Youth Soccer Capitalization Rate

Study of the U.S. Soccer Capitalization Rate

BJ Pheasant

bpheasan@indiana.edu

This is the three step process in calculating the Capitalization Rate of soccer in the U.S.A. Combining Phase 2, Participation Rate, with Phase 3, Developmental Rate, and comparing to Phase 1, Target Participation Rate, reveals the capitalization rate of soccer in the U.S.A.

Phase 1

(Maximum) Target Participation Rate

My goal is to compute a percentage that could be used in conjunction with school district data (grades K-8), or census data (ages 4-14), to calculate the number of children that could be expected to play soccer. This assumes that the local club(s) advertise their services sufficiently and coach the children in such a way to not turn off the children to soccer once enrolled.

A two pronged approach was used by finding actual participation rates from as many clubs as possible across the U.S. and researching youth sport participation. This led to the discovery of university studies on youth development from many sources with Michigan State and Cal State as leading lights for usefulness. I combined the data that I researched and came up with an estimated average percentage of children that should participate in youth soccer.

Using my experience I decided that two standard deviations above the average would supply an achievable target. Two standard deviations produced a percentage that indicated about one in three youth (ages 4-14) should become soccer players. My calculations also include the pyramid shaped distribution in participation patterns from the younger to older age groups in youth activities. While there was an element of estimation involved in my calculations, I gathered participation rate data from youth soccer clubs in communities across the country from Washington State to Florida and from Washington DC to Texas. In particular the high rate of participation in one midsized city in Washington State almost caused me to dismiss it as an outlier, but further exploration in person proved it to be a sustainable rate, which led to inclusion.

Through 2009 I have helped with the process of starting five new high school programs (one in progress) and one new youth soccer club. I have also supplied my statistics as an outside expert to about 25 other high schools and clubs. For clubs this is supplied on a countywide format by age group from US Census data and for high schools this is supplied on a grade level basis through the school system data.

Current Progress:

Currently the Target Participation Rate (TPR) has been calculated. The resulting percentage has been used to compute the TPR for each of the 3,077 counties in the United States, as well as, on a statewide basis for each of the 50 states, and the U.S.A. as a whole.

Phase 2

(Actual) Participation Rate

This phase of the study will provide valuable feedback as to the efficiency of soccer recruitment and retention policies across the U.S. Current registration number will be collected from the various governing bodies for youth soccer. According to my current research the ranking of registration supremacy is as follows: U.S. Youth Soccer (USYS), American Youth Soccer Organization (AYSO), YMCA, Upward Soccer, Boys and Girls Club, other organizations (such as middle school soccer), and unaffiliated organizations. I estimate at this time that about 75% of youth players are registered with USYS, 8% with AYSO, 7% with the other organizations, and 10% unaffiliated – these percentages may change as detailed data becomes available.

Current Progress:

By comparing Phase 2 with Phase 1 a Participation Rate can be established for the U.S., for each state (as seen on the chart), and by county. With detailed data the Participation Rate can be calculated by school, by grade, or by sex.

Phase 3

Participant Development Rate

3.1 Player Development Rate

This section of the study determines how efficiently players have been trained geographically by using college soccer participation as a standard for developed players. By taking a snapshot of college rosters from the NCAA Div. 1, 2, and 3 along with NAIA Div 1 and 2 and comparing the player home towns and states to Phase 2 the development rate of each state and county can be determined. Further study will benchmark this player development rate by comparing it to that of other countries in soccer and other sports in the U.S. to determine a target development rate.

3.2 Systemic Development Rate

During my research into the Participant Development Rate it has become evident the structure of the system in which players are trained can affect their development. Several problems may arise, for example, if players are selected (the age players are selected is systemic) too early in development then poor projections are possible. Another example is that if too wide an age range (selection age range is systemic) is compared during selection then younger players with less training time may be passed over.

Current Progress:

My research in Player Development has centered so far around actual coaching experience as a highly licensed soccer coach. This includes travel to countries in three FIFA regions and extensive reading of youth soccer training methods from countries around the world.

My research to this point in the Systemic Development region has determined several factors that determine participant development. Helpful studies have come from youth academic honors progress studies, Canadian youth hockey, and Czech youth soccer.

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