
2026 Women's Wimbledon Winner
核心摘要
根據「2026 Women's Wimbledon Winner」的最新預測市場資料,交易者已形成強烈共識。
目前,Aryna Sabalenka 以壓倒性的 22.5% 獲勝機率主導市場;Elena Rybakina 以 18% 位居第二,Mirra Andreeva 以 9.3% 排名第三。該市場的下注量已達 $7.7M,反映出市場的高度關注。
競爭梯隊拆解
為了更好地評估各潛在結果的位置,可依據隱含機率與合約定價將市場劃分為三個明顯的交易梯隊:
🥇 第一梯隊:絕對領跑者
- Aryna Sabalenka (22.5%):Aryna Sabalenka 目前擁有最高機率,深受訂單簿青睞。看好該結果的交易者面對的「Buy Yes」合約價為 23¢,顯示出市場的高度確信。僅該合約就已產生 $18.9K 的成交量。
🥈 第二梯隊:主要挑戰者
- Elena Rybakina (18%):作為最可行的替代選項,Elena Rybakina 保持著 18% 的成真機率,其「Buy Yes」份額目前成交價為 18¢。
- Mirra Andreeva (9.3%):以 9.3% 的機率位列第三,市場對 Mirra Andreeva 持謹慎懷疑態度,除非勢頭轉變,否則視其為外圍黑馬。
🥉 第三梯隊:長尾選項(合計約 50.3%)
在前三名之外,還有大量宏觀變數與冷門結果被持續追蹤。儘管單個機率偏低,但它們是投機交易者的重要對沖:
- 替代選項:包括 Iga Świątek (9%)、Amanda Anisimova (5.8%),以及 Coco Gauff (4.6%)。
- 投機成交:儘管統計機率偏低,像 Jessica Pegula 這類長尾合約仍吸引著可觀的關注。
完整訂單簿與定價面板
下表列出了該預測池中所有結果的合約價格、機率與市場深度的完整拆解:
| 排名 | 預測結果 | 獲勝機率 | 成交量 | 買入 Yes(成本) | 買入 No(成本) |
|---|---|---|---|---|---|
| 1 | Aryna Sabalenka | 22.5% | $18.9K | 23¢ | 78¢ |
| 2 | Elena Rybakina | 18.0% | $26.3K | 18¢ | 82¢ |
| 3 | Mirra Andreeva | 9.3% | $792.5K | 9¢ | 91¢ |
| 4 | Iga Świątek | 9.0% | $272.7K | 9¢ | 91¢ |
| 5 | Amanda Anisimova | 5.8% | $1.0M | 6¢ | 94¢ |
| 6 | Coco Gauff | 4.5% | $116.8K | 5¢ | 95¢ |
| 7 | Jessica Pegula | 3.0% | $258.6K | 3¢ | 97¢ |
| 8 | Elina Svitolina | 2.8% | $18.6K | 3¢ | 97¢ |
| 9 | Emma Raducanu | 1.9% | $245.4K | 2¢ | 98¢ |
| 10 | Marta Kostyuk | 1.9% | $41.4K | 2¢ | 98¢ |
| 11 | Karolína Muchová | 1.7% | $166.4K | 2¢ | 98¢ |
| 12 | Barbora Krejčíková | 1.6% | $45.1K | 2¢ | 98¢ |
| 13 | Diana Shnaider | 1.2% | $43.7K | 1¢ | 99¢ |
| 14 | Markéta Vondroušová | 1.1% | $14.9K | 1¢ | 99¢ |
| 15 | Madison Keys | 1.1% | $149.4K | 1¢ | 99¢ |
| 16 | Linda Nosková | 0.9% | $67.4K | 1¢ | 99¢ |
| 17 | Belinda Bencic | 0.8% | $40.0K | 1¢ | 99¢ |
| 18 | Naomi Osaka | 0.8% | $13.6K | 1¢ | 99¢ |
| 19 | Donna Vekić | 0.8% | $33.5K | 1¢ | 99¢ |
| 20 | Qinwen Zheng | 0.7% | $25.0K | 1¢ | 99¢ |
| 21 | Anna Kalinskaya | 0.7% | $52.9K | 1¢ | 99¢ |
| 22 | Jasmine Paolini | 0.4% | $248.3K | 0¢ | 100¢ |
| 23 | Liudmila Samsonova | 0.4% | $100.2K | 0¢ | 100¢ |
| 24 | Emma Navarro | 0.4% | $27.0K | 0¢ | 100¢ |
| 25 | Clara Tauson | 0.4% | $447.3K | 0¢ | 100¢ |
| 26 | Jelena Ostapenko | 0.4% | $901.2K | 0¢ | 100¢ |
| 27 | Ekaterina Alexandrova | 0.4% | $48.7K | 0¢ | 100¢ |
| 28 | Tatjana Maria | 0.4% | $14.3K | 0¢ | 100¢ |
| 29 | Dayana Yastremska | 0.4% | $8.2K | 0¢ | 100¢ |
| 30 | Elise Mertens | 0.4% | $38.5K | 0¢ | 100¢ |
| 31 | Ons Jabeur | 0.3% | $145.7K | 0¢ | 100¢ |
| 32 | Leylah Fernandez | 0.3% | $11.6K | 0¢ | 100¢ |
| 33 | Xinyu Wang | 0.3% | $29.0K | 0¢ | 100¢ |
| 34 | Anastasia Pavlyuchenkova | 0.3% | $43.8K | 0¢ | 100¢ |
| 35 | Yulia Putintseva | 0.3% | $30.4K | 0¢ | 100¢ |
| 36 | Marie Bouzková | 0.3% | $38.8K | 0¢ | 100¢ |
| 37 | Ashlyn Krueger | 0.2% | $735.1K | 0¢ | 100¢ |
| 38 | Paula Badosa | 0.1% | $88.8K | 0¢ | 100¢ |
| 39 | Maya Joint | 0.1% | $317.2K | 0¢ | 100¢ |
| 40 | Olga Danilović | 0.1% | $8.5K | 0¢ | 100¢ |
| 41 | McCartney Kessler | 0.1% | $11.4K | 0¢ | 100¢ |
| 42 | Solana Sierra | 0.1% | $56.9K | 0¢ | 100¢ |
| 43 | Sonay Kartal | 0.1% | $48.9K | 0¢ | 100¢ |
| 44 | Beatriz Haddad Maia | 0.1% | $33.7K | 0¢ | 100¢ |
| 45 | Laura Siegemund | 0.1% | $9.0K | 0¢ | 100¢ |
| 46 | Maria Sakkari | 0.1% | $10.9K | 0¢ | 100¢ |
裁決規則
Wimbledon 2026 is scheduled for June 29 - July 12, 2026.
This market will resolve to the player that wins the 2026 Wimbledon Women’s Singles Tournament.
If at any point it becomes impossible for a listed player to win the 2026 Wimbledon Women’s Singles Tournament per the rules of the tournament, the corresponding market will resolve to “No”.
If the 2026 Wimbledon Women’s Singles Tournament is cancelled, postponed after August 31, 2026, or there is otherwise no winner declared within that timeframe, this market will resolve to “Other”.
The primary resolution source will be official information from Wimbledon (https://www.wimbledon.com/index.html); however, a consensus of credible reporting may also be used.
AI 估值分析:發現市場錯誤定價與 EV 差
人群共識與投機成交塑造了更宏觀的預測市場,而我們的量化演算法提供了資料驅動的反向視角。透過分析基本面訊號、底層趨勢與歷史分布,我們的 AI 估值模型為每個結果獨立測算出一個「公允價值」機率。
將該公允價值與當前交易價值對比,可揭示出重大背離——即期望值(EV)差。正 EV 差代表統計上被低估的結果,而負 EV 差則提示市場可能存在反應過度。
頂級 AI Alpha 與錯誤定價套利機會
根據最新一輪資料模型測算,以下幾個關鍵合約存在顯著偏離:
- 最佳價值標的(最高 EV):我們的模型將 Jasmine Paolini 識別為盤面上最具價值的機會。市場僅給予其 0.4% 的交易機率,而我們 AI 的公允價值評估為 51.9%——形成可觀的 +51.4% EV 差。
- 被忽視的黑馬:其他值得注意的偏離包括 Xinyu Wang(EV 差:+50.8%)以及 Donna Vekić(EV 差:+50.7%)。儘管我們的預測模型給予更強的統計支撐,這些長尾機會仍被即時訂單簿大幅低估。
| Market | Trade Value | Fair Value | EV Gap |
|---|---|---|---|
| Aryna Sabalenka | 22.5% | 46.3% | +23.8% |
| Elena Rybakina | 18.0% | 29.8% | +11.8% |
| Mirra Andreeva | 9.3% | 35.4% | +26.1% |
| Iga Świątek | 9.0% | 45.1% | +36.1% |
| Amanda Anisimova | 5.8% | 35.7% | +29.9% |
| Coco Gauff | 4.5% | 34.5% | +29.9% |
| Jessica Pegula | 3.0% | 31.7% | +28.7% |
| Elina Svitolina | 2.8% | 35.8% | +33.0% |
| Emma Raducanu | 1.9% | 41.3% | +39.4% |
| Marta Kostyuk | 1.9% | 40.5% | +38.6% |
| Karolína Muchová | 1.7% | 35.8% | +34.2% |
| Barbora Krejčíková | 1.6% | 40.3% | +38.7% |
| Diana Shnaider | 1.2% | 51.8% | +50.6% |
| Markéta Vondroušová | 1.1% | 36.7% | +35.6% |
| Madison Keys | 1.1% | 35.6% | +34.6% |
| Linda Nosková | 0.9% | 39.3% | +38.4% |
| Belinda Bencic | 0.8% | 27.6% | +26.8% |
| Naomi Osaka | 0.8% | 36.8% | +36.0% |
| Donna Vekić | 0.8% | 51.4% | +50.7% |
| Qinwen Zheng | 0.7% | 33.0% | +32.4% |
| Anna Kalinskaya | 0.7% | 38.2% | +37.6% |
| Jasmine PaoliniBest EV | 0.4% | 51.9% | +51.4% |
| Liudmila Samsonova | 0.4% | 39.7% | +39.2% |
| Emma Navarro | 0.4% | 37.6% | +37.2% |
| Clara Tauson | 0.4% | 45.9% | +45.4% |
| Jelena Ostapenko | 0.4% | 41.8% | +41.4% |
| Ekaterina Alexandrova | 0.4% | 44.1% | +43.8% |
| Tatjana Maria | 0.4% | 39.2% | +38.8% |
| Dayana Yastremska | 0.4% | 49.5% | +49.1% |
| Elise Mertens | 0.4% | 33.9% | +33.6% |
| Ons Jabeur | 0.3% | 49.6% | +49.3% |
| Leylah Fernandez | 0.3% | 36.3% | +36.1% |
| Xinyu Wang | 0.3% | 51.0% | +50.8% |
| Anastasia Pavlyuchenkova | 0.3% | 38.3% | +38.0% |
| Yulia Putintseva | 0.3% | 49.5% | +49.3% |
| Marie Bouzková | 0.3% | 39.8% | +39.5% |
| Ashlyn Krueger | 0.2% | 38.6% | +38.4% |
| Paula Badosa | 0.1% | 33.3% | +33.1% |
| Maya Joint | 0.1% | 38.1% | +38.0% |
| Olga Danilović | 0.1% | 38.6% | +38.4% |
| McCartney Kessler | 0.1% | 46.5% | +46.3% |
| Solana Sierra | 0.1% | 48.1% | +47.9% |
| Sonay Kartal | 0.1% | 38.3% | +38.2% |
| Beatriz Haddad Maia | 0.1% | 37.4% | +37.2% |
| Laura Siegemund | 0.1% | 50.5% | +50.4% |
| Maria Sakkari | 0.1% | 39.7% | +39.5% |
交易動態
以下是該事件的交易動態。
Jun 30, 2026
- 07:55 AM——$0.00
Sold 3038.5 Yes for Will Dayana Yastremska be the 2026 Women’s Wimbledon Winner? at 0
- 07:55 AM——$0.00
Bought 3038.5 Yes for Will Dayana Yastremska be the 2026 Women’s Wimbledon Winner? at 0
- 07:55 AM——$0.00
Sold 3038.5 Yes for Will Dayana Yastremska be the 2026 Women’s Wimbledon Winner? at 0
- 07:55 AM——$0.00
Bought 3038.5 Yes for Will Dayana Yastremska be the 2026 Women’s Wimbledon Winner? at 0
- 07:54 AM——$0.00
Sold 3037.76 Yes for Will Dayana Yastremska be the 2026 Women’s Wimbledon Winner? at 0
- 07:54 AMNOnorthdrawer$0.00
Sold 0.74 Yes for Will Dayana Yastremska be the 2026 Women’s Wimbledon Winner? at 0
- 07:54 AM——$0.00
Bought 3038.5 Yes for Will Dayana Yastremska be the 2026 Women’s Wimbledon Winner? at 0
- 07:54 AM——$0.00
Sold 3038.5 Yes for Will Dayana Yastremska be the 2026 Women’s Wimbledon Winner? at 0
- 07:54 AM——$0.00
Bought 3038.5 Yes for Will Dayana Yastremska be the 2026 Women’s Wimbledon Winner? at 0
- 07:54 AM——$0.00
Sold 3038.5 Yes for Will Dayana Yastremska be the 2026 Women’s Wimbledon Winner? at 0
- 07:54 AM——$0.00
Bought 3038.5 Yes for Will Dayana Yastremska be the 2026 Women’s Wimbledon Winner? at 0
- 07:51 AM——$67.84
Sold 67.84 No for Will Jasmine Paolini be the 2026 Women’s Wimbledon Winner? at 1
正在押注該事件的鯨魚錢包
常見問題
目前市場對「2026 Women's Wimbledon Winner」的共識是什麼?
截至最新更新,Aryna Sabalenka 以 22.5% 的獲勝機率領跑,其次是 Elena Rybakina(18%),以及 Mirra Andreeva(9.3%)。該市場總成交量已達 $7.7M,顯示出充足的流動性與高交易參與度。
AI 公允價值與即時市場交易價值有何不同?
即時市場交易價值反映的是公眾情緒、訂單簿動能與投機資金。我們的 AI 公允價值則由量化模型獨立計算,剔除情緒炒作、專注底層數據。兩者出現顯著背離時即形成 EV 差,提示市場對某個結果可能存在錯誤定價。
目前哪個結果的期望值(EV)最高?
最新一輪測算顯示,Jasmine Paolini 是最顯著的錯誤定價。市場對其隱含機率僅給到 0.4%,而我們的 AI 測算其公允價值為 51.9%——形成 +51.4% 的期望值差,是該市場中最具價值的標的。
長尾資料中是否藏有高價值的黑馬選項?
當然有。除了頭部結果之外,我們的模型在排名靠後的選項中發現了被低估的潛力。Xinyu Wang 擁有 +50.8% 的正 EV 差,Donna Vekić 則為 +50.7%。儘管量化層面更有支撐,這些合約仍被即時訂單簿低估。
