(05-20-2021 07:20 AM)CliftonAve Wrote: I still maintain these calculations are bullshyte. UCOnn FB #56 on the list, even over some P5 schools— who put this list together?
Clifton, this is why I asked Wahoo for the methodology. Links to a paywall and references in an article to something that is not shown is not a substitute for methodology. I had seen the following years ago but did not want to have to track it down again:
Somehow I just knew I would have to dig this out myself:
“DATA COLLECTION
To analyze how changes in socioeconomic and political status are associated with changes in program value, we used a balanced panel dataset of variables across three presidential election years: 2004, 2008, and 2012. With 100 observations in each year and three years of data, the panel includes 300 observations. Following is a description of the variables used in our analysis:
Revenues: Program revenue is not reported directly and thus must be approximated. The estimate was made using the method described in Brewer and Pedersen (2013b), by summing the pro rata percentage of non-allocated revenues with those revenues directly attributed to football, as reported in the EADA cutting tool (U.S. Department of Education, 2013). The pro rata percentage of non-allocated revenues was established by assessing the fraction of total allocated revenues within each athletic department that comprised football operations, and multiplying this fraction by the non-allocated revenues. This product was estimated to reflect revenues arising from football. No adjustments were made for intangible value attributions such as goodwill to the university, the "Flutie Effect," differential state appropriations, or other indirect revenues arising from sport presence.
Program Value: our second dependent variable represents football program valuations for programs at the 100 public universities in NCAA Division I football (Brewer & Pedersen, 2013b). Program values represent consideration of two distinct valuation methods: revenue multipliers and discounted cash flow analysis. In professional football, teams are valued primarily by their ability to generate revenue, which prospective buyers of teams prefer to consider as expense levels can vary quite significantly among franchises, rendering cash flow analysis less useful than in other industries. Thus, the first valuation method uses NFL-based revenue multiplies for college football program value indication. The general value equation is given below:
... (1)
Football teams are valued on revenue, however, given their ability to cash flow. While financial losses are rare in the NFL, expenses sometimes exceed revenue in NCAA Division I football programs having less brand development. Therefore, valuing college football programs solely on revenues would fail to reflect the risks associated with running expense-intensive football program lacking a market sufficient to produce positive earnings. The second valuation method implements a constant growth model, using the cash flow in the year following the valuation year projected forward at a constant rate, a discount rate in the form of a weighted average cost of capital, and a growth rate. The general valuation model is given below:
... (2)
where CF1 is the program's cash flow in the valuation year, k is the program's estimated weighted average cost of capital, and g is the projected growth rate.
The value (dependent) variable used in this report represents the average of these two valuation methods, both of which are invested capital indications that do not consider debt level or capital structure, and is denominated in millions of dollars. Note that if the resulting value indication was negative, the program was assigned a valuation of zero (0).
Table 1, below, shows the top ten programs, ranked by valuation as of 2012 (Everson, 2013). Revenues and coach's salary are included for reference.
Income: The income variable is the average per capita income in the program's state, denominated in thousands of dollars. Data was retrieved for each state and year from Stats Indiana (Stats Indiana, 2014).”
Freeman, K.M. andBrewer, R.M.(2016). The Politics of American College Football.Journal of Applied Business and Economics. 18(2), 96-107