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- Creators: Wilson, Jeffrey
- Resource Type: Text
sports, banking, and other disciplines. We use predictive analytics and modeling to
determine the impact of certain factors that increase the probability of a successful
fourth down conversion in the Power 5 conferences. The logistic regression models
predict the likelihood of going for fourth down with a 64% or more probability based on
2015-17 data obtained from ESPN’s college football API. Offense type though important
but non-measurable was incorporated as a random effect. We found that distance to go,
play type, field position, and week of the season were key leading covariates in
predictability. On average, our model performed as much as 14% better than coaches
in 2018.
Dreadnought is a free-to-play multiplayer flight simulation in which two teams of 8 players each compete against one another to complete an objective. Each player controls a large-scale spaceship, various aspects of which can be customized to improve a player’s performance in a game. One such aspect is Officer Briefings, which are passive abilities that grant ships additional capabilities. Two of these Briefings, known as Retaliator and Get My Good Side, have strong synergy when used together, which has led to the Dreadnought community’s claiming that the Briefings are too powerful and should be rebalanced to be more in line with the power levels of other Briefings. This study collected gameplay data with and without the use of these specific Officer Briefings to determine the precise impact on gameplay. Linear correlation matrices and inference on two means were used to determine performance impact. It was found that, although these Officer Briefings do improve an individual player’s performance in a game, they do not have a consistent impact on the player’s team performance, and that these Officer Briefings are therefore not in need of rebalancing.
We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.