Michael Grenon CS378 Data Mining Spring 2018 Optimizing...
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Optimizing Baseball Performance and Player Salary Michael Grenon CS378 Data Mining Spring 2018
Transcript of Michael Grenon CS378 Data Mining Spring 2018 Optimizing...
OptimizingBaseball Performance andPlayer Salary
Michael GrenonCS378 Data Mining
Spring 2018
Baseball ♥ Stats
North America ♥ Baseball
Franchise Entertainmentorganization
moneydata
wins
2017*
Optimization
Salary optimizationHow did teams optimize their player salaries?
● No Salary Cap!● Similarity problem
○ Linear correlation○ Pearson correlation coefficient
How to Play to Win?Which aspects of play most strongly correlate with winning?
● Similarity problem○ (Linear) association○ Pearson correlation coefficient
How do the best teams use their players?How frequently are certain players used in games?
● Frequent item set problem○ Apriori algorithm○ Support threshold?
R = 0.253
Price Per Win
E = win% * (ppw rank + win rank)
R = -0.555
BsR ≈ Baserunning WAR
Taken from team_batting(2017)
R = 0.548
wSL ≈ weighted Slider
Taken from team_batting(2017)
R = 0.500
¯\_(ツ)_/¯
What’s next?How frequently are certain players used in games?
● Frequent item set problem○ Apriori algorithm○ Support threshold?
What’s next?To what extent are win-loss record and attendance related?
● Extending Pearson correlation analysis
Preliminary Conclusions
● Hitting coaches: teach how to hit a slider○ Pitch most “cost-efficient” to excel at hitting○ ...but not by much
● Fielding coaches: emphasize speed and skill on baserunning○ More closely associated with salary efficiency than any other performance
■ Even batting, pitching
Preliminary Conclusions
● Chess match: all pieces are important ● 2-sided game● Predictive (vs. descriptive) statistics
○ Time-series analysis
Preliminary Conclusions● Too much data
○ batting_stats(): 287 attributes?● Statcast data
○ More complex mining techniques○ Neural Nets
● Data warehouses incomplete, disorganized○ Private sector
