Strategy selection and outcome prediction in sport using dynamic learning for stochastic processes

Percy, David F.

doi:10.1057/jors.2014.137

Cited by 19 publications

(12 citation statements)

References 40 publications

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“…In the author's opinion, modeling baseball games as a stochastic process and applying dynamic learning using "within-game" data should bring better predictive results than general models (Percy, 2015). In this sense, data mining methods have achieved success for selecting strategies and predicting outcomes in the context of some specific baseball game situations.…”

Section: Discussionmentioning

confidence: 99%

Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods

Soto-Valero

2016

International Journal of Computer Science in Sport

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Baseball is a statistically filled sport, and predicting the winner of a particular Major League Baseball (MLB) game is an interesting and challenging task. Up to now, there is no definitive formula for determining what factors will conduct a team to victory, but through the analysis of many years of historical records many trends could emerge. Recent studies concentrated on using and generating new statistics called sabermetrics in order to rank teams and players according to their perceived strengths and consequently applying these rankings to forecast specific games. In this paper, we employ sabermetrics statistics with the purpose of assessing the predictive capabilities of four data mining methods (classification and regression based) for predicting outcomes (win or loss) in MLB regular season games. Our model approach uses only past data when making a prediction, corresponding to ten years of publicly available data. We create a dataset with accumulative sabermetrics statistics for each MLB team during this period for which data contamination is not possible. The inherent difficulties of attempting this specific sports prediction are confirmed using two geometry or topology based measures of data complexity. Results reveal that the classification predictive scheme forecasts game outcomes better than regression scheme, and of the four data mining methods used, SVMs produce the best predictive results with a mean of nearly 60% prediction accuracy for each team. The evaluation of our model is performed using stratified 10-fold cross-validation.

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Section: Discussionmentioning

confidence: 99%

Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods

Soto-Valero

2016

International Journal of Computer Science in Sport

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“…In simple terms, the Ergodic theorem for a Markov chain states that the chain can go from any state to any other state and that the chain does not repeat the same loop. This means that no samples are replicated [11].…”

Section: Markov Chainsmentioning

confidence: 99%

Betting models using AI: A review on ANN, SVM, and Markov Chain

Kollár¹

2021

Preprint

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In today's modern world, sports generate a great deal of data about each athlete, team, event, andseason. Many people, from spectators to bettors, find it fascinating to predict the outcomes ofsporting events.With the available data, the sports betting industry is turning to Artificial Intelligence.Working with a great deal of data and information is needed in sports betting all over the world.Artificial intelligence and machine learning are assisting in the prediction of sporting trends. The trueinfluence of technology is felt as it offers these observations in real-time, which can have an impacton important factors in betting. An artificial neural network is made up of several small,interconnected processors called neurons, which are similar to the biological neurons in the brain. InANN framework, MLP, the most applicable NN algorithm, are generally selected as the best model forpredicting the outcomes of football matches. This review also discussed another common techniqueof modern intelligent technique, namely Support Vector Machines (SVM). Lastly, we also discussedthe Markov chain to predict the result of a sport. Markov chain is the sequence or chain from whichthe next sample from this state space is sampled.

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“…Other refinements of the basic model have been proposed in which, for example, strengths are time-varying (e.g. Crowder et al, 2002;Owen, 2011;Koopman and Lit, 2015;Percy, 2015), or in which scores are dependent (e.g. Dixon and Coles, 1997;Karlis and Nzoutfras , 2003;Scarf, 2007, 2011).…”

Section: The Poisson-matchmentioning

confidence: 99%

On skill and chance in sport

Scarf

Khare

Alotaibi

2021

IMA Journal of Management Mathematics

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This work studies outcome uncertainty and competitive balance from a broad perspective. It considers four sports with varying scoring rates, from soccer with typically three goals per match to netball with one hundred goals per match. Within a general modelling framework for a two-competitor contest, we argue that outcome uncertainty, the extent to which the outcome of a contest is unpredictable, depends on scoring rate, on strength variation and on score dependence. Score dependence is essentially the tendency for scores to alternate because possession alternates and possession is advantageous. We regard competitive balance as lack of variation in strength or skill, so that when strength variation is large competitive balance is low and vice versa. Thus, we argue that the outcome of a contest depends on skill, scoring rate, score dependence and chance. This description of outcome is useful because it informs policy-making in sport about the design of scoring systems and the control of competitive imbalance. Broadly, we find that: soccer is relatively competitively unbalanced but outcomes are uncertain because the scoring rate is low; the Australian football league is competitively balanced and so outcomes are uncertain in spite of the high scoring rate in this sport; international rugby matches are relatively neither competitive nor uncertain so that little is left to chance; and netball matches have uncertain outcomes because scores are positively dependent.

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Strategy selection and outcome prediction in sport using dynamic learning for stochastic processes

Cited by 19 publications

References 40 publications

Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods

Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods

Betting models using AI: A review on ANN, SVM, and Markov Chain

On skill and chance in sport

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