🎾 Predicting Tennis Betting Dynamics Using Machine Learning

Regression, Classification & Feature Engineering on WTA 2007–2023 Match Data

1. Dataset Overview

• Player rankings (Rank_1, Rank_2)
• Player points (Pts_1, Pts_2)
• Betting odds (Odd_1, Odd_2)
• Match attributes (Surface, Round)

2. Exploratory Data Analysis (EDA)

2.1 Distribution of Pre-Match Odds (Odd_1)

• Most matches have low odds for Player 1 (strong favorites).
• There is a long tail of higher odds representing clear underdogs.

2.2 Distribution of Ranking Difference (Rank_Diff)

• Many matches are played between players with small rank differences.
• Large positive or negative gaps are less common but very influential in betting.

2.3 Relationship Between Ranking Gap and Odds

• A clear relationship between Rank_Diff and Odd_1.
• When Player 1 is much stronger (large negative Rank_Diff), Odd_1 tends to be lower.
• When Player 1 is weaker (positive Rank_Diff), Odd_1 increases.

2.4 Odds by Surface

• Odds distributions differ slightly across Hard, Clay, and Grass.
• Some surfaces exhibit higher variance in odds, reflecting different levels of uncertainty.

3. Feature Engineering

✔ Rank_Diff & Pts_Diff

✔ Favorite_Flag

• 1 → Player 1 is the pre-match favorite (Odd_1 < Odd_2)
• 0 → Player 1 is the underdog

✔ Cluster_Label (K-Means)

• Rank_1, Rank_2, Pts_1, Pts_2

4. Regression Modeling: Predicting Betting Odds (Odd_1)

1. Linear Regression
2. Random Forest Regressor
3. Gradient Boosting Regressor (🏆 Winning regression model)

• MAE (Mean Absolute Error)
• RMSE (Root Mean Squared Error)
• R² (Goodness of fit)

4.1 Feature Importance – Regression (Gradient Boosting)

• Rank_Diff and Pts_Diff are among the most important predictors.
• Cluster-based features (Cluster_Label) and favorite information (Favorite_Flag) also contribute significantly.

• Best R² score
• Lowest errors (MAE/RMSE)
• Handles nonlinear tennis behavior better than linear models

5. Converting Regression to Classification

• Low_Odds — strong favorites
• Medium_Odds — balanced matches
• High_Odds — clear underdogs

• Recall vs Precision:
  Recall is more important, because missing a high-value opportunity (false negative) is more costly than marking a match as risky when it is not.

6. Classification Modeling: Predicting Odd_Class (Risk Level)

1. Logistic Regression
2. Random Forest Classifier
3. Gradient Boosting Classifier

• Macro F1 — to balance performance across all three classes.

6.1 Confusion Matrix – Winning Classification Model (Random Forest)

• Random Forest handles all three classes reasonably well.
• It reduces false negatives in the High_Odds class, which is crucial for not missing risky underdog situations.

6.2 Feature Importance – Classification (Random Forest)

• The same engineered features (Rank_Diff, Pts_Diff, Favorite_Flag, Cluster_Label) are highly influential.
• Match context (Surface, Round) also plays a role in predicting risk levels.

• Highest Macro F1 score
• Most stable performance across all odds classes
• Best trade-off between recall and precision in the risk classification task

7. Model Deployment

• winning_regression_model.pkl
• winning_classification_model.pkl

• Loaded in external Python environments
• Used for batch or near real-time predictions on new matches
• Integrated into a future sports analytics & betting decision-support system

8. Final Insights & Conclusion

✔ Can historical statistics predict betting odds?

✔ Can we classify matches into betting risk levels?

✔ Can this support real-time betting decisions?

• These models work with pre-match data only.
• For full real-time betting, additional information is needed (live score, momentum, injuries, etc.).

✔ What this project demonstrates

• Machine learning can meaningfully model betting markets
• Tennis statistics provide strong predictive signals
• Feature engineering dramatically improves performance
• Combining regression + classification creates a foundation for an automated betting recommender system

9. Repository Structure