SharpServeAI

Data-backed tennis betting for discipline bettors 🎾

[SharpServeAI @SharpServeAI]

Tennis betting is truly one of the most mis-priced market compared to most sports.

I built a AI model then tracked its performance during Wimbledon. My model wasn’t guessing but was able to find real value.

Performance (3 days): 28-34
Hit rate: 82.4%

[SharpServeAI @SharpServeAI]

🎾US OPEN DAY 4

*not high confidence just full model predictions*

Full board predictions:

-Botic Van De Zandschulp
-Tsitsipas
-Bublik
-Y. Wu
-V. Vacherot
-Z. Svajda
-B. Nakashima
-M. Giron
-A. Rinderknech

*rest have been identified as pass/no prediction

[SharpServeAI @SharpServeAI]

🎾US OPEN DAY 4

🎾VALUE SPOTS:
-F. Marozsan ML

High Confidence Low Value:
-Tiafoe ML
-Jodar ML
-Etcheverry ML
-Shelton ML
-Tommy Paul ML

Data. Insight. Real Edge.

[SharpServeAI @SharpServeAI]

🎾US Open day 3 was full of suspended and postponed matches.

Day 4 will provide us with our best day yet.

SharpServeAI has been calibrated and refined with hard court data and statistics.

Day 4 predictions coming soon👀

[SharpServeAI @SharpServeAI]

US OPEN DAY 3🎾

The model has identified 2 value plays:

-Marozsan ML
-Molcan ML

High Confidence picks:
-T Fritz ML
-Z. Bergs ML
-R.Jodar ML
-Cobolli ML

Other:
Mountet ML, Svajda ML

[SharpServeAI @SharpServeAI]

I apologize for misread on Day 2 schedule. Some matchup predictions are for Day 3.

SharpServeAI preformed accurate on Day 2.
Performance breakdown: 🎾

High confidence:
2/2 (Tiafoe still in progress)

Overall:
12/17 (7 matches labeled no prediction)

Stay posted for Day 3👀

[SharpServeAI @SharpServeAI]

SharpServeAI has been calibrated for hard court performance. US Open Day 1 gave us a strong first look.

Results:
Value play: Jamie Faria +112
8/10 on predictions made
4 matches labeled pass

The goal is not to force the board but find value.

Day 2 coming soon👀

[SharpServeAI @SharpServeAI]

SharpServeAI

US Open Day 2 board

The system has identified two value spots:

F. Marozsan ML +127
Alex Molcan ML +194

High confidence/low value leans:
Fritz ML
Tiafoe ML
Darderi ML
Mensik ML
Jodar ML
Z. Bergs ML

Data. Insight. Real Edge.

[SharpServeAI @SharpServeAI]

The system doesn’t just pick favorites. It wasn’t just lucky guesses. The best systems don’t chase hype, they identify edge compared to market mistakes.

Not every prediction is a win, but the goal is to identify value before market adjustments.

Here’s notable predictions:

[SharpServeAI @SharpServeAI]

The goal was to build a system that identifies a gap between true player edge and market odds.

This account will share model backed tennis betting insights, value, and parlay picks.

Welcome to SharpServeAI

[SharpServeAI @SharpServeAI]

Tennis betting is truly one of the most mis-priced market compared to most sports.

I built a AI model then tracked its performance during Wimbledon. My model wasn’t guessing but was able to find real value.

Performance (3 days): 28-34
Hit rate: 82.4%