This analysis uses insights gathered from reputable, research-backed, and publicly accessible football and sports-injury resources to predict potential injury risks, identify patterns, and recommend preventive strategies.
This project analyzes injury risks among 800 Tsinghua University football players using a fully Excel-driven workflow. Through data cleaning, segmentation, and pivot-table analysis, we identify patterns and predictors influencing next-season injuries and provide actionable recommendations for coaches and athletic departments.
- Identify lifestyle, training, and physical factors that affect injury likelihood.
- Build a structured Excel analytics workflow for sports-science insights.
- Recommend strategies to reduce injury risk and improve player performance.
- Source: Kaggle
- Rows: 800 players
- Data Type: Structured tabular data
- No missing or duplicate values
- Standardized numeric fields
- Applied Excel formulas for:
- Nutrition classification
- Injury labeling
- Warm-up adherence
- Age formatting
- Pivot tables, slicers, and conditional formatting used for segmentation
- Nutrition is the strongest injury predictor
- Poor nutrition → 0.74 injury probability
- Good nutrition → 0.21 probability
- Warm-up non-adherence increases injuries
- 40% of players inconsistently warm up
- Sleep directly affects stress
- Peak match involvement at 10–11 training hours/week
- Optimize training load (9–10 hours/week)
- Weekly structured recovery sessions
- Age-targeted reaction training
- Improve warm-up compliance
- Hire/assign nutritionists
Conclusion: This project shows that injury prediction is multidimensional, shaped not only by physical capability but also by nutrition, discipline, warm-up habits, and psychological factors. Excel proved to be an effective platform for uncovering these insights and delivering actionable strategies to reduce preventable injuries and enhance player performance.
Read full Article on Medium: Predicting Injury Risk Among Tsinghua University Football Players: A Complete Excel-Driven Analytical Story