Football Rankings Research Articles

Innovative player evaluation: Dual-possibility Pythagorean fuzzy hypersoft sets for accurate international football rankings

This study introduces an advanced approach for ranking international football players, addressing the inherent uncertainties in performance evaluations. By integrating dual possibility theory and Pythagorean fuzzy sets, the model accommodates varying degrees of ambiguity and imprecision in player attributes. Additionally, the use of hypersoft set theory enriches the analysis by capturing the multifaceted nature of player evaluations. The proposed aggregation operators refine the synthesis of diverse information sources, leading to a comprehensive and nuanced assessment. This research significantly enhances player evaluation methodologies, providing a more adaptable framework for a fair assessment of international football talent. A practical example illustrates the application of dual-possibility Pythagorean fuzzy hypersoft sets (DP-PFHSS). A numerical technique is proposed for solving multi-criteria decision-making (MCDM) challenges with known dual possibility information using the proposed aggregation operators. This decision-making algorithm effectively determines a football player's worth, contributing to the overall ranking and evaluation process. The approach aids in scouting and recruitment by facilitating talent identification and informed player signings. Graphical analysis, comparing existing and proposed methods using average and geometric operators, demonstrates the superiority of the proposed approach in the players evaluation, indicating that F1 is in the top ranking.

Model It! Devising a Model to Represent College Football Rankings

A model eliciting activity based on our students’ outside interests sparks engagement with modeling and interesting debates.

World Cup Team Evaluation Model based on TOPSIS Method through FIFA Player Data Analysis

The purpose of this study is to analyze which teams are stronger and more likely to win the football World Cup. Although there are many classic cases of the weak winning the strong because of the obvious differences between the national football teams, these are only a very small part and will be excluded from consideration in this article. This article will analyze the performance scores of players in their clubs given by FIFA through the TOPSIS method, and finally the ranking of national football teams. As the current world football rankings are based on points accumulated from wins and losses in intercontinental competitions and friendlies, they do not give a specific indication of a team's strength. This article will rate the highest ranked teams in each group stage of the World Cup and finally rank them. Finally, entropy weight method and simple TOPSIS method are analyzed which is more suitable for the analysis of football World Cup.

Metody tworzenia rankingów sportowych na wybranych przykładach

Sports rankings play a very important role in modern sport. They most often determine who will be considered the favorite of a specific match or fight. In other cases, they decide on the seeding of a player (team) in the tournament, or determine whether he will be able to participate in it at all. There is no one consistent methodology for creating sports rankings. Each discipline has its own rules. Some are based on more or less complicated mathematical calculations, others are based on the subjective opinions of the experts who create them. In this study, we will look at sports rankings on the examples of football, chess and professional boxing, which represent completely different approaches to the methodology of creating rankings. The FIFA football ranking is based on multifactorial calculations taking into account, among other things, the type of match played, its result, and the expected result of the match based on the analysis of the strength of both teams. In professional boxing, no single ranking has been created so far and each federation proposes its own rankings, to which an alternative may be the system based on the algorithm proposed by boxrec.com. In chess, the Elo system remains the dominant way of classifying players, although alternative methods often turn out to be more precise.

Hotel Revenue Management and College Athletics: A Ten-Year Study from A Major College Football Team in USA

ABSTRACTCollege athletic competitions draw spectators increasing demand hotel stays during home games. The current study focuses on demand for hotel rooms generated from home games for college football. The current study uses ten-year hotel revenue data from STR for a major campus town with a nationally ranked NCAA football team. This study followed a two-tier approach regarding the location of the hotel with reference to the stadium. Primary Zone defined as a three-mile geographic zone around the football stadium; Secondary Zone defined as an area between 3 and 20 miles from the stadium. Results from this longitudinal study indicated NCAA football rankings have a significant effect on hotel ADR and Occupancy rates. Interestingly win and loss records of football teams have no effect on hotel ADR and Occupancy. As expected, in the Primary Zone (3 miles from stadium), hotels experience higher ADR and Occupancy during home games. However, this effect gradually fades as the distance to the hotel from the stadium increases.

The Political Economy of Football: Democracy, Income Inequality, and Men’s National Football Rankings

The question as to why certain countries outperform others in international football games warrants exploration. Herein, we argue that the synergy of economic development, democratic experience, duration of football associations, and low-income inequality improves the performance of men’s national football teams. Contemporary professional football is a typical “club good” that encourages members to take collective actions such as watching games, organizing associations, and investing in talent and capital for team success. We hypothesized that economic development sustains professional football, particularly in democratic regimes that allow for collective action through football associations. Wealthy democracies most effectively shape institutional incentives that improve the transparency, fairness, and competitiveness of leagues and contribute to their prosperity, which benefits the talent pool of national football teams. In addition, income inequality reduces national football performance. In this study, we used panel data from 121 countries for the 1999–2014 period as well as fixed-effects models to verify our hypotheses. The results show that a hundred units of democracy stock lead to a 0.145 natural logarithm FIFA score gain, as democracy stock exerted the most prominent effect on national football performance and additionally moderated the effects of long football association history to improve national football performance. A lower income inequality sequentially benefited to the performances.

A Logistic Regression/Markov Chain Model for American College Football

Abstract Kvam and Sokol developed a successful logistic regression/Markov chain (LRMC) model for ranking college basketball teams part of Division I of the National Colligate Athletic Association (NCAA). In their 2006 publication, they illustrated that the LRMC model is one of the most successful ranking systems in predicting the outcome of the NCAA Division I Basketball Tournament. However, it cannot directly be extended to college football because of the lack of home-and-home matchups that LRMC exploits in performing its Logistic Regression. We present a common-opponents-based approach that allows us to perform a Logistic Regression and thus create a football LRMC (F-LRMC) model. This approach compares the margin of victory of home teams to their winning percentage in games played against common-opponents with the away team. Computational results show that F-LRMC is among the best of the many ranking systems tracked by Massey's College Football Ranking Composite.

IJARCCE - Computer and Communication Engineering

The rankings of the members fora given domain, if provided, can be thought to be the efficient description of the domain and help us make decisions accordingly. For example, each web search engine like Google retrieves query- relevant documents and shows us highly ranked ones in ascending order of their ranks. However, if we have two or more rankers or ranking systems with their own expertise for one domain, we may be puzzled what to choose or how to fix their differences. In this paper, the issue of how to combine two experts' rankings is treated. We suggest some methods of combining two different rankings. For practical applications, two domains were selected to test and validate our method. First, two different rankings generated by changing some portion of our information retrieval system were selected and our experiments show that the resulting average rank of top ten relevant documents, for example, was considerably improved. The second domain we tried may be thought to be almost improbable. The question is if American college BCS football ranking, officially published from the middle of the football season, can be modified to better predict the four week later BCS ranking itself. Our experiments show that some computer-based ranker help enhance the predictability of the future BCS ranking.

Minimum violations and predictive meta‐rankings for college football

This article presents two meta‐ranking models that minimize or nearly minimize violations of past game results while predicting future game winners as well as or better than leading current systems—a combination never before offered for college football. Key to both is the development and integration of a highly predictive ensemble probability model generated from the analysis of 36 existing college football ranking systems. This ensemble model is used to determine a target ranking that is used in two versions of a hierarchical multiobjective mixed binary integer linear program (MOMBILP). When compared to 75 other systems out‐of‐sample, one MOMBILP was the leading predictive system while getting within 0.64% of the retrodictive optimum; the other MOMBILP minimized violations while achieving a prediction total that was 2.55% lower than the best mark. For bowls, prediction sums were not statistically significantly different from the leading value, while achieving optimum or near‐optimum violation counts. This performance points to these models as potential means of reconciling the contrasting perspectives of predictiveness versus the matching of past performance when it comes to ranking fairness in college football. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 61: 17–33, 2014

The Sensitivity of College Football Rankings to Several Modeling Choices

This paper proposes a multiple-membership generalized linear mixed model for ranking college football teams using only their win/loss records. The model results in an intractable, high-dimensional integral due to the random effects structure and nonlinear link function. We use recent data sets to explore the effect of the choice of integral approximation and other modeling assumptions on the rankings. Varying the modeling assumptions sometimes leads to changes in the team rankings that could affect bowl assignments.

Robust Rankings for College Football

We investigate the sensitivity of the Colley Matrix (CM) rankings---one of six computer rankings used by the Bowl Championship Series---to (hypothetical) changes in the outcomes of (actual) games. Specifically, we measure the shift in the rankings of the top 25 teams when the win-loss outcome of, say, a single game between two teams, each with winning percentages below 30%, is hypothetically switched. Using data from 2006--2011, we discover that the CM rankings are quite sensitive to such changes. To alleviate this sensitivity, we propose a robust variant of the rankings based on solving a mixed-integer nonlinear program, which requires about a minute of computation time. We then confirm empirically that our rankings are considerably more robust than the basic CM rankings. As far as we are aware, our study is the first explicit attempt to make football rankings robust. Furthermore, our methodology can be applied in other sports settings and can accommodate different concepts of robustness besides the specific one introduced here.

Are Surveys of Experts Unbiased? Evidence from College Football Rankings

Policy makers are frequently interested in soliciting unbiased information regarding alternative policies, and expert surveys can be influential. Since ranking policies is an often subjective process, there is always the concern of bias, both intentional and not. Expert bias is difficult to discern in the policy world, but surveys of expert opinion are compiled and for accuracy weekly in college football, allowing for hypothesis testing. While previous research has used college football rankings to determine the ability of surveys to incorporate relevant information, this paper examines the Associated Press and American Football Coaches' Association rankings for evidence of systematic bias. Specifically, more than 1,300 games from the 2003 to 2008 regular seasons are tested for factors that are systematically correlated with upsets. Both polls predict the winner nearly 80 percent of the time, and though there is evidence of systematic conference bias, correcting the rankings would only improve the accuracy of the polls by about 1 percent. There is no evidence of a bias favoring big market teams, nor teams that have strong journalism programs or are from the East Coast.

AccuV College Football Ranking Model

This work extends the work of Coleman (2005) which provides a football rankings system that minimizes game score violations. The modified model, called AccuV, developed in this paper incorporates several other aspects of most rankings systems including: strength of schedule, margin of victory, and home field advantage. These other components of a team's rank are included via an additional variable called the composite index. In addition to these new components, other bounding constraints have been added. These new constraints significantly reduce the solve-time of the model. The model of (Coleman, 2005) achieved a minimum of 55 game score violations for the complete 2002 season in about 36 seconds with 234,313 iterations as reported by LINGO. However, with the addition of the new constraints, the new model (without the additional ranking components) established the same minimum in only 6 seconds with a total of 55,119 iterations. This work is also easily extendable to allow the addition of other components if desired.

A Consistent Weighted Ranking Scheme With an Application to NCAA College Football Rankings

The National Collegiate Athletic Association (NCAA) college football ranking, in which the so-called national champion is determined, has been plagued by controversies the last few years. The difficulty arises because there is a need to make a complete ranking of teams even though each team has a different schedule of games with a different set of opponents. A similar problem arises whenever one wants to establish a ranking of patents or academic journals, etc. This article develops a simple consistent weighted ranking (CWR) scheme in which the importance of (weights on) every success and failure are endogenously determined by the ranking procedure. This consistency requirement does not uniquely determine the ranking, as the ranking also depends on a set of parameters relevant for each problem. For sports rankings, the parameters reflect the importance of winning vs. losing, the strength of schedule, and the relative importance of home vs. away games. Rather than assign exogenous values to these parameters, we estimate them as part of the ranking procedure. The NCAA college football has a special structure that enables the evaluation of each ranking scheme and hence, the estimation of the parameters. Each season is essentially divided into two parts: the regular season and the postseason bowl games. If a ranking scheme is accurate, it should correctly predict a relatively large number of the bowl game outcomes. We use this structure to estimate the four parameters of our ranking function using ‘‘historical’’ data from the 1999-2003 seasons. Finally, we use the parameters that were estimated and the outcome of the 2004-2006 regular seasons to rank the teams each year for 2004-2006. We then calculate the number of bowl games whose outcomes were correctly predicted following the 2004-2006 season. None of the six ranking schemes used by the Bowl Championship Series (BCS) predicted more bowl games correctly over the 2004-2006 period than our CWR scheme.

Technology transfer and convergence of performance: an economic study of FIFA football ranking

The seminal work of Gerschenkron (1962) stated that latecomers borrow advanced technology from their predecessors, resulting in convergence of productivity among nations. Additionally, the diffusion of technology among developing nations has been fulfilled not only directly from developed nations but also from surrounding nations through local spillovers such as learning from others. Recently, the disparity in national football performance among the nations appears to have improved, and it is evident that talented players are mobilized from developing nations to developed ones. The present article attempts to apply the framework of development economics, as introduced earlier, to the case of international football. An empirical examination of FIFA's world ranking points considered as reflecting nations' performances was conducted, applying panel data to control for unobserved nations' specific effects (Baltagi 2005). The results indicate that both technology transfer and the local information spillover play a crucial role in improving the performance of developing nations, but not of developed nations. The presented results are interpreted as implying that developing nations are able to catch up with developed ones due to the effects of technology transfer and local information spillover.

Assessing Methods for College Football Rankings

With the advent of the Bowl Championship Series (BCS) much emphasis has been placed on ranking teams. We consider several mathematical methods for ranking college football teams based on point differential including least squares with fixed or mixed effects. We also consider the use of modifications such as truncating or censoring (such as Harville's method) the result to adjust for the possibility of teams running up the score. We assess the predictive performance of these models using leave-one-out cross validation. The methods and analyses are applied to all major NCAA football data from 1930-2007.

Evidence of Television Exposure Effects in AP Top 25 College Football Rankings

A potential source of bias in the Associated Press (AP) Top 25 football rankings is television exposure. Using the 2003-2004 and 2004-2005 college football seasons, the authors observe, all else equal, that AP voters change the ranking of teams differently on the basis of television exposure: The more often a team is televised, relative to the total number of own- and opponent-televised games, the greater the change in the number of AP votes that team receives, even after accounting for own and opponent's on-field performance.

An Economic Model of the College Football Recruiting Process

College football programs devote an enormous amount of resources in efforts to persuade high school football players to attend their schools. In this study, we develop an empirical model of the factors that recruits consider when selecting a school, using a database that combines school-specific attributes with recruit-specific information. The authors' estimates imply that recruits' decisions are governed by a handful of primary factors such as the geographic distance between the recruit and the college, the school's recent football rankings, and whether the school is a member of one of the six Bowl Championship Series (BCS) conferences.

Every Picture Tells a Story: Racial Representation on Sports Illustrated Covers

It could be stated without hyperbole that America is a society of fanatics. Each spring hundreds of thousands of people across country become caught up in phenomenon known as March Madness, which is a tournament to decide best college basketball team of year. People who possess little interest and even less knowledge of collegiate basketball rush to fill out their brackets for office pools and question wisdom (and parentage) of tournament selection committee for including Nowhere State while Big Time U. gets snubbed. The Super Bowl is consistently highest-rated television program of year and has nearly been elevated to status of a national holiday. Each year now as wanes, fans eagerly await, and debate, release of NCAA college football rankings: and what would holiday season be without latest BCS (Bowl Championship Series) controversy? Spring training is signal to fans from coast to coast that boys of summer are coming back to town: Do you think Cubbies will finally win it this year? Sport is an important social institution in American society. Bloom and Willard write it is a complex cultural form that operates on many levels simultaneously, gaining new meanings as they are experienced and read within different historical, political, and social contexts (4). Describing participation levels, Al Neuharth notes that when excluding professional, collegiate, and high school and elementary school athletes, there are still thousands of Americans who participate in various community and workplace-based sports leagues, tens of millions attending professional sporting events each year, and many more watching sports on television (A15). Furthermore, sport has played a major role in both maintaining and deconstructing various systems of stratification in United States. For example, Title IX of Educational Amendments of 1972 requires educational programs receiving federal money to grant equal access to women. While this access goes well beyond athletics, this component has perhaps received most attention, and many argue this piece of legislation has led to an entire generation of girls and women being able to envision and achieve their dreams. Another example, and primary focus of this paper, concerns American system of racial stratification. According to Bloom and Willard, sports in twenty-first century have become a critically important cultural terrain on which most racialized groups have contested, defined, and represented their racial, national, and ethnic identities (1). Professional sports have historically been segregated. Though official policies were not always in place, it was understood: sports were usually reserved for only. Moreover, sports were used to maintain and reinforce racial status quo. Even as racial barriers began to crumble, de facto segregation restricted minority participation and reinforced notions of white racial superiority. An example of this process can be found in professional baseball. Major league teams, around 1953, operated with what has been referred to as the fifty percent color line. The essence of this unwritten policy was that teams could field up to four blacks out of nine total players, but no more than that. Roger Kahn wrote that this de facto color line was to ensure continued majority presence, and dominance, of whites in America's pastime: to have five blacks playing with four whites supposedly threatened old order (172). In spite of this sordid history of discrimination and segregation, sport was among one of first social institutions to integrate, accept, and eventually embrace minority participants. Some examples include Jack Johnson (boxing) in 1897, Kenny Washington (football) in 1946, Jackie Robinson (baseball) in 1947, Althea Gibson (tennis) and Earl Lloyd (basketball) in 1950, and Lee Elder (golf) in 1975. …

Explaining International Soccer Rankings

Existing research on the determinants of FIFA's international soccer rankings suffers from serious statistical problems, particularly sample selection bias and nonnormal errors. The authors correct for this by extending the data set by an additional 100 countries. Furthermore, they find important roles for new variables in the form of the size of population and a long history of international soccer in explaining world football rankings. The authors also investigate the determinants of an alternative ranking measure to that constructed by FIFA.