Beyond the Big 3: quantifying the “Alcaraz Effect” on future draws

The 2023 Wimbledon final lasted four hours and forty-two minutes. Carlos Alcaraz, at 20 years old, took down Novak Djokovic in five sets: 1-6, 7-6(6), 6-1, 3-6, 6-4. The match marked a shift in professional tennis that extends beyond one player’s trophy case. By age 21, Alcaraz had accumulated four Grand Slam titles, three more than Federer or Djokovic held before turning 22, and one more than Nadal. He’d won five Masters 1000 titles. Those numbers tell part of the story. The rest lives in the data showing how his presence reshapes tournament dynamics, betting markets, and opponent preparation. His playing style doesn’t just win matches. It changes how tennis gets played, predicted, and analyzed.

The betting angle

Oddsmakers started adjusting their models for Alcaraz matches in 2022. His numbers create problems for traditional tennis betting algorithms. At the 2025 US Open, reports indicated he faced only 10 break points across seven matches, one of the rarest stretches of service dominance in modern Grand Slam history. He got broken three times total.

The serving numbers force betting sites by TheLines and other sportsbooks to reconsider their in-match probability models. Standard serve-hold expectations don’t apply when a player maintains that level of service dominance. Research using LightGBM machine learning on match data shows 84% probability of winning when the server is performing at elite levels, a threshold Alcaraz consistently reaches.

Then there’s the drop shot variable. Alcaraz hits drop shots on 5.4% of points, winning 62.1% of those attempts according to Match Charting Project data. He gains 6.5 points per thousand through drop shot selection alone. Live betting markets now factor in court position and rally length differently when he’s playing. If he’s camped two feet behind the baseline after a five-shot rally, odds shift because the drop shot becomes a legitimate scoring option rather than a desperation play.

A momentum prediction model built using LightGBM machine learning shows 76.6% accuracy in predicting point-by-point outcomes when serving patterns and tactical shot selection get properly weighted. The model analyzed eight key factors: games won, sets won, serve advantage, points advantage, break points won, unforced errors, winners, and distance run. When researchers compared momentum-based predictions to random predictions, the results showed 72.4% accuracy for the momentum model versus 51.1% for random guessing. This kind of predictive power matters to betting markets that price in-play odds on every point.

Statistical dominance across categories

His drop shot work deserves a separate analysis. Match Charting Project data covering the 60 most-charted tour regulars from 2015-2024 reveals something distinctive about Alcaraz’s approach. While Kei Nishikori wins 69.6% of points behind his drop shot, he uses it on just 1.9% of points. Alexander Bublik goes to the drop on 7.2% of points but wins only 45.4%, actually losing expected value. Alcaraz found the intersection: frequent enough to keep opponents honest, successful enough to gain points.

Only four players appear on both the high-frequency and high-success lists: Alcaraz, Alejandro Davidovich Fokina, Sebastian Baez, and Andy Murray. That combination, using the shot often while maintaining effectiveness, forces opponents into tactical dilemmas on every baseline rally.

The distance and positioning data from momentum research reveals the control he establishes. In analyzed matches, winners ran an average of 13.40 meters per point while losers covered 14.47 meters. Alcaraz makes opponents run more while moving less himself. His court positioning, aggressive baseline stance combined with net approaches, forces defensive players into reactive modes. The research documented 936 successful net approaches compared to 246 for opponents, showing a dramatic advantage in offensive positioning.

Against Jannik Sinner, the head-to-head record stood at 8-5 in Alcaraz’s favor through mid-2025. The matches between them showcase contrasting styles, with Alcaraz generating break opportunities through variety, drop shots, net approaches, serve-and-volley sequences, while Sinner relies more on baseline power and consistency.

Baez shows a comparable drop shot success rate at 63.2%, but his overall game lacks the serve foundation that makes Alcaraz’s tactical approach so effective. The combination of elite serving with elite court craft creates a compound effect. Opponents can’t camp behind the baseline to defend the power game because the drop shot lurks. They can’t press forward because his passing shots and serve consistency punish aggressive positioning.

The momentum model

Researchers built a quantitative momentum model using match data that identified the statistical factors most predictive of point outcomes. The LightGBM algorithm weighted eight variables, with distance run receiving the highest importance score at 0.487. Other factors included games won, sets won, serve advantage, points advantage, break points won, unforced errors, and winners.

The model’s accuracy reached 76.6% in predicting outcomes when properly trained on match data. This significantly outperforms baseline expectations, the momentum-based approach achieved 72.4% accuracy compared to 51.1% for random predictions.

Three-point runs matter most in the data. Consecutive point victories create the largest momentum swings, with a correlation coefficient of 0.23 for winners versus -0.16 for three-point losses. When Alcaraz strings together three straight points, the probability data suggests a significant advantage in winning the next point compared to baseline expectations.

The research showed that in matches where the eventual winner prevailed, they experienced “positive events”, winning serves, point streaks, successful net approaches, 79.9% of the time. Their opponents experienced positive events only 34% of the time in those same matches. The model tested these patterns across multiple matches and maintained prediction accuracy above 70%.

For the 2023 Wimbledon final specifically, the researchers identified dozens of momentum shifts throughout the match. These turning points occurred when the relative momentum advantage switched from one player to the other, driven by offensive shot-making on one side and errors on the other.

Impact on future draws

Tournament directors now face a calculation problem. Alcaraz’s presence in a quarter of the draw doesn’t just eliminate one seeded player. His style forces opponents to adjust tactics in the matches before they face him. At major tournaments, players facing Alcaraz have reportedly added specialized preparation for defending drop shots and handling rapid transitions from defense to offense.

The seeding system assumes equal probability of upsets across similar ranking differentials. A No. 1 seed should have comparable difficulty beating a No. 16 seed regardless of playing style. Alcaraz challenges that assumption with his court versatility. He doesn’t have a “bad surface” the way clay specialists or grass-court players do, competing effectively across all three surfaces.

The Match Charting Project data suggests opponents alter their tactical approaches when facing him. Players appear to concede the drop shot battle, hitting them less frequently than their seasonal averages rather than engaging in that particular contest. Meanwhile, first-serve percentages often run higher against him as opponents take fewer risks, trying to avoid the exact break point situations where his return game thrives.

The Big 3 era created strategic templates: overpower Federer before he dictates, outlast Nadal on clay, defend until Djokovic makes an error. Alcaraz doesn’t offer that clarity. His game has no obvious weakness to exploit, no consistent tactical approach that yields success across opponents. That’s the real “Alcaraz Effect”, not just winning matches, but forcing the entire tour to rethink preparation, tactics, and what defines elite tennis in 2025.