A Probabilistic Model for Multi-Contestant Races

Gakis, Konstantinos; Pardalos, Pãnos M.; Choi, Chang‐Hwan; Park, Jae-Hyeon

doi:10.30958/ajspo.3-2-1

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Furthermore, the selected player-related performance indicators and the used machine learning techniques are unique for this study. Findings of this study can benefit the entire cricket community and cricket industry as always prediction in sports brings an economical value to the industry (Gakis et al 2016).…”

Section: Introductionmentioning

confidence: 84%

Classification of All-Rounders in the Game of ODI Cricket: Machine Learning Approach

Wickramasinghe¹

2020

AJSPO

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Player classification in the game of cricket is very important, as it helps the coach and the captain of the team to identify each player's role in the team and assign responsibilities accordingly. The objective of this study is to classify allrounders into one of the four categories in one day international (ODI) Cricket format and to accurately predict new all-rounders'. This study was conducted using a collection of 177 players and ten player-related performance indicators. The prediction was conducted using three machine learning classifiers, namely Naive Bayes (NB), k-nearest neighbours (kNN), and Random Forest (RF). According to the experimental outcomes, RF indicates significantly better prediction accuracy of 99.4%, than its counter parts.

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Section: Introductionmentioning

confidence: 84%

Classification of All-Rounders in the Game of ODI Cricket: Machine Learning Approach

Wickramasinghe¹

2020

AJSPO

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“…A "multi-contestant" race is one where the athletes run to finish first by achieving the minimum time. Gakis et al (2016) have presented a discussion of the general problem and have provided insight to the problem. Among the main points has been that most of the research and the literature have been concerned with two contestant games, in many cases with a score, where one wins and one loses or there can be a tie.…”

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confidence: 99%

“…Thus the problem, in theoretical terms, is one of order statistics. Gakis et al (2016) presented two theoretical models, which present a series of challenges and bring the promise of good predictions if further elaborated and appropriately applied.…”

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confidence: 99%

Simulation of a Probabilistic Model for Multi-Contestant Races

Gakis¹,

Pardalos²,

Choi³

et al. 2018

AJSPO

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Predictions of sports games have been recognized as an important area of study for their economic significance. Most models for such games cover two-player games and the resulting championships or study individual players or teams and their resulting comparative position. In this paper, we elaborate on a model for such multi-contestant races based on order statistics of the negative exponential distribution for race times. The games involve only a fixed number of athletes out of a broader pool of several athletes. Several large samples of games of ten athletes that play four at a time are generated, and Maximum Likelihood Estimators are calculated for the relative dominance parameters of the athletes. The estimated parameters are then used to test the predictive validity of the model. The results are discussed. Subsequently the model is modified for Erland-2 distributed race times. New samples are generated and the predictive validity of the new model is discussed. The paper concludes with a discussion of future research directions for improving the predictive validity and establishing confidence levels.

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Historical ranking of vault players in artistic gymnastics using PageRank algorithm

Yoon¹,

Park²

2017

Korean J Sport Sci

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Purpose The purpose of this study is to calculate the ranking of vault players in artistic gymnastics by individual and by country using the PageRank algorithm. The purpose of this study is to provide basic data that can be used in gymnastics events by comparing the performances of historical vault players with those of previous Olympic competitions. Methods The data collected for this purpose is a score of 117 vault players based on the results of the Olympic final event published in the International Gymnastics Federation (FIG). For data analysis, PegeRnak algorithm was used for calculating the ranking of vault players using MS-Excel and NetMiner. Results The results are as follows that. First, the PageRank algorithm is possible to calculate for historical vault players' rankings. Specifically, the ranking of vault players for historical Olympic calculated by PageRank ranked as Gervasio Deferr from Spain (ESP) at 1st, Alexei Nemov from Russia (RUS) at 2nd, and Klaus Koste from Germany (GDR) at 3rd. Second, Network of vault players' ranking is separated by each generation of Olympic games. Conclusions As a conclusions, it is possible to calculate national ranking of vault games of gymnastics by using PageRank algorithm.

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A Probabilistic Model for Multi-Contestant Races

Cited by 3 publications

References 6 publications

Classification of All-Rounders in the Game of ODI Cricket: Machine Learning Approach

Classification of All-Rounders in the Game of ODI Cricket: Machine Learning Approach

Simulation of a Probabilistic Model for Multi-Contestant Races

Historical ranking of vault players in artistic gymnastics using PageRank algorithm

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