SHAP Values in XGBoost – How Each Feature Influences Player Market Value

The Power of SHAP Values in XGBoost

SHAP (SHapley Additive exPlanations) values provide individual-level explanations for model predictions. Unlike feature importances, which show the overall influence of a feature, SHAP values explain how a feature affects a specific player's valuation.

Key SHAP Takeaways from the XGBoost Model

🔹 Elite Creators See the Biggest Gains

• avg_bigChanceCreated_per_game has the highest positive SHAP values, meaning that players excelling in this area consistently see higher market valuations.

• The distribution of SHAP values shows that players creating 1+ big chances per game receive the largest boosts.

🔹 Open Play Impact Varies by Position

• avg_attOpenPlay_value_per_game has high SHAP values for midfielders and attackers but fluctuates for defenders.

• This suggests that XGBoost differentiates positional roles more than RandomForest when assigning market value.

🔹 Team Success Matters More for Lower-Valued Players

• The SHAP distribution for Team_points shows that low-market-value players benefit the most from playing on a successful team.

• High-value players are already valued highly regardless of team performance, while those in mid-tier squads see larger market fluctuations based on success.

Why This Matters for Football Clubs

SHAP values provide clubs with granular insights into individual player valuations. Understanding these nuances allows teams to:✅ Identify undervalued players with high impact in key areas.✅ Justify higher asking prices in transfer negotiations.✅ Optimize squad building by focusing on attributes that drive market value.

📊 Final Takeaway: XGBoost gives a sharper, more individualized breakdown of player valuations compared to RandomForest. Teams looking for high-impact scouting insights should integrate SHAP analysis into their data-driven decision-making.