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dc.contributor.advisorCline, Kelly
dc.contributor.authorCrooks, Sabrina
dc.contributor.authorDavidson, Shirley
dc.contributor.authorNorton, Madeline
dc.date.accessioned2021-05-27T22:34:01Z
dc.date.available2021-05-27T22:34:01Z
dc.date.issued2020-02
dc.identifier.urihttps://scholars.carroll.edu/handle/20.500.12647/10468
dc.descriptionCompetition: The Mathematical Contest in Modeling (MCM)en_US
dc.description.abstractWe found qualitative indicators of team success for the Huskies soccer team. Success was measured in terms of the match outcome: win, loss, or tie. These indicators of success included the total number of passes per game, the type and frequency of node networks used, and the length of passes. These passes in a typical winning game included three different types of passing networks, two-node, three-node, and four-node, where the number of nodes indicates the players involved within the network. We found that while all three of these network types contributed to winning, the best indicator of success was an increase in the frequency of the three-node network utilization. We also found that shorter and simpler passes were more indicative of the team’s success in the match. We created five quantitative models that focused mainly on whether it was a home or away game, the starting formation, and the starting player line-up. We determined the top three players in all three positions, defense, mid-field, and offense, based upon the frequency in which they received a passed ball throughout the season. We hypothesized that other indicators of the team’s success would include which individuals started each game.en_US
dc.language.isoen_USen_US
dc.titleA Winning Design for Soccer Teams: Three-Node Networks and Increased Pass Frequencyen_US
dc.typeResearch Paperen_US
carrollscholars.object.departmentMathematicsen_US
carrollscholars.object.departmentLife & Environmental Sciencesen_US
carrollscholars.object.departmentData Scienceen_US
carrollscholars.object.seasonSpringen_US
carrollscholars.object.majorBiologyen_US
carrollscholars.object.majorData Scienceen_US
carrollscholars.object.majorMathematicsen_US


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