Resources

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
Editorial for Sports Performance section of Journal of Sports Sciences: Moving match analysis forward – Guidelines and perspectives - https://www.tandfonline.com/doi/full/10.1080/02640414.2025.2535850#d1e115
Lessons from Sports Statistics - https://www.tandfonline.com/doi/abs/10.1080/00031305.1997.10474403?s=03
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
Soccermatics - https://soccermatics.readthedocs.io/en/latest/index.html#
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
Optimization of tennis match decision planning based on reinforcement learning model - https://journals.sagepub.com/doi/abs/10.1177/14727978251348624
DRL-driven padel players: Simulating padel matches through deep reinforcement learning in real and hypothetical scenarios - https://www.tandfonline.com/doi/pdf/10.1080/02640414.2025.2518693

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
The Effect of Basketball Analytics Investment on National Basketball Association (NBA) Team Performance

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
An investigation of data-driven player positional roles within the Australian Football League Women’s competition using technical skill match-play data- https://journals.sagepub.com/doi/pdf/10.1177/17479541231203895
Is Stephen Curry really a guard? New perspective on players typology using functional data analysis - https://arxiv.org/abs/2504.21761?s=03
Characterizing player’s playing styles based on player vectors for each playing position in the Chinese Football Super League - https://doi.org/10.1080/02640414.2022.2096771
A Bayesian marked spatial point processes model for basketball shot chart - https://www.degruyter.com/document/doi/10.1515/jqas-2019-0106/html
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball - https://arxiv.org/pdf/1401.0942.pdf
Bayesian Group Learning for Shot Selection of Professional Basketball Players - https://onlinelibrary.wiley.com/doi/full/10.1002/sta4.324

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
BLUE: A Bayesian Approach to College Football Ratings - https://lauerjames.github.io/BLUE/?s=03
How to extend Elo: a Bayesian perspective - https://www.degruyterbrill.com/document/doi/10.1515%2Fjqas-2020-0066/pdf?stream=true
Extension of the Elo rating system to margin of victory - https://www.sciencedirect.com/science/article/pii/S0169207020300157?fr=RR-2&ref=pdf_download&rr=96a20a95ba3729a4

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
Offensive Personnel vs. Men in the Box: A Football Causality Tutorial - https://web.archive.org/web/20230327192059/https://www.footballoutsiders.com/stat-analysis/2019/offensive-personnel-men-box
The use of directed acyclic graphs (DAGs) in physical activity and nutrition research - https://sportrxiv.org/index.php/server/preprint/view/556/version/716

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
Stop Guessing: Optimizing Goalkeeper Policies for Soccer Penalty Kicks - https://arxiv.org/pdf/2505.24629

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/
Introducing Serve and Return Skill Ratings - https://seriousstats.substack.com/p/introducing-serve-and-return-skill
On the Importance of Skill Adjustment - https://seriousstats.substack.com/p/on-the-importance-of-skill-adjustment
Down, set, hut! Explaining variability in snap timing on plays with motion - https://www.kaggle.com/code/tindata/down-set-hut/notebook
Swinging, Fast and Slow: Interpreting variation in baseball swing tracking metrics - https://arxiv.org/pdf/2507.01238
Measuring skill via player dynamics in football dribbling - https://www.nature.com/articles/s41598-023-45914-6?fromPaywallRec=false
Monitoring meaningful rugby ratings - https://www-tandfonline-com.ez.library.latrobe.edu.au/doi/pdf/10.1080/0264041031000102006?needAccess=true&

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
Revisiting Expected Possession Value in Football: Introducing a Benchmark, U-Net Architecture, and Reward and Risk for Passes - https://arxiv.org/abs/2502.02565
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
Joris Bekkers - Cutting Edge Football Analytics in Python PyData London 25 - https://www.youtube.com/watch?v=PUXU3SokbW0&ab_channel=PyData
- https://github.com/UnravelSports/unravelsports
- A Graph Neural Network deep-dive into successful counterattacks - https://arxiv.org/pdf/2411.17450

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
Measuring player pace of play in elite female association football - https://journals.sagepub.com/doi/abs/10.1177/17479541251334022
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
The use of player tracking data to analyze defensive play in professional soccer - A scoping review - https://journals.sagepub.com/doi/full/10.1177/17479541221075734
HMMotion: Using tracking data to predict coverage - https://www.kaggle.com/code/rouvenmichels/hmmotion-using-tracking-data-to-predict-coverage
Fractional tackles: leveraging player tracking data for within-play tackling evaluation in American football - https://www.nature.com/articles/s41598-025-85993-1
Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0263051

Defensive strategy

Optimizing Baseball Fielder Positioning with Consideration for Adaptable Hitters - https://cdn.prod.website-files.com/5f1af76ed86d6771ad48324b/65bfe4996c2c54376b78d238_193963%20-%20Optimizing%20Baseball%20Fielder%20Positioning%20with%20Consideration%20for%20Adaptable%20Hitters.pdf
- https://www.youtube.com/watch?v=1aoVR1yXShs
The Sacred Nine Spots - https://arnavj.substack.com/p/the-sacred-nine-spots

Ball tracking data

Guessing Spin - https://flightedlegggie.wordpress.com/2025/04/30/guesswork-vs-spin-bowling/?s=03
Introducing xCTRL: A Probabilistic Approach to Pitch Location Accuracy - https://wsb.wharton.upenn.edu/introducing-xctrl-a-probabilistic-approach-to-pitch-location-accuracy/

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
Investigating the influence of ecological dynamics on player density metrics in junior Australian Football - https://www.tandfonline.com/doi/full/10.1080/02640414.2025.2521209?src=exp-la#abstract
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
TranSPORTmer: A Holistic Approach to Trajectory Understanding in Multi-agent Sports - https://link.springer.com/chapter/10.1007/978-981-96-0901-7_20?fromPaywallRec=true
2025 big data bowl (pre-snap) - https://storage.googleapis.com/kaggle-forum-message-attachments/3110100/21745/BDB%20announcement%202025%20winners.pdf

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
Spatial roles in hockey special teams - https://www.degruyterbrill.com/document/doi/10.1515/jqas-2023-0019/html?lang=en

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
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball - https://arxiv.org/pdf/1401.0942.pdf
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
Event2Tracking: Reconstructing Multi-Agent Soccer Trajectories Using Long-Term Multimodal Context - https://ojs.aaai.org/index.php/AAAI/article/view/33289
PySport - Patrick Lucey - Generating complete tracking data using event data and remote video - https://www.youtube.com/watch?v=s1Erc6FZoS8&ab_channel=PySport

Game state with tracking data

Self-Supervised Representations for Tracking Data - https://karun.in/blog/ssr-tracking-data.html

Applications of pose data

What Makes a Dribble Successful? Insights From 3D Pose Tracking Data - https://arxiv.org/pdf/2506.22503

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
Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data - https://www.degruyterbrill.com/document/doi/10.1515/jqas-2020-0051/html
Predicting fatigue using countermovement jump force-time signatures: PCA can distinguish neuromuscular versus metabolic fatigue - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0219295

Non-invasive monitoring

Invisible Monitoring for Athlete Health and Performance: A Call for a Better Conceptualization and Practical Recommendations - https://journals.humankinetics.com/view/journals/ijspp/20/6/article-p880.xml
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 - <>

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

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
Re-establishing confidence in Confidence Intervals - https://sportrxiv.org/index.php/server/preprint/view/546
Sports Injury is Self-exciting: Modelling AFL Injuries Using Hawkes Processes - https://link.springer.com/article/10.1007/s42979-025-03939-w
Development and internal validation of machine learning prognostic models of sports injuries using self-reported data in athletics (track and field): The influence of quantity and quality of features - https://doi.org/10.1080/02640414.2025.2517971
Advanced feature engineering in Acute:Chronic Workload Ratio (ACWR) calculation for injury forecasting in elite soccer - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0327960

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
An empirical study of race times in recreational endurance runners - https://bmcsportsscimedrehabil.biomedcentral.com/articles/10.1186/s13102-016-0052-y
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
- How much running is too much? Identifying high-risk running sessions in a 5200-person cohort study - https://bjsm.bmj.com/content/early/2025/07/07/bjsports-2024-109380

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
An integrated dataset of spatiotemporal and event data in elite soccer - https://www.nature.com/articles/s41597-025-04505-y?s=03
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

Data formatting

Common Data Format (CDF): A Standardized Format for Match-Data in Football (Soccer) - https://arxiv.org/abs/2505.15820

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
Broadcast analytics – an evaluation of video-based tracking systems with constrained player visibility - https://www.tandfonline.com/doi/abs/10.1080/24733938.2025.2533808

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
GTA: Global Tracklet Association for Multi-Object Tracking in Sports - https://github.com/sjc042/gta-link

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
Sports video telestration - https://www.taylorfrancis.com/chapters/mono/10.4324/9781003375463-7/sports-video-telestration-adam-cullinane-gemma-davies-peter-donoghue

Markerless mocap

Reliability of artificial intelligence-driven markerless motion capture in gait analyses of healthy adults - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0316119

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
HuggingFace
- SuperGlue - https://huggingface.co/docs/transformers/main/en/model_doc/superglue
- Efficient Loftr - https://huggingface.co/docs/transformers/main/en/model_doc/efficientloftr#efficientloftr

Object detection

RF-DETR - https://github.com/roboflow/rf-detr
GroundingDINO - https://github.com/IDEA-Research/GroundingDINO

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
CAMELTrack: Context-Aware Multi-cue ExpLoitation for Online Multi-Object Tracking - https://arxiv.org/pdf/2505.01257
BoostTrack/BoostTrack++ repository - https://github.com/vukasin-stanojevic/BoostTrack

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

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
Sports Metaresearch: An Emerging Discipline of Sport Science and Medicine - https://link.springer.com/article/10.1007/s40279-025-02181-x

Exploratory research

Exploratory analyses: How to meaningfully interpret and report them - https://onlinelibrary.wiley.com/doi/full/10.1002/pmrj.12980
Exploratory research in sport and exercise science: Perceptions, challenges, and recommendations - https://www.tandfonline.com/doi/full/10.1080/02640414.2025.2486871?s=03#d1e199
Exploratory reports: A new article type for Cortex - https://www.sciencedirect.com/science/article/pii/S0010945217302393?via%3Dihub

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
Sources of variation - https://www.nature.com/articles/nmeth.3224
Sample size estimation revisited - https://www.tandfonline.com/doi/full/10.1080/02640414.2025.2499403#d1e416
Importance of sample size on the quality and utility of AI-based prediction models for healthcare - https://www.thelancet.com/journals/landig/article/PIIS2589-7500(25)00021-4/fulltext

Packages and tools

Introduction to Superpower - https://cran.r-project.org/web/packages/Superpower/vignettes/intro_to_superpower.html

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

Creating DAGs

Evidence synthesis for constructing directed acyclic graphs (ESC-DAGs): a novel and systematic method for building directed acyclic graphs - https://pubmed.ncbi.nlm.nih.gov/31325312/
How to develop causal directed acyclic graphs for observational health research: a scoping review - https://pubmed.ncbi.nlm.nih.gov/39327907/

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/
Generalized Additive Models for Location, Scale and Shape - https://www.gamlss.com/

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://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378
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
Beyond data: leveraging non-empirical information and expert knowledge in Bayesian model calibration - https://arxiv.org/pdf/2505.21934
Visualizing the differences between Bayesian posterior predictions, linear predictions, and the expectation of posterior predictions - https://www.andrewheiss.com/blog/2022/09/26/guide-visualizing-types-posteriors/

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/
Bayesian updating tool to assess effectiveness of S&C interventions - https://www.researchgate.net/publication/354418809_Bayesian_updating_tool_to_assess_effectiveness_of_SC_interventions

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
How hierarchical models improve point estimates of model parameters at the individual level - https://www.sciencedirect.com/science/article/pii/S0022249616300025

Location and scale models

https://bookdown.org/content/4857/adventures-in-covariance.html#learn-more-about-your-data-with-the-melsm.
Generalized Additive Models for Location, Scale and Shape - https://www.gamlss.com/

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

Proportions

Analysing continuous proportions in ecology and evolution: A practical introduction to beta and Dirichlet regression - https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13234

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/
Performance evaluation of predictive AI models to support medical decisions: Overview and guidance - https://arxiv.org/abs/2412.10288

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

Item reduction

Bayesian Design Optimization: An Application to Item Reduction in Scale Development Research - https://haines-lab.com/post/bayesian-design-optimization-an-application-to-item-reduction-in-scale-development-research/

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

Synthetic consumers

Synthetic Consumers: The Promise, The Reality, and The Future - https://www.pymc-labs.com/blog-posts/synthetic-consumers/
The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective - https://arxiv.org/pdf/2312.15524v1

Conferences and presentations

MIT Sloan Sports Analytics https://www.sloansportsconference.com/
New England Symposium on Statistics in Sports (NESSIS) https://www.nessis.org/
The Cascadia Symposium on Statistics in Sports (CASSIS) http://www.cascadiasports.com/
Workshop on Machine Learning and Data Mining for Sports Analytics - https://dtai.cs.kuleuven.be/events/MLSA24/
International Symposium on Computer Science in Sport (IACSS) - https://iacssconference.org/
International Sports Analytics Conference and Exhibition (ISACE) - https://formal-analysis.com/isace/2025
UCD Smurfit School of Business Sports Analytics Conference - https://sites.google.com/view/ucd-ssac/home
Mathsport International - https://www.mathsportinternational.com/
ANZIAM MathSport - https://www.anziam.org.au/Mathsport
MathSport Asia - https://mathsportasia.com/
CVPR Sports - https://vap.aau.dk/cvsports/
http://large-scale-sports-analytics.org

Sloan Sports Analytics Research Papers

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

Videos

Sloan sports analytics conference presentations - https://www.youtube.com/user/42analytics/videos
NESSIS presentations - https://www.youtube.com/@markglickman7999/videos
PySport - https://www.youtube.com/@PySport-fg2mu/videos

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.

Sport Analytics

Textbooks

Artificial Intelligence and Machine Learning in Sports Science - https://link.springer.com/book/10.1007/978-3-662-70155-3
Handbook of Statistical Methods and Analyses in Sports - https://www.routledge.com/Handbook-of-Statistical-Methods-and-Analyses-in-Sports/Albert-Glickman-Swartz-Koning/p/book/9780367331016?srsltid=AfmBOoo2zI72hio9bWdWFYKPAO0cJ78IAINj_dbRc1ckXjCg_5YmWlAV

David L. Carey

Sports analytics topics and problems

Compilations and reviews

Communication

Reinforcement learning/AI in sport

Strategy proposal and simulation

Working in sports analytics (inc. academic collaborations)

Forecasting crowds

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

Forecasting player performance

Extreme values

Drafting

Recruiter perspectives

Randomness and skill vs. luck in sport

Meta-analytics & metric evaluation

In-game win probability & momentum

Match prediction & Team rating models

Tipping models

Causal Inference in sport

Decision making

Player evaluation/rating

Multi-trial/test events

Shooting/Kicking/Passing/Hitting ratings + xGoals

EPV, VAEP, xThreat, Equity

Action valuation

Event stream analysis/Game state

Regression to the mean

Defensive valuation

Defensive strategy

Ball tracking data

Player tracking data

Role assignment

NMF

Pitch control

Pass models/Completion models

Running/rushing models

Trajectory prediction (‘ghosting’)

Game state with tracking data

Applications of pose data

Racing sports

Subjective ratings

Coaching/scouting

E-Sports

Sport science

Technology validation

Fitness-Fatigue models

Training load and monitoring

Non-invasive monitoring

Training plan generation and optimisation

Injuries

Running and wearables

Match demands

Opinions and commentaries

Datasets and competitions

Data formatting

CV in Sport Open data

Open data for science

Competitions

Deep Learning

Reinforcement learning

Graph neural networks

Computer vision

Computer vision in sport

Player detection

Player/Team ID

Player tracking

Ball tracking

Action detection/recognition

Sport camera calibration

Telestration

Markerless mocap

CV in biomech

Vision transformers

Image feature matching/ Homography

Object detection

Multi-object tracking

Sensors and Signal processing

Tutorial papers

Smartball examples

GPS