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Sports Analytics, vt 2020

Lectures and reading material

This page will be updated when new info becomes available.
  • Introduction (Patrick Lambrix)
    • slides
    • reading material
      • Swartz T, Where Should I Publish My Sports Paper?, The American Statistician, 2018. doi, pdf
      • See reference section in slides
  • Ice Hockey Analytics research at IDA (Patrick Lambrix)
    • slides - Player performance in ice hockey
    • reading material
      • Ljung D, Carlsson N, Lambrix P, Player pairs valuation in ice hockey, 5th Workshop on Machine Learning and Data Mining for Sports Analytics, 82-92, Dublin, Ireland, 2018. pdf, doi
      • Sans Fuentes C, Carlsson N, Lambrix P, Player impact measures for scoring in ice hockey, MathSport International 2019 Conference, 307-317, Athens, Greece, 2019. pdf
  • Football Analytics research at IDA (Patrick Lambrix)
    • slides - Player valuation in European football
    • reading material
      • Nsolo E, Lambrix P, Carlsson N, Player valuation in European football, 5th Workshop on Machine Learning and Data Mining for Sports Analytics, 42-54, Dublin, Ireland, 2018. pdf, doi
  • Basketball Analytics research at IDA (Patrick Lambrix)
    • slides - NBA game prediction and season simulation
  • Football Analytics at Signality (Ludvig Jacobsson)
    • reading material
        Lindström, Jacobsson L, Carlsson N, Lambrix P, Predicting Player Trajectories in Shot Situations in Soccer, 6th Workshop on Machine Learning and Data Mining for Sports Analytics, 2019. pdf
  • Seeing in to the future. Using self-propelled particle models to aid player decision-making in soccer (David Sumpter)
    • reading material
      • Francisco Peralta Alguacil, Javier Fernandez, Pablo Pinones Arce, David Sumpter, Seeing in to the future. Using self-propelled particle models to aid player decision-making in soccer, MIT Sloan Sports Analytics Conference, 2020. pdf
  • Baseball Analytics (Marcus Bendtsen)
    • reading material
      • Bendtsen M, Regimes in baseball players' career data, Data Mining and Knowledge Discovery 31(6):1580-1621, 2017. doi
  • Ice hockey Analytics (Mikael Vernblom)
  • student presentations (2020)
    • David: Bransen et al., Measuring soccer players' contributions to chance creation by valuing their passes, Journal of Quantitative Analysis in Sports, 15(2):97-116, 2019. doi
    • Grégoire: Brown and Sandholm, Superhuman AI for multiplayer poker, Science 365:885-890, 2019. doi
    • Lawrence: Lepschy et al., Success factors in football: an analysis of the German Bundesliga, International Journal of Performance Analysis in Sport 20(2):150-164, 2020. doi
    • Nastaran: Fernandez and Born, Wide Open Spaces: A statistical technique for measuring space creation in professional soccer, MIT Sloan Sports Analytics Conference, 2018. paper
    • Nikodimos: Olde Rickert et al., The colour of a football outfit affects visibility and team success, Journal of Sports Sciences, 33:2166-2172, 2015. doi
    • Oriol: Giles et al., A machine learning approach for automatic detection and classification of changes of direction from player tracking data in professional tennis, Journal of Sports Sciences, 38(1):106-113, 2020. doi
  • student presentations (2019)
    • Jon: Stein et al., Revealing the Invisible: Visual Analytics and Explanatory Storytelling for Advanced Team Sport Analysis, International Symposium on Big Data Visual and Immersive Analytics, 2018. doi
    • Kristian: Franks et al., Meta-analytics: tools for understanding the statistical properties of sports metrics, Journal of Quantitative Analysis in Sports 12(4):151-165, 2016. doi
    • Pontus: Drappi and Ting Keh, Predicting golf scores at the shot level, Journal of Sports Analytics, in press, 2018. doi
    • Erik: Doux et al., Detecting Strategic Moves in HearthStone Matches, 3rd Workshop on Machine Learning and Data Mining for Sports Analytics, 2016. paper
    • Teodor: Sidle and Tran, Using multi-class classification methods to predict baseball pitch types, Journal of Sports Analytics 4(1):85-93, 2018. doi
    • Olumide: Setti et al., The S-Hock dataset: A new benchmark for spectator crowd analysis, Computer Vision and Image Understanding 159:47-58, 2017. doi, paper
    • Sijin: Shah and Romijnders, Applying Deep Learning to Basketball Trajectories, KDD Large Scale Sports Analytic Workshop, 2016. paper
    • Stefano: Miller et al., Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball, International Conference on Machine Learning, 235-243, 2014. paper
    • Huanyu: Kovalchick and Ingram, Hot heads, cool heads, and tacticians: Measuring the mental game in tennis, MIT Sloan Sports Analytics Conference, 2016. video and paper
    • Robin: Di Salvo et al., Performance Characteristics According to Playing Position in Elite Soccer, International Journal of Sports Medicine 28(3): 222-227, 2007. doi, paper
    • Martin: Schuckers, DIGR: A Defense Independent Rating of NHL Goaltenders using Spatially Smoothed Save Percentage Maps, MIT Sloan Sports Analytics Conference, 2011. paper
    • Isak: Czuzoj-Shulman et al., Winning Isn't Everything - A contextual analysis of hockey face-offs, MIT Sloan Sports Analytics Conference, 2019. paper




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Last updated: 2020-05-13