Matching Items (4)
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- All Subjects: Data Analytics
- Creators: Wilson, Jeffrey
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
Description
Although the sport and exercise of running has a great amount of benefits to anyone's health, there is a chance of injury that can occur. There are many variables that can contribute to running injury. However, because of the vast amount of footsteps a frequent runner takes during their average run, foot strike pattern is a significant factor to be investigated in running injury research. This study hypothesized that due to biomechanical factors, runners that exhibited a rear foot striking pattern would display a greater incidence of chronic lower extremity injury in comparison to forefoot striking counterparts. This hypothesis would support previous studies conducted on the topic. Student-athletes in the Arizona State University- Men's and Women's Track & Field program, specifically those who compete in distance events, were given self reporting surveys to provide injury history and had their foot strike patterns analyzed through video recordings. The survey and analysis of foot strike patterns resulted in data that mostly followed the hypothesized pattern of mid-foot and forefoot striking runners displaying a lower average frequency of injury in comparison to rear foot strikers. The differences in these averages across all injury categories was found to be statistically significant. One category that displayed the most supportive results was in the average frequency of mild injury. This lead to the proposed idea that while foot strike patterns may not be the best predictor of moderate and severe injuries, they may play a greater role in the origin of mild injury. Such injuries can be the gateway to more serious injury (moderate and severe) that are more likely to have their cause in other sources such as genetics or body composition for example. This study did support the idea that foot strike pattern can be the main predictor in incidence of running injuries, but also displayed that it is one of many major factors that contribute to injuries in runners.
ContributorsBaker-Slama, Garrett Richard (Author) / Harper, Erin (Thesis director) / Cataldo, Donna (Committee member) / Wilson, Jeffrey (Committee member) / Barrett, The Honors College (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2014-05
Description
Predictive analytics have been used in a wide variety of settings, including healthcare,
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.
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.
ContributorsBlinkoff, Joshua Ian (Co-author) / Voeller, Michael (Co-author) / Wilson, Jeffrey (Thesis director) / Graham, Scottie (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Department of Information Systems (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Predictive analytics have been used in a wide variety of settings, including healthcare, 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.
ContributorsVoeller, Michael Jeffrey (Co-author) / Blinkoff, Josh (Co-author) / Wilson, Jeffrey (Thesis director) / Graham, Scottie (Committee member) / Department of Information Systems (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
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%.
ContributorsBarolli, Adeiron (Author) / Jimenez Arista, Laura (Thesis director) / Wilson, Jeffrey (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05