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BEAT BILL BELICHICK
My advice to Pete Carroll

www.h2o.ai
TB + BB
Bill Belichick Tom Brady
+ =
15 years together
3 Super Bowls
PASS OR RUN?
On any given offensive play…
Coach Bill can either call a PASS or a RUN
What determines this?
Game situation
Opposing team
Time remaining, etc, etc
Yards to go (until 1st down)
Basically, lots of s*it.
BUT WHAT IF??
Question:
Can we try to predict whether the next play will be PASS or RUN
using historical data?
Approach:
Download every offensive play from Bilichick-Brady era since 2000
Use various Machine Learning approaches to model PASS / RUN
Disclaimer: I’m not a Seahawks fan!
Extract known features to build model inputs
DATA COLLECTION
Data:
13 years of data (2002 -2013 season)
194 games total
14,547 total offensive plays (excludes punts, kickoffs, returns)
Response Variable: PASS / RUN
Model Inputs:
Quarter, Minutes, Seconds, OpposingTeam, Down, Distance,
Line of Scrimmage, NE-Score, OpposingTeam Score, Season,
Formation, Game Status (is NE losing / winning / tied)
MODEL RESULTS
H20 Model Confusion Matrix Overall Accuracy
Random Forest
Gradient Boosting
(100Trees)
GLM
(100Trees)
74%
75%
72%
SO, COACH PETE CARROLL…
75% Accurate at predicting PASS / RUN*
*Assumes non-deflated footballs!
We’ll ‘play’ our model live on Super Bowl 32!

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Beat Bill Belichick - Deep Learning comes of Age!

  • 1. BEAT BILL BELICHICK My advice to Pete Carroll
 www.h2o.ai
  • 2. TB + BB Bill Belichick Tom Brady + = 15 years together 3 Super Bowls
  • 3. PASS OR RUN? On any given offensive play… Coach Bill can either call a PASS or a RUN What determines this? Game situation Opposing team Time remaining, etc, etc Yards to go (until 1st down) Basically, lots of s*it.
  • 4. BUT WHAT IF?? Question: Can we try to predict whether the next play will be PASS or RUN using historical data? Approach: Download every offensive play from Bilichick-Brady era since 2000 Use various Machine Learning approaches to model PASS / RUN Disclaimer: I’m not a Seahawks fan! Extract known features to build model inputs
  • 5. DATA COLLECTION Data: 13 years of data (2002 -2013 season) 194 games total 14,547 total offensive plays (excludes punts, kickoffs, returns) Response Variable: PASS / RUN Model Inputs: Quarter, Minutes, Seconds, OpposingTeam, Down, Distance, Line of Scrimmage, NE-Score, OpposingTeam Score, Season, Formation, Game Status (is NE losing / winning / tied)
  • 6. MODEL RESULTS H20 Model Confusion Matrix Overall Accuracy Random Forest Gradient Boosting (100Trees) GLM (100Trees) 74% 75% 72%
  • 7. SO, COACH PETE CARROLL… 75% Accurate at predicting PASS / RUN* *Assumes non-deflated footballs! We’ll ‘play’ our model live on Super Bowl 32!