# Thesis Proposal (set 8: prior pre-decision probability)

 .remove_background_ad { border: 1px solid #555555; padding: .75em; margin: .75em; background-color: #e7e7e7; } .rmbg_image { max-height: 80px; } PRIOR PRE-DECISION PROBABILITY 1A If this frame looks similar it's because we are again utilizing Bayes' rule, but this time it is with the posterior as the PRE-DECISION odds of team C winning the game given the game-state, which is shown on the left-hand side of the equation. PRIOR PRE-DECISION PROBABILITY 1B On the right-hand side we again have the prior and conditional likelihood (original and new information). The first term is literally the prior (pre-game) odds of team C winning the game while the second term is the odds of team C being in that game-state given that they ended up winning the game. PRIOR PRE-DECISION PROBABILITY 1C The next step is to calculate these two components. Down here you can see that to estimate the first component (the prior) we will be using a logistic regression to predict game outcomes from closing point spreads. PRIOR PRE-DECISION PROBABILITY 1D For the second component we will use a multinomial logistic regression to predict the probability of the team being in that situation given that they won the game. In this case X-Prime represents the group of variables that make up the game-state: the score margin, time remaining in the game, field position with respect to the offensive team, current down, yards to go to gain a new set of downs, and an indicator variable for possession with respect to the home team. [CLICK] Authorwellerross ID318212 Card SetThesis Proposal (set 8: prior pre-decision probability) Descriptionscript Updated2016-03-31T02:55:57Z Show Answers