Exponentially weighed moving average charts for monitoring zero-inflated proportions with applications in health care

Maravelakis, Petros E.; Rakitzis, Athanasios C.; Castagliola, Philippe

doi:10.1177/09622802221074157

Cited by 3 publications

(1 citation statement)

References 52 publications

(66 reference statements)

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“…20 In another earlier work, the EMWA model has also outperformed other models in modeling datasets with frequent occurrences of zero values, wherein zero was a natural lower bound for the variables. 21 We evaluated three models (EMWA3 , EMWA5, and EMWA7), the difference between these models being the number of preceding match days that were used as training data for predicting match action frequency in the next match instance. Specifically, to predict match action frequency in the next match day ( PF k ), the EMWA3 model utilized the actual match action frequency data from the past three available match days ( AF k−1, AF k−2, AF k−3 ).…”

Section: Methodsmentioning

confidence: 99%

Key considerations in the week-to-week forecasting of individual match actions in football

Komar

Kee

2022

International Journal of Sports Science & Coaching

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Short-term forecasting of performance in football is crucial in week-to-week decision making. The current study presented novel contributions regarding the considerations that should be accounted for in the prediction of match actions performed in competitive matches. First, the study examined whether the quantity and recency of training data used to build a prediction model significantly influenced predictive accuracy. Three prediction models were built with the exponential moving weighted average (EMWA) method, each differing in the quantity of training data used (three, five, and seven preceding match days). Next, the study examined if contextual constraints, such as type of match action being predicted, playing position, or player age, significantly influenced predictive accuracy. Match action data from players in the top five European leagues were collected from the 2014/2015 to the 2019/2020 seasons. The model trained using less but more recent data (three preceding match days) demonstrated the greatest accuracy. Next, within the offensive and defensive phases, match actions differed significantly in predictive accuracy. Lastly, significant differences were found in prediction accuracy between playing positions, whereby actions associated with the primary task of the playing position were more accurately predicted. These findings suggest that in the forecasting of individual match actions, practitioners should seek to train the prediction model using more recent data, instead of including as much data as possible. Furthermore, contextual constraints such as the type of action and playing position of the player must be keenly considered.

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Section: Methodsmentioning

confidence: 99%