Minimizing Game Score Violations in College Football Rankings

One metric used to evaluate the myriad ranking systems in college football is retrodictive accuracy. Maximizing retrodictive accuracy is equivalent to minimizing game score violations: the number of times a past game’s winner is ranked behind its loser. None of the roughly 100 current ranking systems achieves this objective. Using a model for minimizing violations that exploits problem characteristics found in college football, I found that all previous ranking systems generated violations that were at least 38 percent higher than the minimum. A minimum-violations criterion commonly would have affected the consensus top five and changed participants in the designated national championship game in 2000 and 2001—but not in the way most would have expected. A final regular season ranking using the model was perhaps the best prebowl ranking published online in 2004, as it maximized retrodictive accuracy and was nearly the best at predicting the 28 bowl winners

A network-based dynamical ranking system for competitive sports

From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.Comment: 6 figure

BCS Or Just BS: How College Football Could Crown The Wrong National Champion? Just Do The Math - Correctly!

The 2009 college football season is here, but there has been a continuing controversy swirling over how the Football Bowl Subdivision (FBS) selects its national champion.  College football uses a multi-criterion decision matrix (MCDM) evaluation technique to determine which two teams will play for the national championship. We analyzed the BCS method of evaluating which teams play for the national. We found that Texas should have been ranked ahead of Oklahoma.  We conclude that BCS utilizes a mathematically flawed evaluation method to decide which two teams will play each year in the national championship game.  Just do the math--correctly

An NCAA Football Bowl Subdivision Production Function

Each year pundits across the NCAA football landscape debate the validity of various NCAA football teams’ relative worthiness to play for the national championship. Given this debate seems to revolve around which team is the best in terms of total team production, I have developed and statistically estimated a complex invasion NCAA football bowl subdivision production function measuring NCAA football team productivity covering the 2008 to 2017 seasons. The model estimates both points scored and points surrendered for each team during this time period and then is combined to determine each team’s overall productivity. Finally, as an application of the complex invasion college football production function model, I have ranked the overall productivity of the NCAA football bowl subdivision teams for the 2017 season to find the most productive team. The model concludes that the University of Alabama was the most productive team for the 2017 season

NCAA college football pseudo-playoff non-conference games scheduling via constraint and Integer Programming

NCAA Division I-A College Football post-season play is currently determined by a controversial BCS Bowl system. Due to the massive differences in compensation for playing in differing bowl games, heated debates arise every year as to who deserves places in the prestigious BCS bowl games. Without a round-robin approach, in which every team plays every other, there would be no absolute measure of which teams deserve BCS births. We developed a scenario involving a pseudo-playoff system to be implemented at the end of regular season conference play to create unique matchups to increase comparisons of teams across the nation. The system was modeled twice, once using Integer Programming techniques and again with Constraint Programming techniques. Instances of the two models were implemented on the 2010 NCAA football season and compared on their performance. Lastly, we discussed how certain matchups of the resulting solutions would have affected the outcomes of the season and perhaps the assignment of post-season bowl games

Discrimination and Information: Geographic Bias in College Basketball Polls

Voters in the Associated Press college basketball poll vote own-state teams and teams that are fewer miles away to higher rankings than other teams, especially at the bottom of their ballots. Game outcome data show evidence that teams that are fewer miles away are underrated—not overrated—by pollsters, especially at the top of their rankings, perhaps because pollsters fear accusations of geographic bias. When controlling for distance between pollsters and teams, there is some evidence that pollsters overrate local-conference teams at the top of their ballots, but more properly rate them the bottom of their ballots

Discrimination and Information: Geographic Bias in College Basketball Polls

Voters in the Associated Press college basketball poll vote own-state teams and teams that are fewer miles away to higher rankings than other teams, especially at the bottom of their ballots. Game outcome data show evidence that teams that are fewer miles away are underrated—not overrated—by pollsters, especially at the top of their rankings, perhaps because pollsters fear accusations of geographic bias. When controlling for distance between pollsters and teams, there is some evidence that pollsters overrate local-conference teams at the top of their ballots, but more properly rate them the bottom of their ballots