This document describes predicting NHL players' contributions over the life of their contracts using statistical data from a private hockey analytics site. It explores various machine learning models like random forest, elastic net, and support vector regression applied to features including goals, assists, advanced stats, and quality of competition metrics to predict the following season's output. The best models were elastic net and random forest, achieving predictions within .359-.364 accuracy. Improvements could include more data, different target variables, and additional features like age and injury status.

1. The Goal
Predict an individuals NHL player's
contribution to his team over the life
of his contract

2. The Data
• Private stats site (stats.hockeyanalysis.com)
• Rows correspond to individual player's stats by
season
• Data spread across multiple tables
– Joined tables using Pandas with name as key
– Be careful with excel…

3. Y’s
(EN Goals + EN Assists) / TOI
…for the following season

4. X’s
• Basic Feature Selection/Manipulation
– Removed redundant/derived
– Removed noisey nonsense
– Normalized for TOI across seasons

5. Features
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Goals, Assists, First Assists
iFenwick
tFenwick for, tFenwick against
lmFenwick for, lmFenwick against,
HARO/HARD/HART QoC
A few others

6. More about the data
• Advanced stats only go back to 2007
• Aggregated prior 3 seasons of data to predict
the following season
• Players show up as multiple rows
• Y’s in certain row’s become x’s in other rows

7. Models
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RF  .326 (be sure to set oob_score = True)
ElasticNetCV  .359
ElasticNetCV w/PCA  .364
RF w/PCA  .285
SVR(xVal)  .314 (could be improved if I
normalized featureset)
• SVR(xVal) w/PCA  .322
• GBR w/PCA  .187

8. Improvements
• More data!!
– more rows
– Less noise variables (especially Y’s)
• Choose a ‘longer’ Y
• Get age data
• Include injury status