Bracketology: How can math help?

Chartier, Tim; Kreutzer, Erich; Langville, Amy N.; Pedings, Kathryn

doi:10.5948/upo9781614442004.006

Cited by 23 publications

(5 citation statements)

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“…The Massey method utilizes transitivity to include game scores in ranking but does not consider when in the season the games are played (Chartier, et al, 2010). Some also predict outcomes by choosing the teams with the higher NCAA seed.…”

Section: Predicting Tournament Outcomesmentioning

confidence: 99%

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Application of PageRank Algorithm to Division I NCAA men’s basketball as bracket formation and outcome predictive utility

Matthews

McClain

Smith

et al. 2021

Journal of Sports Analytics

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This article examines the use of the PageRank algorithm to rank the teams and predict team performance in the tournament. This method has the potential to be utilized as an alternative method to choose tournament participants, as opposed to the traditional ranking and seeding process currently employed by the NCAA. PageRank allows for the consideration of all games played during the regular season (average of 5832 games per season) and for customizable performance weights in the prediction. The PageRank algorithm is a viable tool in predicting tournament outcomes due to depth and extensiveness of the data. The PageRank analysis helped to predict over half of the tournament game outcomes correctly in 2014-2018 and produced an average bracket score of 81.8 points out of 192 possible over the same 5 years.

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Section: Predicting Tournament Outcomesmentioning

confidence: 99%

“…The edges were also weighted to provide more accurate representation of the value of each game. Point margin, date, and venue were all mentioned as potential areas to add weights (Chartier, et al, 2010). Adding weights to the edges allows for additional factors to be later considered in the probability distribution analysis of PageRank.…”

Section: Weightsmentioning

confidence: 99%

Application of PageRank Algorithm to Division I NCAA men’s basketball as bracket formation and outcome predictive utility

Matthews

McClain

Smith

et al. 2021

Journal of Sports Analytics

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“…Massimo Franceschet, Enrico Bozzo (2017) has applied the temporalized Massey's method to investigate a temporalized version of the popular Massey's technique for rating actors in sport competitions. Tim Chartier ,et.al(2010), applied two popular rating methods , Colley Method and the Massey Method that are also used by the Bowl Championship Series, the organization that determines which college football teams are invited to which bowl games.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of AFL Results from year 2012 – 2017 using Massey Constant Rating models

K¹,

P²,

Rajeswari³

2019

IJITEE

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Australian rules football is a vibrant sport, based on the principle of territorial foray. It adorns with both a large playing field and more number of participants predominantly athletes who are dynamic with multifaceted skills. The research consists of the score data analysis for the years 2012 to 2017. s.From the given score data of the 18 AFL playing teams, it was required to build severable working Massey Constant Rating models to come up with the structured report and understanding of the performance of the teams and interpretation various results from the analysis. Primarily, it was noted that the home advantage does have an impact on the well performing teams. Then the teams’ ratings were calculated based on the standard rating value of 100 to initiate our analysis and calculation to find the actual ratings.It reveals interesting observations about the teams’ performance based ratings and their consistency. Facts show critical aspects of the home advantage and the evenness of the competition for the past six year

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“…The Colley method of sports ranking can be defined by a linear system [7], C r = b, where r n×1 is a column-vector of all the ratings, r i . The right-hand-side vector, b, is defined with the components as follows:…”

Section: B Ranking Friendsmentioning

confidence: 99%

Social network generation and friend ranking based on mobile phone data

Akbaş

Avula

Bassiouni

et al. 2013

2013 IEEE International Conference on Communications (ICC)

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Social networking websites have been increasingly popular in the recent years. The users create and maintain their social networks by themselves in these websites by establishing or removing the connections to friends and sites of interests. The smart phones not only create a high availability for social network applications, but also serve for all forms of digital communication such as voice or video calls, e-mails and texts, which are also the ways to form or maintain our social network.In this paper, we deal with the problem of automatically generating and organizing social networks by analyzing and assessing mobile phone usage and interaction data. We assign weights to the different types of interactions. The interactions among users are then evaluated based on these weight values for certain periods of time. We use these values to rank the friends of users by a sports ranking algorithm, which recognizes the changes in the collected data over time.

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Bracketology: How can math help?

Cited by 23 publications

References 0 publications

Application of PageRank Algorithm to Division I NCAA men’s basketball as bracket formation and outcome predictive utility

Application of PageRank Algorithm to Division I NCAA men’s basketball as bracket formation and outcome predictive utility

Analysis of AFL Results from year 2012 – 2017 using Massey Constant Rating models

Social network generation and friend ranking based on mobile phone data

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