Multivariate Regression
Analysis to Predict UFC
Fight Scoring
Using historical fight statistics from UFCStats.com and fight scoring data from MMADecisions.com, I created a Multivariate Linear Regression model that accepts fight data (strikes, takedowns, submission attempts, control time) and predicts the score of the fight. In addition to evaluating the model’s accuracy for predicting the correct score, I also evaluated the model’s ability to pick the winning fighter correctly. After testing several different algorithms, I settled on LinearRegressor from Sci-Kit Learn for my multivariate linear regression model. In predicting fight scores, the model returned an R-Squared of 0.7168. The model predicted the correct winner of the fight with approximately 85.5% accuracy.
Read. Watch. Review.
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Watch the project presentation.
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Read the full project write-up on Medium.
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Watch the code review.
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Review the project code on my Github.
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Scrape, store, train, predict.
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Sometimes simple is best.
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UFC judges in algebraic form.
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If only predictions were this accurate before the fights.
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Statistics will never tell the full story of a fight.
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Reyes and Barboza were robbed.
