint: Naomi Osaka, Anastasia Potapova, Karolina Pliskova, Iga Swiatek, Sorana Cirstea, Dayana Yastremska, Veronika Kudermetova, Danielle Collins, Anett Kontaveit, Daniela Hantuchova. When two players have rarely met, their records against these lookalikes fill the gap.