2026年9月4日 美国东部时间下午5:40 / 哥伦比亚广播公司新闻（CBS News）
作者：
劳伦·菲希滕 数字记者
劳伦·菲希滕是哥伦比亚广播公司新闻的记者，报道人工智能、数字安全和网络极端主义议题。她毕业于北卡罗来纳大学教堂山分校后加入CBS新闻，此前曾在CBS新闻国内编辑部担任副制片人。
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即便在十年前，网球比赛现场观众举着手机观看都还是一件奇怪的事。但今年的美网公开赛上，数字世界已经与赛事深度融合，实时人工智能分析正彻底改变赛事观赛体验。
周二晚间，可可·高芙对阵土耳其选手泽伊内普·松梅兹时，美网公开赛官方应用实时推送了人工智能生成的赛事分析，涵盖关键回合、两位选手的获胜概率，以及一项名为“发球质量”的全新指标。
纽约皇后区阿瑟·阿什球场周边架设的摄像头追踪网球、球拍和选手肢体动作，采集肘关节和膝关节弯曲角度、手腕挥拍速度等数据点。随后，IBM的智能软件开发平台Watsonx会处理球场上采集到的数据，以此衡量选手发球的效率、精准度和稳定性，最终打出0到100分的评分，赛后可在美网应用中查看该评分。
根据发球质量分析报告，高芙的制胜发球“挥拍准备动作控制精准，准备阶段屈膝充分”。
IBM体育与娱乐合作项目技术总监泰勒·西德尔表示，本届美网赛事结束时，累计产生的数据点将超过10亿个。
他表示，这些基于数据打造的观赛体验旨在为球迷开启话题讨论。
“体育赛事充满不确定性——你可以参考所有数据，但球场上任何情况都可能发生，”他说，“这正是赛事的乐趣所在。我们希望提供赛事洞察，但同时也要让大家专注观看比赛本身。”
人工智能带来的观赛体验升级并非仅面向通过应用程序获取信息的球迷（据IBM数据，这类球迷约有1400万）。这些功能同样成为选手们的实用工具。
周五晋级美网公开赛第四轮的杰西卡·佩古拉，会在对阵对手前利用人工智能分析对手的发球模式。
“网球比赛在球场上很大程度上是在解决问题，其中涉及大量战术模式，而发球无疑是其中非常关键的一环，”她对哥伦比亚广播公司新闻表示，“发球是网球比赛中我们唯一可以主动掌控的击球环节。”
尽管比赛局势随时可能逆转，但对佩古拉而言，这些数据依然极具价值。
“你可以获取大量数据分析，但比赛过程中局势时常变化，有时选手会改变战术，或者做出与你预判完全不同的打法，这时你仍然需要相信自己的直觉，”她说，“但我认为，这些数据能让你在赛前就做好充分准备，从心理上更有底气。”
西德尔表示，“获胜概率”功能已经在美网的数字观赛体验中沿用了约六年，但今年是球迷第二年可以在比赛进行中实时追踪波动的获胜概率。该功能基于权威媒体报道和选手近期表现汇总分析，生成选手的获胜赔率。
周二的比赛中，高芙赛前被预测的获胜概率为72%，随着她在首局建立优势，这一数值持续上升。另一项人工智能驱动的功能“关键回合”会在比赛进行中推送简短的分析评论。第二局比赛中，一条关键评论写道：“高芙手握三个赛点，松梅兹需要打出超水平发挥才能挽回局面。”
当晚晚些时候，男子单打头号种子、德国选手亚历山大·兹维列夫对阵意大利选手洛伦佐·索内戈的比赛扣人心弦，一直打到凌晨才结束，其获胜概率预测出现了戏剧性波动。赛前兹维列夫的获胜概率为87%，但到第四局尾声时，索内戈的获胜概率升至93%，随着兹维列夫完成逆转，概率再次反转。
应用内的人工智能助手“赛事聊天”还可以回答关于选手、赛事和场馆的各类问题。
当被询问在哪里可以买到美网标志性鸡尾酒“Honey Deuce”时，它迅速推送了酒吧的具体位置，并问道：“您是否需要查找离您所在看台最近的Honey Deuce酒吧？”
How AI is reshaping the U.S. Open for players and fans
2026-09-04 5:40 PM EDT / CBS News
By
Lauren Fichten Digital Reporter
Lauren Fichten is a journalist at CBS News covering artificial intelligence, digital safety and online extremism. She joined CBS News after graduating from UNC-Chapel Hill and was previously an associate producer at the CBS News National Desk.
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Seeing fans with their phones out during a tennis match would’ve been a strange sight even a decade ago. But at the U.S. Open this year, the digital world has become intertwined with the game as live AI insights reshape the tournament experience.
On Tuesday night, when Coco Gauff faced off against Zeynep Sönmez of Turkey, AI-generated analysis trickled in through the U.S. Open app, tracking key moments, each player’s likelihood to win and a new metric called “serve quality.”
Cameras around the perimeter of Arthur Ashe Stadium in Queens, New York, collect data on players’ limb movement. Lauren Fichten
Cameras lining the perimeter of Arthur Ashe Stadium track the ball, racket and players’ limbs, collecting data points like elbow and knee flexion and wrist flex velocity. Then, IBM’s agentic software development platform, Watsonx, processes the data from the court to measure the efficiency, accuracy and consistency of a player’s serve, a number out of 100 that can be seen in the U.S. Open app postmatch.
Gauff’s winning serves had a “controlled racket preparation and deep knee bend during her setup,” according to the serve quality summary.
There will be over a billion data points produced by the end of the tournament, said Tyler Sidell, IBM’s technical program director for sports and entertainment partnerships.
The data-driven experiences, he said, are meant to be a conversation starter for fans.
“There’s so much unpredictability in sports — you could take a look at all the data, but anything can happen out there on the court,” he said. “That’s the fun about it. We want to provide an insight, but still, let’s watch the matches play out.”
AI insights are not just about the fans who can access them in the app (about 14 million of them, according to IBM). The features have also become a tool for players.
Jessica Pegula, who advanced to the fourth round of the U.S. Open on Friday, uses AI to identify patterns in her opponents’ serves before facing them in a match.
“Tennis is a lot of problem solving on the court, so it’s a lot of patterns, and I think serve is a really big one,” she told CBS News. “It’s the one controllable shot we have in tennis.”
Although the game can turn around at any moment, for Pegula, the data remains useful.
The IBM Data Operations center processes data throughout the duration of a match. Lauren Fichten
“You can get a lot of analytics, but sometimes things change during the match, sometimes someone changes their strategy, or they maybe go against the grain of what you thought they were going to do, and you still have to really trust your instinct,” she said. “But I think mentally it just gives you that feeling of being prepared before you go into a match.”
The “likelihood to win” feature has been a staple of the U.S. Open digital experience for about six years, Sidell said, but this is the second year fans can track the fluctuating likelihoods in real time as a match plays out. The feature is built on trusted media sources and recent performances, which are aggregated to generate a player’s odds of winning.
Gauff was predicted to win Tuesday by 72%, a number that rose as she built momentum in the first set. Another AI-powered feature, “key moments,” provides brief blurbs of analysis as a game unfolds. “Gauff has three opportunities to win the match, with Sonmez needing a heroic response,” said a key moment in the second set of the match.
A dramatic fluctuation in likelihood to win prediction came later that evening when the men’s No. 1 seed, Alexander Zverev of Germany, played a nail-biting match against Italy’s Lorenzo Sonego that dragged into the early morning. Zverev had an 87% likelihood of winning before the match. But toward the end of the fourth set, Sonego had a 93% chance of winning. It flipped again when Zverev made a comeback.
Match Chat, an AI-powered assistant in the app, also can answer questions about players, matches and the venue.
When asked where one could get a Honey Deuce, the signature cocktail of the U.S. Open, it swiftly sent the locations of the bars and asked, “Do you want the nearest Honey Deuce bar to your gate?”
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