1. MARCH DATA CRUNCH MADNESS
The Shooting Stars
Nan (Miya) Wang
John De Martino
Pritha Sinha
Armi Thassim
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2. INTRODUCTION
 Background: With 68 college basketball teams competing in a single-
elimination tournament, the National Collegiate Athletic Association
(NCAA) is played every spring in the US.
 Objective: Create an optimized model to predict 2016 NCAA Finals,
based on historical regular season data from 2002 to 2015, through
applying various machine learning techniques.
 Results:
http://shootingstarsnyc.azurewebsites.net/
Above link to our machine learning web API can help you make your own
2016 NCAA Predictions!
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3. ANALYSIS KPI
Model Performance Evaluation Metrics
 Find a set of predictions that minimizes Log loss.
 Penalize heavily being simultaneously confident and wrong.
 Balance between being too conservative and too confident.
Actual number of
games played in the
tournament
Predicted probability
that team A beats team
B
Actual binary
outcome of each
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4. ANALYSIS PROCESS
Model Evaluation
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5. DATA PREPARATION
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Feature Transformation and Normalization
Rank to Score
Team 1 Adjusted Seed = 0.5 + 0.03 *
(Team 2 Seed - Team 1 Seed)
Normalization
MinMax Scaler
Derive
differences
Team 1 score of an attribute - Team 2
score of an attribute

6. FEATURE SELECTION
Feature Correlation
Heatmap
Feature Distribution
Histogram
Correlation and Distribution
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A few Features have
linear Correlation
Most Features are
Normal Distributed

7. Importance Plotting and Recursive Elimination
Log Loss for Different Feature
Numbers
Feature Importance
FEATURE SELECTION
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Optimal Number of
Feature: 9
● 97 Features to 9 Features

8. PERFORMANCE VALIDATION
Cross Validation and Different Training Size
Grid Searching/Parameter Tuning
Acceptable Model
Performance Variation
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Learning Curve
Overfitting when
Training Size under 45%
Partition Size:
50% - 50%

9. PERFORMANCE VALIDATION
Model Fusion RF, GBT and Logistic
Regression are Top 3
Majority Voting
 Leverage the information gleaned from different methods
 Minimize the flaws in each model.
 Increase stability and guarantee accuracy
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10. PREDICTION REVIEW
Predicted Prob Distribution
for 2016 NCAA
Our model keeps more
affirmative on “Gonna
Win” Teams while
holding ambiguous to
“Gonna Lose” Teams.
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11. PREDICTION REVIEW
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Predicted Round of 32 for 2016 NCAA
Our Model Accurately
Predicted 25 out of 32.
Accuracy: 78%

12. PREDICTION REVIEW
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Our Model Accurately
Predicted 12 out of 16.
Accuracy: 75%
Predicted Sweet 16 for 2016 NCAA

13. PREDICTION REVIEW
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Predicted Elite Eight for 2016 NCAA
Our Model Accurately
Predicted 6 out of 8.
Accuracy: 75%

14. INTERESTING ANALYSIS
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Top Teams and Cinderella Teams
Top Eight Teams from 2002 to
2015
Detailed performance of
eight top teams in each
season ?

15. INTERESTING ANALYSIS
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Eight Top Teams
UNC Michigan St.
ConnecticutKansas Kentucky Duke
LouisvilleFlorida
Championship Count:
1. Connecticut(3 times)
2. Duke; UNC; Florida(twice)
3. Kansas; Kentucky; Louisville(once)
Years Count:
1. Kansas(12 years)
2. Duke; UNC; Kentucky(11 years)
3. Florida; Michigan St.(10 years)
No Championship:
 Michigan St.

16. INTERESTING ANALYSIS
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Top Teams and Cinderella Teams
Most Frequent “Cinderella”
from 2002 to 2015
We define: In each game, a winning team with higher seed and lower RPI, as Cinderella
Top Teams being Cinderella:
 Michigan St.
 Connecticut
 Kentucky

17. INTERESTING ANALYSIS
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Cinderella Teams
We define: In each game, a winning team with higher seed and lower RPI, as Cinderella
Model Prediction for
Cinderella
Our model accurately
identified all Cinderella.
Mean
Score:
80%

18. CONCLUSION
Self Attribute(importance descending)
 offensive efficiency
 defensive efficiency
 block shots
Opponent Attribute
 2 point field goals shooting
 3 point field goals shooting
On Training Dataset:
 Log_loss: 0.46
 Accuracy: 81%
On 2016 Testing Dataset:
 Accuracy: 75%-78%
Primary Factors for Win-Lose:
Model Accuracy
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Outer Factor
 distance
Useful Indicator
 RPI
 seed