ATP tennis predict — Ryan Seggerman vs Naoki Tajima

Challenger Hard Wuning 3 challenger · qualification - 1. round 08:20

Predicted probability

Ryan Seggerman

USA

85.40%

Naoki Tajima

Japan

14.60%

Average Odds

1.06 / 8.17

Market probability

88.52% / 11.48%

Why This Prediction?

Ryan Seggerman is the favorite because he has a higher Elo rating (1617 vs 1256), a higher Elo rating on hard courts (1529 vs 1309) and a stronger serve (69.6% vs 55.2% of service points won).

Player Comparison

1617.4 Elo 1256.2

1529.5 Elo (surface) 1308.6

52 Wins (Challenger) 6

28 Wins (surface) 6

9 Last 15 form 2

0 Head-to-head wins 0

1.022 Dominance ratio 0.744

612 Rank 1444

27.2 Age 26.0

right Hand right

Performance vs. the Odds

Career record grouped by how big a favorite the market made each player, based on their average odds.

Ryan Seggerman

Heavy favorite (odds below 1.30) 1W–0L · 100.0%

Favorite (odds 1.30-1.80) 2W–1L · 66.7%

Even match (odds 1.80-2.20) 3W–1L · 75.0%

Underdog (odds 2.20-4.00) 0W–3L · 0.0%

Heavy underdog (odds above 4.00) 0W–1L · 0.0%

12 matches with market odds since 2026-05-31.

Naoki Tajima

Heavy favorite (odds below 1.30) 0W–0L · 0.0%

Favorite (odds 1.30-1.80) 0W–0L · 0.0%

Even match (odds 1.80-2.20) 0W–0L · 0.0%

Underdog (odds 2.20-4.00) 0W–0L · 0.0%

Heavy underdog (odds above 4.00) 0W–1L · 0.0%

1 matches with market odds since 2026-07-26.

Serve & Return

Serve and return win rates from each player's last 10 matches at the selected level.

Ryan Seggerman Naoki Tajima

1st Serve In %

55.3%

—

1st Serve Points Won %

70.5%

—

2nd Serve Points Won %

45.5%

—

Return Points Won vs 1st Serve %

26.3%

—

Return Points Won vs 2nd Serve %

42.1%

—

Aces per Service Game

0.79

—

Double Faults per Service Game

0.43

—

Based on each player's last 10 ATP/Grand Slam matches.

1st Serve In %

61.8%

53.4%

1st Serve Points Won %

76.4%

71.0%

2nd Serve Points Won %

55.5%

39.9%

Return Points Won vs 1st Serve %

21.8%

27.3%

Return Points Won vs 2nd Serve %

48.2%

47.7%

Aces per Service Game

0.53

0.51

Double Faults per Service Game

0.26

0.52

Based on each player's last 10 Challenger matches.

Break Point Performance

How each player handles the biggest points - break points saved serving, and converted returning - from their last 10 matches at the selected level.

Ryan Seggerman Naoki Tajima

Break Points Saved %

56.4%

—

Break Points Converted %

34.6%

—

Based on each player's last 10 ATP/Grand Slam matches.

Break Points Saved %

56.5%

46.0%

Break Points Converted %

24.2%

51.2%

Based on each player's last 10 Challenger matches.

Head-to-Head

Ryan Seggerman and Naoki Tajima have never played each other.

Ryan Seggerman vs Naoki Tajima — Prediction