High Level Design
Overview
Data analytics in professional sports has grown exponentially in the last decade. Instead of making decisions to draft or trade on a coaches personal opinion, teams are looking to data for the answers. Professional Soccer/Football is no exception. The need for an increasing edge over your opponents is only increasing the need for accurate player monitoring and performance tracking.Dashboard Example (sample data)
Click on the image to open and interact with the dashboard:
Goals
Analyze player performance
- Better decision making when trading players
- Understand when a player is ready to be substituted during a match
- Gain deeper insight into individual player KPIs
Objectives
Track Performance KPIs:
- Match Attendance
- Passing Accuracy
- Goals
- Successful Shots on Target Rate
- Successful Tackle Rate
KPI Architecture
| Objective | KPI’s | Measures | Data source |
| Track Performance KPIs | Match Attendance | Sum(Appearance) / Count(GameID) *All Games | Fact_Game |
| Passing Accuracy | Sum(SuccessfulPasses) / Sum(Passes) | Fact_Game | |
| Goals | Sum (Goals) | Fact_Game | |
| Successful Shots on Target Rate | Sum(ShotsonTarget) / Sum(Shots) | Fact_Game | |
| Successful Tackle Rate | Sum(SuccssefulTackles) / Sum(Tackles) | Fact_Game |
Entities Relationship Diagram
