US Open DFS Tennis First Look: Odds and Predictions for Monday, August 28th
The US Open DFS is here, and the projections are live in the optimizer! The USTA Billie Jean King National Tennis Center in NY hosts one of the most prestigious sporting events in the world this week, and DraftKings will be hosting some of its biggest Tennis DFS tournaments of the season in celebration.
In this Tennis DFS article, we explore the world of betting and DFS on platforms like DraftKings and PrizePicks. Whether you’re looking for tennis betting odds, DFS tennis projections, Pick’em selections, or insights into today’s slate, our analysis is ready to help you get started. Join us as we review DFS tennis research and survey the landscape for the upcoming US Open slate.
Tennis DFS Matchups and Odds for the US Open
| Player | Opponent | Odds | Opp Odds | Event | Surface | Elo% | Opp Elo% |
|---|---|---|---|---|---|---|---|
| Iga Swiatek | Rebecca Peterson | -5000 | 1400 | WTA – US Open Women | Hard | 100.00% | 74.02% |
| Frances Tiafoe | Learner Tien | -6000 | 1500 | ATP – US Open Men | Hard | 96.92% | 30.00% |
| Taylor Fritz | Steve Johnson | -2500 | 1000 | ATP – US Open Men | Hard | 98.24% | 65.42% |
| Karolina Muchova | Storm Hunter | -1200 | 750 | WTA – US Open Women | Hard | 98.16% | 30.00% |
| Victoria Azarenka | Fiona Ferro | -1100 | 700 | WTA – US Open Women | Hard | 93.56% | 70.57% |
| Lorenzo Musetti | Titouan Droguet | -900 | 550 | ATP – US Open Men | Hard | 96.48% | 54.19% |
| Belinda Bencic | Kamilla Rakhimova | -750 | 500 | WTA – US Open Women | Hard | 96.55% | 69.20% |
| Holger Rune | Roberto Carballes Baena | -750 | 500 | ATP – US Open Men | Hard | 99.12% | 80.84% |
| Casper Ruud | Emilio Nava | -650 | 450 | ATP – US Open Men | Hard | 97.36% | 56.83% |
| Danielle Collins | Linda Fruhvirtova | -650 | 450 | WTA – US Open Women | Hard | 95.86% | 62.30% |
| Ben Shelton | Pedro Cachin | -525 | 380 | ATP – US Open Men | Hard | 76.87% | 82.38% |
| Veronika Kudermetova | Bernarda Pera | -400 | 310 | WTA – US Open Women | Hard | 97.01% | 80.46% |
| Maria Sakkari | Rebeka Masarova | -390 | 300 | WTA – US Open Women | Hard | 97.70% | 85.98% |
| Yuriko Lily Miyazaki | Margarita Betova | -425 | 320 | WTA – US Open Women | Hard | 45.06% | 30.00% |
| Francisco Cerundolo | Zachary Svajda | -320 | 250 | ATP – US Open Men | Hard | 96.04% | 49.34% |
| Alejandro Davidovich Fokina | Marcos Giron | -320 | 250 | ATP – US Open Men | Hard | 95.59% | 85.90% |
| Ilya Ivashka | Juan Manuel Cerundolo | -240 | 195 | ATP – US Open Men | Hard | 77.97% | 59.47% |
| Daria Saville | Clervie Ngounoue | -250 | 200 | WTA – US Open Women | Hard | 79.08% | 30.00% |
| Jeffrey John Wolf | Zhizhen Zhang | -205 | 170 | ATP – US Open Men | Hard | 86.34% | 75.33% |
| Lauren Davis | Danka Kovinic | -245 | 200 | WTA – US Open Women | Hard | 85.52% | 77.01% |
| Adrian Mannarino | Yosuke Watanuki | -205 | 165 | ATP – US Open Men | Hard | 89.21% | 83.26% |
| Xinyu Wang | Katie Volynets | -230 | 185 | WTA – US Open Women | Hard | 81.38% | 63.68% |
| Elisabetta Cocciaretto | Kaja Juvan | -225 | 180 | WTA – US Open Women | Hard | 91.03% | 79.54% |
| Richard Gasquet | Fabian Marozsan | -200 | 165 | ATP – US Open Men | Hard | 80.62% | 70.70% |
| Brandon Nakashima | Laslo Djere | -180 | 145 | ATP – US Open Men | Hard | 82.60% | 89.65% |
| Lin Zhu | Mayar Sherif | -200 | 165 | WTA – US Open Women | Hard | 80.23% | 88.51% |
| Alexander Bublik | Dominic Thiem | -170 | 140 | ATP – US Open Men | Hard | 89.43% | 83.70% |
| Emma Navarro | Magdalena Frech | -165 | 135 | WTA – US Open Women | Hard | 87.13% | 76.09% |
| Sebastian Ofner | Nuno Borges | -145 | 115 | ATP – US Open Men | Hard | 86.12% | 78.19% |
| Taylor Townsend | Varvara Gracheva | -150 | 125 | WTA – US Open Women | Hard | 84.14% | 82.30% |
| Sara Sorribes Tormo | Anhelina Kalinina | -150 | 125 | WTA – US Open Women | Hard | 88.05% | 87.36% |
| Rinky Hijikata | Pavel Kotov | -130 | 105 | ATP – US Open Men | Hard | 62.11% | 71.59% |
| Kateryna Baindl | Anna-Karolina Schmiedlova | -140 | 115 | WTA – US Open Women | Hard | 64.83% | 71.49% |
| Beatriz Haddad Maia | Sloane Stephens | -130 | 105 | WTA – US Open Women | Hard | 95.63% | 90.11% |
| Gregoire Barrere | Jakub Mensik | -125 | 100 | ATP – US Open Men | Hard | 84.36% | 66.74% |
| Novak Djokovic | Alexandre Muller | -10000 | 1700 | ATP – US Open Men | Hard | 99.78% | 70.26% |
| Coco Gauff | Laura Siegemund | -1000 | 600 | WTA – US Open Women | Hard | 99.31% | 86.90% |
| Borna Gojo | Hugo Dellien | -750 | 500 | ATP – US Open Men | Hard | 72.47% | 74.23% |
| Petra Kvitova | Cristina Bucsa | -450 | 340 | WTA – US Open Women | Hard | 98.39% | 79.31% |
| Christopher Eubanks | Soonwoo Kwon | -390 | 300 | ATP – US Open Men | Hard | 88.99% | 30.00% |
| Mirra Andreeva | Olivia Gadecki | -340 | 270 | WTA – US Open Women | Hard | 93.79% | 48.05% |
| Sorana Cirstea | Kayla Day | -350 | 270 | WTA – US Open Women | Hard | 92.18% | 74.48% |
| Stefanos Tsitsipas | Milos Raonic | -310 | 245 | ATP – US Open Men | Hard | 98.90% | 30.00% |
| Jelena Ostapenko | Jasmine Paolini | -230 | 185 | WTA – US Open Women | Hard | 97.47% | 89.89% |
| Bernabe Zapata Miralles | Ethan Quinn | -240 | 190 | ATP – US Open Men | Hard | 81.28% | 30.00% |
| Caroline Wozniacki | Tatiana Prozorova | -215 | 175 | WTA – US Open Women | Hard | 85.29% | 30.00% |
| Quentin Halys | Benjamin Bonzi | -160 | 130 | ATP – US Open Men | Hard | 85.02% | 75.99% |
| Alize Cornet | Elina Avanesyan | -165 | 135 | WTA – US Open Women | Hard | 81.61% | 78.16% |
| Alexei Popyrin | Dominic Stricker | -165 | 135 | ATP – US Open Men | Hard | 87.22% | 77.75% |
| Felix Auger-Aliassime | Mackenzie McDonald | -140 | 115 | ATP – US Open Men | Hard | 95.37% | 91.19% |
| Katerina Siniakova | Anna Kalinskaya | -120 | 100 | WTA – US Open Women | Hard | 89.43% | 93.33% |
| Sho Shimabukuro | Hugo Gaston | -110 | -115 | ATP – US Open Men | Hard | 67.84% | 75.11% |
Odds courtesy of Draftkings Sportsbook as of publication time.
Top DFS Projections for the US Open
| Player | fpts | matches | sets | games | aces | df |
|---|---|---|---|---|---|---|
| Frances Tiafoe | 75.32 | 0.90 | 2.82 | 19.00 | 11.91 | 2.84 |
| Iga Swiatek | 74.94 | 0.90 | 1.69 | 14.00 | 2.04 | 1.80 |
| Novak Djokovic | 74.80 | 0.90 | 2.82 | 17.50 | 7.39 | 2.47 |
| Taylor Fritz | 74.22 | 0.90 | 2.82 | 14.00 | 8.43 | 1.77 |
| Borna Gojo | 71.60 | 0.84 | 2.73 | 20.50 | 15.71 | 4.10 |
| Lorenzo Musetti | 71.14 | 0.85 | 2.75 | 20.50 | 6.97 | 3.15 |
| Karolina Muchova | 70.84 | 0.89 | 1.68 | 13.50 | 3.49 | 2.36 |
| Coco Gauff | 69.81 | 0.86 | 1.66 | 13.50 | 4.98 | 3.47 |
| Casper Ruud | 68.32 | 0.83 | 2.71 | 21.00 | 7.87 | 3.54 |
| Victoria Azarenka | 68.32 | 0.88 | 1.67 | 13.50 | 2.75 | 4.31 |
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Top DFS ownership for the US Open
| Player | own% |
|---|---|
| Frances Tiafoe | 19.34% |
| Taylor Fritz | 18.61% |
| Brandon Nakashima | 18.55% |
| Casper Ruud | 17.97% |
| Adrian Mannarino | 17.31% |
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Top DFS Optimal Rates for the US Open
| Player | pts$ |
|---|---|
| Veronika Kudermetova | 13.53% |
| Danielle Collins | 13.33% |
| Karolina Muchova | 13.00% |
| Belinda Bencic | 12.67% |
| Victoria Azarenka | 12.53% |
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Tennis DFS Picks for the US Open
Lauren Davis – $9,000: Each of her last 3 matches in the U.S. on hard court have come against respectable American opponents to mixed results. She dominated Sloane Stephens (rank 36) to a 6-1 first set victory only to allow a comeback of equal dominance in Cleveland. The matches with Alycia Parks (Rank 28) and Ann Li (Rank 136) were also tight in 3 sets length. Her first opponent at the US Open will be Danka Kovinic, who ranks 94 in the WTA but just 135th in 52 week ELO. This compares to Davis’ 50th world rank and #72 52 week ELO. On hard court, the gap widens with Davis showing better performance with a 58th best rankings and Kovinic ranking down at 176th. Our Tennis simulation shows a nice difference between the optimal rate and ownership of Davis, which is hard to come by on such a large slate. Constructing your lineup with Davis could provide a unique team without sacrificing win expectation.
Katie Volynets – $5,900: It’s not going to do you much good if I list out three of the most expensive players in the field with massive odds to win, so I might as well take a longer shot here with a 6k crapper. It should come as no surprise that a player with the name “Voly – nets” can’t serve all that well, and even less of a surprise that her strength at the ripe old age of 21 has been in the return game. She has an ace rate under 3% in the last 52 weeks, but a return points won rate of 43.5% and a break point conversion rate in the top 100 in the world YTD. Her opponent, Xiyu Wang, is also not a serve-reliant player, and this match figures to have quite a bit of back and forth. Wang is a -210 favorite, but on the women’s side, anything can happen. That could be particularly true in a match like this where we shouldn’t expect either player to outright dominate the serve. Projected ownership for Volynets is extremely low at under 3%, and the optimal rate in the simulation was almost 4x that. Hard to make a really good case why not, so if you’re playing more than one lineup, this one looks like a solid YOLO.
You can also take a shot at the Wang “less” than 4.5 breaks on PrizePicks if you like using this analysis better there.
Iga Swiatek – $11,400: There have been matches recently where she absolutely face-melts weaker opponents, and this could be one of those matches. Danielle Collins somehow defeated Iga in Canada, and the rematch did not go so well in Cincinnati. Iga smashed 6-1, 6-0 and put down a 90 burger for DFS. Iga is a top 5 server in the sport and top 10 in the return game. Peterson is solid in her return stats, but can’t say the same for her service stats. It’s not going be pretty if she allows Iga to convert breaks, and that’s not a wild expectation considering Swiatek is -5000. It might sound like the easy way out touting Iga as a massive favorite, but keep in mind her salary is astronomical at $11,400 and her ownership will be reduced on a massive slate as a result. I wouldn’t shy away as much your opponents will. There is a big fat, juicy >80 burger sitting there well within the range of outcomes.