Explaining match outcome in elite Australian Rules football using team performance indicators

Robertson, Sam; Back, Nicole; Bartlett, Jonathan

doi:10.1080/02640414.2015.1066026

Cited by 125 publications

(134 citation statements)

References 23 publications

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“…Morgan, Williams, and Barnes (2013) apply decision tree induction for identifying characteristics in one-versus-one player interactions that drive the outcome in hockey contests. Robertson, Back, and Bartlett (2015) use logistic regression and decision trees for explaining match outcomes in Australian Rules football. Yuan et al (2015) present a mixture of modelers approach to forecast the 2014 NCAA men's basketball tournament.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…16,17 This increase in popularity has stemmed from the potential ability of these approaches to account for nonlinearity within datasets and thus display improved performance. 18 Therefore, implementation of such approaches may help to better understand TL at the individual level, thus increasing the accuracy in understanding the relationship between internal and external load, and thus, athlete management.…”

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confidence: 99%

Relationships Between Internal and External Training Load in Team-Sport Athletes: Evidence for an Individualized Approach

Bartlett¹,

O’Connor²,

Pitchford³

et al. 2017

International Journal of Sports Physiology and Performance

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149

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Purpose The aim of this study was to quantify and predict relationships between RPE and GPS training load variables in professional Australian Football (AF) players using group and individualised modelling approaches. Methods Training load data (GPS and RPE) for 41 professional AF players was obtained over a period of 27 weeks. A total of 2711 training observations were analysed with a total of 66 13 sessions per player (range; 39 to 89).Separate generalised estimating equations (GEE) and artificial neural network analyses (ANN) were conducted to determine the ability to predict RPE from training load variables (i.e. session distance, high-speed running (HSR), high-speed running %, m·min Further, importance plots generated from the ANN revealed session distance was most predictive of RPE in 36 of the 41 players, whereas, HSR was predictive of RPE in just 3 players and m . min -1 as predictive as session distance in just 2 players. Conclusions This study demonstrates that machine learning approaches may outperform more traditional methodologies with respect to predicting athlete responses to training load. These approaches enable further individualisation of load monitoring, leading to more accurate training prescription and evaluation.

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“…1,2 The expansion in the number of sport performance models therefore raises the question about their validity and practical value. Swimming is a sport at the forefront in the development of performance models, potentially because of the ease with which individual performances can be quantified.…”

Section: Introductionmentioning

confidence: 99%

Confirming the Value of Swimming-Performance Models for Adolescents

Dormehl¹,

Robertson²,

Barker³

et al. 2017

International Journal of Sports Physiology and Performance

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Purpose: To evaluate the efficacy of existing performance models to assess the progression of male and female adolescent swimmers through a quantitative and qualitative mixed-methods approach. Methods: Fourteen published models were tested using retrospective data from an independent sample of Dutch junior national-level swimmers from when they were between 12 and 18 years of age (n=13). The degree of association by Pearson's correlations were compared between the calculated differences from the models and quadratic functions derived from the Dutch junior national qualifying times. Swimmers were grouped based on their differences from the models and compared with their swimming histories that were extracted from questionnaires and follow-up interviews. Results: Correlations of the deviations from both the models and quadratic functions derived from the Dutch qualifying times were all significant except for the 100 m breaststroke and butterfly and the 200 m freestyle female events (p<0.05). Additionally, the 100 m freestyle and backstroke for males and 200 m freestyle male and female events were almost directly proportional. In general deviations from the models were accounted for by the swimmers' training histories. Higher levels of retrospective motivation appeared to be synonymous with higher-level career performance. Conclusion:This mixed-methods approach helped to confirm the validity of the models that were found to be applicable to adolescent swimmers at all levels, allowing coaches to track performance and set goals. The value of the models in being able to account for the expected performance gains during adolescence allows for peripheral factors that could affect performance to be quantified.

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Explaining match outcome in elite Australian Rules football using team performance indicators

Cited by 125 publications

References 23 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

Relationships Between Internal and External Training Load in Team-Sport Athletes: Evidence for an Individualized Approach

Confirming the Value of Swimming-Performance Models for Adolescents

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