
2026 Women's Wimbledon Winner
核心摘要
根据「2026 Women's Wimbledon Winner」的最新预测市场数据,交易者已形成强烈共识。
目前,Aryna Sabalenka 以压倒性的 22.5% 获胜概率主导市场;Elena Rybakina 以 18% 位居第二,Mirra Andreeva 以 9.3% 排名第三。该市场的下注量已达 $7.7M,反映出市场的高度关注。
竞争梯队拆解
为了更好地评估各潜在结果的位置,可依据隐含概率与合约定价将市场划分为三个明显的交易梯队:
🥇 第一梯队:绝对领跑者
- Aryna Sabalenka (22.5%):Aryna Sabalenka 目前拥有最高概率,深受订单簿青睐。看好该结果的交易者面对的「Buy Yes」合约价为 23¢,显示出市场的高度确信。仅该合约就已产生 $18.9K 的成交量。
🥈 第二梯队:主要挑战者
- Elena Rybakina (18%):作为最可行的替代选项,Elena Rybakina 保持着 18% 的成真概率,其「Buy Yes」份额目前成交价为 18¢。
- Mirra Andreeva (9.3%):以 9.3% 的概率位列第三,市场对 Mirra Andreeva 持谨慎怀疑态度,除非势头转变,否则视其为外围黑马。
🥉 第三梯队:长尾选项(合计约 50.3%)
在前三名之外,还有大量宏观变量与冷门结果被持续追踪。尽管单个概率偏低,但它们是投机交易者的重要对冲:
- 替代选项:包括 Iga Świątek (9%)、Amanda Anisimova (5.8%),以及 Coco Gauff (4.6%)。
- 投机成交:尽管统计概率偏低,像 Jessica Pegula 这类长尾合约仍吸引着可观的关注。
完整订单簿与定价面板
下表列出了该预测池中所有结果的合约价格、概率与市场深度的完整拆解:
| 排名 | 预测结果 | 获胜概率 | 成交量 | 买入 Yes(成本) | 买入 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¢ |
裁决规则
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:发现市场错误定价与 EV 差
人群共识与投机成交塑造了更宏观的预测市场,而我们的量化算法提供了数据驱动的反向视角。通过分析基本面信号、底层趋势与历史分布,我们的 AI 估值模型为每个结果独立测算出一个「公允价值」概率。
将该公允价值与当前交易价值对比,可揭示出重大背离——即期望值(EV)差。正 EV 差代表统计上被低估的结果,而负 EV 差则提示市场可能存在反应过度。
顶级 AI Alpha 与错误定价套利机会
根据最新一轮数据模型测算,以下几个关键合约存在显著偏离:
- 最佳价值标的(最高 EV):我们的模型将 Jasmine Paolini 识别为盘面上最具价值的机会。市场仅给予其 0.4% 的交易概率,而我们 AI 的公允价值评估为 51.9%——形成可观的 +51.4% EV 差。
- 被忽视的黑马:其他值得注意的偏离包括 Xinyu Wang(EV 差:+50.8%)以及 Donna Vekić(EV 差:+50.7%)。尽管我们的预测模型给予更强的统计支撑,这些长尾机会仍被实时订单簿大幅低估。
| 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% |
交易动态
以下是该事件的交易动态。
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
正在押注该事件的鲸鱼钱包
常见问题
当前市场对「2026 Women's Wimbledon Winner」的共识是什么?
截至最新更新,Aryna Sabalenka 以 22.5% 的获胜概率领跑,其次是 Elena Rybakina(18%),以及 Mirra Andreeva(9.3%)。该市场总成交量已达 $7.7M,显示出充足的流动性与高交易参与度。
AI 公允价值与实时市场交易价值有何不同?
实时市场交易价值反映的是公众情绪、订单簿动能与投机资金。我们的 AI 公允价值则由量化模型独立计算,剔除情绪炒作、专注底层数据。两者出现显著背离时即形成 EV 差,提示市场对某个结果可能存在错误定价。
当前哪个结果的期望值(EV)最高?
最新一轮测算显示,Jasmine Paolini 是最显著的错误定价。市场对其隐含概率仅给到 0.4%,而我们的 AI 测算其公允价值为 51.9%——形成 +51.4% 的期望值差,是该市场中最具价值的标的。
长尾数据中是否藏有高价值的黑马选项?
当然有。除了头部结果之外,我们的模型在排名靠后的选项中发现了被低估的潜力。Xinyu Wang 拥有 +50.8% 的正 EV 差,Donna Vekić 则为 +50.7%。尽管量化层面更有支撑,这些合约仍被实时订单簿低估。
