sarahzhoo620/tennis-momentum-analysis-data

Dataset Card for Tennis Momentum Analysis Dataset

This dataset contains detailed point-by-point data from a 2023 Wimbledon Championship tennis match between Carlos Alcaraz and Nicolas Jarry, designed to support momentum analysis and sports analytics research. It captures 100 data points with 47 features covering match progression, player performance metrics, and technical details of each point.

Dataset Details

Dataset Description

The Tennis Momentum Analysis Dataset offers granular insights into a professional tennis match, tracking every point from the first two sets. It includes comprehensive metrics such as set/game/point progression, serve statistics, shot types, error counts, movement data, and point outcomes. This data enables in-depth analysis of player momentum shifts, performance patterns, and tactical decision-making throughout the match.

• Curated by: sarahzhoo620

Direct Use

Ideal for sports analysts, data scientists, and tennis researchers to:

• Analyze player momentum shifts during a high-stakes match
• Study serve/return strategies and effectiveness
• Evaluate performance metrics (aces, winners, errors) across match progression
• Develop predictive models for point/game outcomes
• Research tactical decision-making in professional tennis

Out-of-Scope Use

Not suitable for:

• Generalizing player performance across multiple matches or tournaments (single match focus)
• Gender-specific analysis (only men's singles match data)
• Long-term trend analysis (limited to 100 data points from two sets)
• Non-tennis sports analytics applications

Dataset Structure

The dataset consists of 100 tabular data points (each representing a single point in the match) with 47 features organized into key categories:

1. Match Identification: match_id (2023-wimbledon-1301), player1 (Carlos Alcaraz), player2 (Nicolas Jarry)
2. Match Progression: elapsed_time, set_no, game_no, point_no, p1_sets, p2_sets, p1_games, p2_games, p1_score, p2_score
3. Point Details: server, serve_no, point_victor, game_victor, set_victor
4. Performance Metrics: aces, winners, double faults, unforced errors (for both players)
5. Technical Data: winner_shot_type, net points (attempted/won), break points (attempted/won/missed)
6. Physical Metrics: distance_run (for both players), rally_count, serve speed (speed_mph)
7. Tactical Data: serve_width, serve_depth, return_depth

All data points are ordered chronologically by elapsed_time, with consistent indexing for set, game, and point numbers.

Dataset Creation

Curation Rationale

The dataset was curated to address the need for granular, point-level tennis data to support momentum analysis research. Traditional match summaries lack the detail required to study how momentum shifts between players during a match, making this dataset valuable for sports analytics and performance research.

Source Data

Data Collection and Processing

Data was collected from the 2023 Wimbledon Championship match between Carlos Alcaraz and Nicolas Jarry. Each point was recorded in real-time with manual verification to ensure accuracy. Features were standardized into consistent formats (e.g., elapsed_time as HH:MM:SS, speed_mph as float64) and missing values were labeled as "null" or 0 where appropriate. No synthetic data was added—all records represent actual match events.

Who are the source data producers?

Source data was originally captured by Wimbledon Championship official match statisticians, with curation and formatting by sarahzhoo620 for Hugging Face.

Bias, Risks, and Limitations

• Sample Limitation: Limited to a single match between two male players, so results may not generalize to other players, genders, or tournaments.
• Contextual Gaps: Does not include contextual factors like weather, player fatigue, or crowd influence that may impact momentum.
• Feature Completeness: Some fields (e.g., return_depth) have null values for certain points due to data collection constraints.
• Selection Bias: Focuses on a high-profile match at a major tournament, which may not represent typical tennis match dynamics.

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. For broader generalizations, combine this dataset with additional match data from diverse tournaments and players. Supplement with contextual data (e.g., player rankings, match conditions) when possible to enhance analysis validity.

Glossary

• Ace: A serve that lands in the service box and is not touched by the receiver.
• Winner: A shot that the opponent cannot return in play, resulting in an immediate point win.
• Break Point: A point where the receiver can win the game by winning the next point.
• Rally Count: The number of shots exchanged between players during a point.
• Serve Width/Dimension Codes: B (Broad), BC (Broad-Center), BW (Broad-Wide), C (Center), CTL (Control), NCTL (Non-Control), D (Deep), ND (Non-Deep)
• Point Victor Code: 1 (Player 1 wins point), 2 (Player 2 wins point)

Dataset Card Contact

For questions or feedback, contact the dataset curator via Hugging Face (https://huggingface.co/sarahzhoo620)