Artificial Intelligence in Sports Prediction

McCabe, Alan; Trevathan, Jarrod

doi:10.1109/itng.2008.203

Cited by 58 publications

(23 citation statements)

References 3 publications

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“…Researchers also calculated statistics based on players or teams' actions and scores, since sports like football where access to a video of matches in high-ranking teams is quite difficult for external users to access. Thus, statistical data from the matches are used in order to predict future scores [23]. Similarly, in the case of fitness, the data gathered on the athlete cannot accurately describe their effort; rather, one needs to use additional devices to track the movements and state on the machine [24,25].…”

Section: Usable Features For Sportsmentioning

confidence: 99%

Towards an AI-Based Tailored Training Planning for Road Cyclists: A Case Study

2020

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In a world where the data is a central piece, we provide a novel technique to design training plans for road cyclists. This study exposes an in-depth review of a virtual coach based on state-of-the-art artificial intelligence techniques to schedule road cycling training sessions. Together with a dozen of road cycling participants’ training data, we were able to create and verify an e-coach dedicated to any level of road cyclists. The system can provide near-human coaching advice on the training of cycling athletes based on their past capabilities. In this case study, we extend the tests of our empirical research project and analyze the results provided by experts. Results of the conducted experiments show that the computational intelligence of our system can compete with human coaches at training planification. In this case study, we evaluate the system we previously developed and provide new insights and paths of amelioration for systems based on artificial intelligence for athletes. We observe that our system performs equal or better than the control training plans in 14 and 24 week training periods where it was evaluated as better in 4 of our 5 test components. We also report a higher statistical difference in the results of the experts’ evaluations between the control and virtual coach training plan (24 weeks; training load: X2 = 4.751; resting time quantity: X2 = 3.040; resting time distance: X2 = 2.550; efficiency: X2 = 2.142).

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Section: Usable Features For Sportsmentioning

confidence: 99%

Towards an AI-Based Tailored Training Planning for Road Cyclists: A Case Study

2020

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“…Probabilities of some events, like possible player substitute or change of formation, are calculated on the basis of previous data in each tide and are used to fire certain rules to determine the decisions for the next tide. Another common approach is the use of artificial neural networks [12,13] to train the multi-layer perceptron based on a number of statistic data. All of these methods suffer from unavoidable noise corruption in the observed low-level statistics without explicitly first pre-processing them [14].…”

Section: Related Workmentioning

confidence: 99%

Analysis of sports statistics via graph-signal smoothness prior

Zheng

Cheung

2015

2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

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Since teams in a sporting league compete head-tohead according to a structured schedule, it is natural to interpret statistics emanating from competitions as signals on a graph modeling similarities among competing entities. In this paper, we analyse available sports statistics to predict game outcomes from a graph signal processing (GSP) perspective: GSP tools are used to remove (denoise) unwanted variability to reveal underlying predictable trends, and to interpolate missing data-predicted game outcomes in terms of point differential. First, we construct a graph for the desired graph-signal (point differential for every team pair): for an N -team league, we construct N subgraphs Gj, each containing N − 1 nodes representing teams competing against opponent j. We next assign weight to each intra-subgraph edge based on similarity in observed statistics (e.g., total points scored, assists, etc) of the two connecting nodes (teams). We then connect nodes in different subgraphs representing the same teams, where the weight of an inter-subgraph edge connecting nodes in subgraphs G k and G l now reflects the similarity between opponents k and l. Finally, assuming a graph-signal smoothness prior, we compute the desired graph-signal on the constructed graph via an alternating convex programming procedure. Experimental results show that our graph-based scheme achieves better prediction than a competing k-nearest neighbor (kNN) scheme.

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“…As a result, the task of forecasting the outcomes of sporting events, that can include such problems as big data analysis, data In our opinion, the sport event predictive systems based on artificial neural networks are the most promising. The advantage of such systems is their flexibility, versatility and accuracy of prediction [14][15][16][17][18]. Such systems can be considered as universal approximators of nonlinear dependences.…”

Section: Introductionmentioning

confidence: 99%

Deep Multilayer Neural Network for Predicting the Winner of Football Matches

Anfilets¹,

Bezobrazov²,

Golovko³

et al. 2020

IJC

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In this work, we draw attention to prediction of football (soccer) match winner. We propose the deep multilayer neural network based on elastic net regularization that predicts the winner of the English Premier League football matches. Our main interest is to predict the match result (win, loss or draw). In our experimental study, we prove that using open access limited data such as team shots, shots on target, yellow and red cards, etc. the system has a good prediction accuracy and profitability. The proposed approach should be considered as a basis of Oracle engine for predicting the match outcomes.

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Artificial Intelligence in Sports Prediction

Cited by 58 publications

References 3 publications

Towards an AI-Based Tailored Training Planning for Road Cyclists: A Case Study

Towards an AI-Based Tailored Training Planning for Road Cyclists: A Case Study

Analysis of sports statistics via graph-signal smoothness prior

Deep Multilayer Neural Network for Predicting the Winner of Football Matches

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