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TDDE64 Sports Analytics

Timetable


  • March 27, 2024 15:15-17:00, S11, Introduction (Patrick Lambrix)
  • April 5, 2024, 15:15-17:00, R35, Ice Hockey Analytics research at IDA (Patrick Lambrix)
  • April 12, 2024, 15:15-17:00, T23, Football and Basketball Analytics research at IDA (Patrick Lambrix)
  • April 15, 2024, 13:15-15:00, U3, Player roles in ice hockey (Anton Olivestam, Axel Rosendahl)
  • April 24, 2024, 13:15-15:00, U15: Football Analytics at Playmaker AI (Jesper Haglöf, Playmaker AI)
  • April 25, 2024, 10:15-12:00, U3: Baseball Analytics (Marcus Bendtsen)
  • May 8, 2024, 13:15-15:00, U4: Ice Hockey Analytics at Färjestad BK (Erik Wilderoth, FBK)
  • May 10, 2024, 10:15-12:00, SH62 (studenthuset): Ice Hockey Analytics at Linköping Hockey Club (Mikael Vernblom, LHC)
  • May 14, 2024, 13:15-15:00, S35, student presentations, (general) Feraidon, (golf) Filip, (volleyball) Vilgot, (football) Oscar
  • May 15, 2024, 10:15-12:00, S10, student presentations, (football) Axel+Gustaf, John, Joline
  • May 15, 2024, 15:15-17:00, S15, student presentations, (football) Clara+Moa, Fabian, Mustafa
  • May 20, 2024, 15:15-17:00, S10, student presentations, (ice hockey) Erik+Oskar, Gunnar, Hong
  • May 21, 2024, 13:15-15:00, S10, student presentations, (cricket) Priyansh, Ragini, Vignesh, (formula 1) Erik
Student presentations
  • r!* (*) (football) Axel, Gustaf: Upadhyay and Backhaus, Identifying Key players & playing styles of 10 English Premier League Teams during offensive sequences in 2021/2022 season, Statsbomb Conference, 2023. paper
  • r! (football) Clara, Moa: Trower et al., Clustering women's football players: Identifying functional patterns for performance optimisation, Statsbomb Conference, 2023. paper
  • r!! (formula 1) Erik: de Groote, Overtaking in Formula 1 during the Pirelli era: A driver-level analysis, Journal of Sports Analytics 7:119-137, 2021. doi
  • r!! () (*) (ice hockey) Erik, Oskar: Yu et al., Playing Fast Not Loose: Evaluating team-level pace of play in ice hockey using spatio-temporal possession data, MIT Sloan Sports Analytics Conference, 2019. paper
  • r!! (**) (football) Fabian: Bransen and Van Haaren, Player Chemistry: Striving for a Perfectly Balanced Soccer Team, MIT Sloan Sports Analytics Conference, 2020. paper
  • r! (general) Feraidon: Santos-Fernandez et al., Bayesian statistics meets sports: a comprehensive review, Journal of Quantitative Analysis in Sports 15(4):289-312, 2019. doi
  • r! (*) (golf) Filip: McNally et al., Combining Physics and Deep Learning Models to Simulate the Flight of a Golf Ball, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 5119-5128, 2023. paper
  • r!! (**) (ice hockey) Gunnar: Czuzoj-Shulman et al., Winning Isn't Everything - A contextual analysis of hockey face-offs, MIT Sloan Sports Analytics Conference, 2019. paper
  • !! (*) (ice hockey) Hong: Radke et al., Analyzing Passing Metrics in Ice Hockey using Puck and Player Tracking Data, LINHAC, 25-39, 2023. doi
  • r!! (*) (football) John: Bauer et al., Putting team formations in association football into context, Journal of Sports Analytics 9:39-59, 2023. doi
  • r!! (*) (football) Joline: Stöckl et al., Making Offensive Play Predictable - Using a Graph Convolutional Network to Understand Defensive Performance in Soccer, MIT Sloan Sports Analytics Conference, 2021. paper
  • r!! (*) (football) Mustafa: Sahasrabudhe and Bekkers, A Graph Neural Network deep-dive into successful counterattacks, MIT Sloan Sports Analytics Conference, 2023. paper
  • r!* (**) (football) Oscar: Fernandez et al., Decomposing the Immeasurable Sport: A deep learning expected possession value framework for soccer, MIT Sloan Sports Analytics Conference, 2019. paper
  • r! (**) (cricket) Priyansh: Modekurti, Setting final target score in T-20 cricket match by the team batting first, Journal of Sports Analytics 6:205-213, 2020. doi
  • r! (**) (cricket) Ragini: Rafique, cricWAR: A reproducible system for evaluating player performance in limited-overs cricket, MIT Sloan Sports Analytics Conference, 2023. paper
  • r! (**) (cricket) Vignesh: Gurpinar-Morgan et al., You Cannot Do That Ben Stokes: Dynamically Predicting Shot Type in Cricket Using a Personalized Deep Neural Network, MIT Sloan Sports Analytics Conference, 2020. paper
  • r!* (volleyball) Vilgot: Tracy et al., RallyGraph: Specialized Graph Encoding for Enhanced Volleyball Prediction, KDD Workshop on Data Science and AI for Sports, 2023. paper

Page responsible: Patrick Lambrix
Last updated: 2024-05-21