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March Madness Data Crunch Overview

Join the March Madness Data Crunch challenge to predict tournament outcomes creatively and demonstrate your analytical skills with innovative techniques. Utilize various datasets and tools to gain insights and communicate findings effectively.

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March Madness Data Crunch Overview

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  1. March Madness Data Crunch Overview Sponsored By:

  2. Timeline • 02/06/19 – Historical Data Released • 02/13/19 – Registration Deadline for Teams • 03/01/19 – Initial Predictions CSV Submission due • 03/18/19 – 2019 Current Season Data Released by 5PM • 03/20/19 – 2019 Final Tournament Predictions CSV due by 5PM • 03/27/19 – 2019 Final PowerPoint Report & Participation Declaration Form due by 5PM • 04/05/19 – Final Poster Session & Awards Ceremony

  3. Where to Create Teams • Create a team of 4 and upload Excel file to blackboard by, 2/13/19, 11:59 pm • Please email team name and members to: • mdcm@fordham.edu • Teams will then be added to the March Madness Blackboard class • Materials will be uploaded there

  4. Objective • Based on Kaggle’s Machine Learning Mania • https://www.kaggle.com/c/march-machine-learning-mania-2017 • Predict the probability that a team wins any given game in the March Madness Tournament • Predict all possible 2278 matches • Use data from 2002 until 2018 to train and test until data for 2019 is released • Be creative! • See if you can find signal in the noise • Demonstrate your analytical skills • Visualize your findings

  5. Dataset Overview • Glossary available on Blackboard • Game Data: game_id, host name and latitude and longitude and score • KenPom Data: four factor data, tempo, efficiency, etc. • Do not share outside of Fordham • Coaching Data: Coach name, career wins, season wins, NCAA tournament appearances, Sweet 16 appearances, and Final 4 appearances • Team Location Data: Latitude and longitude of team1 and team2 • Team Data: Team Name • Poll Data: AP Preaseason/Final Polls, Coaches Preseason/Final Polls • RPI Data

  6. Grading Criterion • Judges will grade the submissions on the following factors • Model Accuracy • How well did the model perform? • Creativity of Exploratory Analysis & Methodology • Was the team able to find novel ways to improve accuracy and gain new insights into what makes teams succeed in March? • Communication & Visualization • How well was the team able to effectively communicate their findings to the judges • Extremely important to Deloitte!! • Note: Model accuracy is not the most important. Very important to find creative ways to analyze the data and effectively communicate!

  7. Format of Poster Board • Overview & Introduction • Hypothesis & Methodology • Variable Selection, Analytics Explored, Data Mining Techniques • Analytics & Results • Results of Analytics, Results of Data Mining Techniques • Conclusions & Suggestions for Improvement • Performance of Model

  8. Tutorials • What is Log Loss? (Blackboard) • SPSS Logistic Regression Example (Blackboard) • Python Logistic Regression Example (Blackboard) • Other examples (Right)

  9. Prediction Tracking http://fordhamsportsanalytics.com/

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