A Hybrid Approach to Predicting Sports Results and an AccuRATE Rating System

Kyriakides, George; Talattinis, Kyriacos; Stephanides, George

doi:10.1007/s40819-015-0103-1

Cited by 3 publications

(9 citation statements)

References 5 publications

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“…One of these extensions would be the inclusion of tipster data that are also used by Spann & Skiera (2009). Another extension would be the integration of a hybrid ranking method as proposed by Kyriakides et al (2017) and Constantinou (2018). A third extension would be the use of betting exchanges odds regarding a potential improvement to our Sharpe ratio's distributions, as Franck, Verbeek & Nüesch (2010) showed that these outperform classic betting odds.…”

Section: Discussionmentioning

confidence: 99%

“…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%

“…Concluding that accurate models are not always profitable, tried to improve them following another approach Kyriakides et al (2015) and applying risk management as filters on their preferences. Finally, considering the performance of both machine learning and linear algebra ranking systems on prediction of upcoming matches' outcomes, Kyriakides et al (2017) suggested a hybrid method, combining Colley's, mHITS and the novel AccuRate rank/rating systems with machine learning methods (Artificial Neural Networks, Decision Tables, Naive Bayes, Logistic Model Trees, Bagging, Stacking). They suggested that combination models performed the best and allow greater flexibility in terms of the desired goal (accuracy, profit).…”

Section: Profit-orientedmentioning

confidence: 99%

“…Naive Bayes classifier (Kyriakides et al, 2017;Hand & Yu, 2001) is a simple and easy to implement algorithm. It is also considered as a computationally fast and surprisingly powerful technique that performs well in most cases.…”

Section: Naive Bayesmentioning

confidence: 99%

“…Decision tables (Kyriakides et al, 2017) are one of the simplest and easily understandable algorithms for supervised learning. They are consisted of two main components: The schema, which is a set of attributes, and the body, which is a multiset of labeled instances from the space defined by the schema.…”

Section: Decision Tablesmentioning

confidence: 99%

See 4 more Smart Citations

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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