TDDE64 Sports Analytics

Timetable

• March 30, 2026, 10:15-12:00, P18, Introduction (Patrick Lambrix)
• March 31, 2026, 13:15-15:00, P18, Ice Hockey Analytics research at IDA (Patrick Lambrix)
• April 13, 2026, 10:15-12:00, P18, Football and Basketball Analytics research at IDA (Patrick Lambrix)
• April 14, 2026, 13:15-15:00, R26 + online, Football Analytics at Playmaker AI (Ola Lidmark Eriksson, Playmaker AI)
• April 27, 2026, 10:15-12:00, P26, Baseball Analytics (Marcus Bendtsen, LiU)
• CANCELLED --- May 12, 2026, 13:15-15:00, P18, student presentations
• May 18, 2026, 10:15-12:00, P18, student presentations (golf) Adam, Edvin/Felix, Emma, Jonathan, Valdemar
• May 19, 2026, 13:15-17:00, P30, student presentations (football) Abdul/Muhammad, Charles, Christopher, Dan, Daniel, Daneyal, Dennis, Felix, Frederic, Khizra/Muhammad, Zeyuan
• May 21, 2026, 08:15-10:00, U3, student presentations (ice hockey, floorball, skiing) Andreas, Elliot, Morgan, Wiktor, Duo, Eric
• May 21, 2026, 10:15-12:00, U3, student presentations (kabaddi, tennis, e-sports) Vigneshwararaj, Marcus, Robin, Gabriel/Nils
• May 22, 2026, 15:15-17:00, P18, student presentations (basketball/ice hockey, basketball, diving/ski jumping, formula 1) Mervan/Olle, Erik, Heba, Jiahui, Xin

• * (basketball/ice hockey) Mervan, Olle: Swartz et al., Ups and Downs: Team Performance in Best-of-Seven Playoff Series, Journal of Quantitative Analysis in Sports, 2011. doi
• (basketball) Erik: Patton et al., Predicting NBA Talent from Enormous Amounts of College Basketball Tracking Data , MIT Sloan Sports Analytics Conference, 2026. paper
• * (basketball) Heba: Mihalyi et al., Momentum Matters: Investigating High-Pressure Situations in the NBA Through Scoring Probability, 10th International Workshop on Machine Learning and Data Mining for Sports Analytics 77-90, 2023. doi
• (diving/ski jumping) Jiahui: Goller and Späth, 'Good job!' the impact of positive and negative feedback on performance, Sports Economics Review, 2024. doi
• * (e-sports) Gabriel, Nils: Szmida and Toka, Evaluating Player Actions in Professional Counter Strike using Temporal Heterogeneous Graph Neural Networks, MIT Sloan Sports Analytics Conference, 2026. paper
• * (e-sports) Robin: Patnaha et al., CS:GO Multi-Purpose Platform: ML-Driven Strategy and Tactical Analytics, 7th International Conference on Information Systems and Computer Networks, 2025. doi
• * (floorball) Wiktor: Zdercik, Analysis of goal situations at the World Floorball Championship 2022, Scientific Journal of Sport and Performance, 3(4):457-463, 2024. doi
• * (football) Abdul, Muhammad: Yeung et al., A framework of interpretable match results prediction in football with FIFA ratings and team formation, PLOS One, 2023. doi
• * (football) Charles: Baron et al., Miss it like Messi: Extracting value from off-target shots in soccer, Journal of Quantitative Analysis in Sports, 2024. doi
• * (football) Christopher: Mead et al., Expected goals in football: Improving model performance and demonstrating value, PLOS One, 2023. doi
• * (football) Dan: Wilkens, Can simple models predict football - and beat the odds? Lessons from the German Bundesliga, Journal of Sports Analytics, 2026. doi
• (football) Daniel: Ezzeddine et al., Pricing football transfers using video gaming data, Journal of Sports Analytics, 2025. doi
• * (football) Daneyal: Cao, Passing path predicts shooting outcome in football, Scientific Reports, 14, 9572 2024. doi
• * (football) Dennis: Settembre et al., Factors associated with match outcomes in elite European football - insights from machine learning models, Journal of Sports Analytics, 2024. doi
• * (football) Felix: Sjögren et al., Data-Driven Models for Predicting Field Player Market Value in European Football, IEEE International Workshop on Sport, Technology and Research, 2025. doi
• * (football) Frederic: Aalbers and van Haaren, Distinguishing Between Roles of Football Players in Play-by-Play Match Event Data, Machine Learning and Data Mining for Sports Analytics - MLSA 2028, 31-41, 2019. doi
• * (football) Khizra, Muhammad: Bauer et al., Putting team formations in association football into context, Journal of Sports Analytics, 2023. doi
• (football) Zeyuan: Cefis and Carpita, A new xG model for football analytics, Journal of the Operational Research Society 76(1):1-13, 2025. doi
• * (formula 1) Xin: van Kesteren and Bergkamp, Bayesian analysis of Formula One race results: disentangling driver skill and constructor advantage, Journal of Quantitative Analysis in Sports 9(4): 273-293, 2023. doi
• * (golf) Adam: Staufer and Guillot, Golf strategy optimization and the "Drive for show, putt for dough" adage, Computational Statistics 40:5381-5415, 2025. doi
• * (golf) Edvin, Felix: Bacic, Predicting golf ball trajectories from swing plane: An artificial neural networks approach, Expert Systems with Applications 65:423-438, 2016. doi
• * (golf) Emma: Stöckl et al., The ISOPAR Method: A New Approach to Performance Analysis in Golf, Journal of Quantitative Analysis in Sports, 2011. doi
• * (golf) Jonathan: Bliss, Modelling Elite Golf Performance: Predictors of Hole Score on the European Tour From 2017-2019, International Journal of Golf Science 9(1), 2021. paper
• * (golf) Valdemar: Marschall and LLewwllyn, Effects of Flexibility and Balance on Driving Distance and Club Head Speed in Collegiate Golfers, International Journal of Exercise Science 10:954-963. 2016. doi
• * (ice hockey) Andreas: Radke et al., Passing and Pressure Metrics in Ice Hockey, Artificial Intelligence for Sports Analytics Workshop, 2021. paper
• * (ice hockey) Elliot: Moreau et al., Valuation of NHL draft picks using functional data analysis, Journal of Sports Analytics, 2025. doi
• * (ice hockey) Morgan: Olivestam et al., Characterizing Playing Styles for Ice Hockey Players, Linköping Hockey Analytics Conference LINHAC 2024 Research Track, 39-50, 2024. doi
• (kabaddi) Vigneshwararaj: Patel and Chaudhari, Strategic insights in kabaddi: Applying game theory and mathematical modeling for enhanced performance, International Journal of Science and Research Archive, 2020. doi
• * (skiing - alpine) Duo: Audet et al., Insightful skiing: developing explainable models of on-snow performance through physical attribute selection of alpine skis, Sports Engineering 28:35 2025. doi
• * (skiing - cross country) Eric: Johansson et al., Identifying cross country skiing techniques using power meters in ski poles, Nordic Artificial Intelligence Research and Development, 52-57, 2019. doi
• * (tennis) Marcus: Wilkens, Sports prediction and betting models in the machine learning age: The case of tennis, Journal of Sports Analytics, 2021. doi

Page responsible: Patrick Lambrix
Last updated: 2026-05-16