Tennis Score Encoding for Machine Learning Models

Tennis score encoding for machine learning involves converting raw score strings like ‘6-4 6-2’, ‘3-6 3-6’, and ‘6-7 7-5 6-3’ into meaningful numerical features that Random Forest models can effectively use for training and prediction. The optimal approach is to parse each set score and engineer features such as sets won, games won, game differentials, and match completion indicators that capture the competitive dynamics of tennis matches.

Contents

• Understanding Tennis Score Data Structure
• Encoding Technique for Random Forest Models
• Step-by-Step Implementation Guide
• Feature Engineering Strategies
• Advanced Encoding Considerations
• Practical Example with Code

Understanding Tennis Score Data Structure

Tennis scores follow a specific pattern that’s crucial to understand before implementing any encoding technique. A tennis match typically consists of multiple sets, with each set represented as “games_won-player1-games_won-player2”. For example, the score ‘6-4 6-2’ indicates that Player 1 won the first set 6-4 and the second set 6-2, resulting in a 2-0 set victory.

The complexity arises when dealing with different match formats:

• Straight-set victories: ‘6-2 6-3’ (Player wins 2-0 sets)
• Three-set matches: ‘6-7 7-5 6-3’ (Player wins 2-1 sets)
• Five-set matches in Grand Slams: ‘4-6 6-3 6-4 6-7 7-5’ (Player wins 3-2 sets)

Understanding these patterns is essential for effective tennis score encoding. The primary challenge is to transform these string representations into features that capture the match’s competitive narrative while providing numerical values that machine learning algorithms can process efficiently.

Encoding Technique for Random Forest Models

For Random Forest models, the most effective tennis score encoding technique involves a two-step process: parsing the score strings and engineering meaningful numerical features. Random Forest models benefit from features that capture the match’s progression and intensity, rather than just the final outcome.

Why this approach works particularly well for Random Forest:

• Non-linear relationships: Tennis scores exhibit non-linear patterns that Random Forest can capture
• Feature importance: The algorithm can identify which derived features are most predictive
• Robustness: Random Forest handles well-engineered features without extensive normalization

The core encoding technique involves:

1. String parsing: Splitting the score into individual sets and then into individual games
2. Feature extraction: Creating numerical representations of match statistics
3. Aggregation: Combining parsed data into meaningful features

This approach transforms raw tennis score data into a rich feature set that Random Forest models can effectively leverage for predicting match outcomes.

Step-by-Step Implementation Guide

Step 1: Parse the Score Strings

Begin by splitting the tennis score string into individual sets. For example, with ‘6-7 7-5 6-3’:

score_string = '6-7 7-5 6-3'
sets = score_string.split(' ')
# Result: ['6-7', '7-5', '6-3']

Next, parse each set to extract games won by each player:

set_scores = []
for set_result in sets:
 player1_games, player2_games = map(int, set_result.split('-'))
 set_scores.append((player1_games, player2_games))
# Result: [(6,7), (7,5), (6,3)]

Step 2: Calculate Basic Statistics

From the parsed data, compute fundamental match statistics:

# Calculate sets won
sets_won_player1 = sum(1 for p1, p2 in set_scores if p1 > p2)
sets_won_player2 = sum(1 for p1, p2 in set_scores if p2 > p1)

# Calculate total games won
total_games_p1 = sum(p1 for p1, p2 in set_scores)
total_games_p2 = sum(p2 for p1, p2 in set_scores)

Step 3: Engineer Additional Features

Create features that capture more nuanced aspects of the match:

# Game difference
game_diff = total_games_p1 - total_games_p2

# Set difference
set_diff = sets_won_player1 - sets_won_player2

# Whether match went to a deciding set
deciding_set = len(set_scores) > 2

# Tiebreaks (if any)
tiebreaks = sum(1 for p1, p2 in set_scores if p1 == 7 or p2 == 7)

This systematic approach ensures that your tennis score encoding captures multiple aspects of match performance that can be valuable for Random Forest models.

Feature Engineering Strategies

Beyond basic parsing, consider these feature engineering strategies to enhance your tennis score encoding:

1. Progressive Features

Track how the match evolved over time:

• Set-by-set progression: Whether the player won consecutive sets
• Comeback indicators: If the player lost the first set but won the match
• Dominance metrics: Average margin of victory in sets won

# Example: Set progression feature
won_first_set = set_scores[0][0] > set_scores[0][1]
won_consecutive_sets = all(set_scores[i][0] > set_scores[i][1] for i in range(min(2, len(set_scores))))

2. Contextual Features

Incorporate match-specific context:

• Surface adjustments: Adjust game differences based on playing surface
• Tournament importance: Weight features differently for Grand Slams versus regular tournaments
• Player hand: Account for lefty-righty matchups if available

3. Advanced Statistical Features

Create more sophisticated tennis score encodings:

• Efficiency metrics: Games won per set played
• Resilience indicators: Performance in deciding sets or tiebreaks
• Pressure performance: How the player performed when facing match points

# Example: Efficiency metric
games_per_set = total_games_p1 / len(set_scores)

These feature engineering strategies transform your tennis score encoding from a simple numerical representation into a rich feature set that captures the nuances of tennis performance.

Advanced Encoding Considerations

When implementing tennis score encoding for Random Forest models, consider these advanced techniques to improve model performance:

1. Player-Specific Normalization

Normalize features based on player career statistics:

• Adjust game differentials by the player’s typical performance
• Account for whether the player is overperforming or underperforming relative to expectations

# Example: Normalized performance
career_avg_games_per_set = player_stats['avg_games_per_set']
normalized_performance = (games_per_set - career_avg_games_per_set) / career_avg_games_per_set

2. Temporal Encoding

Incorporate time-based factors:

• Recency weighting: Give more importance to recent matches
• Fatigue indicators: Account for matches played in quick succession
• Seasonal adjustments: Normalize for different playing conditions throughout the year

3. Opponent-Adjusted Features

Create features that account for opponent strength:

• Quality of victory: Adjust features based on opponent ranking
• Expected performance: Compare actual performance to pre-match expectations
• Head-to-head context: Encode historical matchup patterns

These advanced considerations take your tennis score encoding beyond simple parsing and into the realm of sophisticated feature engineering that can significantly improve your Random Forest model’s predictive power.

Practical Example with Code

Here’s a complete Python implementation of tennis score encoding for Random Forest models:

import pandas as pd
import numpy as np

def encode_tennis_score(score_string, player=1):
 """
 Encode tennis score strings into numerical features for Random Forest.
 
 Args:
 score_string: String like '6-4 6-2' or '6-7 7-5 6-3'
 player: 1 for first player in score, 2 for second player
 
 Returns:
 Dictionary of encoded features
 """
 if pd.isna(score_string):
 return {
 'sets_won': 0, 'sets_lost': 0,
 'games_won': 0, 'games_lost': 0,
 'set_diff': 0, 'game_diff': 0,
 'deciding_set': 0, 'tiebreaks': 0,
 'straight_set_win': 0, 'comeback': 0
 }
 
 # Parse score string
 sets = score_string.split(' ')
 set_scores = []
 
 for set_result in sets:
 try:
 p1_games, p2_games = map(int, set_result.split('-'))
 set_scores.append((p1_games, p2_games))
 except:
 continue
 
 if not set_scores:
 return {
 'sets_won': 0, 'sets_lost': 0,
 'games_won': 0, 'games_lost': 0,
 'set_diff': 0, 'game_diff': 0,
 'deciding_set': 0, 'tiebreaks': 0,
 'straight_set_win': 0, 'comeback': 0
 }
 
 # Determine which player to track
 if player == 1:
 sets_won = sum(1 for p1, p2 in set_scores if p1 > p2)
 sets_lost = sum(1 for p1, p2 in set_scores if p2 > p1)
 total_games_won = sum(p1 for p1, p2 in set_scores)
 total_games_lost = sum(p2 for p1, p2 in set_scores)
 else:
 sets_won = sum(1 for p1, p2 in set_scores if p2 > p1)
 sets_lost = sum(1 for p1, p2 in set_scores if p1 > p2)
 total_games_won = sum(p2 for p1, p2 in set_scores)
 total_games_lost = sum(p1 for p1, p2 in set_scores)
 
 # Calculate derived features
 set_diff = sets_won - sets_lost
 game_diff = total_games_won - total_games_lost
 deciding_set = len(set_scores) > 2
 tiebreaks = sum(1 for p1, p2 in set_scores if p1 == 7 or p2 == 7)
 straight_set_win = (sets_won == 2 and sets_lost == 0 and len(set_scores) == 2) or \
 (sets_won == 3 and sets_lost == 0 and len(set_scores) == 3)
 
 # Check for comeback (lost first set but won match)
 if len(set_scores) > 1:
 lost_first_set = (set_scores[0][0] < set_scores[0][1]) if player == 1 else \
 (set_scores[0][1] < set_scores[0][0])
 comeback = lost_first_set and sets_won > sets_lost
 else:
 comeback = 0
 
 return {
 'sets_won': sets_won,
 'sets_lost': sets_lost,
 'games_won': total_games_won,
 'games_lost': total_games_lost,
 'set_diff': set_diff,
 'game_diff': game_diff,
 'deciding_set': int(deciding_set),
 'tiebreaks': tiebreaks,
 'straight_set_win': int(straight_set_win),
 'comeback': int(comeback)
 }

# Example usage
score_examples = ['6-4 6-2', '3-6 3-6', '6-7 7-5 6-3']

for score in score_examples:
 features = encode_tennis_score(score)
 print(f"Score: {score}")
 print(f"Features: {features}")
 print()

This implementation creates a comprehensive set of features that capture different aspects of tennis performance. When these features are combined with other match data (player rankings, surface type, etc.), they form a robust input for Random Forest models to predict tennis match outcomes.

Sources

1. VincentAuriau. (n.d.). Tennis-Prediction: Predicts the winner of a tennis match with machine learning. GitHub. Retrieved from https://github.com/VincentAuriau/Tennis-Prediction

2. Vombatkere, K. (n.d.). Predicting Professional Tennis Player Success using Binary Classification. Retrieved from https://kvombatkere.github.io/assets/Vombatkere_TennisPlayerPrediction_WriteUp.pdf

3. jdlamstein. (n.d.). tennispredictor: Machine learning to predict winners in tennis games. GitHub. Retrieved from https://github.com/jdlamstein/tennispredictor

4. fireplume. (n.d.). tennis-score: Python script to process tennis league player scores for singles and doubles. GitHub. Retrieved from https://github.com/fireplume/tennis-score

5. Pillai, K. (2018, October 8). Predicting Tennis Match Outcomes Using Machine Learning. Medium. Retrieved from https://medium.com/@kpillai_17910/predicting-tennis-match-outcomes-using-machine-learning-d0ce3d96dc9e

6. datascience.stackexchange.com. (n.d.). Predicting results of tennis matches based on historical data. Retrieved from https://datascience.stackexchange.com/questions/84682/predicting-results-of-tennis-matches-based-on-historical-data

Conclusion

Effective tennis score encoding is crucial for building accurate Random Forest models that predict match outcomes. By parsing score strings into meaningful numerical features like sets won, games won, game differentials, and contextual indicators like deciding sets and tiebreaks, you transform raw tennis score data into a rich feature set that captures the competitive dynamics of tennis matches.

The recommended approach combines basic parsing with thoughtful feature engineering, creating a comprehensive representation that Random Forest models can effectively leverage. Whether you’re analyzing historical data or building predictive models, this tennis score encoding technique provides the foundation for extracting valuable insights from tennis match scores. Consider enhancing these basic features with player-specific normalization, temporal adjustments, and opponent-based factors to further improve your model’s performance.