Sports Analytics - student presentations

• student presentations 2021
  • Julia: Shaw and Gopaladesikan, Routine Inspection: A playbook for corner kicks, MIT Sloan Sports Analytics Conference, 2021. paper
  • Victor: Williams et al., MAYFIELD: Machine Learning Algorithm for Yearly Forecasting Indicators and Estimation of Long-Run Player Development, MIT Sloan Sports Analytics Conference, 2021. paper
  • Rasmus and Sofie: Berrar et al., Incorporating domain knowledge in machine learning for soccer outcome prediction, Machine Learning 108:97-126, 2019. doi
    (and) Hubacek et al., Learning to predict soccer results from relational data with gradient boosted trees, Machine Learning 108:29-47, 2019. doi
  • Tim: Fernandez and Bornn, Wide Open Spaces: A statistical technique for measuring space creation in professional soccer, MIT Sloan Sports Analytics Conference, 2018. paper
  • Gustav: Bransen et al., Shoke or Shine? Quantifying Soccer Players' Abilities to Perform Under Mental Pressure, MIT Sloan Sports Analytics Conference, 2019. paper
  • Dimitra: Bransen and van Haaren, Player Chemistry: Striving for a Perfectly Balanced Soccer Team, MIT Sloan Sports Analytics Conference, 2020. paper
  • Biswas: Van Roy et al., Leaving Goals on the Pitch: Evaluating Decision Making in Soccer, MIT Sloan Sports Analytics Conference, 2021. paper
  • Varshith: Marty, High-resolution shot capture reveals systematic biases and an improved method for shooter evaluation, MIT Sloan Sports Analytics Conference, 2018. paper
  • Vinod: Cheong et al., Prediction of Defensive Player Trajectories in NFL Games with Defender CNN-LSTM model, MIT Sloan Sports Analytics Conference, 2021. paper
  • Harshavardhan: Senevirathne and Manage, Predicting the winning percentage of limited-overs cricket using the pythagorean formula, Journal of Sports Analytics, 2021. paper
  • Abhinay: Kalman and Bosch, NBA Lineup Analysis on Clustered Player Tendencies: A new approach to the positions of basketball & modeling lineup efficiency of soft lineup aggregates, MIT Sloan Sports Analytics Conference, 2020. paper
  • Dhyey: Dobreff et al., Physical Performance Optimization in Football, International Workshop on Machine Learning and Data Mining for Sports Analytics, 2020. paper
  • Karthikeyan and Mowniesh: Morra et al., SoccER: Computer graphics meets sports analytics for soccer event recognition, SoftwareX, 2020. paper
    (and) Morra et al., Slicing and Dicing Soccer: Automatic Detection of Complex Events from Spatio-Temporal Data, International Conference on Image Analysis and Recognition, 2020. paper
  • Uno: Murray et al., Using a Situation Awareness approach to determine decision-making behaviour in squash, Journal of Sports Sciences, 2018. paper
  • Atieh: Mlakar and Kovalchik, Analysing time pressure in professional tennis, Journal of Sports Analytics, 2020. paper
• 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: 2022-02-07