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TDDE64 Sports Analytics

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Sports Analytics - student presentations

  • student presentations 2024
    • (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
    • (football) Clara, Moa: Trower et al., Clustering women's football players: Identifying functional patterns for performance optimisation, Statsbomb Conference, 2023. paper
    • (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
    • (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
    • (football) Fabian: Bransen and Van Haaren, Player Chemistry: Striving for a Perfectly Balanced Soccer Team, MIT Sloan Sports Analytics Conference, 2020. paper
    • (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
    • (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
    • (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
    • (football) John: Bauer et al., Putting team formations in association football into context, Journal of Sports Analytics 9:39-59, 2023. doi
    • (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
    • (football) Mustafa: Sahasrabudhe and Bekkers, A Graph Neural Network deep-dive into successful counterattacks, MIT Sloan Sports Analytics Conference, 2023. paper
    • (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
    • (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
    • (cricket) Ragini: Rafique, cricWAR: A reproducible system for evaluating player performance in limited-overs cricket, MIT Sloan Sports Analytics Conference, 2023. paper
    • (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
    • (volleyball) Vilgot: Tracy et al., RallyGraph: Specialized Graph Encoding for Enhanced Volleyball Prediction, KDD Workshop on Data Science and AI for Sports, 2023. paper
  • student presentations 2023
    • (biathlon) Nikolaus: Maier et al., Predicting biathlon shooting performance using machine learning, Journal of Sports Sciences 36:20, 2333-2339, 2018. doi
    • (ice hockey) Anton: Schuckers and Curro, Total Hockey Rating (THoR): A comprehensive statistical rating of National Hockey League forwards and defensemen based upon all on-ice events, MIT Sloan Sports Analytics Conference, 2013. paper
    • (ice hockey) Hampus: Liu and Schulte, Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation, Twenty-Seventh International Joint Conference on Artificial Intelligence 3442-3448, 2018. doi
      Schulte, Valuing Actions and Ranking Hockey Players With Machine Learning (Extended Abstract), LINHAC 2-9, 2022. doi
    • (football) Axel: Bransen and van Haaren, Player Chemistry: Striving for a Perfectly Balanced Soccer Team, MIT Sloan Sports Analytics Conference, 2020. paper
    • (football) Lukas: Tuyls et al., Game Plan: What AI can do for Football, and What Football can do for AI, Journal of Artificial Intelligence Research 71:41-88, 2021. doi
    • (sports in general) Adithya: Perin et al., State of the Art of Sports Data Visualization, Computer Graphics Forum, 37:663-686, 2018. doi
  • student presentations 2022
    • (basketball) Ying: Nistala and Guttag, Using Deep Learning to Understand Patterns of Player Movement in the NBA, MIT Sloan Sports Analytics Conference, 2019. paper
    • (baseball) Mina: Martin, Predicting Major League Baseball Strikeout Rates from Differences in Velocity and Movement Among Player Pitch Type, MIT Sloan Sports Analytics Conference, 2019. paper
    • (American football) Fahim: Horton, Learning Feature Representations from Football Tracking, MIT Sloan Sports Analytics Conference, 2020. paper
    • (football) Oscar M and David: Constantinou, Dolores: a model that predicts football match outcomes from all over the world, Machine Learning 108:49-75, 2019. doi
      and Tsokos et al., Modeling outcomes of soccer matches, Machine Learning 108:77-95, 2019. doi
    • (e-sports) Shashi and Rojan: Clark et al., A Bayesian adjusted plus-minus analysis for the esport Dota 2, Journal of Quantitative Analysis in Sports 16:325-341, 2020. doi
      and Gourdeau and Archambault, Discriminative Neural Network for Hero Selection in Professional Heroes of the Storm and DOTA 2, IEEE Transactions on Games, 13:380-387, 2021. doi
    • (football) Farid: Decroos et al., Actions Speak Louder than Goals: Valuing Player Actions in Soccer, 25th ACM SIGKDD International Conference 1851-1861, 2019. doi
    • (football) Oscar K: Berrar et al., Incorporating domain knowledge in machine learning for soccer outcome prediction Machine Learning 108:97-126, 2019. doi
    • (basketball) Adesijibomi: Deshpande and Jensen, Estimating an NBA player's impact on his team's chances of winning, Journal of Quantitative Analysis in Sports 12:51-72, 2016. doi
    • (cricket) Ravinder: Stevenson and Brewer, Finding your feet: A Gaussian process model for estimating the abilities of batsmen in test cricket, Journal of the Royal Statistical Society: Series C (Applied Statistics) 70:481-506, 2021. doi
    • (football) Olof: Bojinov and Bornn, The Pressing Game: Optimal Defensive Disruption in Soccer, MIT Sloan Sports Analytics Conference, 2016. paper
    • (football) Syed: Whitaker et al., Visualizing a Team's Goal Chances in Soccer from Attacking Events: A Bayesian Inference Approach, Big Data 6, 2018. doi
    • (cricket) Siddharth: Asif and McHale, In-play forecasting of win probability in One-Day International cricket: A dynamic logistic regression model, International Journal of Forecasting 32:34-43. doi
    • (volleyball) Rodrigo: Drikos et al., Game variables that predict success and performance level in elite men's volleyball, International Journal of Performance Analysis in Sport 21:767-779, 2021. doi
    • (cricket) Keshav: Rama Iyer and Sharda, Prediction of athletes performance using neural networks: An application in cricket team selection, Expert Systems with Applications: An International Journal 36:5510-5522, 2009. doi
    • (e-sports) Sreenand: Semenov et al., Performance of Machine Learning Algorithms in Predicting Game Outcome from Drafts in Dota 2, International Conference on Analysis of Images, Social Networks and Texts, 26-37, 2016. doi
  • 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


Page responsible: Patrick Lambrix
Last updated: 2025-02-17