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Descriptive Statistics In Table 2

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After conducting an analysis of descriptive statistics, as found in Table I, the results reveal that $224.6 million dollars (M=$4.16 million, SD=$2.99 million) was spent across the 54 multimedia rights agreements during the 2015-2016 school year. The smallest financial payout is $375,000 and belongs to Northern Illinois, while the largest contract $12,358,087 million, belonging to the University of Texas. The disparity among institutions becomes more evident when analyzing the multimedia rights contracts. Of the $224.6 million dollars spent during the 2015-2016 season, $182.3 million was spent on institutions who compete in one of the “Power 5” conferences, meaning that the 31 “Power 5” institutions in this study receive 81.2% of the …show more content…

In terms of total spending, IMG is followed by Learfield (sum = $82.3 million), JMI (sum = $11.9 million). It is important to note that JMI is relatively new when it comes to college multimedia rights contracts, and only has two institution, which is reflected in their low total payout. However, as illustrated in Table VI, JMI has a higher average payout than the two larger companies, IMG and Learfield, (M=$5.93 million, SD= $4.7 million). JMI will see an increase in their average payout during the 2016-2017 as well as during the 2017-2018 seasons with addition of Clemson who will make $2.6 million to $7.7 million during the respective seasons. IMG and Learfield are relatively similar in average payouts, with IMG averaging $4.53 million (SD = $3.52 million) and Learfield averaging $3.74 million (SD= $1.87 million). IMG and Learfield, both with over 20 contracts a piece have a far greater range of contract sizes, with IMG controlling both the smallest payout, $375,000, and the largest payout, $12.36 million. The variables outlined in Table VII were used in an attempt to explain the effect that numerous factors have on the guaranteed rights fee that a university receives from a third party rights holder. Those variables were separated into four categories; property-related, football performance, basketball performance, and demand indicators. From there, a Pearson product-moment correlation was applied

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