-Expected results including poll data
-Expected win values this week
The major update this week solves the major predictive problem that expected computer rankings will not match expected poll rankings, and so teams like Northern Illinois are going to be vastly overpredicted. There was also a small error in the code for computing conference champions, which gave teams like Northern Illinois an extra 10% chance of winning their championship. After a big win against Toledo, it's still likely that Northern Illinois will finish in the Top 16, finish ahead of the Big East champion and any other non-automatic qualifying team, and so they will get a BCS bowl, but it's extremely unlikely they will be ranked high enough in the polls to get into the championship. As polls are subjective, they are difficult to quantify.
To fix this problem, I added a prior distribution for each conference, essentially adjusting each team's rating up and down by a number of points depending on two factors. The first was the overall strength of the conference, derived from the Central mean of their Sagarin Pure Points rating. The second was, looking back at the end results since 2005, how teams poll rankings were compared to their computer rankings. These were averaged by conference, and conferences were maximally adjusted up or down half a game against a neutral team.
In other words, an ACC team that went 10-2 would be comparable with an MAC team that went 11-1 against the same teams, as the ACC is an average conference that does very well in polls compared to computer rankings, and the MAC is a mediocre conference that does not do well in polls.
The precision of the end result will be lower, and so you can uncheck the "Use Poll Data" box to view results purely by computer predictions, but the results are probably much closer to what will actually happen.
Each team that plays this week is also displayed with their imputed likelihood of winning this week's game.
This was designed by Jack Cackler, a doctoral student in Biostatistics at Harvard University.
To make winter travel plans with better information, this site graphically illustrates of all of the bowls a team might play in, how likely they are, and where and when each bowl will be.
Click the radio buttons to switch between viewing by team or by bowl, and mouseover any bar to see the chance of that event occurring.
Feel free to contact me at email@example.com.
These predictions were generated largely based on the pure points predictor ratings created by Jeff Sagarin, and will be updated weekly.
For each game remaining in the schedule a probability of winning was determined based on the rating of each team and home field advantage.
Based on these predictions, each game was sampled 10,000 times and for each sample, conference championship games and subsequently bowl games were computed.
Overall ratings were given by an Elo rating system used by the computer rankings in the BCS standings. For each team, the number of times it ended up in each bowl was counted and is given as a percentage. The maximum standard deviation of any estimate is .5%. Bowls that occurs .1% of the time or less are grouped as "Other", and are theoretically possible, but highly unlikely
The predictions are robust, but have several limitations that could impact accuracy. Final BCS Standings average Computer rankings, the USA Today Poll, and the Harris poll, the latter two of which are more subjective.
As such, these predictions will over-rank teams with good numerical rankings but poor poll standings.
While most bowl seats have contracts with conferences, in some cases a bowl may choose any available team if no team they are contracted to is eligible.
These predictions simply assign the highest rated team not yet in a bowl, but there may be instances in which a bowl might pick a lower-ranked team that they believe has a larger fan base.
Analysis coded in R, graphs constructed with the D3 Library.