1. Fantasy Football Optimization Kennon Young, Nick Ashton, Austin Cline

2. Informal Definition Current player performance projections lack accuracy and are unreliable in properly predicting offensive player’s weekly performance via their individual statistical performance which is measured by number of touchdowns, yards gained after a catch, rushing yards, etc. These measures are important in predicting a weekly performance but fall short of incorporating other player performance in what is known to be a team sport.

3. Formal Definition

4. Fantasy Omatic Only source that investigated this problem Utilized Strength of Schedule and Linear Regression in an attempt to solve the problem Our idea was largely formulated around these two concepts of Strength of Schedule and implementing Linear Regression analysis

5. Data Representation (Defense) seattle13 = 16 carolina13 = 15 cincinnati13 = 14 newOrleans13 = 13 sanFrancisco13 = 12 arizona13 = 11 houston13 = 10 nyGiants13 = 9 cleveland13 = 8 buffalo13 = 7 nyJets13 = 6 baltimore13 = 5 pittsburgh13 = 4 tennessee13 = 3 losAngeles13 = 2 detroit13 = 1 tampaBay13 = 0 washington13 = -1 denver13 = -2 indianapolis13 = -3 miami13 = -4 oakland13 = -5 sanDiego13 = -6 kansasCity13 = -7 greenBay13 = -8 newEngland13 = -9 atlanta13 = -10 jacksonville13 = -11 philidelphia13 = -12 chicago13 = -13 minnesota13 = -14 dallas13 = -15 seattle14 = 16 detroit14= 15 denver14 = 14 buffalo14 = 13 sanFrancisco14 = 12 nyJets14 = 11 kansasCity14 = 10 baltimore14 = 9 sanDiego14 = 8 carolina14 = 7 indianapolis14 = 6 miami14 = 5 newEngland14 = 4 minnesota14 = 3 greenBay14 = 2 houston14 = 1 losAngeles14 = 0 pittsburgh14 = -1 dallas14 = -2 washington14 = -3 oakland14 = -4 cincinnati14 = -5 cleveland14 = -6 arizona14 = -7 tampaBay14 = -8 jacksonville14 = -9 tennessee14 = -10 philidelphia14 = -11 nyGiants14 = -12 chicago14 = -13 newOrleans14 = -14 atlanta14 = -15

6. Data Representation (Wide Receivers) #Demaryius Thomas demaryiusThomas_2013 = [161, 52, 94, 86, 57, 78, 82, 75, 108, 121, 41, 106, 88, 45, 123, 113] demaryiusThomas_2013_Defense = [baltimore13, nyGiants13, oakland13, philidelphia13, dallas13, jacksonville13, indianapolis13, washington13, sanDiego13, kansasCity13,newEngland13,kansasCity13, tennessee13, sanDiego13, houston13, oakland13] demaryiusThomas_2013_passerRating = [141.1, 105.5, 135.8, 146.0, 129.6, 92.9, 96.1, 94.3, 135.2, 94.1, 70.4, 118.2, 107.8, 92.4, 113.2, 145.8] demaryiusThomas_2014 = [48, 62, 31, 226, 124, 171, 105, 127, 108, 103, 87, 63, 11, 123, 115, 115] demaryiusThomas_2014_Defense = [indianapolis14, kansasCity14, seattle14, arizona14, nyJets14, sanFrancisco14, sanDiego14, newOrleans14, oakland14, losAngeles14, miami14, kansasCity14, buffalo14, sanDiego14, cincinnati14, oakland14] demaryiusThomas_2014_passerRating = [111.9, 143.9, 85.7, 110.2, 117.9, 157.2, 124.2, 80.9, 111.9, 75.3, 135.4, 85.3, 56.9, 125.6, 61.8, 80.1] #AJ Green ajGreen_2013 = [162, 41,46, 51, 61, 103, 155, 115, 128, 151, 7, 83, 72, 93, 97, 61] ajGreen_2013_Defense = [chicago13, pittsburgh13, greenBay13, cleveland13, newEngland13, buffalo13, detroit13, nyJets13, miami13, baltimore13, cleveland13, sanDiego13, indianapolis13, pittsburgh13, minnesota13, baltimore13] ajGreen_2013_passerRating = [97.2, 81.7, 105.5, 58.2, 81.1, 105.9, 135.9, 125.7, 55.4, 52.2, 62.7, 83.6, 120.5, 86.4, 136.2, 62.2] ajGreen_2014 = [131, 0, 102, 81, 0, 0, 0, 44, 23, 127, 121, 57, 224, 49, 0, 82] ajGreen_2014_Defense = [baltimore14, atlanta14, tennessee14, newOrleans14, carolina14, indianapolis14, baltimore14, jacksonville14, cleveland14, newOrleans14, houston14, tampaBay14, pittsburgh14, cleveland14, denver14, pittsburgh14] ajGreen_2014_passerRating = [98.7, 116.6, 68.9, 117.4, 93.5, 55.4, 89.3, 79.1, 2.0, 143.9, 84.6, 60.6, 128.8, 53.6, 93.1, 83.7, 83.5]

7. Strength Of Schedule Goal: Manipulate players production based of the difficulty of the defense they played. -Rank Defense on a Scale -Implement linear regression formula that will affect players production for each game -Order players based on those results

8. Strength of Schedule Results WR 13 With Strength of Schedule Considered, Antonio Brown yards are: 1535.0. Antonio Brown actual yards are: 1499 With Strength of Schedule Considered, Pierre Garcon yards are: 1170.0. Pierre Garcon actual yards are: 1346 With Strength of Schedule Considered, Demaryius Thomas yards are: 1310.0. Demaryius Thomas actual yards are: 1430 With Strength of Schedule Considered, AJ Green yards are: 1408.0. AJ Green actual yards are: 1426 With Strength of Schedule Considered, Larry Fitzgerald yards are: 1070.0. Larry Fitzgerald actual yards are: 954 With Strength of Schedule Considered, Greg Olsen yards are: 900.0. Greg Olsen actual yards are: 816 With Strength of Schedule Considered, Eric Decker yards are: 1168.0. Eric Decker actual yards are: 1288 With Strength of Schedule Considered, Doug Baldwin yards are: 902.0. Doug Baldwin actual yards are: 778 With Strength of Schedule Considered, Emmanuel Sanders yards are: 776.0. Emmanuel Sanders actual yards are: 740 With Strength of Schedule Considered, Ashon Jefery yards are: 1391.0. Ashon Jefery actual yards are: 1421

9. Compliment Player Goal: Prove that a Wide Receiver’s Quarterback affects game production For each Wide Receiver: Determine quarterback reliance based on average yards per game Manipulate player’s yards per game by the QB’s Pass Rating, which is based on certain thresholds, using linear regression See if player’s on field production increased or decreased in relation to the quarter back they play with

10. Compliment Player Results ESPN 2013: Antonio Brown: 93 yards/game Demaryius Thomas: 89.4 yards/game AJ Green: 89.1 yards/game Alshon Jeffery: 88.8 yards/game ….. Emmanuel Sanders: 46 yards/game Our 2014 prediction: Demaryius Thomas: 112 yards/game Antonio Brown: 110 yards/game AJ Green: 92 yards/game Emmanuel Sanders: 75 yards/game Alshon Jeffery: 73 yards/game

11. Weighted Optimization

12. Confidence Interval -Find X Bar -Find Sample Standard Deviation -Find Square Root of sample Size -Calculate low Confidence Interval -Calculate High Confidence interval

13. Compare: Our Prediction To Actual Our Combined Weighted prediction: Antonio Brown: 107 yards/game Demaryius Thomas: 102.9 yards/game AJ Green: 90.8 yards/game Alshon Jeffery: 77 yards/game Emmanuel Sanders: 67 yards/game Actual ESPN 2014: Antonio Brown: 106 yards/game Demaryius Thomas: 101 yards/game AJ Green: 94 yards/game Emmanuel Sanders: 87 yards/ game Alshon Jeffery: 70 yards/game

15. Pitfalls Quarterback rating does not have a big impact on a running back’s production Unpredictable variables that occur in sports, such as weather, injuries, and other unforeseen events can have an impact on the desired results Injuries created large holes in our data sets and did not allow for us to conduct large scale analysis using consistent data

16. Conclusions A player’s schedule difficulty plays a large role in the player’s ranking among other players A good quarterback does not necessarily mean increased production for players of all positions, only wide receivers will be affected With strength of schedule and compliment player equally weighted, the results were not accurate. With a 95% confidence interval had no bearing on accuracy in our experiment

17. Conclusion Continued -Time Complexity -Strength Of Schedule: O(n 2 ) -Compliment Player: O(n) -Weighted Optimization: O(n 2 ) -Confidence Interval: O(n 2 )

18. Questions What were the two methods/ideas used by fantasy-o-matic? Strength of schedule and linear regression Does a quarterback’s rating have a huge impact on a running back’s production? No, a running back’s stats have little to do with the quarterback’s efficiency What was the main pitfall we encountered that limited us from using large sets of data? Player injuries and missed games limited the amount of data we could use