{"id":210,"date":"2019-07-31T10:38:10","date_gmt":"2019-07-31T09:38:10","guid":{"rendered":"https:\/\/research.reading.ac.uk\/football-research\/?page_id=210"},"modified":"2019-08-14T09:22:13","modified_gmt":"2019-08-14T08:22:13","slug":"research","status":"publish","type":"page","link":"https:\/\/research.reading.ac.uk\/football-research\/research\/","title":{"rendered":"Research"},"content":{"rendered":"

The University has an active research agenda in Sports and related topics but in particular there is keen interest in different areas of research around the ‘beautiful game’\u00a0<\/em>of Football.\u00a0 Current projects include:<\/p>\n

RED Score Predictor Model<\/h4>\n

Dr James Reade and Dr Carl Singleton in the Department of Economics at the University are interested in Sports Economics and are members of the Economic Analysis Research Group (EARG).\u00a0 As part of their ongoing research on evaluating forecasting methods and behaviour they have developed a computer model they have named ‘RED’ which has been programmed to predict the outcome of weekly football fixtures.\u00a0 This original predictor model estimates how many goals each team scores in a given match as a function of their own historical attacking and defending abilities, the historical abilities of their opponents, recent form, home advantage, the disruption of international breaks and European matches, and whether the match takes place on a midweek evening in November (though not, as yet, whether that evening was also rainy).\u00a0 The model is used to predict the most likely score in upcoming matches which is no mean feat when in general, even the most likely scoreline has just a 10-15% chance of happening. So, on top of predicting the most likely scores, the model suggests the probability of them happening.\u00a0 After each week of matches the model and end-of-season predictions is updated – Scorecasting Economists<\/a> . As more information comes to light on the relative strengths of the teams, both these predictions and weekly scoreline forecasts should become increasingly accurate.<\/p>\n

\"\"<\/a><\/p>\n

Football Manager Performance Model<\/h4>\n

Professor Adrian Bell, Professor Chris Brooks and Dr Tom Markham have developed a performance management tool with particular reference to its application to the football industry. Specifically, the resulting model evaluates the extent to which the performance of English Premier League football club managers can be attributed to skill or luck when measured separately from the characteristics of the team. A specification that models managerial skill as a fixed effect is used first and then a bootstrapping approach is implemented to generate a simulated distribution of average points that could have taken place after the impact of the manager has been removed. The findings suggest that there are a considerable number of highly skilled managers but also several who perform below expectations. The model is also used to illustrate how the approach adopted could be used to determine the optimal time for a club to part company with its manager.\u00a0 Details of the research can be found in Publications<\/a> and the Football blog posts.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

The University has an active research agenda in Sports and related topics but in particular there is keen interest in different areas of research around the ‘beautiful game’\u00a0of Football.\u00a0 Current…Read More ><\/a><\/p>\n","protected":false},"author":219,"featured_media":361,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"__cvm_playback_settings":[],"__cvm_video_id":"","footnotes":""},"coauthors":[6],"class_list":["post-210","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"aioseo_notices":[],"yoast_head":"\nResearch - Football Research<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/research.reading.ac.uk\/football-research\/research\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Research - 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