View PDF ↗
PDF Viewer
Open Full PDF

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (19)

[1]
Disequilibrium Play in Tennis
2023Axel Anderson, J. Rosen et al.
[2]
Expertise, gender, and equilibrium play
2023Romain Gauriot, Lionel Page et al.
[3]
The analysis of serve decisions in tennis using Bayesian hierarchical models
2022P. Tea, T. Swartz
[4]
Tactical Analyses in Professional Tennis
2019Arie-Willem de Leeuw, A. Hoekstra et al.
[5]
SciPy 1.0: fundamental algorithms for scientific computing in Python
2019Pauli Virtanen, R. Gommers et al.
[6]
Do expert tennis players actually demonstrate anticipatory behavior when returning a first serve under representative conditions? A systematic review including quality assessment and methodological recommendations
2019Carlos Avilés, J. Navia et al.
[7]
Randomization and serial dependence in professional tennis matches: Do strategic considerations, player rankings and match characteristics matter?
2018Leonidas Spiliopoulos
[8]
Returning Serve in Tennis: A Qualitative Examination of the Interaction of Anticipatory Information Sources Used by Professional Tennis Players
2018G. Vernon, D. Farrow et al.
[9]
A shot taxonomy in the era of tracking data in professional tennis
2018S. Kovalchik, M. Reid
[10]
Spatial characteristics of professional tennis serves with implications for serving aces: A machine learning approach
2017D. Whiteside, M. Reid
[11]
The serve impact in tennis: First large‐scale study of big Hawk‐Eye data
2016Sami Mecheri, François Rioult et al.
[12]
What Can Hawk-Eye Data Reveal about Serve Performance in Tennis?
2015François Rioult, Sami Mecheri et al.
[13]
Predicting Serves in Tennis using Style Priors
2015Xinyu Wei, P. Lucey et al.
[14]
Scikit-learn: Machine Learning in Python
2011Fabian Pedregosa, G. Varoquaux et al.
[15]
Data Structures for Statistical Computing in Python
2010Wes McKinney
[16]
The Importance of Service in Grand Slam Singles Tennis
2008G. O’Donoghue, E. Brown
[17]
Minimax Play at Wimbledon: Comment
2007S. Hsu, Chen-Ying Huang et al.
[18]
Minimax Play at Wimbledon
2001M. Walker, J. Wooders
[19]
The Cognitive Structure of Emotions
1988A. Ortony, G. Clore et al.

Founder's Pitch

"Leverage machine learning to predict tennis serve directions and enhance gameplay strategy."

Sports AnalyticsScore: 2View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

0/4 signals

0

Quick Build

2/4 signals

5

Series A Potential

0/4 signals

0

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/26/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.