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

Suggested data model for call center analytics

Plugins