1. Fantasy Football Forecasting
By Derick Owens and Amanda Zimecki

2. Fantasy Football: What is it?
Standard League – Conventions
Draft:
Nine Starting Players
Quarterback (QB) (Max 3)
Two Running Backs (RB) (Max 4)
Two Wide Receivers (WR) (Max 4)
Tight End (TE) (Max 3)
Flex (RB, WR, or TE)
Kicker (K) (Max 2)
Defense/Special Teams (D/ST) (Max 3)
Seven Bench Players
Any permutation of the above listed players

3. Fantasy Football – Scoring
By standard league conventions, scoring is as follows:
All Players
Touchdown = 6 Points
Turnover = -2 Points QB
15 Passing Yards = 1 Point
10 Rushing Yards = 1 Point RB WR
10 Receiving Yards = 1 Point

4. Fantasy Football - ScoringTE
10 Receiving Yards = 1 Point K
Extra Point Made = 1 Point
Field Goal (0-39 Yards) = 3 Points
Field Goal (40-49 Yards) = 4 Points
Field Goal (50+ Yards) = 5 Points

5. Fantasy Football - Scoring
D/ST
Turnover Forced = 2 points
Blocked Kick = 2 Points
Safety = 2 Points
Sack = 1 Point
Points Allowed
0 Points Allowed = 5 Points
1-6 Points Allowed = 4 Points
7-13 Points allowed = 3 Points
14-17 Points Allowed = 1 Point
18-27 Points Allowed = 0 Points
28-34 Points Allowed = -1 Point
35-45 Points Allowed = -3 Points
46+ Points Allowed = -5 Points
Yards Allowed
Under 100 Yards Allowed = 5 Points
100 to 199 Yards Allowed= 3 Points
200 to 299 Yards Allowed = 2 Points
300 to 349 Yards Allowed = 0 Points
350 to 399 Yards Allowed = -1 Points
400 to 449 Yards Allowed = -3 Points
450 to 499 Yards Allowed = -5 Points
500 to 549 Yards Allowed = -6 Points
550+ Yards Allowed = -7 Points

6. “Hidden” Values
Players have “hidden” statistics that make them more (or less) valuable, although their fantasy scoring may be the same or similar to other players
Such statistics include, but are not limited to:
Touches (RB)
Targets (WR, TE)
Injuries/Suspensions (Games Played)
Performance in Specific Conditions (Cold/Wind)
There are also statistics that can be derived from basic statistics to better “describe” a player, such as:
Completion % (QB)
Catch % (TE, WR, RB)

7. Objective
Our objective is to create an algorithm to forecast and thus draft the “best” team based on past statistics
To create this algorithm, there are a few things we need:
Past Statistics
Weights for those statistics
A scoring/rating system
Neural Network Algorithm
Output comparisons (Are all teams produced optimal?)

8. Data Source & Benchmark
– comparison site
As a benchmark, we’re going to compare our rating to how many fantasy points the player had in the previous season
We will also compare our ratings to expert rankings

9. Neural Network wSum = weighted sum of the inputs
T = threshold/bias value

10. Weights
Each statistic of a position is given a specific weight
Determined by us from previous knowledge and research
Weights are calculated based on their importance to that position
Σwp = 1

11. Our Threshold Tp = Sp / [ ( ΣSq / q ) * 2], where: T = Threshold
S = Statistic (being calculated)
q = All Players
p = Individual Player

12. Scoring/Rating System
Yards per season, TDs per season, Games Played, Touches, Targets, Extra Points Made
R = (Sp / (ΣSq / Σq)) * w, where:
S = statistic
p = individual player
w = weight
q = all players
R = individual player rating
Turnovers
R = 1 - ( T *0.05) / 1 * w
Catch & Field Goal Percentage
a = Base score of average ( ΣSq / Σq )
(+ / -) .1 each standard deviation (b) below/above the position average
R = a(+ / -) * .1(b)

13. Output Comparisons

14. Million Dollar Algorithm?
Fantasy Kings
Weekly Draft
“Salary Cap” of $50,000
Each player is assigned a dollar value
You can draft any permutation of players given that you don’t go over the salary cap
Positions:
1 QB, 3 WR, 2 RB, 1 TE, 1 FLEX (TE, RB, OR WR), 1 D/ST
92,867,930,357,760 permutations based on 32 starting players at each position

15. Our Objective Now Limit the number of permutations based on match-ups
Mid-ranked RBs vs. last-ranked defenses, for example.
Maximize dollar value
Min($) = Max(Points)
The objective is not to find the best permutation
The odds of accomplishing this are 1/92,867,930,357,760
Making this an intractable problem
The objective is to optimize and beat the other players
While also exploring the possibility of making this problem tractable
How will we do this?

16. Neural Network & Knapsack
In order to optimize the permutations, we’ll use our current neural network algorithm combined with the knapsack problem
In order to implement the Knapsack problem, we’ll need to calculate a player’s dollar value to go along with that player’s rating.
This will create a test set of optimized permutations.
Even with the optimized set of permutations, nothing is guaranteed.
Even with a completely optimized set that uses the full salary cap, there are chaotic factors that constantly interfere with a forecast:
Weather
Injuries
Suspensions
Etc.

17. Chaos Theory Tackles Adrian Peterson
Many teams drafted Adrian Peterson as their #1 overall pick
Results:

18. Knapsack – Greedy Implementation
We’re using the greedy implementation of the Knapsack Problem
Use as much as the knapsack as possible
“Fitting” the most possible “stuff” into the sack
Complexity is m/2 (given that you can use the same item an unlimited amount of times), where:
m is the maximum value of the items to fit into the sack (as close to salary cap as possible)
Value is equal to the dollar amount assigned to the player
Dollar amounts are predetermined by Draft Kings based on player performance
However, we have a limited amount of items to fit into the sack
9, which is the number of players
Each item will be unique (item = player(our rating, $ value))
Since these constraints will be applied to the greedy implementation, complexity will get worse. Output from a programmed implementation should tell us more about its complexity.