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<span>patrick</span></div>
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<div id="divRplyFwdMsg" dir="ltr"><font color="#000000" face="Calibri, sans-serif" style="font-size:11pt"><b>Från:</b> Fredrik Lindsten <fredrik.lindsten@liu.se><br>
<b>Skickat:</b> den 20 april 2020 19:57<br>
<b>Till:</b> ml-seminars@lists.liu.se <ml-seminars@lists.liu.se>; idaint@ida.liu.se <idaint@ida.liu.se><br>
<b>Ämne:</b> IDA Machine Learning Seminar 22/4 - David Sumpter</font>
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Welcome to the IDA Machine Learning Seminar on Wednesday, April 22, 3.15 pm, 2020
<div><span>Due to the current situation the seminar will be over video using Zoom, </span><a id="LPlnk931104" style="color: rgb(0, 0, 238);font-family:Calibri,Arial,Helvetica,sans-serif; font-size:12pt; border-color:currentcolor; color:rgb(0,102,204); margin-bottom:0px; margin-top:0px" href="https://uu-se.zoom.us/j/67286808836" rel="noopener noreferrer">https://uu-se.zoom.us/j/67286808836</a> </div>
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<div><span><b>Seeing in to the future. Using self-propelled particle models to aid player decision-making in soccer.</b></span></div>
<div><b>David Sumpter</b>, Department of Mathematics, Uppsala University.</div>
<div><a style="color: rgb(0, 0, 238);" href="https://www.david-sumpter.com/">https://www.david-sumpter.com/</a><br>
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<i>Abstract:</i> <span>Soccer has some of the most complex team movement patterns of any team sport. Recently, several measurements have been proposed for evaluating the value of dribbles, passes or shots. The next step is to automatically identify the alternative
actions available to players both on and off the ball. We address this challenge by building a ‘self-propelled player’ model, simulating attacking roles by maximizing three criteria: pass probability, pitch Impact and pitch control. The model assumes that
players can anticipate the movement of the other players on the pitch a few seconds in to the future and maximize the future value of their position. We compared these simulations to player decisions during matches by top-flight men’s teams of Hammarby IF
and FC Barcelona. In simulations, we found that the two or three players nearest to the ball tended to optimize the product of pass probability and pitch impact. In a first-team coaching intervention at Hammarby, players re-watched attacking situations in
which they had been involved, and were asked to discuss their own actions in comparison with the model. The players often agreed that the model captured complex game patterns, including off-ball actions. The model also recommended runs that the players hadn’t
taken, which the players also found realistic and aided discussions. Despite the novelty of these discussions, the players showed a high willingness to engage with them. We further explored how these techniques can be used to provide automated feedback to
players within the match cycle. Bio: David Sumpter is professor of applied mathematics and author of Soccermatics (2016), Outnumbered (2018) and The Ten Equations that Rule the World (due 2020). His research, resulting in over 100 publications, covers everything
from the inner workings of fish schools and ant colonies, through social psychology and segregation in society, to machine learning and artificial intelligence. He has consulted for leading football clubs and works actively with outreach to schools, industry
and the social sector. His talks at Google, TedX, the Oxford Mathematics Public Lecture and The Royal Institution are available online. </span>
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<i>Location:</i> Zoom<span>, </span><a id="LPlnk931104" style="color: rgb(0, 0, 238);font-size:12pt; font-family:Calibri,Arial,Helvetica,sans-serif; border-color:currentcolor; color:rgb(0,102,204); margin-bottom:0px; margin-top:0px" href="https://uu-se.zoom.us/j/67286808836" rel="noopener noreferrer">https://uu-se.zoom.us/j/67286808836</a><span> </span><br>
<i>Organizer:</i> Patrick Lambrix</div>
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The list of future seminars in the series is available at <a style="color: rgb(0, 0, 238);" href="http://www.ida.liu.se/research/machinelearning/seminars/">http://www.ida.liu.se/research/machinelearning/seminars/</a>.<br>
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<div>Welcome!<br>
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IDA Machine Learning Group<br>
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