UVA Data Science Institute MSDS student Abhimanyu Roy ('18) presented a talk at the 2018 Tom Tom Applied Machine Learning Conference in Charlottesville, Va. His presentation highlights how data science can be used to predict results in sporting events. Learn more about Abhimanyu at https://dsi.virginia.edu/people/abhimanyu-roy.

1. How to Beat the
House
Predicting Football results with
Hyperparameter Optimization
Abhimanyu Roy, Data Science Institute

2. “There are no shortcuts to building a team each season. You build the foundation
brick by brick.”
- Bill Belicheck
TIME SERIES!
Min et. al. (2008) used a rule based approach (NBC) combined with an in-
game time series component to predict results from the 2002 Soccer world
cup with 70% accuracy
~2014 - Enter Deep Learning
4
It takes time to build a winning team

3. 8
Neural Networks
Some Input variables (30 years of NFL, NCAA, CFL) -
1. Number of games played in the season before the observation
under consideration
2. Number of wins and losses in the season
3. Win/loss ratio against the opponent
4. Number of wins in the last 5 games
5. Number of players from NFL Fantasy rankings in team
6. Number of top 10 players from NFL Fantasy rankings in team

4. 4
Recurrent Neural Networks
• Type of neural network where connections between units form a
directed graph along a sequence
• Can model the behavior of a time sequence
• RNNs can use their internal state to remember or forget sequences of
inputs
• Long-short Term Memory, Gated Recurrent Unit, Generic RNN Cell
(Simple RNN). Same concept the only difference is cell design
• Hyperparameter Tuning to get the best parameters
• Activation function: Defines the output of a node given an input or set
of inputs
• Recurrent Activation function: Defines the output of a node when
receiving input from nodes in the same hidden layer
• Learning Rate: Adjustment of weights
• Loss function: Cost of inaccurate prediction

5. 8
Results
Best performing parameters-
LSTM
Activation: Sigmoid
Recurrent Activation: Hard Sigmoid
Learning Rate: 0.05
Loss Function: Binary Crossentropy

6. THANK YOU