Big Data and Data Analytics in Sports

Brandon To

Formula One

• McLaren Team

• 150 sensors outfitted on cars

• Transmit 200GB of data per race weekend

• Up to 3TB for a full season

• Use of SAP HANA to manage all the data

• 14,000 times faster than what they had before

SAP HANA

• In-memory database

• Dynamic tiering

• Column oriented

• Combines OLAP and OLTP

• Provides real-time analytics

Formula One – Data Storage

• Circuits are all around the world – have to bring data centers along

• Mercedes-AMG team

• Replaced legacy storage system with Pure Storage’s FlashArray (SSDs)

• Save space and weight

• Performance and reliability

• Reduced processing time for SQL requests by 95%

NBA

• Have been using video player tracking since 2013

• Recently changed from SportVu to Second Spectrum

• Speed, distance, location of shots

• Combined with traditional stats = 4.5 quadrillion combinations of data

NBA – SAP HANA

• Access data from the past 70 seasons

• Base stats are updated in real time, advanced stats minutes after game

• Able to take intangible stats and represent it into an easier way

• Time spend on website increased by 30%

Player Impact Estimate - % of game events player acheived(PTS + FGM + FTM - FGA - FTA + DREB + (.5 * OREB) + AST + STL + (.5 * BLK) - PF - TO) / (GmPTS + GmFGM + GmFTM - GmFGA -

GmFTA + GmDREB + (.5 * GmOREB) + GmAST + GmSTL + (.5 * GmBLK) - GmPF - GmTO)

NHL – SAP HANA

• 100 seasons of data

• Able to display advanced stats – Corsi, Fenwick

• Thousands of ways to filter stats

• 25% increase in new visitors, 45% increase in time spent on site

MLB

• Player and ball tracking with cameras and radar – Statcast

• Movements, speeds, distance

• 7TB of data per game, 17PB of data each season

• Uses Amazon Web Services to parse data

NFL

• RFID tags on players and football

• 3TB of data a week

• Also uses Amazon Web Services

The End