Research paper on college football players
METHOD This study focused on the sixty-four universities in the Power Five conferences as of and the football players who were published commits to one of the sixty-four universities during the years of to Three online sources were utilized to build large datasets that were used for the study.
First, a Football Commit dataset was built utilizing the published commitment list for each Power Five university for each recruiting class from to from Rivals. The incoming class was used as the final recruiting class in the study because this allowed for tracking of all commits through the football season, ensuring a minimum of at least four college football rosters seasons for each recruit in the dataset. To best represent both the commits as well as the commits that went on to become players, the term commits was applied to everyone in the study regardless if they transitioned into being a player on the roster or not.
The researchers gathered the rosters that were produced by the NCAA for each Power Five football program for each year from to The NCAA statistics rosters also included the official number of games each player appeared in during each season. The Football Roster dataset was much larger than the Football Commit dataset because the NCAA statistics rosters included all players who ever appeared on the roster, not just recruited commits, but also all walk-ons and transferred-in players.
For example, a football player that played for four seasons would show up on four different NCAA statistics rosters.
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The NCAA also has various rules including redshirting and medical exemptions that when combined allow commits to extend the number of years in a program beyond five years. Therefore, a few players appear on a total of six NCAA statistics rosters. In order to study the ACLC two subgroups in the datasets needed to be removed from the final analysis. The first subgroup was the commits that excelled on the football field during their first several years at the college level and made the decision to leave early prior to appearing on a fourth roster for the NFL once they were eligible to do so, which allowed them to continue to participate in their sport at the next level.
The players who were drafted early were excluded from the final analysis. These players were excluded because they made a personal choice to continue to participate in the sport but at a higher level and the researchers did not want to penalize the universities for commits leaving before completing their ACLC for a roster spot at a higher level. The second subgroup were commits listed as juniors or seniors on their first roster appearance.
This subgroup was treated as Transfer-ins because they did not appear to come in as high school recruits. This subgroup was removed from the final analysis because the ACLC was based on high school commits and how they fared in roster survival at the university they committed to right out of high school.
After removing the Drafted Early and Transferred-in subgroups, Excel was utilized to index and match the two datasets to determine how many rosters each football commit appeared on at the university he had originally committed to, as well as how many games he played in. The researchers then sought to identify commits based on the number of rosters on which they appeared, between zero and six rosters. Those who stopped appearing on rosters prior to a fourth season appearing on zero to three rosters were classified as having incomplete ACLCs while those who appeared on four or more rosters were classified as having complete ACLCs.
In terms of games played, as mentioned above, the Football Roster dataset utilized the football-statistics section of the NCAA website. Therefore, the researchers utilized the games played stat provided on the roster list but did not attempt to ascertain the amount of time actually spent on the field by the commits.
These differences included spelling variations as well as errors. The researchers utilized the Excel add-in Fuzzy Lookup to assist in finding differences such as full first names as compared to shortened first names, use of nicknames, typos, misspellings, and punctuation differences.
RESULTS The Football Roster dataset contained 98, roster spots and the Football Commit dataset included 17, high school football commits recruited between to from one of the six-four universities in the Power Five conferences. The subgroups Drafted Early and Transferred-in 1, were removed from the final analysis. This reduced the final number in the analysis by 1, commits, leaving 15, in the final analysis. Table 1 illustrates the percentage breakdown of the roster appearances of commits during their ACLC by conference. The table shows the distribution of the 15, commits.
The SEC had the most commits, with 3,, and the Big 12 had the least commits, with 2,, between and As a conference, the Big 12 had the highest percentage of commits that never appeared on a roster at The variance between conferences dissipates slightly once commits appear on at least one roster and then there is roughly a ten-percent loss each year from the first to the third year, across all conferences. Next, the commits were separated by the number of rosters they appeared on, with those appearing on zero to three rosters placed in the incomplete ACLC group and those who appeared on four or more rosters placed in the complete ACLC group.
The Big 12 conference was the only conference with a greater number of commits with incomplete ACLCs The ACC Table 3 displays the ten universities within the Power Five conferences with the largest positive percentage differential between commits classified as completing their ACLCs and those classified as having incomplete ACLCs. The table also provides the total number of commits for each university along with the percentages for the zero to three roster appearances. The university with the highest percentage of football commits with completed ACLCs was Northwestern This means that only This resulted in Northwestern also having the highest positive differential Table 4 displays the ten universities from the Power Five conferences on the other end of the ACLC spectrum, with the largest negative percentage differential between commits classified as completing their ACLCs and those classified as having incomplete ACLCs.
That produced a negative differential of Beyond roster appearances, the number of games played by each commit was also recorded. Table 5 provides the weighted average of games played for commits by conference. The table breaks down the average games played based on the number of roster appearances from zero to six rosters.
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The weighted average used the percentages of rosters made for all football commits including those who never appeared on a roster zero rosters to reflect the average number of games for all commits at their chosen universities. Overall, the number of games played is similar across conferences, with all commits in the study averaging slightly over 24 games during their ACLC.
However, when examined by the number of roster appearances those commits who appeared on only one or two rosters and saw very few games, averaging just under 3 games for those that only appeared on one roster and less than four games per season for those who appeared on only two rosters 7. A commit that appeared on at least four rosters and survived the many years of roster management saw a large increase in the average number of games played Traditionally the focus has been on team-based graduation rates using academic measures such as the FGR or the GSR to show how many athletes progressed and earned degrees.
The importance of this study is that it moves the point of our attention from matriculation at the university back to the point at which the player had made a commitment to the university, and ostensibly the university to the player. By doing so we see the number of commits that never make a single roster at their chosen university.
This method provided raw data for the discussion on the issue of over signing going forward. As the results showed 1, The issue of over signing and roster management are complex because the reason a commit does not make an initial roster or why a commit does not continue on the team after appearing on one or more rosters varies. There are valid reasons in which the athlete makes the decision to leave the team, there are legitimate reasons for players to be dismissed or retire from a team, and then there are dubious instances in which coaches make decisions to force their once highly-recruited commit off the team 13, As an exploratory study with a large dataset, the researchers did not attempt to go back and do in-depth research on why each commit with an incomplete ACLC did not progress onto the next roster or why some commits never appeared on a single roster but rather focused on a longitudinal approach to view the movement of commits over time.
The results raise questions of why there was a substantial difference between universities in terms of maintaining the athletes on rosters.
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The universities with the largest positive differential between complete and incomplete ACLCs had between While the universities with the largest negative differential between complete and incomplete ACLCs only had between Because there are no rules or regulation that require athletic departments to document what happened to each commit that did not matriculate to the school or never made it onto a roster, further university by university in-depth research would be required to ascertain why these universities had higher numbers of commits never appear on a single roster.
Nevertheless, the ACLC data alone could be valuable information for potential recruits weighing their options between Power Five universities. If future recruits have this information during the recruiting process it would force coaches from programs with negative ACLC differentials to try to explain why their football program has had such high turnover rates throughout the years. The results of the study also provided the average number of games played by commits in each conference.
The overall weighted average was just over 24 games, however, those with shorter ACLCs played in far fewer games. Viewing these results could better prepare future recruits to understand the reality of college football and how the years of long hours practicing might not pay off in as many college football games as they think it will.
The NCAA could also require documentation from universities on each athlete that failed to complete their ACLC and the reasons why they never made a single roster or were removed from the team after one, two, or three years. Adler, P. From idealism to pragmatic detachment: The academic performance of college athletes. Sociology of Education, 58, Bateman, J. Marquette Sports Law Review, 22 1 Beamon, K.
The Journal of Negro Education, 77 4 , Big stage, small screen. Dallas News. Botkin, B. Five years later, SEC not hurt by its football recruiting signing cap. Case, B. Academic clustering in athletics: Myth or reality?
Research paper on college football players
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