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KICKIN’ BACK Predictive Analytics and Fantasy Football Kicking GEORGE GREEN DSS680: PREDICTIVE ANALYTICS.

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Presentation on theme: "KICKIN’ BACK Predictive Analytics and Fantasy Football Kicking GEORGE GREEN DSS680: PREDICTIVE ANALYTICS."— Presentation transcript:

1 KICKIN’ BACK Predictive Analytics and Fantasy Football Kicking GEORGE GREEN DSS680: PREDICTIVE ANALYTICS

2 INTRODUCTION Recent popularity of fantasy football Use of kickers in real life Fantasy scoring Only thirty-two kickers, usually ten to sixteen team fantasy leagues Not a position considered important enough to risk using a draft pick

3 ASSUMPTIONS Kickers don’t tend to move around the league a lot The lone exception being if they become inconsistent, they can be replaced relatively easily and very cheaply Another play after a TD not involving the kicker was ignored. It has low usage and is usually only successful half the time Missed extra point attempts, worth 1 in both real life and fantasy scoring, were not considered due to the extreme rarity.

4 Consistent|Unemployed -180 points total -4 50+ FGs -High 21 points -Low 2 points -11.25 AVG -Tied for most points with consensus #1 preseason kicker ‘11 – 120 points ‘12 – 106 ‘13 – 116 points ‘14 – Out of the league. Also missed an extra point attempt. Will never be forgiven for it.

5 Variables Used Target Variable: Fantasy Points per Game Points Per Game Defensive Points Per Game Yards Per Game Plays Per Game Field Goal % Dome (Binary)

6 Cleaning the Data 5800+ Rows Data for entirety of game in one row for both teams Had to combine into Offensive Yards Copy and Paste after Splitting Creation of fantasy point statistic

7

8 Stat Explore Input Correlation FG_ 0.66133 PPG 0.53455 YPDS 0.34832 PlaysPG 0.24472 DPG -0.13547

9 Variable Selection

10 Dome Rejected! Kind of surprising!.002233

11 Model Comparison Model Model Node Model Description VASE TASE Y AutoNeural AutoNeural 5.68388 5.75652 Boost Gradient Boosting 5.97723 6.02730 Neural Neural Network 6.00761 6.15125 Ensmbl Ensemble 6.02437 6.07232. Neural2 NeuralVar 6.08551 6.11350. Reg Regression 6.71333 6.74621. Reg2 Stepwise 6.71333 6.74621. Reg3 StepwiseVar 6.73230 6.75552. Tree Decision Tree 7.42829 7.31521.

12 Importance of Variables Variable Importance Obs NAMELABEL NRULES IMPORTANCE VIMPORTANCE RATIO H 1 FG_ 36 1.00000 1.00000 1.00000 0.020536 2 PPG 53 0.64015 0.64545 1.00828 0.022600 3 YPDS 7 0.13057 0.14258 1.09198 0.001611 4 PlaysPG 12 0.12535 0.06431 0.51302 0.001041 5 DPG 11 0.10921 0.00000 0.00000 0.000496

13 Future Research Other data in the set such as wind speed and direction Other positions The significance of the insignificance of the dome

14 Questions? No? Awesome, next victim!


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