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Article ; Online: A framework of interpretable match results prediction in football with FIFA ratings and team formation.

Yeung, Calvin C K / Bunker, Rory / Fujii, Keisuke

2023 Volume 18, Issue 4, Page(s) e0284318

Abstract: While forecasting football match results has long been a popular topic, a practical model for football participants, such as coaches and players, has not been considered in great detail. In this study, we propose a generalized and interpretable machine ... ...

Abstract	While forecasting football match results has long been a popular topic, a practical model for football participants, such as coaches and players, has not been considered in great detail. In this study, we propose a generalized and interpretable machine learning model framework that only requires coaches' decisions and player quality features for forecasting. By further allowing the model to embed historical match statistics, features that consist of significant information, during the training process the model was practical and achieved both high performance and interpretability. Using five years of data (over 1,700 matches) from the English Premier League, our results show that our model was able to achieve high performance with an F1-score of 0.47, compared to the baseline betting odds prediction, which had an F1-score of 0.39. Moreover, our framework allows football teams to adapt for tactical decision-making, strength and weakness identification, formation and player selection, and transfer target validation. The framework in this study would have proven the feasibility of building a practical match result forecast framework and may serve to inspire future studies.
MeSH term(s)	Humans ; Athletic Performance ; Forecasting ; Gambling ; Soccer
Language	English
Publishing date	2023-04-13
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0284318
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Evaluating Soccer Match Prediction Models

Yeung, Calvin / Bunker, Rory / Umemoto, Rikuhei / Fujii, Keisuke

A Deep Learning Approach and Feature Optimization for Gradient-Boosted Trees

2023

Abstract: Machine learning models have become increasingly popular for predicting the results of soccer matches, however, the lack of publicly-available benchmark datasets has made model evaluation challenging. The 2023 Soccer Prediction Challenge required the ... ...

Abstract	Machine learning models have become increasingly popular for predicting the results of soccer matches, however, the lack of publicly-available benchmark datasets has made model evaluation challenging. The 2023 Soccer Prediction Challenge required the prediction of match results first in terms of the exact goals scored by each team, and second, in terms of the probabilities for a win, draw, and loss. The original training set of matches and features, which was provided for the competition, was augmented with additional matches that were played between 4 April and 13 April 2023, representing the period after which the training set ended, but prior to the first matches that were to be predicted (upon which the performance was evaluated). A CatBoost model was employed using pi-ratings as the features, which were initially identified as the optimal choice for calculating the win/draw/loss probabilities. Notably, deep learning models have frequently been disregarded in this particular task. Therefore, in this study, we aimed to assess the performance of a deep learning model and determine the optimal feature set for a gradient-boosted tree model. The model was trained using the most recent five years of data, and three training and validation sets were used in a hyperparameter grid search. The results from the validation sets show that our model had strong performance and stability compared to previously published models from the 2017 Soccer Prediction Challenge for win/draw/loss prediction.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2023-09-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union.

Bunker, Rory / Fujii, Keisuke / Hanada, Hiroyuki / Takeuchi, Ichiro

PloS one

2021 Volume 16, Issue 9, Page(s) e0256329

Abstract: Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence. However, in sport, these techniques cannot determine the importance of ... ...

Abstract	Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence. However, in sport, these techniques cannot determine the importance of particular patterns of play to good or bad outcomes, which is often of greater interest to coaches and performance analysts. In this study, we apply a recently proposed supervised sequential pattern mining algorithm called safe pattern pruning (SPP) to 490 labelled event sequences representing passages of play from one rugby team's matches in the 2018 Japan Top League season. We obtain patterns that are the most discriminative between scoring and non-scoring outcomes from both the team's and opposition teams' perspectives using SPP, and compare these with the most frequent patterns obtained with well-known unsupervised sequential pattern mining algorithms when applied to subsets of the original dataset, split on the label. From our obtained results, line breaks, successful line-outs, regained kicks in play, repeated phase-breakdown play, and failed exit plays by the opposition team were found to be the patterns that discriminated most between the team scoring and not scoring. Opposition team line breaks, errors made by the team, opposition team line-outs, and repeated phase-breakdown play by the opposition team were found to be the patterns that discriminated most between the opposition team scoring and not scoring. It was also found that, probably because of the supervised nature and pruning/safe-screening mechanisms of SPP, compared to the patterns obtained by the unsupervised methods, those obtained by SPP were more sophisticated in terms of containing a greater variety of events, and when interpreted, the SPP-obtained patterns would also be more useful for coaches and performance analysts.
MeSH term(s)	Algorithms ; Athletic Performance/statistics & numerical data ; Competitive Behavior/physiology ; Football/statistics & numerical data ; Humans ; Japan ; Sports/statistics & numerical data
Language	English
Publishing date	2021-09-23
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0256329
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Performance Indicators Contributing To Success At The Group And Play-Off Stages Of The 2019 Rugby World Cup

Bunker, Rory / Spencer, Kirsten

2020

Abstract: Performance indicators that contributed to success at the group stage and play-off stages of the 2019 Rugby World Cup were analysed using publicly available data obtained from the official tournament website using both a non-parametric statistical ... ...

Abstract	Performance indicators that contributed to success at the group stage and play-off stages of the 2019 Rugby World Cup were analysed using publicly available data obtained from the official tournament website using both a non-parametric statistical technique, Wilcoxon's signed rank test, and a decision rules technique from machine learning called RIPPER. Our statistical results found that ball carry effectiveness (percentage of ball carries that penetrated the opposition gain-line) and total metres gained (kick metres plus carry metres) were found to contribute to success at both stages of the tournament and that indicators that contributed to success during the group stages (dominating possession, making more ball carries, making more passes, winning more rucks, and making less tackles) did not contribute to success at the play-off stage. Our results using RIPPER found that low ball carries and a low lineout success percentage jointly contributed to losing at the group stage, while winning a low number of rucks and carrying over the gain-line a sufficient number of times contributed to winning at the play-off stage of the tournament. The results emphasise the need for teams to adapt their playing strategies from the group stage to the play-off stage at tournament in order to be successful.
Keywords	Statistics - Applications ; Computer Science - Machine Learning
Publishing date	2020-10-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: The Application of Machine Learning Techniques for Predicting Results in Team Sport

Bunker, Rory / Susnjak, Teo

A Review

2019

Abstract: Over the past two decades, Machine Learning (ML) techniques have been increasingly utilized for the purpose of predicting outcomes in sport. In this paper, we provide a review of studies that have used ML for predicting results in team sport, covering ... ...

Abstract	Over the past two decades, Machine Learning (ML) techniques have been increasingly utilized for the purpose of predicting outcomes in sport. In this paper, we provide a review of studies that have used ML for predicting results in team sport, covering studies from 1996 to 2019. We sought to answer five key research questions while extensively surveying papers in this field. This paper offers insights into which ML algorithms have tended to be used in this field, as well as those that are beginning to emerge with successful outcomes. Our research highlights defining characteristics of successful studies and identifies robust strategies for evaluating accuracy results in this application domain. Our study considers accuracies that have been achieved across different sports and explores the notion that outcomes of some team sports could be inherently more difficult to predict than others. Finally, our study uncovers common themes of future research directions across all surveyed papers, looking for gaps and opportunities, while proposing recommendations for future researchers in this domain. Comment: 48 pages, 10 figures
Keywords	Computer Science - Machine Learning ; Statistics - Applications ; Statistics - Machine Learning
Subject code	006
Publishing date	2019-12-25
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Multi-agent statistical discriminative sub-trajectory mining and an application to NBA basketball

Bunker, Rory / Duy, Vo Nguyen Le / Tabei, Yasuo / Takeuchi, Ichiro / Fujii, Keisuke

2023

Abstract: Improvements in tracking technology through optical and computer vision systems have enabled a greater understanding of the movement-based behaviour of multiple agents, including in team sports. In this study, a Multi-Agent Statistically Discriminative ... ...

Abstract	Improvements in tracking technology through optical and computer vision systems have enabled a greater understanding of the movement-based behaviour of multiple agents, including in team sports. In this study, a Multi-Agent Statistically Discriminative Sub-Trajectory Mining (MA-Stat-DSM) method is proposed that takes a set of binary-labelled agent trajectory matrices as input and incorporates Hausdorff distance to identify sub-matrices that statistically significantly discriminate between the two groups of labelled trajectory matrices. Utilizing 2015/16 SportVU NBA tracking data, agent trajectory matrices representing attacks consisting of the trajectories of five agents (the ball, shooter, last passer, shooter defender, and last passer defender), were truncated to correspond to the time interval following the receipt of the ball by the last passer, and labelled as effective or ineffective based on a definition of attack effectiveness that we devise in the current study. After identifying appropriate parameters for MA-Stat-DSM by iteratively applying it to all matches involving the two top- and two bottom-placed teams from the 2015/16 NBA season, the method was then applied to selected matches and could identify and visualize the portions of plays, e.g., involving passing, on-, and/or off-the-ball movements, which were most relevant in rendering attacks effective or ineffective.
Keywords	Computer Science - Multiagent Systems
Subject code	006
Publishing date	2023-11-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Supervised sequential pattern mining of event sequences in sport to identify important patterns of play

Bunker, Rory / Fujii, Keisuke / Hanada, Hiroyuki / Takeuchi, Ichiro

an application to rugby union

2020

Abstract	Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence. However, in sport, these techniques cannot determine the importance of particular patterns of play to good or bad outcomes, which is often of greater interest to coaches and performance analysts. In this study, we apply a recently proposed supervised sequential pattern mining algorithm called safe pattern pruning (SPP) to 490 labelled event sequences representing passages of play from one rugby team's matches from the 2018 Japan Top League. We compare the SPP-obtained patterns that are the most discriminative between scoring and non-scoring outcomes from both the team's and opposition teams' perspectives, with the most frequent patterns obtained with well-known unsupervised sequential pattern mining algorithms when applied to subsets of the original dataset, split on the label. Our obtained results found that linebreaks, successful lineouts, regained kicks in play, repeated phase-breakdown play, and failed exit plays by the opposition team were identified as as the patterns that discriminated most between the team scoring and not scoring. Opposition team linebreaks, errors made by the team, opposition team lineouts, and repeated phase-breakdown play by the opposition team were identified as the patterns that discriminated most between the opposition team scoring and not scoring. It was also found that, by virtue of its supervised nature as well as its pruning and safe-screening properties, SPP obtained a greater variety of generally more sophisticated patterns than the unsupervised models, which are likely to be of more utility to coaches and performance analysts.
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2020-10-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Exploration of Methods to Remove Implanted $^{210}$Pb and $^{210}$Po Contamination from Silicon Surfaces

Arnquist, Isaac J. / Bunker, Raymond / Dohnalek, Zdenek / Ma, Runze / Uhnak, Nicolas

2022

Abstract: Radioactive contaminants on the surfaces of detector components can be a problematic source of background events for physics experiments searching for rare processes. Exposure to radon is a specific concern because it can result in the relatively long- ... ...

Abstract	Radioactive contaminants on the surfaces of detector components can be a problematic source of background events for physics experiments searching for rare processes. Exposure to radon is a specific concern because it can result in the relatively long-lived $^{210}$Pb (and progeny) being implanted to significant subsurface depths such that removal is challenging. In this article we present results from a broad exploration of cleaning treatments to remove implanted $^{210}$Pb and $^{210}$Po contamination from silicon, which is an important material used in several rare-event searches. We demonstrate for the first time that heat treatments ("baking") can effectively mitigate such surface contamination, with the results of a 1200 $^{\circ}$C bake consistent with perfect removal. We also report results using wet-chemistry and plasma-based methods, which show that etching can be highly effective provided the etch depth is sufficiently aggressive. Our survey of cleaning methods suggests consideration of multiple approaches during the different phases of detector construction to enable greater flexibility for efficient removal of $^{210}$Pb and $^{210}$Po surface contamination Comment: 8 pages, 7 figures
Keywords	Physics - Instrumentation and Detectors ; High Energy Physics - Experiment ; Nuclear Experiment
Subject code	660
Publishing date	2022-06-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Effect of weather parameters on development of early blight of Tomato caused by Alternaria solani in polyhouse and field conditions

Soni, Rajendra / Bunker, R.N / Tanwar, V.K

Annals of plant protection sciences. 2017 Sept., v. 25, no. 2

2017

Abstract: Field studies were conducted to determine the environmental factors associated with infection of field and protected plants. The overall mean disease incidence and intensity was 84.8% in protected environment and 82.2% in field condition during last week ...

Abstract	Field studies were conducted to determine the environmental factors associated with infection of field and protected plants. The overall mean disease incidence and intensity was 84.8% in protected environment and 82.2% in field condition during last week of October in 2014 and 2015, respectively. Results revealed that correlation coefficient (r) showed strong positive correlation (r = +841) with minimum temperature ranged (24.8–23.8°C) with maximum relative humidly (r = +0.792) ranged (91.1–84%) and negatively correlated with maximum temperature and minimum relative humidity (r =-0.170 &-0.826), (30.3–33.9°C) and (70.7–47.1%), respectively. In the case of field condition, r was (−0.37) with maximum (+0.94) minimum temperature, (+0.92 &-0.92) with maximum and minimum relative humidity and-0.62 rainfall, respectively. Disease severity was found comparatively higher in the temperature range from (24.8–35.4°C) and (20.0–33.9°C) coupled with relative humidity (66.5–91.1%) and (38.4–84.1%), respectively in polyhouse (protected) and field conditions.
Keywords	Alternaria solani ; Solanum lycopersicum ; blight ; disease incidence ; disease severity ; environmental factors ; rain ; relative humidity ; temperature ; tomatoes
Language	English
Dates of publication	2017-09
Size	p. 351-354.
Publishing place	Society of Plant Protection Sciences
Document type	Article
ISSN	0974-0163
DOI	10.5958/0974-0163.2017.00024.6
Database	NAL-Catalogue (AGRICOLA)

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Book ; Conference proceedings: Film cooling science and technology for gas turbines: state-of-the-art experimental and computational knowledge ; April 16 - 20, 2007

Bunker, R. S

(Lecture series / Von Karman Institute for Fluid Dynamics ; 2007-06)

2007

Event/congress	Lecture series (2007.04.16-20, RhodeSaintGenèse)
Author's details	ed. by R. S. Bunker
Series title	Lecture series / Von Karman Institute for Fluid Dynamics ; 2007-06
Language	English
Size	Getr. Zählung [ca. 780 S.]
Publisher	Von Karman Inst. for Fluid Dynamics
Publishing place	Rhode Saint Genèse
Document type	Book ; Conference proceedings
Note	CD-ROM enth. "Slides of S. Acharya"
Accompanying material	1 CD-ROM
Database	Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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