Rating Systems Vs Machine Learning on the context of sports

Kyriakides, George; Talattinis, Kyriacos; Stephanides, George

doi:10.1145/2645791.2645846

Cited by 5 publications

(7 citation statements)

References 9 publications

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“…The availability of multiple forecasting methods raised questions about their effective use and the potential of systematic profitability for investors in sport markets. Among the works that tried to come up with this challenge, Forrest, Goddard & Simmons (2005), Spann & Skiera (2009), Constantinou (2018), Kyriakides, Talattinis & George (2014), Kyriakides, Talattinis & Stephanides (2015) and Kyriakides, Talattinis & Stephanides (2017) proposed interesting approaches. Some of these cases showed abnormal positive returns from betting strategies.…”

Section: Profit-orientedmentioning

confidence: 99%

“…The model performed remarkably in the case of English Premier League managing to reach a 38% ROI. Kyriakides et al (2014) also worked on English Premier League to predict final outcomes and identify profitable methods. They compared the performance of linear algebra ranking algorithms (mHITS, Colley, Massey) to a machine learning approach (Neural Networks, Decision Trees, Random Forests).…”

Section: Profit-orientedmentioning

confidence: 99%

“…A decision tree (Bhargava, Sharma, Bhargava, & Mathuria, 2013;Kyriakides et al, 2014) is a tree-like graph that is used to describe and classify data. Algorithms usually construct decision trees top-down, by choosing the variable that best splits the data into appropriate sets.…”

Section: Decision Treementioning

confidence: 99%

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Forecasting Soccer Outcome Using Cost-Sensitive Models Oriented to Investment Opportunities

Talattinis

Kyriakides

Kapantai

et al. 2019

International Journal of Computer Science in Sport

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Realizing the significant effect that misprediction has on many real-world problems, our paper is focused on the way these costs could affect the sports sector in terms of soccer outcome predictions. In our experimental analysis, we consider the potential influence of a cost-sensitive approach rather than traditional machine-learning methods. Although the measurement of prediction accuracy is a very important part of the validation of each model, we also study its economic significance. As a performance metric for our models, the Sharpe ratio metric is calculated and analyzed. Seeking to improve Sharpe ratio value, a genetic algorithm is applied. The empirical study and evaluation procedure of the paper are primarily based on English Premier League’s games, simple historical data and well-known bookmakers’ markets odds. Our research confirms that it is worthwhile to employ cost-sensitive methods for the successful predictions of soccer results and better investment opportunities.

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Section: Profit-orientedmentioning

confidence: 99%

Section: Profit-orientedmentioning

confidence: 99%

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Forecasting Soccer Outcome Using Cost-Sensitive Models Oriented to Investment Opportunities

Talattinis

Kyriakides

Kapantai

et al. 2019

International Journal of Computer Science in Sport

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“…[30] Data from 275 male golfers. [38] Website data http://www.football-data.co.uk/. [40] Real-life statistical data from cfbstats.com for past college football games.…”

Section: Rq7: Appliedmentioning

confidence: 99%

“…[30] A data analysis to identify important aspects separating skilled golfers from poor. [38] Compared the performance of algebraic methods to some machine learning approaches, particularly in the field of match prediction.…”

mentioning

confidence: 99%

Computational Intelligence in Sports: A Systematic Literature Review

Bonidia

Rodrigues

Avila-Santos

et al. 2018

Advances in Human-Computer Interaction

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Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.

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