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 team name and members to:
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
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)
Method
Software
Link
Decision Trees R
Iterative Strength Rating
Python (NumPy)
Probability Distribution
Excel
Network Analysis
STATA
Generalized Boosted Model (Decision Trees)
Ordinal Logistic Regression and Expectation
Various
SAS
Proprietary (Interesting Video Discussion)
Ensemble (Support Vector, Naïve Bayes, KNN, Decision Tree, Random Trees, Neural Nets)
N/A
Random Forest
Python (scikit-learn)

9. Prediction Tracking