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bibliography.bib
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% This file was created with Citavi 6.3.0.0
@misc{.2212020,
abstract = {Advanced soccer analytics for the soccer (football) industry. Empowering soccer's data revolution.},
year = {2/21/2020},
title = {Capturing context and space in football match analysis | Soccermetrics Research, LLC},
url = {https://www.soccermetrics.net/high-level-discussions/network-modeling-capture-contextual-and-spatial-match-analysis},
urldate = {2/21/2020}
}
@misc{.2212020b,
abstract = {Controlling space in football - data modelling with our analytics database, and visual analysis in Tableau Football isn't just about controlling the ball},
year = {2/21/2020},
title = {Controlling space in football - Exasol},
url = {https://www.exasol.com/en/blog/controlling-space-in-football/},
urldate = {2/21/2020}
}
@misc{.2212020c,
abstract = {Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.},
year = {2/21/2020},
title = {David Sumpter: {\textquotedbl}Soccermatics{\textquotedbl} | Talks at Google - YouTube},
url = {https://www.youtube.com/watch?v=AnYRTdOW0Uk},
keywords = {camera phone;free;sharing;upload;video;video phone},
urldate = {2/21/2020}
}
@misc{.2212020d,
abstract = {Our research and development{\&}nbsp;team{\&}nbsp;was selected to present{\&}nbsp;at the 2017 OptaPro {\&}{\&}nbsp;MIT Sloan Analytics Conferences on{\&}nbsp;their recent work{\&}nbsp;analyzing{\&}nbsp;passing probabilities{\&}nbsp;in football (soccer).{\&}nbsp;},
year = {2/21/2020},
title = {Don't Waste Your Data: Using Tracking Data to Find Key Moments in Soccer with Open Space | Hudl Blog},
url = {https://www.hudl.com/blog/open-space-and-passing-in-football},
urldate = {2/21/2020}
}
@article{Fairchild.2018,
author = {Fairchild, Alexander and Pelechrinis, Konstantinos and Kokkodis, Marios},
year = {2018},
title = {Spatial analysis of shots in MLS: A model for expected goals and fractal dimensionality},
pages = {165--174},
volume = {4},
number = {3},
issn = {2215020X},
journal = {Journal of Sports Analytics},
doi = {10.3233/JSA-170207},
file = {6f031672-24bb-4e00-a23c-da4281fc97c6:C\:\\Users\\davidlamb\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\h2p2d8w6rnp4noyofzxt2p8iyg8b0l6i5mdgvfy1gkh\\Citavi Attachments\\6f031672-24bb-4e00-a23c-da4281fc97c6.pdf:pdf}
}
@misc{.2212020e,
year = {2/21/2020},
title = {Footoscope: a deciphering tool for football amateurs - Near Future Laboratory},
url = {http://blog.nearfuturelaboratory.com/2012/07/31/footoscope-a-deciphering-tool-for-football-amateurs/},
urldate = {2/21/2020}
}
@article{Goncalves.2019,
abstract = {Spatiotemporal patterns of play can be extracted from competitive environments to design representative training tasks and underlying processes that sustain performance outcomes. To support this statement, the aims of this study were: (i) describe the collective behavioural patterns that relies upon the use of player positioning in interaction with teammates, opponents and ball positioning; (ii) and define the underlying structure among the variables through application of a factorial analysis. The sample comprised a total of 1,413 ball possession sequences, obtained from twelve elite football matches from one team (the team ended the season in the top-5 position). The dynamic position of the players (from both competing teams), as well as the ball, were captured and transformed to two-dimensional coordinates. Data included the ball possession sequences from six matches played against top opponents (TOP, the three teams classified in the first 3 places at the end of the season) and six matches against bottom opponents (BOTTOM, the three teams classified in the last 3 at the end of the season). The variables calculated for each ball possession were the following: ball position; team space in possession; game space (comprising the outfield players of both teams); position and space at the end of ball possession. Statistical comparisons were carried with magnitude-based decisions and null-hypothesis analysis and factor analysis to define the underlying structure among variables according to the considered contexts. Results showed that playing against TOP opponents, there was {\~{}}38 meters game length per {\~{}}43 meters game width with 12{\%} of coefficient of variation ({\%}). Ball possessions lasted for {\~{}}28 seconds and tended to end at {\~{}}83m of pitch length. Against BOTTOM opponents, a decrease in the game length with an increase in game width and in the deepest location was observed in comparison with playing against TOP opponents. The duration of ball possession increased considerable ({\~{}}37 seconds), and the ball speed entropy was higher, suggesting lower levels of regularity in comparison with TOP opponents. The BOTTOM teams revealed a small EPS. The Principal Component Analysis showed a strong association of the ball speed, entropy of the ball speed and the coefficient of variation ({\%}) of the ball speed. The EPS of the team in possession was well correlated with the game space, especially the game width facing TOP opponents. Against BOTTOM opponents, there was a strong association of ball possession duration, game width, distance covered by the ball, and length/width ratio of the ball movement. The overall approach carried out in this study may serve as the starting point to elaborate normative models of positioning behaviours measures to support the coaches' operating decisions.},
author = {Gon{\c{c}}alves, Bruno and Coutinho, Diogo and Exel, Juliana and Travassos, Bruno and Lago, Carlos and Sampaio, Jaime},
year = {2019},
title = {Extracting spatial-temporal features that describe a team match demands when considering the effects of the quality of opposition in elite football},
pages = {e0221368},
volume = {14},
number = {8},
journal = {PloS one},
doi = {10.1371/journal.pone.0221368},
file = {daf350bd-b0cd-42d1-80f6-4ae01ecba624:C\:\\Users\\davidlamb\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\h2p2d8w6rnp4noyofzxt2p8iyg8b0l6i5mdgvfy1gkh\\Citavi Attachments\\daf350bd-b0cd-42d1-80f6-4ae01ecba624.pdf:pdf}
}
@article{Gudmundsson.2017,
author = {Gudmundsson, Joachim and Horton, Michael},
year = {2017},
title = {Spatio-Temporal Analysis of Team Sports},
pages = {1--34},
volume = {50},
number = {2},
issn = {03600300},
journal = {ACM Computing Surveys},
doi = {10.1145/3054132},
file = {c3739e5d-80a7-4dda-bd13-9272902392a9:C\:\\Users\\davidlamb\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\h2p2d8w6rnp4noyofzxt2p8iyg8b0l6i5mdgvfy1gkh\\Citavi Attachments\\c3739e5d-80a7-4dda-bd13-9272902392a9.pdf:pdf}
}
@article{Gudmundsson.2014,
author = {Gudmundsson, Joachim and Wolle, Thomas},
year = {2014},
title = {Football analysis using spatio-temporal tools},
pages = {16--27},
volume = {47},
issn = {01989715},
journal = {Computers, Environment and Urban Systems},
doi = {10.1016/j.compenvurbsys.2013.09.004},
file = {fbd54051-7d71-491e-803d-4f1dc7828052:C\:\\Users\\davidlamb\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\h2p2d8w6rnp4noyofzxt2p8iyg8b0l6i5mdgvfy1gkh\\Citavi Attachments\\fbd54051-7d71-491e-803d-4f1dc7828052.pdf:pdf}
}
@misc{.2212020f,
abstract = {The visual exploration and analysis of passing patterns helps professional football clubs to understand how individual players perform and how their s...},
year = {2/21/2020},
title = {How Geoinformation Enhances Professional Football},
url = {https://www.gim-international.com/content/article/geovisual-football-analytics},
keywords = {GIS;Pitch},
urldate = {2/21/2020},
file = {296b8c5c-bf52-4177-a8cc-0188e46205de:C\:\\Users\\davidlamb\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\h2p2d8w6rnp4noyofzxt2p8iyg8b0l6i5mdgvfy1gkh\\Citavi Attachments\\296b8c5c-bf52-4177-a8cc-0188e46205de.pdf:pdf}
}
@misc{.2212020g,
year = {2/21/2020},
title = {How GIS Can Help With Football Game Analysis {\~{}} GIS Lounge},
url = {https://www.gislounge.com/game-analysis-gis-football-soccer/},
urldate = {2/21/2020}
}
@article{Pena.,
abstract = {We showcase in this paper the use of some tools from network theory to describe the strategy of football teams. Using passing data made available by FIFA during the 2010 World Cup, we construct for each team a weighted and directed network in which nodes correspond to players and arrows to passes. The resulting network or graph provides a direct visual inspection of a team's strategy, from which we can identify play pattern, determine hot-spots on the play and localize potential weaknesses. Using different centrality measures, we can also determine the relative importance of each player in the game, the `popularity' of a player, and the effect of removing players from the game.},
author = {Pe{\~n}a, Javier L{\'o}pez and Touchette, Hugo},
title = {A network theory analysis of football strategies},
url = {https://arxiv.org/pdf/1206.6904},
keywords = {Combinatorics (math.CO);Physics and Society (physics.soc-ph);Statistics Theory (math.ST)},
journal = {In C. Clanet (ed.), Sports Physics: Proc.},
file = {https://arxiv.org/pdf/1206.6904.pdf}
}
@book{Sumpter.2016,
author = {Sumpter, David},
year = {2016},
title = {Soccermatics: Mathematical adventures in the beautiful game},
address = {London},
publisher = {{Bloomsbury Sigma}},
isbn = {1472924126},
file = {d4c8efb0-e271-47fa-bb59-1239bc087658:C\:\\Users\\davidlamb\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\h2p2d8w6rnp4noyofzxt2p8iyg8b0l6i5mdgvfy1gkh\\Citavi Attachments\\d4c8efb0-e271-47fa-bb59-1239bc087658.pdf:pdf}
}
@misc{.12182018,
year = {12/18/2018},
title = {Using ArcGIS for sports analytics},
url = {https://www.esri.com/arcgis-blog/products/arcgis-desktop/analytics/using-arcgis-for-sports-analytics/},
keywords = {3D Visualization {\&}amp;Analytics;Mapping},
urldate = {2/21/2020}
}
@misc{.12182018b,
year = {12/18/2018},
title = {Using spatial analytics to study spatio-temporal patterns in sport},
url = {https://www.esri.com/arcgis-blog/products/arcgis-desktop/analytics/using-spatial-analytics-to-study-spatio-temporal-patterns-in-sport/},
keywords = {3D Visualization {\&}amp;Analytics;Mapping},
urldate = {2/21/2020}
}
@misc{.2212020h,
abstract = {By Damien Demaj, Cartographic Product Engineer at ESRI Late last year I introduced ArcGIS users to sports analytics, an emerging and exciting field within},
year = {2/21/2020},
title = {Using Spatial Analytics to Study Spatio-temporal Patterns in Sport, by Damien Demaj, Geospatial Product Engineer at ESRI - MIT Sloan Analytics Conference News},
url = {http://www.sloansportsconference.com/mit_news/using-spatial-analytics-to-study-spatio-temporal-patterns-in-sport-by-damien-demaj-geospatial-product-engineer-at-esri/},
urldate = {2/21/2020}
}