Visual search and analytics platform startup Zegami wins 2018 MIT Sloan Sports Conference Startup Award

Visual search and analytics platform startup, Zegami, has won 2018 MIT Sloan Sports Conference Startup Award. Zegami was one of the five startups that competed in the Technology Services Track of the startup competition. The MIT Sloan Sports Conference provides a forum for industry professionals (executives and leading researchers) and students to discuss the increasing role of analytics in the global sports industry. MIT Sloan is dedicated to fostering growth and innovation in this arena, and the conference enriches opportunities for learning about the sports business world. The conference is open to anyone interested in sports. The Conference was founded by Daryl Morey (MIT Sloan ’00) and Jessica Gelman (HBS ’02) in 2006. It is chaired by Gelman and organized by MIT Sloan students.

“MIT Sloan host the most prestigious Sports Analytics event on the planet, and we are thrilled to be recognized by them as a key emerging vendor in this field,” Sam Conway, CEO of Zegami says. Zegami is a revolution in data and image exploration that uncovers insights in your player data. Other startups that participated in the competition include:

Fansure Fansure provides fans reassurance that their favorite players will play in the game – or get 50% of their ticket cost back!
FanWide FanWide helps sports fans find the closest watch party or fan club, for any team in any city, while measuring out-of-home TV viewership.
LogoBase.com   LogoBase leverages league and team brand assets, enabling data analytics at the logo level, product automation, and brand compliance.
PlayersVote  PlayersVote provides fan-based performance ratings to organizations + teams, bringing the fan much closer to the live game via FB Messenger.
Timecode Archive Inc.  We build Narrative Graph – the world’s largest timecode archive on sports and entertainment.

Founded in  2016 by Australians Samuel Conway, Roger Noble and Stephen Taylor, the Oxford, England-based Zegami is a data discovery toolset that challenge’s the way organisations manage visual data. It allows users to explore, search, sort, filter, group and analyse large collections of images and data in a simple and intuitive way. The startup is backed by innovative Oxford University thinking, Zegami combines the power of machine learning with human cognitive abilities. This ensures that people are immersed in the information allowing them to recognise patterns, find hidden insights and make new discoveries. Zegami makes information more visual and accessible, enabling intuitive exploration, search and discovery.

Zegami works by allowing images to be attached to  data. Then using the power of machine learning and human pattern recognition to reveal hidden insights and new perspectives. This allows users to find what they are looking for in a fraction of the time, and helps to make new discoveries that were previously unreachable. The company raised $4 million back in 2017.