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BosskOnASegway Ballot for 2017 Week 8

Ballot Type: Computer

Submitted: Oct. 17, 2017, 8:41 a.m.

Overall Rationale: Below are the contributing models.  Format for reasons is (change) Score: score (votes: in order below) **MLE** - This is my "Most Likely Elo" model.  Do not let the name fool you, it is not an Elo model.  Elo is simply the function used to determine the stabilitization point.  This model takes the games played thus far and treats them as the result of one function.  The function takes 2 teams evaluations as inputs to get their games result.  It finds the best fit evaluation for which our test function has the closest results to the actual game results.  Games are scored by percentage chance to win a rematch based on each teams score and lots of regression testing.  The biggest issue with this model is until week 7 or 8 there are multiple solutions to our function so results can get wonky if we hit a valley.**Page** - This model is a much more complex derivative of the Google Page Rank reapplication I have talked about at lenght.  In the models I have traditionally discussed here, losses are forwarded linked and we solve our matrix and go on our way.  In this new model, it runs 2 Page Rank variants, in one losses are forward linked in the other wins are forward linked.  We take the scores from the loss forward linked run and subtract half the win forward linked scores.  This gives us a much smoother range and prevents one big win from pushing a team to the top despite a large number of losses.  This model favors teams that have played more games as they have more chances to form links into themselves.  This leads to teams like Ohio State who have their bye early being slightly deflated.  This will correct itself as the season goes on.**QElo** - This is a varation of an Elo model.  Unlike MLE, this is actually an Elo based model.  It is lightly seeded with G5 starting 200 points lower than P5 and FCS starting at half.  This model looks at games down to DII.  To prevent early seeding or order of games from having to great an impact it does 2 passes.  The first pass is down with the seeding and runs through the season forward, the second pass runs through the season backwards and continues from the forward pass results.  This model, as an Elo model, can suffer from some recency bias or beating a team at the right time being a huge boon though this helps prevent it.  This model also suffers slightly from byes being heavily punished early.  The Q stands for quality.  This measure the quality of the win rather than using a 1 or 0 to revelaute Elo after each game.  This model uses score, luck margin, and overtime to determine, rematch win rate.  The winning team always is guarrenteed to be given a 52% rematch win rate, but is capped at 60% if overtime occurs.  Luck is determined by unforced turnovers, forced turnovers are not factored into luck. (This is super subjective and done by hand so it doesn't get a huge say). **Naive** - This is my naive strength of record model.  In this context naive doesn't mean dumb or bad, it means that it doesn't use any form of regression or machine learning to refine its results.  This is a simple run through a create a strength of record based on basic ratings of the opponent's offense and defense.  This model does not use a true record despite its name.  I find the Pythagorean record much more accurate representation of a team thus any place in this model a record is used the Pythagorean record is used instead.**Margin** - This model is another log likelihood based model.  This model takes scores of very game and tries to find a score for each team that minimizes the rating of team A - the rating of team B to be as close to the actual margin of victory or loss for every team across all games.  It places a soft cap on margin of victory of 28 anything above that and points are weighted less and less.  The functional cap is around 42 points past this and the impact of additional points is completely negligible.**Wins Over** - This is my latest creation.  This model is designed to lineup very closely with the CFP committees rankings as a similar concept seems tobe the driving factor in their rankings. I had toyed with the idea for a while based on models I had seen around.  It is also based on the concept of expected performance.  The model runs an initial estimated ranking.  These rankings are used to generate an expected Pythagorean wins an above average team (a team on the border of the top 25) would have against this schedule.  It compares this to the Pythagorean wins a team actually had to see how far over above expected wins they are performing.

Rank Team Reason
1 Alabama Crimson Tide (+3) Score: 0.9782 (Votes: 3,5,5,5,3,2)
2 Penn State Nittany Lions (6) Score: 0.9718 (Votes: 8,10,6,1,2,1)
3 Georgia Bulldogs (-2) Score: 0.9705 (Votes: 4,1,1,12,6,5)
4 Clemson Tigers (-2) Score: 0.9641 (Votes: 7,4,3,6,11,3)
5 Notre Dame Fighting Irish (2) Score: 0.9628 (Votes: 5,7,10,2,7,4)
6 UCF Knights (-3) Score: 0.9538 (Votes: 1,2,7,4,4,24)
7 TCU Horned Frogs (-2) Score: 0.9538 (Votes: 6,3,4,11,8,10)
8 Washington Huskies (-2) Score: 0.941 (Votes: 9,6,9,9,12,7)
9 Miami Hurricanes (1) Score: 0.9231 (Votes: 13,8,11,3,15,16)
10 Ohio State Buckeyes (-1) Score: 0.9218 (Votes: 14,15,16,15,1,6)
11 Wisconsin Badgers (1) Score: 0.9192 (Votes: 10,12,13,10,9,15)
12 Michigan Wolverines (2) Score: 0.8936 (Votes: 11,14,19,14,20,11)
13 Oklahoma Sooners (0) Score: 0.891 (Votes: 16,11,17,18,10,19)
14 Iowa Hawkeyes (5) Score: 0.8897 (Votes: 20,17,23,8,16,8)
15 USC Trojans (1) Score: 0.8808 (Votes: 19,9,15,25,22,9)
16 Auburn Tigers (1) Score: 0.8795 (Votes: 12,19,22,21,13,13)
17 Michigan State Spartans (1) Score: 0.8731 (Votes: 15,23,12,17,26,12)
18 Virginia Tech Hokies (3) Score: 0.8718 (Votes: 17,28,26,7,14,14)
19 Washington State Cougars (-8) Score: 0.8628 (Votes: 28,16,2,20,30,17)
20 USF Bulls (-5) Score: 0.8564 (Votes: 2,13,14,22,18,49)
21 Oklahoma State Cowboys (-1) Score: 0.8538 (Votes: 18,21,21,24,5,31)
22 Wake Forest Demon Deacons (4) Score: 0.809 (Votes: 33,33,34,16,21,18)
23 NC State Wolfpack (0) Score: 0.8026 (Votes: 21,26,24,39,27,23)
24 Stanford Cardinal (4) Score: 0.791 (Votes: 27,29,28,44,19,22)
25 San Diego State Aztecs (-1) Score: 0.7846 (Votes: 47,18,8,37,37,27)

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