2013 French Open women's tournament: Advanced Baseline forecast

Jasper Juinen

It's the most wide open Grand Slam of the year -- a nice counter-balance if you're suffering from Rafatigue. Tournament forecasts, draw analysis and unsolicited gambling advice below.

Single-elimination tournaments for individual sports are an interesting clash of two competing schools of sports fandom. In most other individual sports, the biggest drivers of interest are usually athletes like Tiger Woods and Michael Phelps dominating their entire fields by themselves, which works great for sports like golf and swimming where no two players are directly heads-up against one another. On the other hand, the single-elimination tournament format is great for inducing chaos and unpredictability, where underdogs can go on a lucky run and favorites are always one bad day from going home early.

Tennis is the only major sport where both of these somewhat contradictory elements are in play, which makes for an interesting question: Which would you rather see at a Grand Slam: individual dominance or anarchy? If you prefer the former, the men's side of the French Open will be on its ninth iteration of Nadal's reign of terror and shows no sign of slowing down. If you prefer the latter, though, the women's side is the most wide open of any of the Grand Slams this year -- or at least what passes for wide open in a top-heavy sport.

The top women's players don't have nearly as much of a clay-specialist presence as the men's side does. There are only four women's players in the top 25 who are at their best on clay according to Advanced Baseline ratings (and only two in the top 10), as opposed to eight in the men's top 25 and four in the top 10. This has a flattening effect on the field, where the top five women are playing the equivalent of a road game every match and are a lot more vulnerable to an upset.

In addition, the players in the bottom half of the top 10 have faltered a bit in clay events leading up to the French Open, so the top isn't quite heavy as it was just two months ago. Combine all of this with the best-of-three-sets format, and one should probably expect the unexpected.

Year in Review: Tracking the Top Four

Below is a 52-week tracking graph of the clay-adjusted AB ranks of the top four seeds from last year's French Open until now. It's a quick way to see what's changed at the top of the ladder, and how it might cause things to be different this year. (click to enlarge)

Womenstrack_medium

After Rome and Madrid, Serena has really separated herself from the pack. It's really difficult to get that kind of separation at the top in just a month, which illustrates how dominant she's been on clay in her most recent surge. Meanwhile, Azarenka's inconsistency leaves her a little bit down from where she was a year ago, and Radwanksa has plummeted. Even though she's roughly the same in the official WTA standings, don't be fooled: She's not nearly the threat she was just a year ago.

The Forecast

Generated from simulating the tournament 100,000 times, before the tournament began, using win probabilities from AB ratings.

Like I said before, "wide open" doesn't quite mean the same thing in tennis as it does in something like the NCAA Tournament. The top three favorites have a collective 80 perent chance of winning, as opposed to a collective 57 percent chance from this year's wide-open basketball tournament. As much as Serena has surged this month, this forecast is a reminder that it's difficult for anyone not named Rafael Nadal to win seven tennis matches in a row, especially on clay, and there's a better than 50 percent chance that Serena will not win this tournament.

As far as potential unseeded players that could make a deep run, Kaia Kanepi is nicely set up to capitalize on her recent success this week in Brussels, and Simona Halep is set to do some damage if she can get past a tough first-round matchup.

Player 2nd Round 3rd Round 4th Round Quarterfinals Semifinals Finals Winner Expected Points
Serena Williams 96.9% 94.6% 89.8% 82.7% 73.6% 60.2% 39.0% 1181.4
Maria Sharapova 95.5% 90.7% 86.9% 77.9% 63.9% 43.6% 25.4% 945.0
Victoria Azarenka 89.5% 85.9% 77.1% 70.0% 55.1% 31.0% 16.3% 753.2
Sara Errani 94.8% 81.2% 64.6% 47.0% 31.5% 12.0% 4.8% 432.1
Samantha Stosur 88.1% 76.9% 57.2% 40.4% 14.8% 6.7% 2.4% 303.9
Ana Ivanovic 89.2% 73.4% 56.4% 32.9% 17.0% 5.2% 1.6% 283.9
Agnieszka Radwanska 90.2% 75.9% 51.4% 31.3% 15.6% 4.7% 1.4% 268.3
Kaia Kanepi 76.6% 65.2% 43.5% 27.6% 11.7% 4.2% 1.4% 228.5
Na Li 73.4% 55.4% 39.6% 23.6% 9.9% 3.6% 1.2% 201.1
Maria Kirilenko 91.9% 66.8% 34.9% 18.9% 6.6% 1.8% 0.5% 174.1
Venus Williams 78.2% 65.1% 33.2% 17.8% 7.8% 1.9% 0.5% 169.9
Angelique Kerber 70.3% 54.4% 36.2% 22.2% 5.4% 2.4% 0.7% 167.8
Petra Kvitova 81.9% 57.5% 35.9% 17.2% 4.6% 1.5% 0.4% 154.5
Roberta Vinci 90.2% 67.6% 46.9% 8.1% 4.2% 1.6% 0.3% 154.3
Dominika Cibulkova 77.0% 66.0% 45.4% 10.5% 4.8% 1.6% 0.4% 154.1
Simona Halep 53.8% 48.3% 33.8% 15.3% 8.1% 2.2% 0.7% 153.4
Julia Goerges 83.8% 67.4% 27.0% 10.6% 3.6% 0.7% 0.1% 130.3
Yaroslava Shvedova 85.8% 56.9% 25.7% 11.2% 3.4% 0.8% 0.2% 126.3
Jelena Jankovic 69.8% 52.8% 23.4% 13.0% 3.3% 1.0% 0.2% 121.3
Svetlana Kuznetsova 50.5% 41.1% 26.9% 15.4% 3.3% 1.4% 0.3% 119.4
Caroline Wozniacki 65.0% 51.8% 26.1% 13.3% 2.5% 0.9% 0.2% 118.6
Nadezda Petrova 73.6% 53.8% 24.8% 9.3% 3.9% 0.8% 0.2% 118.5
Carla Suarez Navarro 46.2% 40.8% 26.7% 11.1% 5.3% 1.3% 0.3% 116.2
Ekaterina Makarova 49.5% 40.1% 26.3% 14.7% 3.1% 1.3% 0.3% 115.2
Lucie Safarova 61.9% 46.4% 26.5% 12.0% 3.0% 0.9% 0.2% 115.0
Varvara Lepchenko 77.7% 43.5% 20.8% 10.2% 1.7% 0.6% 0.1% 103.9
Romina Oprandi 75.8% 42.7% 20.5% 9.9% 1.7% 0.6% 0.1% 101.7
Sabine Lisicki 79.0% 48.1% 15.2% 7.0% 2.8% 0.5% 0.1% 99.3
Flavia Pennetta 59.6% 36.8% 25.3% 5.7% 2.1% 0.4% 0.1% 90.3
Alize Cornet 85.4% 48.2% 9.1% 5.2% 1.8% 0.3% 0.1% 87.4
Francesca Schiavone 75.8% 36.3% 22.7% 4.4% 1.3% 0.2% 0.0% 87.1
Petra Cetkovska 82.2% 46.9% 20.0% 2.2% 0.8% 0.2% 0.0% 85.9
Maria-Teresa Torro-Flor 82.7% 42.2% 11.7% 4.9% 1.7% 0.3% 0.0% 84.8
Paula Ormaechea 81.7% 36.2% 12.9% 4.3% 1.0% 0.2% 0.0% 79.1
M. Duque-Marino 76.4% 37.8% 14.2% 2.0% 0.4% 0.0% 0.0% 71.9
Sloane Stephens 56.7% 34.8% 17.0% 2.9% 1.0% 0.2% 0.0% 70.7
A. Pavlyuchenkova 67.8% 36.9% 15.5% 1.7% 0.6% 0.2% 0.0% 70.3
Marion Bartoli 60.0% 35.7% 15.3% 2.4% 0.6% 0.1% 0.0% 67.7
Shuai Peng 64.0% 26.1% 12.3% 4.2% 0.7% 0.2% 0.0% 66.3
Christina McHale 72.0% 23.5% 10.2% 3.9% 0.5% 0.1% 0.0% 64.5
Irina-Camelia Begu 62.8% 33.6% 6.0% 3.4% 1.1% 0.2% 0.0% 63.6
Lucie Hradecka 69.5% 25.1% 8.8% 3.2% 0.7% 0.1% 0.0% 62.6
Madison Keys 67.6% 25.8% 7.4% 1.7% 0.4% 0.0% 0.0% 56.7
Jamie Hampton 38.1% 24.7% 11.1% 3.8% 0.7% 0.2% 0.0% 54.8
B. Mattek-Sands 55.3% 17.9% 8.6% 3.1% 0.8% 0.1% 0.0% 54.2
Garbine Muguruza 65.6% 22.3% 6.0% 2.1% 0.3% 0.1% 0.0% 53.6
Alexandra Cadantu 55.3% 24.0% 10.0% 1.3% 0.4% 0.1% 0.0% 53.1
Kirsten Flipkens 40.4% 21.4% 12.5% 2.2% 0.7% 0.1% 0.0% 51.9
Mallory Burdette 68.4% 16.1% 5.9% 1.7% 0.4% 0.0% 0.0% 51.3
Galina Voskoboeva 59.6% 19.6% 9.2% 0.9% 0.3% 0.1% 0.0% 50.9
Jie Zheng 54.0% 33.5% 3.2% 1.1% 0.3% 0.0% 0.0% 50.1
Chanelle Scheepers 59.6% 14.9% 7.0% 2.0% 0.5% 0.1% 0.0% 49.4
Karin Knapp 43.3% 23.8% 10.0% 1.4% 0.4% 0.1% 0.0% 49.3
Laura Robson 35.0% 23.8% 8.8% 3.2% 0.4% 0.1% 0.0% 48.7
Sorana Cirstea 51.2% 30.0% 2.8% 1.3% 0.4% 0.1% 0.0% 48.3
Yanina Wickmayer 58.4% 18.4% 6.2% 1.5% 0.2% 0.0% 0.0% 47.9
M. Rybarikova 65.4% 14.4% 5.3% 1.4% 0.1% 0.0% 0.0% 47.2
Ayumi Morita 60.3% 11.8% 5.2% 1.8% 0.6% 0.1% 0.0% 46.8
Kiki Bertens 48.8% 27.7% 2.5% 1.1% 0.4% 0.1% 0.0% 45.4
Virginie Razzano 64.0% 18.3% 3.4% 0.6% 0.1% 0.0% 0.0% 45.1
Melanie Oudin 58.8% 25.6% 1.9% 0.5% 0.1% 0.0% 0.0% 45.0
Mona Barthel 29.7% 17.5% 8.1% 3.3% 0.5% 0.1% 0.0% 44.0
Monica Niculescu 53.5% 23.5% 1.8% 0.7% 0.2% 0.0% 0.0% 43.0
B. Zahlavova Strycova 58.1% 15.7% 3.9% 0.9% 0.1% 0.0% 0.0% 42.9
Olga Govortsova 40.0% 21.0% 7.3% 0.9% 0.2% 0.0% 0.0% 42.5
Vesna Dolonts 46.0% 26.7% 2.2% 0.7% 0.1% 0.0% 0.0% 41.9
L. Dominguez-Lino 44.7% 12.8% 5.6% 1.8% 0.4% 0.1% 0.0% 41.6
Vania King 44.7% 17.4% 6.4% 0.8% 0.2% 0.0% 0.0% 41.4
Eugenie Bouchard 60.9% 4.9% 2.9% 1.1% 0.3% 0.0% 0.0% 39.3
A. Medina Garrigues 26.6% 13.9% 6.7% 2.4% 0.6% 0.1% 0.0% 38.7
Shelby Rogers 64.3% 8.2% 2.0% 0.3% 0.0% 0.0% 0.0% 38.6
Marina Erakovic 55.9% 11.6% 3.3% 0.3% 0.0% 0.0% 0.0% 38.4
Dinah Pfizenmaier 52.7% 12.4% 2.7% 0.6% 0.1% 0.0% 0.0% 37.9
Daniela Hantuchova 30.3% 17.6% 4.9% 1.8% 0.3% 0.0% 0.0% 37.2
Johanna Larsson 46.5% 18.7% 1.3% 0.5% 0.1% 0.0% 0.0% 37.2
Lauren Davis 51.8% 9.3% 3.1% 0.9% 0.1% 0.0% 0.0% 37.1
Heather Watson 50.5% 10.2% 2.8% 0.7% 0.1% 0.0% 0.0% 36.4
Annika Beck 63.0% 4.5% 1.3% 0.4% 0.1% 0.0% 0.0% 36.3
Klara Zakopalova 23.4% 14.8% 5.8% 2.1% 0.4% 0.1% 0.0% 35.8
Stefanie Voegele 49.5% 9.8% 2.7% 0.7% 0.1% 0.0% 0.0% 35.7
Kristina Mladenovic 48.2% 7.9% 2.5% 0.7% 0.1% 0.0% 0.0% 34.3
Silvia Soler-Espinosa 37.2% 15.5% 1.9% 0.8% 0.2% 0.0% 0.0% 33.9
Mandy Minella 47.3% 9.9% 1.9% 0.4% 0.0% 0.0% 0.0% 33.6
Grace Min 40.4% 10.4% 4.0% 0.3% 0.1% 0.0% 0.0% 33.3
K. Anna Schmiedlova 41.7% 10.5% 2.9% 0.6% 0.1% 0.0% 0.0% 33.3
Camila Giorgi 36.0% 10.2% 3.5% 0.8% 0.1% 0.0% 0.0% 32.2
Mathilde Johansson 40.4% 7.8% 3.1% 0.7% 0.1% 0.0% 0.0% 32.1
Tamira Paszek 41.2% 14.2% 0.8% 0.2% 0.0% 0.0% 0.0% 32.0
Lesya Tsurenko 23.0% 14.7% 5.9% 0.7% 0.2% 0.0% 0.0% 31.9
Elena Baltacha 44.1% 7.7% 1.9% 0.1% 0.0% 0.0% 0.0% 31.0
Bojana Jovanovski 41.9% 8.7% 1.8% 0.3% 0.0% 0.0% 0.0% 30.8
Andrea Hlavackova 32.2% 12.1% 3.2% 0.2% 0.0% 0.0% 0.0% 30.1
Yuliya Beygelzimer 51.9% 2.0% 0.7% 0.2% 0.0% 0.0% 0.0% 30.1
Monica Puig 26.4% 12.9% 3.4% 0.6% 0.1% 0.0% 0.0% 29.7
Yulia Putintseva 39.7% 5.7% 1.9% 0.5% 0.1% 0.0% 0.0% 29.6
Elena Vesnina 10.5% 8.0% 4.1% 2.3% 0.8% 0.1% 0.0% 28.8
Caroline Garcia 48.1% 1.7% 0.5% 0.1% 0.0% 0.0% 0.0% 28.4
Urszula Radwanska 21.8% 12.6% 3.2% 0.8% 0.2% 0.0% 0.0% 28.3
Karolina Pliskova 34.4% 7.3% 1.2% 0.3% 0.0% 0.0% 0.0% 26.9
Claire Feuerstein 36.0% 6.9% 0.9% 0.1% 0.0% 0.0% 0.0% 26.7
Misaki Doi 32.4% 7.5% 1.5% 0.2% 0.0% 0.0% 0.0% 26.5
Tsvetana Pironkova 39.1% 2.3% 1.1% 0.3% 0.0% 0.0% 0.0% 26.4
Ashleigh Barty 30.5% 6.6% 1.4% 0.3% 0.0% 0.0% 0.0% 25.6
Pauline Parmentier 34.6% 4.4% 1.1% 0.2% 0.0% 0.0% 0.0% 25.5
Elina Svitolina 24.2% 7.4% 1.7% 0.4% 0.0% 0.0% 0.0% 24.2
Sandra Zahlavova 37.0% 1.7% 0.4% 0.1% 0.0% 0.0% 0.0% 24.2
Irena Pavlovic 35.7% 2.7% 0.4% 0.0% 0.0% 0.0% 0.0% 24.2
Donna Vekic 31.6% 4.2% 0.9% 0.1% 0.0% 0.0% 0.0% 24.1
Jana Cepelova 28.0% 4.6% 1.1% 0.2% 0.0% 0.0% 0.0% 23.3
Melinda Czink 24.2% 5.5% 1.8% 0.2% 0.0% 0.0% 0.0% 22.9
Mirjana Lucic 22.3% 6.4% 1.4% 0.3% 0.0% 0.0% 0.0% 22.6
Kristyna Pliskova 23.6% 5.5% 0.9% 0.1% 0.0% 0.0% 0.0% 21.7
Aravane Rezai 18.1% 6.2% 1.7% 0.3% 0.0% 0.0% 0.0% 21.4
Sofia Arvidsson 21.0% 6.4% 0.8% 0.1% 0.0% 0.0% 0.0% 21.3
Zuzana Kucova 16.2% 7.3% 1.0% 0.1% 0.0% 0.0% 0.0% 20.1
Kimiko Date-Krumm 11.9% 5.8% 1.8% 0.5% 0.1% 0.0% 0.0% 19.4
Olga Puchkova 17.8% 4.1% 0.7% 0.0% 0.0% 0.0% 0.0% 18.7
Tatjana Malek 18.3% 3.3% 0.4% 0.0% 0.0% 0.0% 0.0% 18.4
Julia Glushko 17.3% 3.3% 0.3% 0.0% 0.0% 0.0% 0.0% 17.9
Coco Vandeweghe 14.2% 3.6% 0.5% 0.1% 0.0% 0.0% 0.0% 17.1
Petra Martic 10.8% 3.9% 1.2% 0.2% 0.0% 0.0% 0.0% 17.1
Maria Joao Koehler 14.6% 2.7% 0.1% 0.0% 0.0% 0.0% 0.0% 16.5
Shahar Peer 9.8% 3.8% 0.9% 0.1% 0.0% 0.0% 0.0% 16.2
S. Foretz Gacon 9.8% 2.4% 0.5% 0.0% 0.0% 0.0% 0.0% 15.0
Su-Wei Hsieh 4.5% 2.1% 1.1% 0.3% 0.1% 0.0% 0.0% 14.4
Nina Bratchikova 8.1% 1.5% 0.2% 0.0% 0.0% 0.0% 0.0% 13.7
Anna Tatishvili 3.1% 1.7% 0.7% 0.1% 0.0% 0.0% 0.0% 12.8
Arantxa Rus 5.2% 1.3% 0.3% 0.0% 0.0% 0.0% 0.0% 12.7

Winners and Losers of the Draw

Comparing the expected points for each player from their specific draw to all possible draws. Full explanation here.

It could've been even more open, but the draw was unbelievably beneficial to the top three favorites. Usually you'd expect one of the three to draw the short stick, but they somehow all benefited. Sharapova has a cakewalk to the quarterfinals, and Azarenka benefits from Na Li drawing the unluckiest path; there's a significant chance Azarenka won't even face her. Meanwhile, Serena benefitted a lot from drawing Radwanska instead of Azarenka in her quarter, and is likelier to face Sara Errani anyway. On the flip side, it's nice to see a qualifier get a stroke of good luck. Mariana Duque-Marino drew a very winnable first round against Kristyna Pliskova (a shockingly low 176th in AB rankings) instead of being a typical cannon-fodder offering. Kaia Kanepi and Petra Cetkovska are the best-positioned unseeded players to make a deep run, since they were dangerous to begin with and got a favorable draw to boot. Full luck scores for each player below:

Player Change in Expected Points from Draw
Serena Williams 124.8
Ana Ivanovic 58.7
Victoria Azarenka 37.6
Mariana Duque-Marino 33.8
Sara Errani 33.1
Petra Cetkovska 32.5
Maria Sharapova 32.0
Kaia Kanepi 31.2
Francesca Schiavone 30.2
Julia Goerges 28.3
Venus Williams 23.4
Roberta Vinci 21.1
Romina Oprandi 18.4
Flavia Pennetta 18.3
Virginie Razzano 16.4
Paula Ormaechea 16.4
Maria-Teresa Torro-Flor 14.6
Melanie Oudin 14.5
Yaroslava Shvedova 13.8
Shelby Rogers 12.1
Marina Erakovic 11.5
Jie Zheng 11.1
Christina McHale 10.5
Magdalena Rybarikova 10.0
Barbora Zahlavova Strycova 9.6
Madison Keys 8.9
Dominika Cibulkova 8.7
Olga Govortsova 8.4
Galina Voskoboeva 8.2
Mallory Burdette 8.1
Vesna Dolonts 7.7
Elena Baltacha 7.5
Garbine Muguruza 7.3
Annika Beck 6.9
Alize Cornet 6.4
Marion Bartoli 5.8
Alexandra Cadantu 5.8
Dinah Pfizenmaier 5.7
Claire Feuerstein 5.7
Varvara Lepchenko 5.2
Irena Pavlovic 4.8
Simona Halep 4.3
Bojana Jovanovski 4.1
Mandy Minella 3.7
Chanelle Scheepers 3.5
Yanina Wickmayer 3.3
Lucie Hradecka 2.9
Yuliya Beygelzimer 2.6
Sandra Zahlavova 2.5
Kristyna Pliskova 2.3
Caroline Garcia 2.2
Irina-Camelia Begu 2.0
Tamira Paszek 1.9
Shuai Peng 1.7
Vania King 1.5
Grace Min 1.1
Monica Niculescu 1.0
Anastasia Pavlyuchenkova 1.0
Pauline Parmentier 0.6
Andrea Hlavackova 0.5
Lauren Davis -0.1
Johanna Larsson -0.1
Svetlana Kuznetsova -0.7
Stefanie Voegele -0.8
Heather Watson -0.8
Kristina Mladenovic -0.9
Olga Puchkova -1.0
Donna Vekic -1.3
Karin Knapp -1.5
Karolina Anna Schmiedlova -1.5
Karolina Pliskova -1.7
Misaki Doi -1.8
Melinda Czink -2.0
Samantha Stosur -2.4
Mathilde Johansson -2.5
Zuzana Kucova -2.8
Eugenie Bouchard -3.2
Maria Joao Koehler -3.5
Kiki Bertens -3.7
Tatjana Malek -3.9
Julia Glushko -4.3
Jana Cepelova -4.4
Stephanie Foretz Gacon -4.4
Tsvetana Pironkova -4.5
Laura Robson -5.2
Lesya Tsurenko -5.2
Sabine Lisicki -5.3
Silvia Soler-Espinosa -5.6
Nina Bratchikova -5.6
Ashleigh Barty -5.9
Coco Vandeweghe -6.0
Mirjana Lucic -7.0
Camila Giorgi -7.3
Elina Svitolina -7.3
Maria Kirilenko -7.6
Sofia Arvidsson -7.9
Ayumi Morita -8.7
Yulia Putintseva -9.6
Monica Puig -9.6
Shahar Peer -9.8
Arantxa Rus -10.2
Urszula Radwanska -10.2
Petra Martic -10.4
Aravane Rezai -11.2
Bethanie Mattek-Sands -12.6
Jamie Hampton -12.7
Daniela Hantuchova -12.8
Lourdes Dominguez-Lino -14.3
Kimiko Date-Krumm -14.9
Sorana Cirstea -15.4
Sloane Stephens -15.4
Kirsten Flipkens -15.9
Mona Barthel -16.1
Lucie Safarova -16.2
Anna Tatishvili -18.4
Agnieszka Radwanska -18.6
Caroline Wozniacki -19.0
Su-Wei Hsieh -19.7
Jelena Jankovic -23.7
Nadezda Petrova -25.4
Anabel Medina Garrigues -28.9
Petra Kvitova -32.2
Elena Vesnina -33.1
Ekaterina Makarova -36.3
Klara Zakopalova -42.5
Carla Suarez Navarro -43.0
Angelique Kerber -47.3
Na Li -74.0

Food For Thought

The two most likely finals are Williams-Sharapova (Serena is 13-2 in this series) and Williams-Azarenka (Serena: 11-2). If either of those finals were to happen, Advanced Baseline will perhaps underrate Serena's chances of winning. Historically, AB has Serena's expected win total, the sum of all her win probabilities against Sharapova and Azarenka, at about 8-9 wins each, so she's outperformed AB's historical expectations.

In general, when a player or team begins to outperform a model's expectations, there are two explanations: They've been lucky, or there's something the model's not capturing. In other sports, like baseball and football, there are good supplementary metrics to identify instances of good or bad luck, like Batting Average on Balls in Play (BABIP) or fumble recovery rate, but there aren't a lot of good ones in tennis to help see if Serena's gotten lucky in her matchups (which deserves a post all by itself). However, I'm more inclined to believe there is in fact something AB isn't capturing that will affect Serena's heads-up record down the road: her heads-up record itself.

When Serena beat Sharapova in Miami earlier this year, a lot was written about Sharapova's confidence, or possible lack thereof, heading into the final. Sharapova had to know she was an underdog, and according to plenty, you could see that lack of confidence negatively affecting her play. Normally, I'd say this kind of analysis is the same tea-leaf reading that also asserts statistically dubious claims like "Player X Is Clutch" or "Team Y Has All The Momentum." But in an individual sport like tennis, I think a player's mental toughness is not only very real, but it also has a greater effect on the outcome than in team sports, where success depends less on a single individual. So if toughness/confidence is a real thing, is there any way we can measure it?

Consider the Serena/Sharapova example. Let's say you believe Sharapova went into her final match afraid to play Serena because of her 2-12 lifetime record. At what point does their head-to-head record begin to negatively affect Sharapova's confidence: 2-6? 2-8? 2-10? Is her confidence level against Serena also influenced by her confidence level against other players, i.e. if she played really well in all matches leading up to the final? And do other players like Azarenka and Li Na also have their confidence affected by their prior record against Serena? If so, how can you tell if they're more or less affected than Sharapova?

Attempting to quantify all of this is an awfully tall order -- maybe it can't be reliably incorporated into a model, and that's just a permanent limitation of AB. But if it can be done, it'd probably be easier to start by process of elimination and figure out better ways to measure luck in tennis. The more results we can definitely identify as lucky, the better understanding we'll have of what to look for when trying to quantify something as difficult as confidence and mental toughness.

Favorite Bets

Your Bovada account that you haven't looked at since football season ended? It has a tennis section too. Instant rooting interest! Here are my favorite bets:

Round 1 head-to-head:

Johanna Larsson (+200) vs. Monica Niculescu
Mallory Burdette (-140) vs. Donna Vekic
Flavia Pennetta (-115) vs. Kirsten Flipkens
Caroline Wozniacki (+110) vs. Laura Robson
Irina-Camelia Begu (+110) vs. Silvia Soler-Espinosa

Favorite futures:

Maria Sharapova (+485) to win outright
Kaia Kanepi (250-1) to win outright
Simona Halep (375-1) to win outright

Final Thoughts

I don't know if we'll ever get a Grand Slam that is truly wide open in the sense with which most people would associate: a dozen legitimate contenders, landmines at every step and a true sense of everyone having a shot.

As fun as it is to try and predict the future, I'd love a tournament where I look at the field and say I have absolutely no idea what's going to happen, because uncertainty makes sports so fun. It probably won't get any more wide open than this for the rest of the year, since Serena and Sharapova will be even bigger favorites at Wimbledon, and the U.S. Open's hard court plays to the strengths of the top 10. Still, a one-in-five chance of a champion outside the top three isn't all that bad, especially in light of the Rafa-like surge Serena's had in the past couple weeks. Anyone who's played poker knows that aces lose to kings enough that one-in-five isn't insignificant; so who's to say this isn't the year in which the 2-outer won't hit?

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