An Oracle method to predict NFL games

Balreira, E. Cabral; Miceli, Brian K.; Tegtmeyer, Thomas

doi:10.1515/jqas-2013-0063

Cited by 8 publications

(10 citation statements)

References 15 publications

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“…In this paper, we used p ¼ 0.75 in our computations, as this is the value generally used in applications of sports rankings (Balreira et al, 2014;Callaghan et al, 2007). 7.…”

Section: Resultsmentioning

confidence: 99%

“…8. Oracle: This method was recently developed by Balreira et al (2014) and addresses a flaw encountered when using certain Markov methods to rank a tournament. Namely, when an undefeated team loses to a winless team, the previously winless team rises to near the top of the rankings.…”

Section: Resultsmentioning

confidence: 99%

“…Namely, when an undefeated team loses to a winless team, the previously winless team rises to near the top of the rankings. The solution proposed by Balreira et al (2014) is to add a new node to the system, called the Oracle node, and to add edges to and from each team to the Oracle. These edges can be customized to include additional tournament statistics, such as wins and scores.…”

Section: Resultsmentioning

confidence: 99%

“…There are many different ranking algorithms developed for network theory and sports ranking applications that emphasize different statistics of the tournaments, games or competitors (Balreira et al, 2014;Langville & Meyer, 2012). A ranking system is a method to determine a rating vector, r ¼ (r 1 ,…,r n ), for a group of n teams, {T 1 ,T 2 ,…,T n }.…”

Section: Appendix Description Of Mathematical Ranking Algorithms Usementioning

confidence: 99%

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How do lizards determine dominance? Applying ranking algorithms to animal social behaviour

et al. 2016

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Dominance relationships are a defining feature of the social organization of many animal species. Populations structured by absolute dominance usually maintain a generally linear hierarchy, while relative dominance occurs, for example, within territorial populations where an animal is likely to be dominant within its territory. Because relative dominance is dependent on social context, the traits associated with it are often unclear. Green anole lizards, Anolis carolinensis, are an ideal system in which to determine dominance-related traits, as anoles use territorial defence in most natural environments, but establish a dominance hierarchy at high densities such as those that occur in captivity. We hypothesized that anoles use similar morphological and behavioural traits to determine social status under both forms of social organization. To test this, we studied a natural population of anoles to determine the traits most predictive of male territory size and quality (as measured by the number of females overlapping a male's territory). While these measures of territory may be related, they measure different components of territorial success. We then used mathematical ranking algorithms to quantify dominance in a tournament of paired arena trials, and identified traits associated with rank. Our results showed that lizards with wider heads had higher social rank, while those with longer heads were more successful at territorial defence. We also found that, independently of morphology, lizards who behaved more aggressively ranked higher in dominance trials, although behaviour did not predict measures of territory. Together, our results indicate that different traits may determine absolute and relative dominance in the green anole.

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“…In this paper, we used p ¼ 0.75 in our computations, as this is the value generally used in applications of sports rankings (Balreira et al, 2014;Callaghan et al, 2007). 7.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Appendix Description Of Mathematical Ranking Algorithms Usementioning

confidence: 99%

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How do lizards determine dominance? Applying ranking algorithms to animal social behaviour

et al. 2016

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“…With this in mind, Massey is a σ-type model by design, although we modify it to also make a corresponding ω-type, whereas Colley is strictly an ω-type model. For our Markov, or network, methods we use the model in [6], the Biased Voter model developed in [10], the PageRank model in [11], and the Oracle model developed in [12]. All of these methods may be derived from a directed network associated to the game outcomes where i. each of our N teams is represented by a node,…”

Section: Other Rating Methodsmentioning

confidence: 99%

Improving Foresight Predictions in the 2002-2018 NFL Regular-Seasons: A Classic Tale of Quantity vs. Quality

Balreira

Miceli

2019

JAMCS

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Utilizing a modied Bradley-Terry model, we develop a method of making foresight predictions of 2002-2018 NFL games by incorporating a home-eld parameter into previously established ranking models. Knowing only the home team and score of each contest, and taking into account previous predictions, we optimize this parameter considering one of two things: the quantity of correct picks to date or the quality of predictions to date as measured by a quadratic scoringfunction. Our main results establish that optimization of quality-rather than quantity-when making a prediction has higher overall accuracy.

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A novel application of PageRank and user preference algorithms for assessing the relative performance of track athletes in competition

et al. 2017

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Ranking enables coaches, sporting authorities, and pundits to determine the relative performance of individual athletes and teams in comparison to their peers. While ranking is relatively straightforward in sports that employ traditional leagues, it is more difficult in sports where competition is fragmented (e.g. athletics, boxing, etc.), with not all competitors competing against each other. In such situations, complex points systems are often employed to rank athletes. However, these systems have the inherent weakness that they frequently rely on subjective assessments in order to gauge the calibre of the competitors involved. Here we show how two Internet derived algorithms, the PageRank (PR) and user preference (UP) algorithms, when utilised with a simple ‘who beat who’ matrix, can be used to accurately rank track athletes, avoiding the need for subjective assessment. We applied the PR and UP algorithms to the 2015 IAAF Diamond League men’s 100m competition and compared their performance with the Keener, Colley and Massey ranking algorithms. The top five places computed by the PR and UP algorithms, and the Diamond League ‘2016’ points system were all identical, with the Kendall’s tau distance between the PR standings and ‘2016’ points system standings being just 15, indicating that only 5.9% of pairs differed in their order between these two lists. By comparison, the UP and ‘2016’ standings displayed a less strong relationship, with a tau distance of 95, indicating that 37.6% of the pairs differed in their order. When compared with the standings produced using the Keener, Colley and Massey algorithms, the PR standings appeared to be closest to the Keener standings (tau distance = 67, 26.5% pair order disagreement), whereas the UP standings were more similar to the Colley and Massey standings, with the tau distances between these ranking lists being only 48 (19.0% pair order disagreement) and 59 (23.3% pair order disagreement) respectively. In particular, the UP algorithm ranked ‘one-off’ victors more highly than the PR algorithm, suggesting that the UP algorithm captures alternative characteristics to the PR algorithm, which may more suitable for predicting future performance in say knockout tournaments, rather than for use in competitions such as the Diamond League. As such, these Internet derived algorithms appear to have considerable potential for objectively assessing the relative performance of track athletes, without the need for complicated points equivalence tables. Importantly, because both algorithms utilise a ‘who beat who’ model, they automatically adjust for the strength of the competition, thus avoiding the need for subjective decision making.

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An Oracle method to predict NFL games

Cited by 8 publications

References 15 publications

How do lizards determine dominance? Applying ranking algorithms to animal social behaviour

How do lizards determine dominance? Applying ranking algorithms to animal social behaviour

Improving Foresight Predictions in the 2002-2018 NFL Regular-Seasons: A Classic Tale of Quantity vs. Quality

A novel application of PageRank and user preference algorithms for assessing the relative performance of track athletes in competition

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