After the WC and DIV week - Superbowl and Fantasy Football Playoff Roster winner predictions

After today's unexpected loss of NE to the NYJ a lot of clarity was brought to the RKB playoff fantasy football pool predictions. Firstly the model probabilities for the SB matchup and winners.


The most likely outcome and matchup is PIT playing GB in the superbowl and GB winning.

Because many rosters in the playoff pool had NE players a large number of potential winning rosters (including my own) were wiped out and have no chance of winning. Also, the GB and PIT games this weekend generated a lot of points for rosters which had those players. Finally, not very many people had NYJ and CHI players in their rosters. The combination of these factors means that for the purposes of my simulation model there is not difference in the 1st and 2nd place winners among the several superbowl mathcups and outcomes.

The expected winner of the pool is roster 64, Rich V 3, built on a GB/PIT superbowl matchup with GB kicker, QB and DEF, and filled in with PIT players for the extra RB and WR. The second place winner is roster 9, Bruschi Drink 4, which contains all GB players. The roster just placing out of the money in third place is roster 58, RDK 2, my roster built around GB kicker, QB and DEF, with NE and PIT players filling out the RB and WR positions. There is no graph because these are the results in 2000 simulations no matter the team outcomes. I didn't bother with 10,000 simulations because for this model the results look pretty unequivocal.

The results show a failure in the model in that there is no link between team performance and player performance except for the number of games played. Additionally it doesn't take into account good or bad days for players since it only uses the average number of points per game for the year. There is good and bad news in this. A weird combination of player performance could let me squeak through and win the pool, but that same combination could let a 4th or 5th place through to win. The only way to get better information is to get game by game data to try to get the variation in player performance into the simulation.