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.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 |