Thesis Defense (8. Results 3)

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  1. Pre-game win probability summary data across game outcomes
    So to estimate the in-game win probability using a Bayesian approach, I first needed to estimate the pre-game win probabilities. In this table you can see the summary data for the pre-game win probabilities across actual game outcomes.

    These numbers are all with respect to the home team and as I mentioned before, there are 3,072 games in this data set. The home team won 1,760 of those, and the away team won 1,308, while the other four ended in ties.

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  2. Pre-game win probability across point spreads
    This graph provides another way to view the pre-game win probabilities. The home team's closing point spreads are shown across the x-axis and the line represents the corresponding pre-game win probability for each point spread.

    As you can see, a point spread of zero translates to a predicted pre-game win probability of almost exactly 0.5, which is what we would expect to see.

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  3. Conditional likelihood of decision model results
    While the previous graph illustrated the results of the pre-game component of the in-game win probability model, this table presents the results of the conditional likelihood component. The results are shown with respect to the multinomial logistic resgression's omitted option, which in this case is first down attemtps.

    As you can see with the p-values, all but three of these estimates are statistically significant at the 0.05 level and all but six are statistically significant at the 0.01 level.

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Author:
wellerross
ID:
323497
Filename:
Thesis Defense (8. Results 3)
Updated:
2016-09-22 22:16:21
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thesis
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thesis
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