[CLUBHOUSE MEETUP] How to Build a Better March Madness Bracket in 2022

Thanks for your interest in this Dataiku Washington DC Meetup! The health & safety of our attendees & speakers is our primary concern. While this currently proves to be a tricky time for public gatherings, Dataiku is still committed to providing great tech content & facilitating discussions in the data science space. As such, we’ve decided to pivot towards online webinars.

IMPORTANT - JOIN HERE:
https://www.clubhouse.com/event/mJzDaGDQ

Tentative Schedule: (ET)

5:00pm: Intro
5:05pm: How to Build a Better March Madness Bracket in 2022
5:45pm: Q&A

Talk Abstract:

One of the most exciting events in American sports is the NCAA March Madness tournament, where 68 college basketball teams battle it out in a single elimination format to decide the season’s champion. In advance of this year’s tournament, Dataiku’s Data Scientists Neil Menghani and Kwasi Arhin tasked themselves with using Dataiku to help model outcomes of the tournament and generate bracket predictions. They presented their picks in a panel discussion with ESPN’s Bracketologist Joe Lunardi, who felt that they were strong. The actual results of the tournament corroborated the strength, with the bracket registering in the 99.8 percentile, selecting three out of four of the correct Final Four and correctly choosing Baylor over Gonzaga in the championship game. It’s no secret that sports analytics is now considered an essential tool for every sports organization’s playbook. Teams rely on analytics to stay competitive, make the best decisions, and recruit the best players for their franchise. Kwasi and Neil’s approach involved incorporating key statistics and building a machine learning model to predict outcomes. While we know the 2021 tournament is now in the past, we hope that these predictions might inspire you to make a data-backed bracket in 2022.

Speaker Bios:

Kwasi Arhin is a Data Scientist at Dataiku working with top companies across industries to deliver impactful AI solutions. Kwasi was drawn to data science by the power of data-driven decision making. As a former athlete, Kwasi is passionate about sports and found sports analytics the perfect way to bring together his personal and professional interests.

Neil Menghani is a Data Scientist at Dataiku working with high-value clients from a variety of industries to build out sophisticated use cases with DSS. While he has extensive experience in the financial services space, Neil’s passion is for sports analytics, developing personal projects for analysis across multiple leagues

Disclaimer: All views, thoughts, & opinions expressed in the webinar belong solely to the panelists, & not to the panelists’ employer, organization, committee, other group or individual.