Tennis Predictions
Model win probabilities for ATP & WTA matches, with a confidence rating per pick and a public accuracy record over every settled match.
154Upcoming
64.3%Accuracy
9972Settled
Segovia, Spain
hard
✓ Correct
Low · 52%
vs
1–0
Hamburg
clay
✓ Correct
Medium · 58%
vs
0–2
Hamburg
clay
✗ Missed
Low · 53%
vs
2–0
Segovia, Spain
hard
✓ Correct
High · 66%
vs
0–2
Tampere, Finland
clay
✓ Correct
Medium · 61%
vs
2–0
Zug, Switzerland
clay
✓ Correct
Low · 50%
vs
2–1
Kitzbuhel
clay
✓ Correct
Low · 52%
vs
0–2
Prague
hard
✓ Correct
High · 79%
vs
2–1
Hamburg
clay
✓ Correct
High · 65%
vs
1–2
Zug, Switzerland
clay
✓ Correct
Medium · 58%
vs
0–2
Kitzbuhel
clay
✗ Missed
Medium · 62%
vs
2–1
Prague
hard
✗ Missed
Medium · 59%
vs
0–2
Prague
hard
✗ Missed
Medium · 58%
vs
2–0
Segovia, Spain
hard
✗ Missed
Medium · 59%
vs
0–2
Tampere, Finland
clay
✗ Missed
High · 67%
vs
0–2
Tampere, Finland
clay
✓ Correct
High · 82%
vs
0–2
Tampere, Finland
clay
✓ Correct
Low · 53%
vs
1–2
Segovia, Spain
hard
✓ Correct
Low · 51%
vs
1–2
Hamburg
clay
✓ Correct
High · 69%
vs
0–2
Segovia, Spain
hard
✓ Correct
Low · 55%
vs
1–2
Palermo, Italy
clay
✗ Missed
Medium · 62%
vs
0–2
Bloomfield Hills, USA
hard
✓ Correct
High · 75%
vs
1–1
Palermo, Italy
clay
✓ Correct
High · 74%
vs
2–0
Winnipeg, Canada
hard
✓ Correct
Medium · 61%
vs
2–0
Winnipeg, Canada
hard
✓ Correct
Medium · 58%
vs
2–1
Estoril
clay
✗ Missed
Low · 54%
vs
0–2
Bloomfield Hills, USA
hard
✓ Correct
High · 81%
vs
2–0
Bloomfield Hills, USA
hard
✓ Correct
Medium · 58%
vs
0–2
Palermo, Italy
clay
✗ Missed
High · 66%
vs
2–1
Zug, Switzerland
clay
✓ Correct
High · 66%
vs
2–1
⚡ This page, via API Pro
Everything on this page is available as JSON via the Tennis API (Sports Addon, $5/mo):
GET
/tennis/api/v2/predictions/?upcoming=true
How to read this: the bar shows our model's win probability for each player; the confidence pill rates how much signal the model had for this match-up. Accuracy is measured over every settled prediction — not a curated subset. Not betting advice; no outcome is guaranteed.