This is a semi-structured collection of sports analytics, statistics, data science, and programming resources that I maintain. It’s primary purpose is to help students find resources for their sport analytics projects (appearing on this list does not constitute endorsement).

Sports analytics topics and problems

Compilations and reviews

• Presenting Multiagent Challenges in Team Sports Analytics - https://arxiv.org/pdf/2303.13660.pdf
  • AI Seminar Series 2023: Dr. David Radke, Presenting Multiagent Challenges in Team Sports Analytics - https://www.youtube.com/watch?v=uclrohXi4ts&ab_channel=AmiiIntelligence
• Big ideas in sports analytics and statistical tools for their investigation - https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/wics.1612
  • Expected value of a game state
  • Win probability
  • Team strength
  • Sports betting market data
• Methodology and evaluation in sports analytics: challenges, approaches, and lessons learned - https://link.springer.com/article/10.1007/s10994-024-06585-0
• Methods of performance analysis in team invasion sports: A systematic review - https://www.tandfonline.com/doi/full/10.1080/02640414.2020.1785185
• Methods of performance analysis in women’s Australian football: a scoping review - https://peerj.com/articles/14946/
• Devin Pleuler: Soccer Analytics Handbook https://github.com/devinpleuler/analytics-handbook
• Tim Swartz - https://www.sfu.ca/~tswartz/
• “A curated list of awesome machine learning applications in the sports domain” - https://github.com/AtomScott/awesome-sports-analytics
• SFU seminars: http://www.sfu.ca/sportsanalytics/Seminars.html
• Soccer Analytics 2020 Review https://janvanhaaren.be/2020/12/30/soccer-analytics-review-2020.html
• Soccer Analytics 2021 Review https://janvanhaaren.be/2021/12/30/soccer-analytics-review-2021.html
• The collection, analysis and exploitation of footballer attributes: A systematic review - https://content.iospress.com/download/journal-of-sports-analytics/jsa200554?id=journal-of-sports-analytics%2Fjsa200554
• F1 - https://twitter.com/F1DataAnalysis
• Basketball analytics - https://squared2020.com/
• UFC analytics - https://literalfightnerd.com/

• STATS LLC patents: https://scholar.google.com.au/citations?hl=en&user=bn4x2d8AAAAJ&view_op=list_works&sortby=pubdate

• Assessment of Physical, Technical, and Tactical Analysis in the Australian Football League: A Systematic Review - https://link.springer.com/article/10.1186/s40798-022-00518-8

Communication

• How to watch basketball: https://cleaningtheglass.com/how-to-watch-basketball/

Reinforcement learning/AI in sport

• How A Bot Made Team New Zealand Faster and Smarter https://www.sailingworld.com/story/racing/how-a-bot-made-team-new-zealand-faster-and-smarter/
• Discovering Diverse Athletic Jumping Strategies https://arpspoof.github.io/project/jump/jump.html
• Game Plan: What AI can do for Football, and What Football can do for AI - https://arxiv.org/pdf/2011.09192.pdf
• Advancing sports analytics through AI research - https://deepmind.com/blog/article/advancing-sports-analytics-through-ai
• A Reinforcement Learning Based Approach to Play Calling in Football - https://drive.google.com/file/d/1j0kBqbRUL3HTdEDWYVLVYc6B21MT56G_/view
• TOWARDS OPTIMIZED ACTIONS IN CRITICAL SITUATIONS OF SOCCER GAMES WITH DEEP REINFORCEMENT LEARNING - https://arxiv.org/pdf/2109.06625v1.pdf
• Markov Cricket: Using Forward and Inverse Reinforcement Learning to Model, Predict And Optimize Batting Performance in One-Day International Cricket - https://arxiv.org/ftp/arxiv/papers/2103/2103.04349.pdf
• Learning to play Table Tennis using Multi-agent Reinforcement Learning - https://sowmyavoona96.github.io/csci527/TP%20(2).pdf
• Q-Ball: Modeling Basketball Games Using Deep Reinforcement Learning - https://www.aaai.org/AAAI22Papers/AAAI-8152.YanaiC.pdf
• Deep reinforcement learning in a racket sport for player evaluation with technical and tactical contexts - https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9775086
• How to run a world record? A Reinforcement Learning approach
• TacticAI: an AI assistant for football tactics - https://arxiv.org/pdf/2310.10553.pdf
• Towards maximizing expected possession outcome in soccer - https://journals.sagepub.com/doi/pdf/10.1177/17479541231154494
• Risk, Reward, and Reinforcement Learning in Ice Hockey Analytics - https://www2.cs.sfu.ca/~oschulte/files/pubs/11_ML%20ice%20hockey_Schulte_englisch.pdf

Strategy proposal and simulation

• Insights from the Application of an Agent-Based Computer Simulation as a Coaching Tool for Top-Level Rugby Union - https://journals.sagepub.com/doi/10.1260/1747-9541.8.3.493
• When to rush a ‘behind’ in Australian rules football: a dynamic programming approach - https://www.tandfonline.com/doi/abs/10.1057/palgrave.jors.2600544

Working in sports analytics (inc. academic collaborations)

• Ben Baumer: https://www.tandfonline.com/doi/pdf/10.1080/00031305.2017.1375985?needAccess=true
• Identifying the characteristics, constraints, and enablers to creating value in applied performance analysis - https://journals.sagepub.com/doi/pdf/10.1177/17479541231180243
• Data analytics in the football industry: a survey investigating operational frameworks and practices in professional clubs and national federations from around the world - https://www.tandfonline.com/doi/pdf/10.1080/24733938.2024.2341837
• Data analytics practices and reporting strategies in senior football: insights into athlete health and performance from over 200 practitioners worldwide - https://www.tandfonline.com/doi/full/10.1080/24733938.2025.2476478
• The game insight group: A model for academic-industry partnerships for sports statistics innovation - https://www.tandfonline.com/doi/pdf/10.1080/08982112.2018.1519578?casa_token=Txrmz-I3ENYAAAAA:vl_Tn49SiI-02iCzlDGKwpehJ1fkPQpEIse7DqnCaEyBwQRb53FLfCtags393hp36pHGVJ8nIItG

Forecasting crowds

• Exploratory modeling on how win uncertainty affects baseball game attendance - https://ssp3nc3r.github.io/post/2020-01-03-exploratory-modeling-on-how-win-uncertainty-affects-game-attendance/

Playing ‘style’ and player ‘similarity’ (teams and players)

• Player Vectors: Characterizing Soccer Players’ Playing Style from Match Event Streams - https://ecmlpkdd2019.org/downloads/paper/701.pdf
• A pilot study to measure game style within Australian football - https://www.tandfonline.com/doi/pdf/10.1080/24748668.2017.1372163
• MEASURING THE SIMILARITY BETWEEN PLAYERS IN AUSTRALIAN FOOTBALL - https://www.researchgate.net/profile/Karl-Jackson-6/publication/305388519_MEASURING_THE_SIMILARITY_BETWEEN_PLAYERS_IN_AUSTRALIAN_FOOTBALL/links/578c16f308ae59aa667c4c91/MEASURING-THE-SIMILARITY-BETWEEN-PLAYERS-IN-AUSTRALIAN-FOOTBALL.pdf
• 6MapNet: Representing Soccer Players from Tracking Data by a Triplet Network - https://arxiv.org/pdf/2109.04720v1.pdf
• Pass2vec: Analyzing soccer players’ passing style using deep learning
• Archetypoid analysis for sports analytics - https://dl.acm.org/doi/abs/10.1007/s10618-017-0514-1
• A scalable framework for NBA player and team comparisons using player tracking data - https://content.iospress.com/articles/journal-of-sports-analytics/jsa0022
• Tired: PCA + kmeans, Wired: UMAP + GMM - https://tonyelhabr.rbind.io/posts/dimensionality-reduction-and-clustering/
• Coach2vec: autoencoding the playing style of soccer coaches - https://arxiv.org/ftp/arxiv/papers/2106/2106.15444.pdf
• The origins of goals in the German Bundesliga - https://www.tandfonline.com/doi/full/10.1080/02640414.2021.1943981
• Classifying ball trajectories in invasion sports using dynamic time warping: A basketball case study - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0272848
• An Analysis of the Influence of Game Context on Team Playing Style - https://statsbomb.com/wp-content/uploads/2023/10/An-Analysis-of-the-Influence-of-Game-Context-on-Team-Playing-Style-2.pdf
• A multilayer network framework for soccer analysis - https://www.sciencedirect.com/science/article/pii/S0960077923012572
• Clustering Offensive Strategies in Australian-Rules Football Using Social Network Analysis - https://www.mdpi.com/2078-2489/15/6/364
• Will Bazball finally knock its critics for six? - https://academic.oup.com/jrssig/article/21/1/32/7596178?login=false
• A data-driven analysis of the technical and tactical evolution of elite women’s football - https://journals.sagepub.com/doi/abs/10.1177/17479541241257809
• Finding repeatable progressive pass clusters and application in international football - https://journals.sagepub.com/doi/10.3233/JSA-220732
• Classifying and quantifying team playing styles in the Australian Football League - https://www.tandfonline.com/doi/full/10.1080/24748668.2024.2325269#abstract
• A Novel Clustering Framework to Identify Team Playing Styles Within Australian Football - https://link.springer.com/article/10.1007/s42979-025-03748-1

Forecasting player performance

• PECOTA - https://en.wikipedia.org/wiki/PECOTA
• CARMELO - https://fivethirtyeight.com/features/how-were-predicting-nba-player-career/
• DARKO - https://apanalytics.shinyapps.io/DARKO//
• ZiPS - https://www.mlb.com/glossary/projection-systems/szymborski-projection-system
• Steamer - https://www.fangraphs.com/projections.aspx?pos=all&stats=bat&type=steamer
• Marcel - https://en.wikipedia.org/wiki/Marcel_(projection_system)
• Predicting the Potential of Professional Soccer Players - http://ceur-ws.org/Vol-1971/paper-02.pdf
• Predicting the future performance of soccer players - https://onlinelibrary.wiley.com/doi/full/10.1002/sam.11321
• Can Elite Australian Football Player’s Game Performance Be Predicted? - https://sciendo.com/de/article/10.2478/ijcss-2021-0004
• Estimation of player aging curves using regression and imputation - https://link.springer.com/article/10.1007/s10479-022-05127-y - and https://arxiv.org/pdf/2110.14017.pdf
• Large data and Bayesian modeling—aging curves of NBA players - https://link.springer.com/article/10.3758/s13428-018-1183-8
• Forecasting basketball players’ performance using sparse functional data - https://onlinelibrary.wiley.com/doi/pdf/10.1002/sam.11436?casa_token=OwxCz_66t3MAAAAA:yqa3qwEjzcj_O0H6MphXXyydwyvZr8S3KmJQqGqJrVBT2VaG35Un_8jvdwHZn-SewKpdC_cJhqiMuQ
• Bayesian Hierarchical Modeling Applied to Fantasy Football Projections for Increased Insight and Confidence - https://srome.github.io/Bayesian-Hierarchical-Modeling-Applied-to-Fantasy-Football-Projections-for-Increased-Insight-and-Confidence/
• Bayesian prediction of winning times for elite swimming events - https://www.tandfonline.com/doi/full/10.1080/02640414.2021.1976485
• Next-Generation Models for Predicting Winning Times in Elite Swimming Events: Updated Predictions for the Paris 2024 Olympic Games - https://journals.humankinetics.com/view/journals/ijspp/18/11/article-p1269.xml
• Estimating human limits of running speed - https://ssp3nc3r.github.io/post/estimating-human-limits-to-running-speed/
• Modeling forces in 100m Olympic sprint, a study in physics and probability - https://ssp3nc3r.github.io/post/estimating-force-of-100m-olympic-sprint-with-physics/
• CAREFUL WHAT YOU THROW OUT: MODELLING RANK DATA (Models for rank data from racing sports) - https://statsbystokes.wordpress.com/2022/02/15/careful-what-you-throw-out-modelling-rank-data/
• Bayesian modelling of elite sporting performance with large databases - https://www.degruyter.com/document/doi/10.1515/jqas-2021-0112/html
• Estimating the effects of age on NHL player performance - https://www.degruyter.com/document/doi/10.1515/jqas-2013-0085/html
• Modelling the dynamics of change in the technical skills of young basketball players: The INEX study - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0257767#abstract0
• Modelling age-related changes in executive functions of soccer players - https://arxiv.org/pdf/2105.01226.pdf
• A mixed effects multinomial logistic-nrmal model for forecasting baseball performance - https://www.degruyter.com/document/doi/10.1515/jqas-2020-0007/html?s=03
• WHKYE AGING CURVES, A PARTIAL INLA TUTORIAL? - https://statsbystokes.wordpress.com/2022/02/15/whkye-aging-curves-a-partial-inla-tutorial/
• Which League is Best? Using Paired Comparison Models to Estimate Hockey League Strength and Project Player Performance - https://drive.google.com/file/d/1WpfAeUZ43qI1VQs2Bp8onRZ1dFeO5sAY/view
• Flexible Aging in the NHL Using GAM - https://rpubs.com/cjtdevil/nhl_aging
• A New Look at Aging Curves for NHL Skaters - https://hockey-graphs.com/2017/03/23/a-new-look-at-aging-curves-for-nhl-skaters-part-1/
• Aging Patterns: Determing aging patterns, and explaining analysis techniques - https://www.tangotiger.net/aging.html
• Bayesian GARCH modeling of functional sports data - https://link.springer.com/article/10.1007/s10260-022-00656-z
• Filling the gaps: A multiple imputation approach to estimating aging curves in baseball - https://content.iospress.com/download/journal-of-sports-analytics/jsa240744?id=journal-of-sports-analytics%2Fjsa240744

Drafting

• Draft efficiency - https://statsbylopez.com/2017/04/25/evaluating-the-evaluators/
• What Does It Mean to Draft Perfectly in the NHL?
  • https://hockeygraphsdotcom.files.wordpress.com/2016/10/2-nandakumar-vanhac-slides.pdf - slides
  • https://repository.upenn.edu/joseph_wharton_scholars/26/ - paper
• Major League Draft WARs: An Analysis of Wins Above Replacement in Player Selection - https://content.iospress.com/download/journal-of-sports-analytics/jsa200586?id=journal-of-sports-analytics%2Fjsa200586
• Combine performance, draft position and playing position are poor predictors of player career outcomes in the Australian Football League - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0234400
• Optionality in Australian Football League draftee contracts - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0291439
• Valuing Australian football league draft picks - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0292395
• Compounding endowment effects when trading draft picks in the Australian Football League - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0300546
• The AFL Pick Trading Market as a Coasian Utopia - https://journals.sagepub.com/doi/full/10.1177/1527002520948108
• How Did the AFL National Draft Mitigate Perverse Incentives? - https://journals.sagepub.com/doi/abs/10.1177/1527002519873128
• Predicting successful draft outcome in Australian Rules football: Model sensitivity is superior in neural networks when compared to logistic regression - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0298743
  • Open data: https://opal.latrobe.edu.au/articles/dataset/Meta_xlsx/24967014

Recruiter perspectives

• Exploring the skill of recruiting in the Australian Football League - https://journals.sagepub.com/doi/full/10.1177/1747954118809775
• An eye for talent: The recruiters’ role in the Australian Football talent pathway - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241307

Randomness and skill vs. luck in sport

• HOW OFTEN DOES THE BEST TEAM WIN? A UNIFIED APPROACH TO UNDERSTANDING RANDOMNESS IN NORTH AMERICAN SPORT - https://arxiv.org/pdf/1701.05976.pdf
• IDENTIFICATION_AND_MEASUREMENT_OF_LUCK_IN_SPORT - https://www.researchgate.net/publication/305388606_IDENTIFICATION_AND_MEASUREMENT_OF_LUCK_IN_SPORT
• When can we trust a team’s stats? - https://fansided.com/2017/12/21/nylon-calculus-team-stats-noise-stabilization-thunder/
• How Long Does It Take For Three Point Shooting To Stabilize? - https://fansided.com/2014/08/29/long-take-three-point-shooting-stabilize/
• Baseball Therapy: It’s a Small Sample Size After All - https://www.baseballprospectus.com/news/article/17659/baseball-therapy-its-a-small-sample-size-after-all/
• Are Launch Angles Skills? - Analyzing Baseball Data with R, Second Edition

Meta-analytics & metric evaluation

• Meta-analytics: tools for understanding the statistical properties of sports metrics - https://www.degruyter.com/view/journals/jqas/12/4/article-p151.xml
• https://tonyelhabr.rbind.io/posts/soccer-meta-analytics/
• The SIDO Performance Model for League of Legends - https://arxiv.org/pdf/2403.04873
• The Do’s and Don’ts of Sports Metrics: The Tennis ATP Leaderboard - https://www.tandfonline.com/doi/full/10.1080/09332480.2017.1302717

In-game win probability & momentum

NFL:

• https://medium.com/@technocat79/building-a-basic-in-game-win-probability-model-for-the-nfl-54600e57fe1c
• https://statsbylopez.com/2017/03/08/all-win-probability-models-are-wrong-some-are-useful/
• Going deep: models for continuous-time within-play valuation of game outcomes in American football with tracking data - https://www.degruyter.com/document/doi/10.1515/jqas-2019-0056/html
• iWinRNFL: A Simple, Interpretable & Well-Calibrated In-Game Win Probability Model for NFL - https://arxiv.org/pdf/1704.00197.pdf
• In-game win probability models for Canadian football - https://www.tandfonline.com/doi/abs/10.1080/2573234X.2021.2015252
• Using random forests to estimate win probability before each play of an NFL game - https://www.degruyter.com/document/doi/10.1515/jqas-2013-0100/html
• NFL win probability from scratch using xgboost in R (2021): https://www.opensourcefootball.com/posts/2021-04-13-creating-a-model-from-scratch-using-xgboost-in-r/

AFL:

• https://thearcfooty.com/within-game-win-probabilities/
• https://thearcfooty.com/2017/02/07/win-probability-estimates-what-are-they-good-for/
• AFLaytics - Quantifying what makes a good game of footy - https://www.aflalytics.com/blog/2018/7/quantifying-good-match-footy/
• Real time prediction of match outcomes in Australian football - https://www.tandfonline.com/doi/full/10.1080/02640414.2023.2259266

Rugby:

• In-game win probabilities for the National Rugby League - https://projecteuclid.org/journals/annals-of-applied-statistics/volume-16/issue-1/In-game-win-probabilities-for-the-National-Rugby-League/10.1214/21-AOAS1514.short

Soccer:

• A Bayesian Approach to In-Game Win Probability in Soccer https://dl.acm.org/doi/10.1145/3447548.3467194
  • blog: https://dtai.cs.kuleuven.be/sports/blog/a-bayesian-approach-to-in-game-win-probability
• In-play forecasting in football using event and positional data - https://www.nature.com/articles/s41598-021-03157-3
• A copula-based multivariate hidden Markov model for modelling momentum in football - https://link.springer.com/article/10.1007/s10182-021-00395-8

Basketball:

• A Data Snapshot Approach for Making Real-Time Predictions in Basketball
• Bayesian estimation of in-game home team win probability for college basketball. - https://arxiv.org/abs/2204.11777
• Understanding team collapse via probabilistic graphical models - https://arxiv.org/pdf/2402.10243

Match prediction & Team rating models

• Forecasting football matches by predicting match statistics - https://content.iospress.com/download/journal-of-sports-analytics/jsa200462?id=journal-of-sports-analytics%2Fjsa200462
  • Soccer. Predict shots on/off target, corners, and goals for each team. Combine those forecasts to predict match result.
• A Critical Comparison of Machine Learning Classifiers to Predict Match Outcomes in the NFL - https://sciendo.com/de/article/10.2478/ijcss-2020-0009
• Multifactorial analysis of factors influencing elite Australian football match outcomes: a machine learning approach - https://sciendo.com/article/10.2478/ijcss-2019-0020
• A Two-Stage Bayesian Model for Predicting Winners in Major League Baseball - http://citeseerx.ist.psu.edu/viewdoc/download?rep=rep1&type=pdf&doi=10.1.1.124.4257
• Modelling Australian Rules Football as spatial systems with pairwise comparisons - https://www.degruyter.com/document/doi/10.1515/jqas-2021-0035/html
• A Data-Driven Machine Learning Algorithm for Predicting the Outcomes of NBA Games - https://www.mdpi.com/2073-8994/15/4/798
• THE REPLICATION PROJECT: IS XG THE BEST PREDICTOR OF FUTURE RESULTS? - https://www.americansocceranalysis.com/home/2022/7/19/the-replication-project-is-xg-the-best-predictor-of-future-results
• Predicting Football Results With Statistical Modelling: Dixon-Coles and Time-Weighting - https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling-dixon-coles-and-time-weighting/
• Extending the Dixon and Coles model: an application to women’s football data - https://arxiv.org/pdf/2307.02139
• True Skill - https://rdrr.io/cran/trueskill/man/trueskill-package.html
• A comprehensive survey of the home advantage in American football - https://doi.org/10.1515/jqas-2024-0016
• Revising home advantage in sport – home advantage mediation (HAM) model - https://www.tandfonline.com/doi/full/10.1080/1750984X.2024.2358491#abstract
• Bye-Bye, Bye Advantage: Estimating the competitive impact of rest differential in the National Football League - https://arxiv.org/pdf/2408.10867

Tipping models

• AFL Lab - SOLDIER Model: https://theafllab.wordpress.com/2019/03/02/the-soldier-model/
• AFL Gains: https://ricporteous.netlify.com/post/machine-learning-in-afl/#creating-a-machine-learning-model-to-predict-afl-matches
• AFLaytics - A Brownian Motion Inspired ELO Model: https://www.aflalytics.com/blog/2019/1/brownian-motion-inspired-elo-model/
• Build an AFL Elo with FitzRoy: https://analysisofafl.netlify.com/models/2018-07-23-build-a-quick-elo/
• AFL teams Elo ratings and footy-tipping: http://freerangestats.info/blog/2019/03/23/afl-elo

Causal Inference in sport

• Causal problems involving football strategy - http://www.mathsportinternational.com/MathSport2023Proceedings.pdf#page=37
• Is Soccer Wrong About Long Shots?
  • https://fivethirtyeight.com/features/is-soccer-wrong-about-long-shots/ - blog
  • https://dtai.cs.kuleuven.be/sports/ssac21/MaaikeVanRoyLeavingGoalsRPpaper.pdf - paper
• A contextual analysis of crossing the ball in soccer - https://www.degruyter.com/document/doi/10.1515/jqas-2020-0060/html
• Modeling Player and Team Performance in Basketball - https://www.annualreviews.org/doi/pdf/10.1146/annurev-statistics-040720-015536
  • We conclude with a discussion on the future of basketball analytics and, in particular, highlight the need for causal inference in sports.
• Understanding causal inference: the future direction in sports injury prevention - https://pubmed.ncbi.nlm.nih.gov/17513917/
• What Might a Theory of Causation Do for Sport? - https://www.mdpi.com/2409-9287/4/2/34/pdf
• Derrick Yam, Michael J. Lopez, “What was lost? A causal estimate of fourth down behavior in the National Football League”, Journal of Sports Analytics, 2019. - https://content.iospress.com/articles/journal-of-sports-analytics/jsa190294
• Analytics, have some humility: a statistical view of fourth-down decision making - https://arxiv.org/abs/2311.03490
• https://mladenjovanovic.github.io/bmbstats-book/causal-inference.html
• Implementation of path analysis and piecewise structural equation modelling to improve the interpretation of key performance indicators in team sports: An example in professional rugby union - https://www.tandfonline.com/doi/full/10.1080/02640414.2021.1943169?s=03&journalCode=rjsp20#.YM-z2hohnOg.twitter
• A holistic analysis of collective behaviour and team performance in Australian Football via structural equation modelling - https://www.tandfonline.com/doi/full/10.1080/24733938.2022.2046286
• Estimating the causal effect of defensive formation on yards gained in run plays - https://operations.nfl.com/media/4199/bdb_kruchten.pdf
• FROM GRAPES AND PRUNES TO APPLES AND APPLES: USING MATCHED METHODS TO ESTIMATE OPTIMAL ZONE ENTRY DECISION-MAKING IN THE NATIONAL HOCKEY LEAGUE - https://rpubs.com/atoumi/zone-entries-nhl

Decision making

• Jointly modeling choice to swing with ball contact - https://ssp3nc3r.github.io/post/jointly-modeling-choice-to-swing-with-ball-contact/
• Play Call Strategies and Modeling for Target Outcomes in Football - https://www.tandfonline.com/doi/full/10.1080/00031305.2023.2223582#d1e124

Player evaluation/rating

• Introducing Grid WAR: Rethinking WAR for Starting Pitchers - https://arxiv.org/abs/2209.07274
• cricWAR: A reproducible system for evaluating player performance in limited-overs cricket - https://www.sloansportsconference.com/research-papers/cricwar-a-reproducible-system-for-evaluating-player-performance-in-limited-overs-cricket
• A first model to rate Formula 1 drivers - https://martiningram.github.io/f1-model/?s=03
• Finding Your Feet: A Gaussian Process Model for Estimating the Abilities of Batsmen in Test Cricket - https://academic.oup.com/jrsssc/article/70/2/481/7033927?login=false
• A Bayesian Approach for Determining Player Abilities in Football – https://academic.oup.com/jrsssc/article/70/1/174/7033964#395473026
• Modelling player performance in basketball through mixed models - https://www.tandfonline.com/doi/abs/10.1080/24748668.2013.11868632
• Estimating an NBA player’s impact on his team’s chances of winning - https://www.degruyter.com/document/doi/10.1515/jqas-2015-0027/html?lang=en
• A Bayesian two-stage framework for lineup-independent assessment of individual rebounding ability in the NBA - https://nessis.org/nessis23/Nicholas-Kiriazis-approved.pdf
• Investigating the multivariate nature of NHL player performance with structural equation modeling - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5590964/
• Deep Dive on Regularized Adjusted Plus Minus
  • https://squared2020.com/2017/09/18/deep-dive-on-regularized-adjusted-plus-minus-i-introductory-example/
  • https://squared2020.com/2017/09/18/deep-dive-on-regularized-adjusted-plus-minus-ii-basic-application-to-2017-nba-data-with-r/
  • https://squared2020.com/2018/12/24/regularized-adjusted-plus-minus-part-iii-what-had-really-happened-was/
• Estimating the marginal value of baseball events - https://ssp3nc3r.github.io/post/estimating-the-marginal-value-of-baseball-events/
• Lasso Multinomial Performance Indicators for in-play Basketball Data - https://arxiv.org/pdf/2406.09895
• Randomized Time Trial - https://betanalpha.github.io/assets/chapters_html/racing.html
• Multiplayer Elo - http://www.tckerrigan.com/Misc/Multiplayer_Elo/

Multi-trial/test events

• When Is a Test Score Fair for the Individual Who Is Being Tested? Effects of Different Scoring Procedures across Multiple Attempts When Testing a Motor Skill Task - https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2017.00619/full
• Reliability of Measures Obtained During Single and Repeated Countermovement Jumps - https://journals.humankinetics.com/view/journals/ijspp/3/2/article-p131.xml
• Skewed performancedistributions as evidence ofmotor constraint in sportsand animal displays - https://royalsocietypublishing.org/doi/epdf/10.1098/rsos.230692

Shooting/Kicking/Passing/Hitting ratings + xGoals

• Statistical modelling of goalkicking performance in the Australian Football League (BAYESIAN) - https://www.sciencedirect.com/science/article/pii/S1440244022001335
• Factors Affecting Set Shot Goal-kicking Performance in the Australian Football League - https://journals.sagepub.com/doi/full/10.1177/0031512518781265
• Things may not always be as they seem: The set shot in AFL football - https://files.eric.ed.gov/fulltext/EJ906695.pdf
• Modelling the Influence of Task Constraints on Goal Kicking Performance in Australian Rules Football - https://sportsmedicine-open.springeropen.com/articles/10.1186/s40798-021-00393-9
• Shots at goal in Australian Football: Historical trends, determinants of accuracy and common strategies - https://www.jsams.org/article/S1440-2440(24)00075-6/fulltext?s=03
• Upgrading Expected Goals - https://statsbomb.com/articles/soccer/upgrading-expected-goals/
• Expected Metrics as a Measure of Skill: Reflections on Finishing in Soccer - https://people.cs.kuleuven.be/~pieter.robberechts/repo/davis-mlsa23-xmskill.pdf
• Biases in Expected Goals Models Confound Finishing Ability - https://arxiv.org/abs/2401.09940
• Rao-Blackwellizing field goal percentage - https://www.degruyter.com/document/doi/10.1515/jqas-2018-0064/html?lang=en
• Miss it like Messi: Extracting value from off-target shots in soccer - https://www.degruyter.com/document/doi/10.1515/jqas-2022-0107/html
• “WhyWould I Trust Your Numbers?” Onthe Explainability of Expected Values in Soccer - https://arxiv.org/pdf/2105.13778
• Women’s football analyzed: interpretable expected goals models for women - https://lirias.kuleuven.be/retrieve/622532
• Introduction – Measuring Average Exit Velocity - https://baseballwithr.wordpress.com/2024/07/29/modeling-to-extract-a-players-competitive-swings/
  • Modeling Player’s Exit Velocities, Part II - https://baseballwithr.wordpress.com/2024/08/05/modeling-players-exit-velocities-part-ii/

EPV, VAEP, xThreat, Equity

Basketball

• Cervone, D., D’Amour, A., Bornn, L., & Goldsberry, K. (2016). A multiresolution stochastic process model for predicting basketball possession outcomes. Journal of the American Statistical Association, 111(514), 585–599.
• Expected Possession Value: An Evaluation Framework for Decision-Making, Strategy, and Execution in Basketball, Ivan C. Jutamulia, (B.S. Computer Science and Engineering) - http://dspace.mit.edu/bitstream/handle/1721.1/139205/Jutamulia-ivanj-meng-eecs-2021-thesis.pdf?sequence=1&isAllowed=y

AFL

• O’Shaughnessy, D. M. (2006). Possession versus position: strategic evaluation in AFL. Journal of sports science & medicine, 5(4), 533.
• ASSESSING PLAYER PERFORMANCE IN AUSTRALIAN FOOTBALL USING SPATIAL DATA - Karl Jackson - https://researchbank.swinburne.edu.au/file/248ec147-72d7-448c-a19d-49f01d90b12f/1/Karl%20Jackson%20Thesis.pdf
• Predicting and Understanding Australian Rules Football Using Markov Processes - https://link.springer.com/chapter/10.1007/978-3-030-99333-7_5

Soccer

• Possession Is The Puzzle Of Soccer Analytics. These Models Are Trying To Solve It - https://fivethirtyeight.com/features/possession-is-the-puzzle-of-soccer-analytics-these-models-are-trying-to-solve-it/
• Explaining Expected Threat - https://soccermatics.medium.com/explaining-expected-threat-cbc775d97935
• Uppsala: Expected possession value - https://uppsala.instructure.com/courses/28112/pages/8-expected-possession-value
• Introducing Expected Threat (xT) - https://karun.in/blog/expected-threat.html
• Valuing On-the-Ball Actions in Soccer: A Critical Comparison of xT and VAEP - https://tomdecroos.github.io/reports/xt_vs_vaep.pdf
• INTRODUCING A POSSESSION VALUE FRAMEWORK - https://www.statsperform.com/resource/introducing-a-possession-value-framework/
• A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions - https://link.springer.com/article/10.1007/s10994-021-05989-6
• Decroos, T., Bransen, L., Van Haaren, J., & Davis, J. (2019). Actions speak louder than goals: Valuing player actions in soccer. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 1851–1861).
• Link, D., Lang, S., & Seidenschwarz, P. (2016). Real time quantification of dangerousity in football using spatiotemporal tracking data. PLoS ONE, 11(12), e0168768.
• Rudd, S. (2011). A framework for tactical analysis and individual offensive production assessment in soccer using markov chains. In New England symposium on statistics in sports. http://nessis.org/nessis11/rudd.pdf.
• Spearman, W. (2018). Beyond expected goals. In Proceedings of the 12th MIT sloan sports analytics conference.
• Unpacking Ball Progression - https://statsbomb.com/articles/soccer/unpacking-ball-progression/
• Soccer as a Markov process: modelling and estimation of the zonal variation of team strengths - https://academic.oup.com/imaman/advance-article-abstract/doi/10.1093/imaman/dpab042/6512363
• An evaluation of characteristics of teams in association football by using a Markov process model - https://www.jstor.org/stable/4128133
• Guide to Expected Possession Value - https://abhiamishra.github.io/ggshakeR/articles/Guide_to_EPV.html
• Guide to Expected Threat - https://abhiamishra.github.io/ggshakeR/articles/Guide_to_Exp_Threat.html
• Creating an augmented possession framework to evaluate phases of play and application in international football - https://journals.sagepub.com/doi/10.1177/22150218241290988

NFL

• Yurko, R., Matano, F., Richardson, L. F., Granered, N., Pospisil, T., Pelechrinis, K., & Ventura, S.L. (2020). Going deep: models for continuous-time within-play valuation of game outcomes in American football with tracking data. Journal of Quantitative Analysis in Sports 1(ahead-of-print).

Rugby

• Integrating machine learning and decision support in tactical decision-making in rugby union - https://www.tandfonline.com/doi/full/10.1080/01605682.2020.1779624
• Development of an expected possession value model to analyse team attacking performances in rugby league - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589207/
• The expected value of possession in professional rugby league match-play - https://pubmed.ncbi.nlm.nih.gov/26190116/
• A Bayesian Mixture Model Approach to Expected Possession Values in Rugby League - https://arxiv.org/abs/2212.10904
  • https://twitter.com/Tom_Sawczuk/status/1605912679033409536?t=9NvfKA107sTXa-wjkPyhuQ&s=03

Action valuation

• Valuing actions intro: The principles of valuing actions - https://www.youtube.com/watch?v=xyyZLs_N1F0&ab_channel=FriendsofTracking
• Evaluating actions in football using machine learning - https://soccermatics.medium.com/evaluating-actions-in-football-using-machine-learning-69517e376e0c
• Valuing Player Actions in Counter-Strike: Global Offensive - https://arxiv.org/pdf/2011.01324v2.pdf
• Fitting your own football xG model - https://www.datofutbol.cl/xg-model/
• Space-Time VON CRAMM: Evaluating Decision-Making in Tennis with Variational generatiON of Complete Resolution Arcs via Mixture Modeling - https://arxiv.org/abs/2005.12853
• A Markov Framework for Learning and Reasoning About Strategies in Professional Soccer - https://www.jair.org/index.php/jair/article/view/13934/26940
• Beyond action valuation: A deep reinforcement learning framework for optimizing player decisions in soccer - https://www.janvanhaaren.be/assets/papers/mitssac-2022-decision-making.pdf

Event stream analysis/Game state

• Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: an application to rugby union - https://arxiv.org/pdf/2010.15377v4.pdf
• Towards a foundation large events model for soccer - https://link.springer.com/article/10.1007/s10994-024-06606-y
• Action rate models for predicting actions in soccer - https://link.springer.com/article/10.1007/s10182-022-00435-x
• A pressure index for the team batting second in T20I cricket - https://journals.sagepub.com/doi/10.3233/JSA-240792

Regression to the mean

• REGRESSION TO THE MEAN: AN EXAMPLE USING GOALKICKING - https://analysisofafl.netlify.app/models/2018-06-20-regression-to-the-mean/

Defensive valuation

• What Happened Next? Using Deep Learning to Value Defensive Actions in Football Event-Data - https://arxiv.org/pdf/2106.01786.pdf
• https://fivethirtyeight.com/features/a-better-way-to-evaluate-nba-defense/
• Counterpoints: Advanced Defensive Metrics for NBA Basketball - http://www.lukebornn.com/papers/franks_ssac_2015.pdf
• Using In-Game Shot Trajectories to Better Understand Defensive Impact in the NBA - https://arxiv.org/pdf/1905.00822.pdf
• The effect of team formation on defensive performance in Australian football - https://www.sciencedirect.com/science/article/abs/pii/S1440244021002358
• Making Offensive Play Predictable - Using a Graph Convolutional Network to Understand Defensive Performance in Soccer - https://o7dkx1gd2bwwexip1qwjpplu-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/1617733444_PaulPowerOffensivePlaySoccerRPpaper-1.pdf
  • Paul Power: neural networks for understanding defending - https://www.youtube.com/watch?v=d5NBm4CFygo&ab_channel=FriendsofTracking
• The Success Factors of Rest Defense in Soccer – A Mixed-Methods Approach of Expert Interviews, Tracking Data, and Machine Learning - https://jssm.org/volume22/iss4/cap/jssm-22-707.pdf
• Evaluation of Off-the-Ball Actions in Soccer - https://www.sfu.ca/~tswartz/papers/off-the-ball.pdf
• NFL Big Data Bowl 2024 - Help evaluate tackling tactics and strategy https://www.kaggle.com/competitions/nfl-big-data-bowl-2024/discussion/472712
  • https://operations.nfl.com/gameday/analytics/big-data-bowl/2024-big-data-bowl-winner-and-finalists/
  • Uncovering Missed Tackle Opportunities - https://www.kaggle.com/code/matthewpchang/uncovering-missed-tackle-opportunities/
• Optimally Disrupting Opponent Build-ups - http://statsbomb.com/wp-content/uploads/2021/11/DTAI-Research-Paper.pdf
• exPress: Contextual Valuation of Individual Players Within Pressing Situations in Soccer - https://www.researchgate.net/publication/390137259_exPress_Contextual_Valuation_of_Individual_Players_Within_Pressing_Situations_in_Soccer

Player tracking data

• Special Issue on Player Tracking Data in the National Football League (NFL) - https://www.degruyter.com/view/journals/jqas/16/2/jqas.16.issue-2.xml
• Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball - https://arxiv.org/pdf/1401.0942.pdf
• Characterizing the spatial structure of defensive skill in professional basketball - https://arxiv.org/pdf/1405.0231.pdf
• A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes - https://www.tandfonline.com/doi/full/10.1080/01621459.2016.1141685
• The role of intrinsic dimension in high-resolution player tracking data—Insights in basketball - https://arxiv.org/pdf/2002.04148.pdf
• Automatic event detection in basketball using HMM with energy based defensive assignment - https://www.degruyter.com/document/doi/10.1515/jqas-2017-0126/html?lang=en
• Route identification in the National Football League - https://www.degruyter.com/document/doi/10.1515/jqas-2019-0047/html
  • Conference talk: https://www.youtube.com/watch?v=rnAzURpLLbs&ab_channel=MarkGlickman
• Template matching route classification - https://www.degruyter.com/document/doi/10.1515/jqas-2019-0051/html
• Possession Sketches: Mapping NBA Strategies - http://www.lukebornn.com/papers/miller_ssac_2017.pdf
• Using Data To Determine Blitz Strategy - https://www.kaggle.com/code/dominicborsani/using-data-to-determine-blitz-strategy?s=03
• A method for evaluating player decision-making in the Australian Football League - https://www.researchgate.net/profile/Bart-Spencer/publication/335101736_A_method_for_evaluating_player_decision-making_in_the_Australian_Football_League/links/5d4f512792851cd046b26add/A-method-for-evaluating-player-decision-making-in-the-Australian-Football-League.pdf
• Routine Inspection: Measuring Playbooks for Corner Kicks - https://global-uploads.webflow.com/5f1af76ed86d6771ad48324b/606e51c17bf6c8ba83d69a01_LaurieShaw-CornerKicks-RPpaper.pdf
  • https://www.youtube.com/watch?v=yfPC1O_g-I8&t=3002s&ab_channel=MarkGlickman
• Effects of collective tactical variables and predictors on the probability of scoring in elite netball - https://www.tandfonline.com/doi/full/10.1080/24748668.2023.2225274
• The influence of match phase and field position on collective team behaviour in Australian Rules football - https://www.tandfonline.com/doi/full/10.1080/02640414.2019.1586077
• Quantifying congestion with player tracking data in Australian football - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0272657
• Team numerical advantage in Australian rules football: A missing piece of the scoring puzzle? - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0254591
• A STATISTICAL MODEL OF SERVE RETURN IMPACT PATTERNS IN PROFESSIONAL TENNIS - https://arxiv.org/pdf/2202.00583.pdf
• Is it worth the effort? Understanding and contextualizing physical metrics in soccer - https://arxiv.org/pdf/2204.02313.pdf
• Putting team formations in association football into context - https://content.iospress.com/download/journal-of-sports-analytics/jsa220620?id=journal-of-sports-analytics%2Fjsa220620
• Detection of tactical patterns using semi-supervised graph neural networks - https://cdn.prod.website-files.com/5f1af76ed86d6771ad48324b/6227709e4d7acb78147f7bcf_Detection%20of%20Tactical%20Patterns%202.pdf
• Parking the bus - https://www.degruyter.com/document/doi/10.1515/jqas-2021-0059/html?casa_token=FPIob6V8DhoAAAAA:QJTYaBBqksQfR4bbShXnzs7iasyKLKz1bjK6SgkA2ErREzo9g0g-n9T9OgZM1V1V6ekmSmg-0Lg
• A framework for player movement analysis in team sports - https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2024.1375513/full
• Assigning goal-probability value to high intensity runs in football - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308749
• NFL Big Data Bowl - How many yards will an NFL player gain after receiving a handoff?
  • 1st place solution The Zoo - https://www.kaggle.com/c/nfl-big-data-bowl-2020/discussion/119400
  • https://operations.nfl.com/media/4204/bdb_ploenzke.pdf
• The Anatomy of Corner 3s in the NBA: What makes them efficient, how are they generated and how can defenses respond? - https://arxiv.org/pdf/2105.12785
• Masked Autoencoder Pretraining for Event Classification in Elite Soccer
• HoopTransformer: Advancing NBA Offensive Play Recognition with Self-Supervised Learning from Player Trajectories - https://link.springer.com/article/10.1007/s40279-024-02030-3
• Hoop-MSSL: Multi-Task Self-supervised Representation Learning on Basketball Spatio-Temporal Data - https://openreview.net/pdf?id=PGqaqOA9s9

Role assignment

• Fixed to Fluid: Frame-by-Frame Role Classification - https://github.com/devinpleuler/research/blob/master/frame-by-frame-position.md
• Dynamic analysis of team strategy in professional football - https://static.capabiliaserver.com/frontend/clients/barcanew/wp_prod/wp-content/uploads/2020/01/56ce723e-barca-conference-paper-laurie-shaw.pdf

NMF

• Understanding Trends in the NBA: How NNMF Works - https://squared2020.com/2018/10/04/understanding-trends-in-the-nba-how-nnmf-works/
• Finding Patterns in Statsbomb Data: Non-Negative Matrix Factorization Applications - https://znstrider.github.io/2018-11-14-SBData-Non-Negative-Matrix-Factorization/
• A Bayesian marked spatial point processes model for basketball shot chart - https://www.degruyter.com/document/doi/10.1515/jqas-2019-0106/html
• Decomposing and Smoothing Soccer Spatial Tendencies - https://tonyelhabr.rbind.io/posts/decomposition-smoothing-soccer/

Pitch control

• Spearman - Quantifying Pitch Control: https://www.researchgate.net/publication/334849056_Quantifying_Pitch_Control
• Space and Control in Soccer - https://www.frontiersin.org/articles/10.3389/fspor.2021.676179/full
• Contextual movement models based on normalizing flows

Pass models/Completion models

• Frame by frame completion probability of an NFL pass - https://arxiv.org/pdf/2109.08051v1.pdf
• Unsupervised methods for identifying pass coverage among defensive backs with NFL player tracking data - https://www.degruyter.com/document/doi/10.1515/jqas-2020-0017/html
• Expected passes: Determining the difficulty of a pass in football (soccer) using spatio-temporal data - https://link.springer.com/content/pdf/10.1007/s10618-021-00810-3.pdf
• Extracting NFL tracking data from images to evaluate quarterbacks and pass defenses - https://www.degruyter.com/document/doi/10.1515/jqas-2019-0052/html?lang=en
• Quarterback evaluation in the national football league using tracking data - https://link.springer.com/article/10.1007/s10182-021-00406-8
• Passing and Pressure Metrics in Ice Hockey - https://www.semanticscholar.org/paper/4ea87ef8e84a461722b7381323ad6a93fd530362
• un-xPass: Measuring Soccer Player’s Creativity - https://statsbomb.com/wp-content/uploads/2022/09/Pieter-Robberechts-et-al-un-xPass-Measuring-Soccer-Players-Creativity.pdf

Running/rushing models

• Parametric modeling and analysis of NFL run plays - https://journals.sagepub.com/doi/full/10.3233/JSA-220657
• Predicting NFL running back rushing yards using Hierarchical Bayesian Linear Regression - https://rpubs.com/JamesRAngus/BayesianStatsAss3

Trajectory prediction (‘ghosting’)

• Basketball GAN: Sportingly Acceptable Trajectory Prediction - https://drive.google.com/file/d/1eZV5mIutg5aoiKqD3jSLUwerueoNFfzH/view
• Where will they go? predicting fine-grained adversarial multi-agent motion using conditional variational autoencoders.
• Bhostgusters: Realtime interactive play sketching with synthesized nba defenses.
• Generating multi-agent trajectories using programmatic weak supervision.
• A Graph Attention Based Approach for Trajectory Prediction in Multi-agent Sports Games - https://arxiv.org/pdf/2012.10531v1.pdf
• baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents - https://arxiv.org/pdf/2104.11980v2.pdf
• Simulating Defensive Trajectories in American Football for Predicting League Average Defensive Movements - https://www.frontiersin.org/articles/10.3389/fspor.2021.669845/full
• Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations - https://arxiv.org/abs/2302.06569
  • https://github.com/GregSoton/PlayerImputation/tree/main
• Multiagent off‑screen behavior prediction in football - https://www.nature.com/articles/s41598-022-12547-0
• Modeling Conditional Dependencies in Multiagent Trajectories - https://ml3.leuphana.de/publications/modeling_conditional_dependencies_in_multiagent_trajectories.pdf
• NFL Ghosts: A framework for evaluating defender positioning with conditional density estimation - https://arxiv.org/pdf/2406.17220
• Masked autoencoder for multiagent trajectories - https://link.springer.com/article/10.1007/s10994-024-06647-3
• Interactive sequential generative models for team sports - https://link.springer.com/article/10.1007/s10994-024-06648-2

Racing sports

• An analysis of pacing profiles in sprint kayak racing using functional principal components and Hidden Markov Models - https://arxiv.org/pdf/2407.07120

Subjective ratings

• Capturing the “expert’s eye”: Towards a better understanding and implementation of subjective performance evaluations in team sports - https://sportrxiv.org/index.php/server/preprint/view/6/20

Coaching/scouting

• Full Jose Mourinho Scouting Report on FC Barcelona from 2005/2006 - https://twitter.com/_DaliborPlavsic/status/1106722625470889984?s=19
• Barriers to coach decision-making during Australian football matches and how it can be supported by artificial intelligence - https://journals.sagepub.com/doi/10.1177/17479541231206682

E-Sports

• Examining the game-specific practice behaviors of professional and semi-professional esports players: A 52-week longitudinal study - https://www.sciencedirect.com/science/article/pii/S0747563222002436

Sport science

Technology validation

• Development of a sports technology quality framework - https://doi.org/10.1080/02640414.2024.2308435
• Methods to assess validity of positioning systems in team sports: can we do better? - https://bmjopensem.bmj.com/content/bmjosem/9/1/e001496.full.pdf
• Challenges and considerations in determining the quality of electronic performance & tracking systems for team sports - https://www.frontiersin.org/articles/10.3389/fspor.2023.1266522/abstract

Fitness-Fatigue models

• Bayesian inference of the impulse-response model of athlete training and performance - https://www.tandfonline.com/doi/full/10.1080/24748668.2023.2268480
• The Use of Fitness-Fatigue Models for Sport Performance Modelling: Conceptual Issues and Contributions from Machine-Learning - https://link.springer.com/article/10.1186/s40798-022-00426-x?utm_source=getftr&utm_medium=getftr&utm_campaign=getftr_pilot
• A Deep Learning Approach for Fatigue Prediction in Sports Using GPS Data and Rate of Perceived Exertion - https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9881489

Training load and monitoring

• Understanding training load as exposure and dose - https://sportrxiv.org/index.php/server/preprint/view/186
• Training-Load Management Ambiguities and Weak Logic: Creating Potential Consequences in Sport Training and Performance - https://journals.humankinetics.com/view/journals/ijspp/aop/article-10.1123-ijspp.2024-0158/article-10.1123-ijspp.2024-0158.xml

Training plan generation and optimisation

• Carey: Optimizing preseason training loads in Australian Football - https://journals.humankinetics.com/view/journals/ijspp/13/2/article-p194.xml
• Connor: Adaptive Athlete Training Plan Generation: An intelligent control systems approach - https://www.sciencedirect.com/science/article/pii/S1440244021004679
• Connor: Optimising Team Sport Training Plans With Grammatical Evolution - https://ieeexplore.ieee.org/document/8790369
• Modelling the Training Practices of Recreational Marathon Runners to Make Personalised Training Recommendations
• Learning to Run Marathons: On the Applications of Machine Learning to Recreational Marathon Running
• Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners
• Classification system for AI-enabled consumer-grade wearable technologies aiming to automatize decision-making about individualization of exercise procedures - https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2024.1500563/abstract

Non-invasive monitoring

• The paradox of “invisible” monitoring: The less you do, the more you do! - https://hiitscience.com/the-paradox-of-invisible-monitoring-the-less-you-do-the-more-you-do/
• A Methodological Comparison of Protocols and Analytical Techniques to Assess Submaximal Fitness Tests Outcome Measures - <>

Injuries

• Untangling the NFL Injury Web - https://www.footballoutsiders.com/stat-analysis/2018/untangling-nfl-injury-web
• Blood sample profle helps to injury forecasting in elite soccer players - https://link.springer.com/content/pdf/10.1007/s11332-022-00932-1.pdf
• Characteristics of Complex Systems in Sports Injury Rehabilitation: Examples and Implications for Practice - https://sportsmedicine-open.springeropen.com/articles/10.1186/s40798-021-00405-8
• Just How Confident Can We Be in Predicting Sports Injuries? A Systematic Review of the Methodological Conduct and Performance of Existing Musculoskeletal Injury Prediction Models in Sport - https://link-springer-com.ez.library.latrobe.edu.au/article/10.1007/s40279-022-01698-9
• Modeling time loss from sports-related injuries using random effects models: an illustration using soccer-related injury observations - https://www.degruyter.com/view/journals/jqas/ahead-of-print/article-10.1515-jqas-2019-0030/article-10.1515-jqas-2019-0030.xml
• Training Load and Injury Part 1: The Devil Is in the Detail—Challenges to Applying the Current Research in the Training Load and Injury Field - https://www.jospt.org/doi/full/10.2519/jospt.2020.9675
• Training Load and Injury Part 2: Questionable Research Practices Hijack the Truth and Mislead Well-Intentioned Clinicians - https://www.jospt.org/doi/full/10.2519/jospt.2020.9211
• A new statistical approach to training load and injury risk: separating the acute from the chronic load - https://www.termedia.pl/A-new-statistical-approach-to-training-load-and-injury-risk-r-nseparating-the-acute-from-the-chronic-load,78,50672,0,1.html
• The Trade Secret Taboo: Open Science Methods are Required to Improve Prediction Models in Sports Medicine and Performance - https://link.springer.com/article/10.1007/s40279-023-01849-6
• Flexible modelling of time-varying exposures and recurrent events to analyse training load effects in team sports injuries - https://academic.oup.com/jrsssc/advance-article/doi/10.1093/jrsssc/qlae059/7909398
• Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis - https://bjsm.bmj.com/content/early/2024/11/29/bjsports-2024-108576.abstract
• Injuries in Baseball: How (Self-)Exciting? - https://sharpestats.com/mlb-injury-point-process/
• Towards a complex systems approach in sports injury research: simulating running-related injury development with agent-based modelling - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6579554/
• How do sports injury epidemiological outcomes vary depending on athletes’ response rates to a weekly online questionnaire? An analysis of 39-week follow-up from 391 athletics (track and field) athletes - https://pubmed.ncbi.nlm.nih.gov/38441349
• Predicting noncontact injuries of professional football players using machine learning - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0315481
• A multi-season machine learning approach to examine the training load and injury relationship in professional soccer - https://content.iospress.com/download/journal-of-sports-analytics/jsa240718?id=journal-of-sports-analytics%2Fjsa240718

Running and wearables

• Feasibility and usability of GPS data in exploring associations between training load and running-related knee injuries in recreational runners - https://bmcsportsscimedrehabil.biomedcentral.com/articles/10.1186/s13102-022-00472-8
• Association Between Temporal Spatial Parameters and Overuse Injury History in Runners: A Systematic Review and Meta-analysis - https://link.springer.com/article/10.1007/s40279-019-01207-5
• Athlete Monitoring in Professional Road Cycling Using Similarity Search on Time Series Data - https://link.springer.com/chapter/10.1007/978-3-031-27527-2_9
• The “impacts cause injury” hypothesis: Running in circles or making new strides? - https://www.sciencedirect.com/science/article/abs/pii/S0021929023002634
• Comparison of different measures to monitor week-to-week changes in training load in high school runners - https://journals.sagepub.com/doi/full/10.1177/1747954120970305
• Accelerometer-based prediction of running injury in National Collegiate Athletic Association track athletes - https://www.sciencedirect.com/science/article/pii/S0021929018302653
• A 2-Year Prospective Cohort Study of Overuse Running Injuries: The Runners and Injury Longitudinal Study (TRAILS) - https://journals.sagepub.com/doi/full/10.1177/0363546518773755
• Predicting vertical ground reaction force characteristics during running with machine learning - https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2024.1440033/full
• Towards a complex systems approach in sports injury research: simulating running-related injury development with agent-based modelling - https://bjsm.bmj.com/content/bjsports/53/9/560.full.pdf
• The biomechanics of running and running styles: a synthesis - https://www.tandfonline.com/doi/pdf/10.1080/14763141.2021.1873411
• Garmin-RUNSAFE
  • The Garmin-RUNSAFE Running Health Study on the aetiology of runningrelated injuries: rationale and design of an 18-month prospective cohort study including runners worldwide
  • Runners with a high body mass index and previous running-related problems is a high-risk population for sustaining anew running-related injury: A 18-month cohort study - https://onlinelibrary.wiley.com/doi/epdf/10.1002/ejsc.12206
  • A Paradigm Shift in Understanding Overuse Running-related Injuries: Findings from the Garmin-RUNSAFE Study Point to a Sudden not Gradual Onset
  • Running-Related Injuries Among More Than 7000 Runners in 87 Different Countries: The Garmin-RUNSAFE Running Health Study
  • Using Self-Reported Training Characteristics to Better Understand Who Is More Likely to Sustain Running-Related Injuries Than Others: The Garmin-RUNSAFE Running Health Study

Match demands

• Analysis of the worst-case scenarios in an elite football team: Towards a better understanding and application - https://www.tandfonline.com/doi/full/10.1080/02640414.2021.1902138
• Benchmarking the Physical Performance Qualities in Women’s Football: A Systematic Review and Meta-Analysis Across the Performance Scale - https://osf.io/preprints/osf/th72c and https://fifawomensdevelopmentprogramme.shinyapps.io/FIFA_Womens_Profiling/
• The influence of tactical and match context on player movement in football - https://www.tandfonline.com/doi/abs/10.1080/02640414.2022.2046938
• Critical speed models of high-resolution speed-duration profiles describe peak running demands in soccer - https://journals.sagepub.com/doi/pdf/10.1177/17479541241246951

Opinions and commentaries

• Innovation in sport performance: GPS shows us where we are, but we decide where we’re going - https://www.sportsmith.co/articles/innovation-in-sport-performance-gps-units-show-us-where-we-are-but-we-decide-where-were-going/

Datasets and competitions

• CRAN Sport Analytics Task View - https://cran.r-project.org/web/views/SportsAnalytics.html
• SportsDataVerse - https://sportsdataverse.org/
• Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset - https://arxiv.org/pdf/2011.00958v2.pdf
• PERSIST: A Multimodal Dataset for the Prediction of Perceived Exertion during Resistance Training - https://www.mdpi.com/2306-5729/8/1/9
• A large-scale multivariate soccer athlete health, performance, and position monitoring dataset - https://www.nature.com/articles/s41597-024-03386-x
• A public data set of spatio-temporal match events in soccer competitions - https://doi.org/10.1038/s41597-019-0247-7
• Tennis - https://github.com/skoval/deuce
• NRL rugby - https://github.com/fredgj/rugby_scraper
• Rugby - https://github.com/walsh06/python_rugby
• ncaahoopR - https://github.com/lbenz730/ncaahoopR
• statsbomb - soccer - https://github.com/statsbomb/open-data
• NFL - big data bowl - https://github.com/nfl-football-ops/Big-Data-Bowl
• Harvard sports analytics - http://harvardsportsanalysis.org/
• NBA player tracking - https://github.com/PatrickChodowski/NBAr
• Multi-sport - https://github.com/meysubb/Sports_Data_Reference
• https://github.com/meysubb/Sports_Data_Reference/blob/master/R/Data.md
• Multi-sport - https://github.com/octonion?tab=repositories
• NBL - https://jaseziv.github.io/nblR/
• WNBL - https://github.com/jacquietran/wnblr
• NBA - https://www.kaggle.com/datasets/wyattowalsh/basketball

• Swimming - https://cran.r-project.org/web/packages/SwimmeR/index.html

• https://twitter.com/DSamangy/status/1492206283214114817?t=by17xVuXOVQBr0-eacK7QQ&s=03

CV in Sport Open data

• Soccer video and player position dataset - https://doi.org/10.1145/2557642.2563677
• Sset: a dataset for shot segmentation, event detection, player tracking in soccer videos - https://doi.org/10.1007/s11042-020-09414-3
• Scaling up SoccerNet with multi-view spatial localization and re-identification - https://www.nature.com/articles/s41597-022-01469-1
• DeepSport Dataset: 300+ high-resolution professional basketball images with multiple annotations - https://www.kaggle.com/gabrielvanzandycke/deepsport-dataset
• Comprehensive Dataset of Broadcast Soccer Videos - https://ieeexplore.ieee.org/document/8397046 & http://media.hust.edu.cn/dataset.htm
• MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions - https://arxiv.org/pdf/2105.07404v2.pdf
• TeamTrack: An Algorithm and Benchmark Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos - https://github.com/AtomScott/TeamTrack
• SoccerNet-GSR: SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap
• WorldPose: A World Cup Dataset for Multi-Person Global 3D Human Pose Estimation - https://eth-ait.github.io/WorldPoseDataset/

Open data for science

• The Impact of Sharing Clinical Research Data: Insights from Sydney Health Partners and the NHMRC Clinical Trials Centre - https://ardc.edu.au/article/the-impact-of-sharing-clinical-research-data-insights/?utm_source=ARDC+Connect&utm_campaign=84276a2d68-EMAIL_CAMPAIGN_2023_09_15_COPY_01&utm_medium=email&utm_term=0_-86d5dc93a4-%5BLIST_EMAIL_ID%5D
• GUIDELINES FOR THE CREATION OF ANALYSIS READY DATA - https://arxiv.org/pdf/2403.08127

Competitions

• AO Data Slam 2023 - Predict the next serve - https://www.crowdanalytix.com/contests/ao-data-slam-2023—predict-the-next-serve
• NFL 1st and Future - Playing Surface Analytics - https://www.kaggle.com/competitions/nfl-playing-surface-analytics
• NFL 1st and Future - Impact Detection - https://www.kaggle.com/competitions/nfl-impact-detection
• 1st and Future - Player Contact Detection - https://www.kaggle.com/competitions/nfl-player-contact-detection/overview

Deep Learning

• Troubleshooting Deep Neural Networks - http://josh-tobin.com/assets/pdf/troubleshooting-deep-neural-networks-01-19.pdf
• An overview of gradient descent optimization algorithms - https://ruder.io/optimizing-gradient-descent/

• Andrej Karpathy - Neural Networks: Zero to Hero - https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ

  • https://github.com/karpathy/nn-zero-to-hero

Reinforcement learning

• https://www.youtube.com/playlist?list=PLEhdbSEZZbDaFWPX4gehhwB9vJZJ1DNm8
• Deep Reinforcement Learning: Pong from Pixels - http://karpathy.github.io/2016/05/31/rl/

Graph neural networks

• A Gentle Introduction to Graph Neural Networks - https://distill.pub/2021/gnn-intro/
• Understanding Convolutions on Graphs - https://distill.pub/2021/understanding-gnns/
• An attempt at demystifying graph deep learning - https://ericmjl.github.io/essays-on-data-science/machine-learning/graph-nets/

Computer vision

• EECS 4422 Computer Vision - https://www.eecs.yorku.ca/~kosta/Courses/EECS4422/

Computer vision in sport

• Computer vision in sport papers - https://github.com/avijit9/awesome-computer-vision-in-sports
• SportLogiQ research - https://www.sportlogiq.com/publications/
• SoccerNet 2023 Challenges Results - https://arxiv.org/pdf/2309.06006.pdf
• SoccerNet 2025 - https://www.soccer-net.org/challenges/2025
• https://github.com/roboflow/supervision

Player detection

• Accelerating the creation of instance segmentation training sets through bounding box annotation - https://arxiv.org/pdf/2205.11563.pdf
• Multimodal and multiview distillation for real-time player detection on a football field
• Semi-Supervised Training to Improve Player and Ball Detection in Soccer - https://arxiv.org/abs/2204.06859
  • https://github.com/rvandeghen/SST
• Homography based player identification in live sports - https://www.amazon.science/publications/homography-based-player-identification-in-live-sports
• Learning Football Body-Orientation as a Matter of Classification - https://arxiv.org/pdf/2106.00359
• DeepSportLab: a Unified Framework for Ball Detection, Player Instance Segmentation and Pose Estimation in Team Sports Scenes - https://arxiv.org/abs/2112.00627

Player/Team ID

• Pose Guided Gated Fusion for Person Re-identification https://openaccess.thecvf.com/content_WACV_2020/papers/Bhuiyan_Pose_Guided_Gated_Fusion_for_Person_Re-identification_WACV_2020_paper.pdf
• Contrastive Learning for Sports Video: Unsupervised Player Classification - https://arxiv.org/pdf/2104.10068v2.pdf
• Associative embedding for team discrimination

Player tracking

• Automated repair of fragmented tracks with 1D CNNs
• Comparison of a computer vision system against three-dimensional motion capture for tracking football movements in a stadium environment - https://link.springer.com/article/10.1007/s12283-021-00365-y
• SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos - https://openaccess.thecvf.com/content/CVPR2022W/CVSports/papers/Cioppa_SoccerNet-Tracking_Multiple_Object_Tracking_Dataset_and_Benchmark_in_Soccer_Videos_CVPRW_2022_paper.pdf
• SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes - https://arxiv.org/abs/2304.05170
  • https://github.com/MCG-NJU/SportsMOT
  • https://paperswithcode.com/dataset/sportsmot
• SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap - https://arxiv.org/abs/2404.11335
  • https://github.com/SoccerNet/sn-gamestate
• Visualizing Skiers’ Trajectories in Monocular Videos - https://openaccess.thecvf.com/content/CVPR2023W/CVSports/papers/Dunnhofer_Visualizing_Skiers_Trajectories_in_Monocular_Videos_CVPRW_2023_paper.pdf
• Individual Locating of Soccer Players from a Single Moving View - https://www.mdpi.com/1424-8220/23/18/7938
• Extraction of Positional Player Data From Broadcast Soccer Videos - https://openaccess.thecvf.com/content/WACV2022/papers/Theiner_Extraction_of_Positional_Player_Data_From_Broadcast_Soccer_Videos_WACV_2022_paper.pdf
• Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Tracking - https://dl.acm.org/doi/pdf/10.1145/3606038.3616172
• COMPUTER VISION IN NETBALL - https://www.researchgate.net/profile/Paul-Smith-95/publication/347304516_COMPUTER_VISION_IN_NETBALL/links/5fd98d5da6fdccdcb8cc9fdb/COMPUTER-VISION-IN-NETBALL.pdf

Ball tracking

• 3D Ball Localization From A Single Calibrated Image - https://openaccess.thecvf.com/content/CVPR2022W/CVSports/papers/Van_Zandycke_3D_Ball_Localization_From_a_Single_Calibrated_Image_CVPRW_2022_paper.pdf

Action detection/recognition

• Group Activity Detection from Trajectory and Video Data in Soccer https://arxiv.org/pdf/2004.10299.pdf
• Actor-Transformers for Group Activity Recognition https://arxiv.org/pdf/2003.12737.pdf
• Sport action mining: Dribbling recognition in soccer - https://link.springer.com/article/10.1007/s11042-021-11784-1
• Pose is all you need: the pose only group activity recognition system (POGARS) - https://link.springer.com/article/10.1007/s00138-022-01346-2
• Group activity recognition using unreliable tracked pose - https://link.springer.com/article/10.1007/s00521-024-10470-1
• Deep Learning Analysis of Group Activity Dynamics in Sports - Haritha Thesis
• A survey of video-based human action recognition in team sports - https://link.springer.com/article/10.1007/s10462-024-10934-9
• F 3 SET: TOWARDS ANALYZING FAST, FREQUENT, AND FINE-GRAINED EVENTS FROM VIDEOS - https://github.com/F3Set/F3Set
• Action Recognition in Australian Rules Football Through Deep Learning - https://link.springer.com/chapter/10.1007/978-3-031-08757-8_47
  • https://github.com/stephenkl/Research-project

Sport camera calibration

• SoccerNet Camera Calibration - https://www.soccer-net.org/tasks/camera-calibration
• Optimizing Through Learned Errors for Accurate Sports Field Registration https://openaccess.thecvf.com/content_WACV_2020/papers/Jiang_Optimizing_Through_Learned_Errors_for_Accurate_Sports_Field_Registration_WACV_2020_paper.pdf
• Fast Camera Calibration for the Analysis of Sport Sequences - https://www.dirk-farin.net/publications/data/Farin2005d_slides.pdf
• End-to-End Camera Calibration for Broadcast Videos
• Sports Field Recognition Using Deep Multi-task Learning - https://www.jstage.jst.go.jp/article/ipsjjip/29/0/29_328/_pdf
• Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning - https://arxiv.org/pdf/2101.05388.pdf
  • https://github.com/DonsetPG/narya
• BirdsPyView - https://github.com/rjtavares/BirdsPyView
• TVCalib: Camera Calibration for Sports Field Registration in Soccer - https://mm4spa.github.io/tvcalib/ and https://arxiv.org/pdf/2207.11709.pdf
• Self-Supervised Shape Alignment for Sports Field Registration - https://openaccess.thecvf.com/content/WACV2022/papers/Shi_Self-Supervised_Shape_Alignment_for_Sports_Field_Registration_WACV_2022_paper.pdf
• Individual Locating of Soccer Players from a Single Moving View - https://www.mdpi.com/1424-8220/23/18/7938
• Extraction of Positional Player Data From Broadcast Soccer Videos - https://openaccess.thecvf.com/content/WACV2022/papers/Theiner_Extraction_of_Positional_Player_Data_From_Broadcast_Soccer_Videos_WACV_2022_paper.pdf
• KaliCalib: A Framework for Basketball Court Registration - https://arxiv.org/pdf/2209.07795.pdf
• Sports Field Registration via Keypoints-aware Label Condition - https://cgv.cs.nthu.edu.tw/KpSFR_data/KpSFR_paper.pdf
  • https://ericsujw.github.io/KpSFR/
  • https://github.com/ericsujw/KpSFR/
• No Bells Just Whistles: Sports Field Registration by Leveraging Geometric Properties - https://openaccess.thecvf.com/content/CVPR2024W/CVsports/papers/Gutierrez-Perez_No_Bells_Just_Whistles_Sports_Field_Registration_by_Leveraging_Geometric_CVPRW_2024_paper.pdf
  • https://github.com/mguti97/No-Bells-Just-Whistles
• Creating Better Data: How To Map Homography - https://statsbomb.com/articles/football/creating-better-data-how-to-map-homography/
• Evaluating the Accuracy of a Generic Field Template for Camera Calibration in Soccer Broadcast Footage - https://link.springer.com/article/10.1007/s42979-024-03636-0?fromPaywallRec=false
• A review on camera calibration in soccer videos - https://link.springer.com/article/10.1007/s11042-023-16145-8?fromPaywallRec=false

Telestration

• Assessing the Efficacy of Video Telestration in Aiding Memory Recall Among Elite Professional Football Players - https://journals.iupui.edu/index.php/sij/article/view/26317/24440
• The use and perceived value of telestration tools in elite football - https://www.tandfonline.com/doi/abs/10.1080/24748668.2020.1753965?journalCode=rpan20

CV in biomech

• OpenCap - https://nmbl.stanford.edu/wp-content/uploads/OpenCap.pdf
  • https://www.opencap.ai/

Vision transformers

• AN IMAGE IS WORTH 16X16 WORDS (ViT) - https://openreview.net/pdf?id=YicbFdNTTy

Image feature matching/ Homography

• DFM: A Performance Baseline for Deep Feature Matching https://github.com/ufukefe/DFM
• Perceptual Loss for Robust Unsupervised Homography Estimation https://arxiv.org/pdf/2104.10011.pdf
• LoFTR: Detector-Free Local Feature Matching with Transformers https://zju3dv.github.io/loftr/
• Reprojecting the Perseverance landing footage onto satellite imagery - https://matthewearl.github.io/2021/03/06/mars2020-reproject/
• Smooth Globally Warp Locally: Video Stabilization using Homography Fields - https://cs.adelaide.edu.au/~tjchin/lib/exe/fetch.php?media=papers:fields_preprint.pdf

Multi-object tracking

• https://github.com/luanshiyinyang/awesome-multiple-object-tracking
• MAT: Motion-aware multi-object tracking - https://www.sciencedirect.com/science/article/pii/S0925231221019627
• Selection of object detections using overlap map predictions - https://link.springer.com/article/10.1007/s00521-022-07469-x
• Keypoint Promptable Re-Identification - https://arxiv.org/pdf/2407.18112

Sensors and Signal processing

Tutorial papers

• A method for processing inertial measurement unit data for para-Nordic sit-ski race analysis: a step-by-step guide - https://link.springer.com/article/10.1007/s12283-025-00496-6

Smartball examples

• EVALUATING A QBS ‘ARM’ FROM SMART BALL METRICS - https://www.sportable.com/evaluating-a-qbs-arm-smart-ball-metrics
• INTERNATIONAL MATCH KICKING SKILLS DATA SET= PERFORMANCE VALUE FOR TEAMS & PLAYERS - https://www.sportable.com/international-match-kicking-skills-data-set-performance-value-for-teams-players

GPS

• GPS - https://ciechanow.ski/gps/

Kalman filter

• How a Kalman filter works, in pictures - https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/

Sensor fusion

• Estimating Orientation Using Inertial Sensor Fusion and MPU-9250 - https://au.mathworks.com/help/fusion/ug/Estimating-Orientation-Using-Inertial-Sensor-Fusion-and-MPU-9250.html;jsessionid=ebb750d5ad1abe079f51ecc7acf7?s_eid=PSM_15028
• IMU and GPS Fusion for Inertial Navigation - https://au.mathworks.com/help/fusion/ug/imu-and-gps-fusion-for-inertial-navigation.html

Signal dimensionality reduction

• PCA of waveforms and functional PCA: A primer for biomechanics - https://www.sciencedirect.com/science/article/pii/S0021929020305303
  • https://github.com/johnwarmenhoven/PCA-FPCA
• https://www.st-andrews.ac.uk/~wjh/dataview/tutorials/principal%20component%20analysis.html
• https://www.psych.mcgill.ca/misc/fda/examples.html

Statistical parametric mapping

• Basic Principles Statistical Parametric Mapping for hypothesis testing with 1D curves - https://www.youtube.com/watch?v=4WoDuBkUF9U&ab_channel=JosVanrenterghem

Functional data analysis

• Over 30 years of using functional data analysis in human movement. What do we know, and is there more for sports biomechanics to learn? - https://doi.org/10.1080/14763141.2024.2398508
• Functional Data Analysis in Biomechanics - https://link.springer.com/book/10.1007/978-3-031-68862-1

Multi-modal time series

• Unlocking the power of time-series data with multimodal models - https://research.google/blog/unlocking-the-power-of-time-series-data-with-multimodal-models/

Statistics, data science, and modelling

• Statistics Notes in the British Medical Journal (Altman & Bland) - https://www-users.york.ac.uk/~mb55/pubs/pbstnote.htm?s=03

Modelling

• The Lost Art of Mathematical Modelling - https://arxiv.org/pdf/2301.08559.pdf

• https://mc-stan.org/docs/stan-users-guide/index.html

• Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning (Sebastian Raschka) - https://arxiv.org/pdf/1811.12808.pdf

• https://discourse.datamethods.org/

  • Harrell - Statistical Problems to Document and to Avoid - https://discourse.datamethods.org/t/author-checklist/3407
  • Reference Collection to push back against “Common Statistical Myths” - https://discourse.datamethods.org/t/reference-collection-to-push-back-against-common-statistical-myths/1787
• Publish your raw data and your speculations, then let other people do the analysis: track and field edition - https://statmodeling.stat.columbia.edu/2017/08/21/publish-raw-data-speculations-let-people-analysis-track-field-edition/

• Packages:

  • https://github.com/adefazio/classifierplots

Reporting and interpreting

• Marginal effects / slopes, contrasts, means and predictions - https://larmarange.github.io/broom.helpers/articles/marginal_tidiers.html#marginal-predictions
• Marginalia: A guide to figuring out what the heck marginal effects, marginal slopes, average marginal effects, marginal effects at the mean, and all these other marginal things are - https://www.andrewheiss.com/blog/2022/05/20/marginalia/

Uncertainty estimation and visualisation

• An illusion of predictability in scientific results: Even experts confuse inferential uncertainty and outcome variability - https://www.pnas.org/doi/abs/10.1073/pnas.2302491120?s=03
• Conformal prediction intervals in R - https://marginaleffects.com/vignettes/conformal.html
• Meta-analysis prediction intervals are under reported in sport and exercise medicine - https://pubmed.ncbi.nlm.nih.gov/38501202/
• NGBoost: Natural Gradient Boosting for Probabilistic Prediction - https://stanfordmlgroup.github.io/projects/ngboost/

Probability distributions & Data generating processes

• Probabilistic Building Blocks - https://betanalpha.github.io/assets/case_studies/probability_densities.html
• (What’s the Probabilistic Story) Modeling Glory? - https://betanalpha.github.io/assets/case_studies/generative_modeling.html

‘Significance’ and testing

• Abandon Statistical Significance - http://www.stat.columbia.edu/~gelman/research/unpublished/amstat.draft2.pdf
• Scientists rise up against statistical significance - https://www.nature.com/articles/d41586-019-00857-9

Meaningful change & effect sizes

• The Minimal Clinically Important Difference Changes Greatly Based on the Different Calculation Methods - https://journals.sagepub.com/doi/full/10.1177/03635465231152484?s=03
• Caldwell, A., & Vigotsky, A. D. (2020). A case against default effect sizes in sport and exercise science. PeerJ, 8, e10314. - https://peerj.com/articles/10314/#p-1
• Standardized or simple effect size: What should be reported? - https://bpspsychub.onlinelibrary.wiley.com/doi/pdf/10.1348/000712608X377117
• Why I don’t like standardised effect sizes - https://janhove.github.io/reporting/2015/02/05/standardised-vs-unstandardised-es
• Interpreting magnitude of change in strength and conditioning: Effect size selection, threshold values and Bayesian updating - https://www.tandfonline.com/doi/full/10.1080/02640414.2022.2128548?s=03
• Minimal important clinical difference values are not uniformly valid in the active duty military population recovering from shoulder surgery - https://www.sciencedirect.com/science/article/abs/pii/S1058274624002465?dgcid=coauthor

MBI

• Systematic review of the use of “magnitude-based inference” in sports science and medicine - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0235318#pone.0235318.ref018
• Gelman on MBI - https://statmodeling.stat.columbia.edu/2018/11/15/the-state-of-the-art/
• Can we trust “Magnitude-based inference”? - https://www.tandfonline.com/doi/full/10.1080/02640414.2018.1516004
• How to Interpret Changes in an Athletic Performance Test, Will G Hopkins - http://www.sportsci.org/jour/04/wghtests.htm
• Design and analysis of research on sport performance enhancement - https://journals.lww.com/acsm-msse/Fulltext/1999/03000/Design_and_analysis_of_research_on_sport.18.aspx
• Progressive Statistics for Studies in Sports Medicine and Exercise Science - https://journals.lww.com/acsm-msse/Fulltext/2009/01000/Progressive_Statistics_for_Studies_in_Sports.2.aspx
• Making Meaningful Inferences About Magnitudes - https://research.tees.ac.uk/ws/files/5918054/58195.pdf

Statistics in sport science

• Wish List for Improving the Quality of Statistics in Sport Science - https://journals.humankinetics.com/view/journals/ijspp/17/5/article-p673.xml?s=03
• Current Research and Statistical Practices in Sport Science and a Need for Change - https://www.mdpi.com/2075-4663/5/4/87
• Expanding the Statistical Toolkit of Sports Scientists - https://thesis.timnewans.com/
• Applications of regularized regression models in sports biomechanics research - https://doi.org/10.1080/14763141.2022.2151932
• With Great Power Comes Great Responsibility: Common Errors in Meta-Analyses and Meta-Regressions in Strength & Conditioning Research - https://link.springer.com/article/10.1007/s40279-022-01766-0?s=03#Bib1
• A systematic review of sport-related packages within the R CRAN repository - https://journals.sagepub.com/doi/10.1177/17479541221136238?s=03
• Replication concerns in sports and exercise science: a narrative review of selected methodological issues in the field - https://royalsocietypublishing.org/doi/10.1098/rsos.220946?s=03

Power and sample size

• The tyranny of power: is there a better way to calculate sample size? - https://www.bmj.com/content/339/bmj.b3985
• Sample Size Planning for Statistical Power and Accuracy in Parameter Estimation
  • This review examines recent advances in sample size planning, not only from the perspective of an individual researcher, but also with regard to the goal of developing cumulative knowledge. Psychologists have traditionally thought of sample size planning in terms of power analysis. Although we review recent advances in power analysis, our main focus is the desirability of achieving accurate parameter estimates, either instead of or in addition to obtaining sufficient power. Accuracy in parameter estimation (AIPE) has taken on increasing importance in light of recent emphasis on effect size estimation and formation of confidence intervals. The review provides an overview of the logic behind sample size planning for AIPE and summarizes recent advances in implementing this approach in designs commonly used in psychological research.
• Power, precision, and sample size estimation in sport and exercise science research - https://www.tandfonline.com/doi/pdf/10.1080/02640414.2020.1776002
• TWO SAMPLE-SIZE PRACTICES THAT I DON’T RECOMMEND - http://homepage.divms.uiowa.edu/~rlenth/Power/2badHabits.pdf
• Exploratory analyses: How to meaningfully interpret and report them - https://onlinelibrary.wiley.com/doi/full/10.1002/pmrj.12980

Sample size calculations for clinical prediction models

• R package: https://cran.r-project.org/web/packages/pmsampsize/index.html
• Continuous variables: https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7993

• Binary/Time to event: https://onlinelibrary.wiley.com/doi/full/10.1002/sim.7992

• Sample size and optimal designs for reliability studies CALCULATOR - https://wnarifin.github.io/ssc/ssicc.html

Decision making

• Decision making in health and medicine - https://www.researchgate.net/profile/Paul-Glasziou/publication/37621420_Decision_Making_in_Health_and_Medicine_Integrating_Evidence_and_Values/links/00b49518af3c2b7add000000/Decision-Making-in-Health-and-Medicine-Integrating-Evidence-and-Values.pdf

Courses

• Statistical Rethinking (2022 Edition) - https://github.com/rmcelreath/stat_rethinking_2022
  • Statistical Rethinking with brms, ggplot2, and the tidyverse - https://bookdown.org/ajkurz/Statistical_Rethinking_recoded/
• Harrell - Biostatistics - http://hbiostat.org/bbr/?s=03
• Course material for Stat 451: Introduction to Machine Learning and Statistical Pattern Classification - https://github.com/rasbt/stat451-machine-learning-fs21

Causal inference

• An Introduction to Causal Inference - https://osf.io/preprints/psyarxiv/b3fkw
• Demystifying causal inference estimands: ATE, ATT, and ATU - https://www.andrewheiss.com/blog/2024/03/21/demystifying-ate-att-atu/
• Reducing bias through directed acyclic graphs - https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-8-70
• Examples of solid causal inferences from purely observational data - https://discourse.datamethods.org/t/examples-of-solid-causal-inferences-from-purely-observational-data/1686

• The Effect: An Introduction to Research Design and Causality

  • https://theeffectbook.net/index.html

• Jennifer Hill - Causal inferences that capitalizes on machine learning and statistics: opportunities and challenges

  • http://www.birs.ca/events/2018/5-day-workshops/18w5054/videos/watch/201801180952-Hill.html
  • https://cds.nyu.edu/wp-content/uploads/2014/04/causal-and-data-science-and-BART.pdf
• Causal design patterns for data analysts - https://emilyriederer.netlify.app/post/causal-design-patterns/?s=03
• Preventing churn like a bandit - https://medium.com/bigdatarepublic/preventing-churn-like-a-bandit-49b7c51b4929
• For effective treatment of churn, don’t predict churn - https://medium.com/bigdatarepublic/for-effective-treatment-of-churn-dont-predict-churn-58328967ec4f

Matching

• https://stats.stackexchange.com/questions/544926/why-do-we-do-matching-for-causal-inference-vs-regressing-on-confounders
• https://stats.stackexchange.com/questions/481110/propensity-score-matching-what-is-the-problem/481620#481620

Statistical tests vs. Linear models

Common statistical tests are linear models (or: how to teach stats) - https://lindeloev.github.io/tests-as-linear/#1_the_simplicity_underlying_common_tests

Spline models and GAMs

• Piecewise Regression and Splines - https://bookdown.org/tpinto_home/Beyond-Linearity/piecewise-regression-and-splines.html

• Smoothing splines - https://bookdown.org/tpinto_home/Beyond-Linearity/smoothing-splines.html
• https://m-clark.github.io/documents.html
• https://www.fromthebottomoftheheap.net/2018/04/21/fitting-gams-with-brms/
• Hierarchical generalized additive models in ecology: an introduction with mgcv - https://peerj.com/articles/6876/

Interpretable machine learning

https://christophm.github.io/interpretable-ml-book/

Bayesian

• Bayesian workflow, Gelman - https://arxiv.org/pdf/2011.01808.pdf
• Visualization in Bayesian workflow - https://arxiv.org/pdf/1709.01449
• Bayesian statistics and modelling - https://www.nature.com/articles/s43586-020-00001-2
• brms: An R Package for Bayesian Multilevel Models using Stan - https://cran.r-project.org/web/packages/brms/vignettes/brms_overview.pdf
• Advanced Bayesian Multilevel Modeling with the R Package brms - https://cran.r-project.org/web/packages/brms/vignettes/brms_multilevel.pdf
• Practical Bayes Part I - https://m-clark.github.io/posts/2021-02-28-practical-bayes-part-i/
• A visual introduction to Gaussian Belief Propagation - https://gaussianbp.github.io/
• BOOK: Bayesian Modeling and Computation in Python - https://bayesiancomputationbook.com/welcome.html
• Bayesian Analytical Methods in Cardiovascular Clinical Trials: Why, When, and How - https://onlinecjc.ca/article/S0828-282X%2824%2901130-9/fulltext

Bayesian and sport

• Dwarfs on the Shoulders of Giants: Bayesian Analysis With Informative Priors in Elite Sports Research and Decision Making - https://pubmed.ncbi.nlm.nih.gov/35368412/
• Bayesian statistics meets sports: a comprehensive review - https://www.degruyter.com/document/doi/10.1515/jqas-2018-0106/html?lang=en
• Bayesian Estimation of Small Effects in Exercise and Sports Science - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147311
• Bayesian Approaches to Quantifying Uncertainty in Sport and Exercise Measurements - https://sportrxiv.org/index.php/server/preprint/view/261
• Understanding empirical Bayes estimation (using baseball statistics) - http://varianceexplained.org/r/empirical_bayes_baseball/
  • Empirical Bayes - https://m-clark.github.io/posts/2019-06-21-empirical-bayes/

Mixed effect models

• Notes on mixed models - https://docs.google.com/document/d/1pxABPqUGUR1tCQvS-7KNt0mWK_CeoP4fXBhD7dhW0Wk/edit?usp=sharing

• Fitting linear mixed models in R - http://staff.pubhealth.ku.dk/~jufo/courses/rm2018/nlmePackage.pdf
• Fitting Linear Mixed-Effects Models Using lme4 - https://cran.r-project.org/web/packages/lme4/vignettes/lmer.pdf
• Getting started with the glmmTMB package - https://cran.r-project.org/web/packages/glmmTMB/vignettes/glmmTMB.pdf
• Plotting partial pooling in mixed-effects models https://www.tjmahr.com/plotting-partial-pooling-in-mixed-effects-models/?s=03
• https://web.stanford.edu/class/psych252/section/Mixed_models_tutorial.html
• INTRODUCTION TO LINEAR MIXED MODELS - https://ourcodingclub.github.io/2017/03/15/mixed-models.html
• M-Clark: Mixed Models - https://m-clark.github.io/mixed-models-with-R/random_intercepts.html
• https://www.stat.cmu.edu/~hseltman/309/Book/chapter15.pdf
• A Beginner’s Introduction to Mixed Effects Models - https://meghan.rbind.io/blog/2022-06-28-a-beginner-s-guide-to-mixed-effects-models/
• A brief introduction to mixed effects modelling and multi-model inference in ecology - https://peerj.com/articles/4794/
• A very basic tutorial for performing linear mixed effects analyses - https://jontalle.web.engr.illinois.edu/MISC/lme4/bw_LME_tutorial.pdf
• Random effects and penalized splines are the same thing - https://www.tjmahr.com/random-effects-penalized-splines-same-thing/
• Elements of Applied Biostatistics: Chapter 16 Models with random factors – linear mixed models - https://www.middleprofessor.com/files/applied-biostatistics_bookdown/_book/lmm.html
• Hierarchical Modeling - https://betanalpha.github.io/assets/case_studies/hierarchical_modeling.html

Covariance structures & temporal models

• Temporal analysis of variation in random effects - https://stats.stackexchange.com/questions/19911/temporal-analysis-of-variation-in-random-effects
• Dealing with temporal autocorrelation - https://www.flutterbys.com.au/stats/tut/tut8.3b.html
• Guidelines for Selecting the Covariance Structure in Mixed Model Analysis - https://support.sas.com/resources/papers/proceedings/proceedings/sugi30/198-30.pdf
• Modelling covariance structure in the analysis of repeated measures data - https://faculty.washington.edu/heagerty/Courses/VA-longitudinal/private/Littell-StatMed2000.pdf
• Covariance structures with glmmTMB - https://cran.r-project.org/web/packages/glmmTMB/vignettes/covstruct.html
• fitting mixed models with (temporal) correlations in R - https://bbolker.github.io/mixedmodels-misc/notes/corr_braindump.html
• R Code for Repeated Measures - https://dnett.github.io/S510/24RepeatedMeasuresR.pdf

Sport specific

• The Utility of Mixed Models in Sport Science: A Call for Further Adoption in Longitudinal Data Sets - https://journals.humankinetics.com/view/journals/ijspp/aop/article-10.1123-ijspp.2021-0496/article-10.1123-ijspp.2021-0496.xml
• Multilevel data collection and analysis for weight training (with R code) - https://statmodeling.stat.columbia.edu/2018/09/22/38708/

Time series

• MultiVariate (Dynamic) Generalized Addivite Models - https://nicholasjclark.github.io/mvgam/index.html

Ordinal models

• Ordinal Regression - https://betanalpha.github.io/assets/case_studies/ordinal_regression.html
• Mixed effect ordinal models - https://drizopoulos.github.io/GLMMadaptive/articles/Ordinal_Mixed_Models.html

Zero inflated data

• A guide to modeling outcomes that have lots of zeros with Bayesian hurdle lognormal and hurdle Gaussian regression models - https://www.andrewheiss.com/blog/2022/05/09/hurdle-lognormal-gaussian-brms/#3-hurdle-lognormal-model
• Hurdle Models - https://m-clark.github.io/models-by-example/hurdle.html
• Getting Started with Hurdle Models - https://data.library.virginia.edu/getting-started-with-hurdle-models/
• Statistical Models for the Analysis of Zero-Inflated Pain Intensity Numeric Rating Scale Data - https://pubmed.ncbi.nlm.nih.gov/27919777/
• A comparison of zero-inflated and hurdle models for modeling zero-inflated count data - https://jsdajournal.springeropen.com/articles/10.1186/s40488-021-00121-4

Stein’s Pardox

• Baseball paper - Efron and Morris - http://statweb.stanford.edu/~ckirby/brad/other/Article1977.pdf
• https://solomonkurz.netlify.com/post/stein-s-paradox-and-what-partial-pooling-can-do-for-you/
• The weirdest paradox in statistics (and machine learning) - https://www.youtube.com/watch?v=cUqoHQDinCM&ab_channel=Mathemaniac
  • James-Stein estimator + bias-variance tradeoff

Gaussian processes

• Gaussian Processes for Machine Learning - http://www.gaussianprocess.org/gpml/chapters/RW.pdf
• Robust Gaussian Process Modeling - https://betanalpha.github.io/assets/case_studies/gaussian_processes.html

Clinical prediction models

Developing and reporting models

• https://www.ncbi.nlm.nih.gov/pubmed/25560730
• https://www.ncbi.nlm.nih.gov/pubmed/22397945
• https://www.ncbi.nlm.nih.gov/pubmed/22397946
• https://www.ncbi.nlm.nih.gov/pubmed/29741602
• https://www.ncbi.nlm.nih.gov/pubmed/27362778
• https://www.ncbi.nlm.nih.gov/pubmed/23393430
• https://www.ncbi.nlm.nih.gov/pubmed/20010215
• https://www.ncbi.nlm.nih.gov/pubmed/24898551

Evaluation

• In Machine Learning Predictions for Health Care the Confusion Matrix is a Matrix of Confusion - https://www.fharrell.com/post/mlconfusion/

Data viz

• Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm - https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128&ref=https://githubhelp.com
• Principles of Effective Data Visualization - https://www.sciencedirect.com/science/article/pii/S2666389920301896#fig1
• Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing - https://dl.acm.org/doi/pdf/10.1145/3025453.3025912
• Friends Don’t Let Friends Make Bad Graphs - https://github.com/cxli233/FriendsDontLetFriends

Dimensionality reduction & Variable selection

• Between groups comparison of PCA - https://www.jstor.org/stable/2286995?seq=5
• intRinsic: An R Package for Model-Based Estimation of the Intrinsic Dimension of a Dataset - https://www.jstatsoft.org/article/view/v106i09
• embed R package - https://embed.tidymodels.org/
• Ten quick tips for effective dimensionality reduction - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006907
• Towards a comprehensive evaluation of dimension reduction methods for transcriptomic data visualization - https://www.nature.com/articles/s42003-022-03628-x
• https://yliapis.github.io/Non-Negative-Matrix-Factorization/
• Learning the parts of objects by non-negative matrix factorization - https://www.nature.com/articles/44565
• https://goldinlocks.github.io/Non-negative-matrix-factorization/
• https://blog.acolyer.org/2019/02/18/the-why-and-how-of-nonnegative-matrix-factorization/

• Exactly Uncorrelated Sparse Principal Component Analysis - https://www.tandfonline.com/doi/abs/10.1080/10618600.2023.2232843?af=R&journalCode=ucgs20

• Is there any good reason to use PCA instead of EFA? Also, can PCA be a substitute for factor analysis? - https://stats.stackexchange.com/questions/123063/is-there-any-good-reason-to-use-pca-instead-of-efa-also-can-pca-be-a-substitut

• mixOmics: An R package for ‘omics feature selection and multiple data integration - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005752
• PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data - https://bioconductor.org/packages/release/bioc/html/ropls.html
• ALASCA: An R package for longitudinal and cross-sectional analysis of multivariate data by ASCA-based methods - https://www.frontiersin.org/articles/10.3389/fmolb.2022.962431/full

Clustering

• Novel diabetes subgroups (Maarten van Smeden, Frank E Harrell Jr, Darren L Dahly) - https://www.thelancet.com/journals/landia/article/PIIS2213-8587(18)30124-4/fulltext

Synthetic data generation

• https://cran.r-project.org/web/packages/synthpop/index.html
• https://pypi.org/project/synthcity/

• https://docs.sdv.dev/sdv/

• Synthetic Data as a Strategy to Resolve Data Privacy and Confidentiality Concerns in the Sport Sciences: Practical Examples and an R Shiny Application - https://journals.humankinetics.com/view/journals/ijspp/18/10/article-p1213.xml
• Synthetic data for sharing and exploration in high performance sport - https://sportrxiv.org/index.php/server/preprint/view/394
• A comprehensive review on GANs for time-series signals - https://link.springer.com/article/10.1007/s00521-022-06888-0

Conferences and presentations

Sloan Sports Analytics Research Papers

• https://www.sloansportsconference.com/research-paper-competition#Research-Paper-Finalists

KDD-Sports Analytics

http://large-scale-sports-analytics.org

Euro-KDD Sports analytics

https://dtai.cs.kuleuven.be/events/MLSA19/links.php

CVPR-sports

http://www.vap.aau.dk/cvsports/

Videos

https://www.youtube.com/watch?v=WjFdD7PDGw0&t=9s&index=2&list=WL Imitation Learning Tutorial ICML 2018 Tutorial session at the International Conference on Machine Learning (ICML 2018) - Yisong Yue (Caltech) & Hoang M. Le (Caltech). This is a high level talk about the machine learning techniques that people are using to train AI sports players like the ‘Ghosting’ video we watched in class.

https://www.youtube.com/watch?v=VkhPT2cPGLA&index=4&list=PLRPywWPWMCkoTF6yQQsI5Mes95ystQbXU&t=2248s Lecture: Machine Learning in Sports by Sam Robertson Good overview lecture on machine learning applications in sports.

https://www.youtube.com/watch?v=YBY9viGTdU0&index=2&list=PLRPywWPWMCkoTF6yQQsI5Mes95ystQbXU&t=388s 2015 NESSIS - Talk by Sam Robertson (Western Bulldogs) “A method to assess the influence of individual player performance distribution on match outcome in team sports” presented by Sam Robertson at the 2015 New England Symposium on Statistics in Sports, held on Sept 26, 2015, at the Harvard University

https://www.youtube.com/watch?v=O0rKs6P0rnY&index=5&list=PLRPywWPWMCkoTF6yQQsI5Mes95ystQbXU&t=62s Statistical Models for Sport in R – Stephanie Kovalchik (Tennis Australia) A hand on tutorial and walkthrough on doing sports analytics in R.

https://www.youtube.com/watch?v=djD-yL3vWNQ 2017 NESSIS - Talk by Ronald Yurko “NFLWAR: A reproducible method for offensive player evaluation in football” presented by Ronald Yurko at the 2017 New England Symposium on Statistics in Sports, held on Sept 23, 2017, at the Harvard University Science Center.

https://www.youtube.com/watch?v=RN2FLKoKC50 2017 NESSIS - Talk by Nathan Sandholtz “Replaying the NBA: Using Markov Decision Processes to test decision-making from the 2015-2016 regular season” presented by Nathan Sandholtz at the 2017 New England Symposium on Statistics in Sports, held on Sept 23, 2017, at the Harvard

https://www.youtube.com/user/42analytics/videos Sloan sports analytics conference presentations Library of many past sports analytics presentations.

https://www.anziam.org.au/MathSport+Proceedings MathSport Proceedings ANZIAM Mathsport has placed conference proceedings online to make the papers available to researchers everywhere.

Books

Applied Predictive Modeling - by Max Kuhn and Kjell Johnson

http://appliedpredictivemodeling.com/

This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

An Introduction to Statistical Learning with Applications in R - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

https://www-bcf.usc.edu/~gareth/ISL/

This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

Computer Age Statistical Inference

https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. ‘Big data’, ‘data science’, and ‘machine learning’ have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

Programming

R

• R Programming - https://www.coursera.org/learn/r-programming
• Statistical Inference via Data Science: A ModernDive into R and the Tidyverse - https://moderndive.com/
• https://github.com/uc-r/Intro-R

Python

• How to create a Python package in 2022 - https://mathspp.com/blog/how-to-create-a-python-package-in-2022