{"id":7345,"date":"2025-01-03T12:40:21","date_gmt":"2025-01-03T12:40:21","guid":{"rendered":"https:\/\/www.tennisabstract.com\/blog\/?p=7345"},"modified":"2025-01-03T12:40:21","modified_gmt":"2025-01-03T12:40:21","slug":"the-52-week-ranking-forecast","status":"publish","type":"post","link":"https:\/\/www.tennisabstract.com\/blog\/2025\/01\/03\/the-52-week-ranking-forecast\/","title":{"rendered":"The 52-Week Ranking Forecast"},"content":{"rendered":"
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A healthy Karolina Muchova is a top-tenner. Credit: Hameltion<\/a><\/em><\/figcaption><\/figure>\n<\/div>\n\n\n

What will the men’s and women’s ranking lists look like at the end of the 2025 season? A few days ago, I attempted to predict which players<\/a> would crack the top 100. Today, we’re playing for bigger stakes: The names at the top the table.<\/p>\n\n\n\n

As with the top-100-breakthrough forecast, the most important inputs are current Elo rank<\/a> and current ATP or WTA rank. Elo tells us how well someone is playing, and the official ranking tells us how well that translated into points. After all, ranking points are what will determine the list in a year’s time, too.<\/p>\n\n\n\n

The cumulative ATP and WTA rankings reflect whether a player missed time in the previous year; while that isn’t always indicative of whether he or she will be absent again, injuries often recur and some pros have a hard time staying on court. The official ranking also gives some players a head start over others: The 32nd seed at the Australian Open is more likely to reach the second week than the best unseeded player, even if they have roughly the same skill level.<\/p>\n\n\n\n

Age is crucial, as well. The younger the player, the more we expect him or her to improve over the course of the year. Later than the mid-20s, however, results trend (usually!) in the other direction.<\/p>\n\n\n\n

I tested the usefulness of myriad other variables, including height, handedness, and surface preference. None unambiguously improved the model. I ended up using just one more input: last<\/em> year’s Elo rank. Current ranks have more predictive value, but last year’s position helps, as it offers a clue as to whether a player’s current level is sustainable.<\/p>\n\n\n\n

Enough chatter–let’s start with the forecast for the 2025 year-end women’s rankings:<\/p>\n\n\n\n

YE 25    Player                     Age  YE 24  Elo 24  Elo 23  \n1        Aryna Sabalenka           26.7      1       1       3  \n2        Iga Swiatek               23.6      2       2       1  \n3        Coco Gauff                20.8      3       3       2  \n4        Qinwen Zheng              22.2      5       4       8  \n5        Elena Rybakina            25.5      6       6       5  \n6        Jasmine Paolini           29.0      4       9      28  \n7        Jessica Pegula            30.9      7       8       4  \n8        Paula Badosa              27.1     12       5      24  \n9        Emma Navarro              23.6      8      16      53  \n10       Mirra Andreeva            17.7     16      15      26  \n11       Diana Shnaider            20.7     13      12     100  \n12       Daria Kasatkina           27.7      9      19      16  \n13       Karolina Muchova          28.4     22       7       6  \n14       Barbora Krejcikova        29.0     10      22      14  \n15       Marta Kostyuk             22.5     18      20      38  \n16       Anna Kalinskaya           26.1     14      23      31  \n17       Madison Keys              29.9     21      11      12  \n18       Beatriz Haddad Maia       28.6     17      17      18  \n19       Jelena Ostapenko          27.6     15      29      13  \n20       Marketa Vondrousova       25.5     39      10       9  \n21       Danielle Collins          31.0     11      31      22  \n22       Linda Noskova             20.1     26      35      42  \n23       Donna Vekic               28.5     19      27      41  \n24       Liudmila Samsonova        26.1     27      26      11  \n25       Leylah Fernandez          22.3     31      30      20  \n                                                                \nYE 2025  Player                     Age  YE 24  Elo 24  Elo 23  \n26       Victoria Azarenka         35.4     20      13      29  \n27       Elina Svitolina           30.3     23      24      19  \n28       Ons Jabeur                30.3     42      14       7  \n29       Maria Sakkari             29.4     32      21      15  \n30       Katie Boulter             28.4     24      33      62  \n31       Amanda Anisimova          23.3     36      28       \n32       Anastasia Potapova        23.8     35      36      36  \n33       Emma Raducanu             22.1     56      18       \n34       Yulia Putintseva          30.0     29      25      55  \n35       Magdalena Frech           27.0     25      51      85  \n36       Elise Mertens             29.1     34      37      33  \n37       Xin Yu Wang               23.3     37      59      57  \n38       Ekaterina Alexandrova     30.1     28      48      25  \n39       Anastasia Pavlyuchenkova  33.5     30      32      35  \n40       Marie Bouzkova            26.4     44      44      30  \n41       Elina Avanesyan           22.3     43      60     131  \n42       Lulu Sun                  23.7     40      56     182  \n43       Peyton Stearns            23.2     47      53     113  \n44       Katerina Siniakova        28.6     45      38      40  \n45       Olga Danilovic            23.9     51      50      82  \n46       Ashlyn Krueger            20.7     64      54      67  \n47       Camila Osorio             23.0     59      49      56  \n48       Dayana Yastremska         24.6     33     104      96  \n49       Clara Tauson              22.0     50      83      64  \n50       Karolina Pliskova         32.8     41      40      39\n<\/pre>\n\n\n\n
No big surprises here–that’s the nature of a model like this. Where players are predicted to move up or down, it’s usually because their Elo rank is notably higher or lower than their official position, like Muchova or Paolini. Mirra Andreeva, the youngest woman in the top 175, is expected to gradually work her way<\/a> into the top ten.<\/p>\n\n\n\n
Getting fuzzier<\/strong><\/p>\n\n\n\n
Of course, there’s considerable uncertainty. When we check in at the end of the 2025 season, we’ll find some substantial moves, like Paolini in 2024<\/a>. We can get a better idea of that uncertainty by forecasting the likelihood that players reach certain thresholds.<\/p>\n\n\n\n
Here is each top player’s probability of becoming the 2025 year-end number one:<\/p>\n\n\n\n
Player              p(#1)  \nAryna Sabalenka     42.3%  \nIga Swiatek         32.6%  \nCoco Gauff          21.1%  \nQinwen Zheng         6.9%  \nElena Rybakina       4.3%  \nJasmine Paolini      2.8%  \nJessica Pegula       2.4%  \nEmma Navarro         0.9%  \nPaula Badosa         0.9%  \nDaria Kasatkina      0.9%  \nBarbora Krejcikova   0.7%  \nMirra Andreeva       0.7%  \nDiana Shnaider       0.5%  \nKarolina Muchova     0.5%\n<\/pre>\n\n\n\n
This is not the list I would have made. Again, this type of model isn’t going to give you big surprises, and there’s no consideration for things like playing styles. Intuitively, a big breakthrough from Andreeva (or Shnaider) seems more likely than a belated push from Kasatkina, or even Pegula.<\/p>\n\n\n\n
In any event, we get an idea of how much the ranking list can shuffle itself in a year’s time. Even beyond these 14 names, the model gives another 20 women at least a one-in-a-thousand chance to end the year at the top.<\/p>\n\n\n\n
We can run a similar exercise to get the odds that each player ends the season in the top 5, 10, or 20:<\/p>\n\n\n\n
Player                    p(top 5)  p(top 10)  p(top 20)  \nAryna Sabalenka              82.4%      95.8%      99.3%  \nIga Swiatek                  81.0%      94.9%      98.9%  \nCoco Gauff                   75.5%      92.7%      98.3%  \nQinwen Zheng                 50.3%      80.3%      95.5%  \nElena Rybakina               32.5%      65.5%      90.3%  \nJessica Pegula               15.5%      42.0%      78.4%  \nPaula Badosa                 15.2%      41.5%      81.7%  \nMirra Andreeva               13.7%      34.5%      68.3%  \nJasmine Paolini              13.1%      38.4%      77.7%  \nKarolina Muchova             10.6%      30.2%      69.8%  \nDiana Shnaider                8.8%      25.7%      64.6%  \nEmma Navarro                  7.9%      24.0%      60.2%  \nMarketa Vondrousova           6.6%      19.2%      53.8%  \nDaria Kasatkina               5.8%      18.3%      49.6%  \nMarta Kostyuk                 4.9%      14.9%      43.4%  \nMadison Keys                  4.9%      15.8%      49.7%  \nBarbora Krejcikova            4.2%      13.5%      40.2%  \nBeatriz Haddad Maia           3.8%      12.1%      39.7%  \nAnna Kalinskaya               3.5%      11.2%      35.8%  \nJelena Ostapenko              3.0%       9.4%      28.8%  \nLeylah Fernandez              2.9%       8.5%      25.8%  \nLiudmila Samsonova            2.8%       8.6%      27.0%  \nLinda Noskova                 2.8%       8.2%      24.9%  \nOns Jabeur                    2.8%       8.7%      31.7%  \nMaria Sakkari                 1.9%       6.1%      23.1%  \n                                                          \nPlayer                    p(top 5)  p(top 10)  p(top 20)  \nDanielle Collins              1.9%       6.3%      22.5%  \nElina Svitolina               1.7%       5.7%      21.6%  \nDonna Vekic                   1.7%       5.4%      21.1%  \nVictoria Azarenka             1.6%       5.9%      28.2%  \nAnastasia Potapova            1.5%       4.5%      15.8%  \nEmma Raducanu                 1.5%       4.7%      21.6%  \nAmanda Anisimova              1.1%       3.5%      15.4%  \nYulia Putintseva              1.0%       3.4%      15.1%  \nKatie Boulter                 1.0%       3.3%      13.5%  \nMarie Bouzkova                0.8%       2.4%       8.8%  \nElise Mertens                 0.8%       2.5%      10.1%  \nXin Yu Wang                   0.8%       2.3%       7.8%  \nAshlyn Krueger                0.8%       2.1%       7.3%  \nCamila Osorio                 0.7%       2.0%       7.4%  \nEkaterina Alexandrova         0.7%       2.1%       7.9%  \nMagdalena Frech               0.6%       2.0%       8.0%  \nKaterina Siniakova            0.6%       2.0%       8.1%  \nOlga Danilovic                0.6%       1.8%       6.8%  \nPeyton Stearns                0.6%       1.7%       6.6%  \nAnastasia Pavlyuchenkova      0.6%       1.9%       8.9%  \nElina Avanesyan               0.6%       1.7%       6.2%  \nClara Tauson                  0.5%       1.4%       4.3%  \nLulu Sun                      0.5%       1.5%       5.9%  \nEva Lys                       0.4%       1.2%       4.8%  \nElisabetta Cocciaretto        0.4%       1.2%       4.1% <\/pre>\n\n\n\n
Most interesting to me in this table is where the columns diverge. Andreeva, with her unrealized potential, ranks higher on the top-5 list than by top-10 or top-20 probability. Azarenka, though she has little chance of returning to the top ten, is more likely than her list-neighbors to hang inside the top 20.<\/p>\n\n\n\n
The same variation means that there are some new names in the table. Eva Lys, for instance, is forecast to land at #65 ahead of the 2026 season. But because she is young and has already posted multiple top-100 seasons by Elo rating, she has an outsized chance of a major breakout. The women who were displaced are either fringy veterans, like Pliskova, or those whose Elo ratings didn’t match their WTA rank, such as Yastremska.<\/p>\n\n\n\n
(These forecasts are probably more accurate than the year-end-number-one table above. There haven’t been many year-end number ones, by definition, so there’s less data to draw upon.)<\/p>\n\n\n\n
Long may Sinner reign<\/strong><\/p>\n\n\n\n
Now for the men. I’ve extended this list to 51 for obvious reasons:<\/p>\n\n\n\n
YE 25  Player                  Age  YE 24  Elo 24  Elo 23  \n1      Jannik Sinner          23.4      1       1       2  \n2      Carlos Alcaraz         21.7      3       3       3  \n3      Alexander Zverev       27.7      2       4       5  \n4      Taylor Fritz           27.2      4       6      10  \n5      Daniil Medvedev        28.9      5       5       4  \n6      Novak Djokovic         37.6      7       2       1  \n7      Holger Rune            21.7     13      10      12  \n8      Jack Draper            23.0     15       8      19  \n9      Casper Ruud            26.0      6      21      16  \n10     Alex de Minaur         25.9      9      16      11  \n11     Andrey Rublev          27.2      8      18       6  \n12     Stefanos Tsitsipas     26.4     11      14       9  \n13     Tommy Paul             27.6     12      11      18  \n14     Hubert Hurkacz         27.9     16       9       8  \n15     Grigor Dimitrov        33.6     10       7       7  \n16     Ugo Humbert            26.5     14      17      13  \n17     Lorenzo Musetti        22.8     17      20      50  \n18     Arthur Fils            20.6     20      25      38  \n19     Ben Shelton            22.2     21      22      17  \n20     Sebastian Korda        24.5     22      15      22  \n21     Tomas Machac           24.2     25      12      33  \n22     Karen Khachanov        28.6     19      19      23  \n23     Felix Auger Aliassime  24.4     29      28      15  \n24     Frances Tiafoe         26.9     18      33      26  \n25     Matteo Berrettini      28.7     34      13      14  \n                                                           \nYE 25  Player                  Age  YE 24  Elo 24  Elo 23  \n26     Alexei Popyrin         25.4     24      27      75  \n27     Jiri Lehecka           23.1     28      39      46  \n28     Flavio Cobolli         22.7     32      30     136  \n29     Alex Michelsen         20.4     41      35     134  \n30     Jakub Mensik           19.3     48      37     119  \n31     Mpetshi Perricard      21.5     31      43     192  \n32     Francisco Cerundolo    26.4     30      36      25  \n33     Matteo Arnaldi         23.9     37      48      31  \n34     Sebastian Baez         24.0     27      67      40  \n35     Brandon Nakashima      23.4     38      42      70  \n36     Jordan Thompson        30.7     26      29      51  \n37     Juncheng Shang         19.9     50      52       \n38     Tallon Griekspoor      28.5     40      32      24  \n39     Alejandro Tabilo       27.6     23      54     121  \n40     Denis Shapovalov       25.7     56      34      34  \n41     T M Etcheverry         25.5     39      58      65  \n42     Alexander Bublik       27.5     33      59      44  \n43     Davidovich Fokina      25.6     61      46      28  \n44     Roman Safiullin        27.4     60      38      27  \n45     Nicolas Jarry          29.2     35      63      20  \n46     Nuno Borges            27.9     36      53      88  \n47     Thanasi Kokkinakis     28.7     77      24      61  \n48     Luciano Darderi        22.9     44     106     122  \n49     Miomir Kecmanovic      25.3     54      65      71  \n50     Jan Lennard Struff     34.7     42      26      35  \n51     Joao Fonseca           18.4    145      45     \n<\/pre>\n\n\n\n
The men’s ranking model is more accurate than the women’s version, though that may be because it is built, in part, on the unusually stable Big Three\/Big Four era. That stability might be gone, taking the reliability of this model with it. (The men’s model predicted the log of next year’s ranking with an adjusted r-squared of .631, compared to .580 for the women.) So again, if it looks boring, that’s the nature of the beast.<\/p>\n\n\n\n
Still: We have Carlos Alcaraz taking back the number two spot, Holger Rune returning to the top ten, and Jack Draper following him in. In the other direction, we see Grigor Dimitrov’s age catching up to him, dropping five spots from his current position.<\/p>\n\n\n\n
At the bottom of the list, we find Joao Fonseca bounding up nearly 100 ranking spots in a single season. That already feels conservative, less than one week into his season. All of these numbers are based on 2024 year-end rankings, yet Fonseca is up 18 places in the live rankings<\/a> with his run to the Canberra Challenger final. He’d gain another 14 with a win tomorrow.<\/p>\n\n\n\n
What about Novak?<\/strong><\/p>\n\n\n\n
The table above shows Novak Djokovic in 6th place, a prediction that aggregates a vast range of possibilities. Here are the odds of various players ending 2025 at the top of the list:<\/p>\n\n\n\n
Player             p(#1)  \nJannik Sinner     56.4%  \nCarlos Alcaraz    22.5%  \nNovak Djokovic    14.6%  \nAlexander Zverev   3.8%  \nDaniil Medvedev    3.4%  \nTaylor Fritz       1.3%  \nHolger Rune        1.2%  \nJack Draper        1.2%  \nHubert Hurkacz     1.0%  \nGrigor Dimitrov    0.7% <\/pre>\n\n\n\n
No one else is even half as likely as Dimitrov to end the season ranked #1. Sinner is the clear favorite, with virtually every stat in his favor. Alcaraz is expected to improve. Djokovic, though, is the clear number three, far ahead of the other players above him in the previous table.<\/p>\n\n\n\n
This is partly to be expected: He ended 2024 in second place on the Elo list<\/a>. He didn’t play a full schedule, but he posted great results much of the time he played, and Alcaraz wasn’t consistent enough to capitalize on the veteran’s step back. Beyond that, remember that the model considers last year’s Elo rank as well. Twelve months ago, Djokovic still had a strong claim to be the best player in the world. His age counts against him, but he is one of only a few players in the 2025 field who has proven he can reach the top.<\/p>\n\n\n\n
Novak’s 6th-place forecast, then, averages a disproportionately high probability of a resurgence with all the things that can happen to 37-year-old athletes. He’s more likely than, say, (projected) #5 Medvedev or #7 Rune to claim the top spot, but he’s also more likely to fall down the list due to injury or lack of interest.<\/p>\n\n\n\n
Djokovic looks like less of an outlier when we see the chances of top-5, top-10, and top-20 finishes this year:<\/p>\n\n\n\n
Player                  p(5)  p(10)  p(20)  \nJannik Sinner          95.6%  98.9%  99.8%  \nCarlos Alcaraz         84.5%  95.7%  99.2%  \nAlexander Zverev       61.7%  88.4%  97.5%  \nDaniil Medvedev        38.5%  71.8%  92.6%  \nTaylor Fritz           34.1%  72.0%  92.9%  \nNovak Djokovic         32.4%  59.8%  86.4%  \nHolger Rune            20.3%  52.8%  86.1%  \nJack Draper            15.6%  46.3%  82.2%  \nHubert Hurkacz          9.8%  29.9%  68.2%  \nAndrey Rublev           9.8%  31.8%  70.8%  \nStefanos Tsitsipas      9.6%  31.6%  70.6%  \nAlex de Minaur          9.5%  32.9%  72.1%  \nGrigor Dimitrov         8.3%  27.0%  63.1%  \nCasper Ruud             7.7%  31.1%  70.6%  \nTommy Paul              7.1%  26.8%  65.0%  \nUgo Humbert             5.3%  20.2%  56.9%  \nBen Shelton             4.8%  18.5%  55.8%  \nSebastian Korda         4.5%  17.8%  53.5%  \nTomas Machac            4.4%  18.3%  54.3%  \nArthur Fils             3.7%  17.0%  54.0%  \nLorenzo Musetti         3.4%  16.6%  52.3%  \nMatteo Berrettini       2.4%   8.7%  32.2%  \nFelix Auger Aliassime   2.1%   8.2%  32.7%  \nKaren Khachanov         2.0%   8.8%  32.8%  \nFrances Tiafoe          1.3%   6.3%  25.9%  \n                                            \nplayer                  p(5)  p(10)  p(20)  \nJiri Lehecka            1.0%   5.0%  22.7%  \nAlexei Popyrin          0.9%   5.4%  23.1%  \nFrancisco Cerundolo     0.8%   3.8%  17.3%  \nFlavio Cobolli          0.7%   4.5%  20.7%  \nJakub Mensik            0.7%   4.1%  20.0%  \nAlex Michelsen          0.7%   4.2%  20.2%  \nMatteo Arnaldi          0.7%   3.0%  14.7%  \nTallon Griekspoor       0.6%   2.4%  11.0%  \nBrandon Nakashima       0.5%   2.8%  13.9%  \nDenis Shapovalov        0.5%   2.2%  10.5%  \nSebastian Baez          0.5%   2.5%  12.6%  \nMpetshi Perricard       0.5%   3.3%  16.2%  \nJordan Thompson         0.4%   2.3%  10.3%  \nDavidovich Fokina       0.4%   1.5%   7.6%  \nRoman Safiullin         0.4%   1.5%   7.1%  \nJuncheng Shang          0.3%   2.0%  10.8%  \nNicolas Jarry           0.3%   1.2%   5.7%  \nThanasi Kokkinakis      0.3%   1.2%   5.7%  \nAlexander Bublik        0.3%   1.3%   6.6%  \nT M Etcheverry          0.3%   1.4%   7.1%  \nAlejandro Tabilo        0.2%   1.5%   7.5%  \nJan Lennard Struff      0.2%   1.0%   4.3%  \nJoao Fonseca            0.2%   1.0%   5.7%  \nNuno Borges             0.2%   1.0%   5.2%  \nMiomir Kecmanovic       0.2%   0.9%   4.5%<\/pre>\n\n\n\n
The various models don’t quite agree: It can’t really be the case that if Djokovic cracks the top five (32.4% here), it’s nearly 50\/50 whether he ends the season at number one. From outside of the models, we can be particularly skeptical, since we know that Novak isn’t likely to play a full schedule. Still, we can glean something from the juxtaposition: There’s not a lot of middle ground for the all-time-great.<\/p>\n\n\n\n
Again, it’s worth peeking at the bottom of the list. Fonseca makes this one, too, with a nearly 6% chance of a top-20 debut this year. (Actually, a debut<\/em> is even more likely, since this is the stricter probability of a year-end top-20 finish.) It seems a bit crazy to say that the 18-year-old has the same top-20 chances as Nicolas Jarry. On the other hand, Fonseca leads Jarry on the Elo table by a healthy margin. He may already be the stronger player.<\/p>\n\n\n\n
Few pros are likely catapult up or down the rankings like Fonseca. Plenty will make moves that these models don’t foresee. With the information available at the beginning of the season, we can get a general sense of how things will change over the next twelve months. Now for the good part: We get to find out how the models were wrong.<\/p>\n\n\n\n
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What will the men’s and women’s ranking lists look like at the end of the 2025 season? A few days ago, I attempted to predict which players would crack the top 100. Today, we’re playing for bigger stakes: The names at the top the table. As with the top-100-breakthrough forecast, the most important inputs are … Continue reading The 52-Week Ranking Forecast<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[32,40,91],"tags":[],"class_list":["post-7345","post","type-post","status-publish","format-standard","hentry","category-elo-ratings","category-forecasting","category-rankings"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/posts\/7345","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/comments?post=7345"}],"version-history":[{"count":3,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/posts\/7345\/revisions"}],"predecessor-version":[{"id":7350,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/posts\/7345\/revisions\/7350"}],"wp:attachment":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/media?parent=7345"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/categories?post=7345"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/tags?post=7345"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}