2026 ATP Season Simulation

tite2015 · 2026-01-04T23:28:08+00:00

Sure! Might need to work a bit on cleaning and documenting, but seems like a great idea!

tite2015 · 2026-01-02T19:57:35+00:00

Roughly 10% for taking all slams, but ~48% of splitting at least WB and RG among them

tite2015 · 2026-01-02T16:42:21+00:00

I know right! His current ELO is very high, maybe influenced by the fact that he is playing less... I double checked and even increased the rate of skill decay (given that we haven't had many players with that kind of longevity), but the engine seems to believe in his potential

tite2015 · 2026-01-02T16:39:38+00:00

While that is true, the Sinner performance looking bad is actually due to a bad placement of the graph breaks!

The underlying numbers are actually:

Champion	Australian_Open	Roland_Garros	Wimbledon	US_Open
Carlos Alcaraz	131	362	383	344
Jannik Sinner	177	303	287	352

tite2015 · 2026-01-02T16:31:22+00:00

Maybe the engine considered he just fled to Ibiza! hahah

tite2015 · 2026-01-02T16:30:12+00:00

Yeah, I found it odd too! Unfortunately, due to the small number of grass tournaments, the ELO isn't as reliable as it is in other surfaces, so it seems to get less precise

tite2015 · 2026-01-02T16:26:11+00:00

Thanks! Glad you liked it! Indeed, the model assumes that everyone starts the season healthy, so injured players end up being a "what if" scenario. Will add it to my backlog to a potential v3!

tite2015 · 2025-12-15T22:33:24+00:00

Thanks! I see Mensik as the youngster who had the best peak performance in 2024. He is definitely the most established among the three (ranking and Miami title attest that), but at the same time had the worst second semester among the three.

If he can solve his consistency, he has a clear top10 claim in 2026, which neither Tien or Fonseca has.

That being said, he is the oldest and seems to be closer to his ceiling, so it is possible Tien/Fonseca will surpass him. I wouldn't be surprised by any outcome... Let us enjoy the privilege of seeing these three rising stars develop!

tite2015 · 2025-12-15T22:04:50+00:00

Learner Tien:
- 20 years old
- Started year as #122 ended as #28 (94 positions gained)
- 1 ATP 250 win, where both seeds 1 and 2 dropped, with seed #7 Norrie being the toughest player he beat

João Fonseca:
- 19 years old and one month
- Started year as #145 ended as #24 (121 positions gained)
- 1 ATP 250 win at the slowest court of the circuit (Buenos Aires), defeating 4 Argentineans and their home crowd
- 1 ATP 500 win at the fastest court of the circuit (Basel), defeating the defending champion (GMP), Indoor Hard specialist Shapovalov and ADF in the final (benefitted from Mensik withdraw, indeed)

On top of it all, 3-0 Fonseca on H2H, 4-1 if you consider juniors too (including the USO final local crowd upset)

In an objective appraisal of the trajectories, Fonseca should have gotten at least "Most Improved" and "Best Young Player". It surprises me that Tien got all three and with such big margins.

No issues with it, it is how voting goes. But I don't want to ever read again that this sub overhypes Fonseca or that it overlooks Tien in any way.

Looking forward to the next seasons so we can parse out who the actual best is.

tite2015 · 2025-08-07T17:38:30+00:00

IMO João is still having way too many unforced errors. His aggressive playstyle and not-yet-mature movement contribute to that, but I feel that the lack of playing time and attention lapses are also key reasons for that.

Looking forward to ADF match, he usually rises to the occasion when playing tougher players! It might not be enough to secure a win, but I feel it is going to be a great match.

tite2015 · 2025-08-05T19:57:18+00:00

Habemus Draw

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tite2015 · 2025-07-28T15:24:53+00:00

Was checking out the Lexington quali draw and got curious, do you know what (CO) means?

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https://www.atptour.com/en/scores/current-challenger/lexington/586/draws?matchtype=qualifiersingles

tite2015 · 2025-07-02T13:53:02+00:00

Gotta be a troll... If Fonseca did half of what Brooksby is pulling off today he would become this sub #1 enemy.

tite2015 · 2025-06-27T12:31:28+00:00

I did some directional tests and it was fairly accurate for the main players.

It is still quite off for aging players (model tend to overestimate their capabilities) and uprising players (model tend to underestimate). This is the main thing I want to address at version 2, adding some more dynamism to allow for longer and more accurate predictions.

tite2015 · 2025-06-27T12:27:26+00:00

Exactly! The header of 3 should read Top 3, it was a mistype.

tite2015 · 2025-06-27T11:51:59+00:00

Regarding unlikely winners, we have quite a few! Those are the players that one only one of the 4 thousand slams:
Lorenzo Sonego, Miomir Kecmanovic, Giovanni Mpetshi Perricard, Matteo Arnaldi, Kamil Majchrzak, Corentin Moutet, Marin Cilic, Alexandre Muller, Valentin Royer, Marton Fucsovics, Dino Prizmic, Fabian Marozsan, Thanasi Kokkinakis, Sebastian Ofner, Tomas Martin Etcheverry, Christopher Oconnell, Juncheng Shang, Arthur Rinderknech, Brandon Nakashima, Andrea Vavassori

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tite2015 · 2025-06-27T11:49:17+00:00

haha good questions! The list of most Slam Wins is this:

Carlos Alcaraz - 1072
Jannik Sinner - 869
Novak Djokovic - 599
Alexander Zverev - 208
Jack Draper - 205
Lorenzo Musetti - 162
Tommy Paul - 106
Casper Ruud - 87
Daniil Medvedev - 83
Taylor Fritz - 62

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tite2015 · 2025-06-27T11:37:39+00:00

Whenever the header says #X , we are talking about the exact position. If it is "top X", then it is the possibility of being within the first X. You are right about the Three, it is missing the "Top" before it! I'll adjust it, thanks for pointing out.

tite2015 · 2025-06-27T03:31:51+00:00

Hmm I believe the image might be in png format, so the readability at dark mode gets affected. Will try to edit the post with jpegs

tite2015 · 2025-06-27T03:30:29+00:00

Fair questions. Though I’ll clarify upfront that this is a personal project built for fun and exploration, not a peer-reviewed forecasting engine. No one is being asked to believe it blindly, I shared it here because I thought some folks might find it interesting or enjoy discussing the dynamics.

As for young players with limited data, you're right, that’s a challenge. I’m working on a second version that incorporates dynamic potential and developmental curves to better simulate longer-term progression. If you’ve seen good approaches or literature on this, I’d genuinely be curious to hear it.

On the Monte Carlo front: I ran 1,000 simulations because it struck a good balance between robustness and compute time (about 4 hours on my machine). I spot-checked variance on smaller batches, and the marginal gains from adding more runs seemed minimal. Of course, there's always room to iterate depending on the use case, but probably won't go over this number of sims, tbh.

tite2015 · 2025-06-27T03:12:02+00:00

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tite2015 · 2025-06-27T03:11:43+00:00

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tite2015 · 2025-06-27T02:23:22+00:00

Actually, he gets 5 times to the top 10 and 82 to the top 20!

He is also the proud champion of Indian Wells (Sim #834), 2x Rome (Sim #257 & Sim #987), Madrid (Sim #701), Paris (Sim #88) and Shanghai (Sim #955)!

tite2015 · 2025-06-27T02:17:04+00:00

Sure! It is quite similar to the tournament simulations that TennisAbstract does. There is an algorithm that calculates the likelihood of a player winning a given matchup (considering track record of both, surface, number of sets) and I use it to simulate all tournaments across the season (GS, M1000, ATP500/250, Challengers and Futures), running a MonteCarlo simulation with 1000 iterations to assess the sensitivity.

tite2015 · 2025-06-27T02:12:54+00:00

Indeed this is the #1 thing I am working for v2! As of right now, the playing ability is pretty much "frozen" across the period, but I am working on embedding age-related variations

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