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https://github.com/AtomScott/awesome-sports-analytics


https://github.com/AtomScott/awesome-sports-analytics

List: awesome-sports-analytics

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# Awesome Sports Analytics: [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)



A curated list of awesome machine learning applications in the sports domain.

An up to date version of this awesome list can be found [here](https://atomscott.me/Awesome-Sports-Analytics-a1a6595efc6e498b8a827dc72179239e)

(I'll be writing in this notion page for the time being).



[![HitCount](http://hits.dwyl.com/AtomScott/awesome-sports-analytics.svg)](http://hits.dwyl.com/AtomScott/awesome-sports-analytics)



## Contributing



Check the [contribution guidelines](https://github.com/AtomScott/awesome-sports-analytics/blob/master/contributing.md).

Or DM me on [twitter](https://twitter.com/AtomJamesScott) for a ~~fast~~ response.



## Table of Contents



- [People](https://www.notion.so/atomscott/Awesome-S-8e673396ce474d059b5f5d4558d153ea#People)

- [Books](https://www.notion.so/atomscott/Awesome-S-8e673396ce474d059b5f5d4558d153ea#books)

- [Papers](https://www.notion.so/atomscott/Awesome-S-8e673396ce474d059b5f5d4558d153ea#papers)

- [Software](https://www.notion.so/atomscott/Awesome-S-8e673396ce474d059b5f5d4558d153ea#software)

- [Datasets](https://www.notion.so/atomscott/Awesome-S-8e673396ce474d059b5f5d4558d153ea#datasets)

- [Tutorials and Talks](https://www.notion.so/atomscott/Awesome-S-8e673396ce474d059b5f5d4558d153ea#tutorials-and-talks)

- [Resources for students](https://www.notion.so/atomscott/Awesome-S-8e673396ce474d059b5f5d4558d153ea#resources-for-students)

- [Blogs](https://www.notion.so/atomscott/Awesome-S-8e673396ce474d059b5f5d4558d153ea#blogs)

- [Links](https://www.notion.so/atomscott/Awesome-S-8e673396ce474d059b5f5d4558d153ea#links)



## People



- Kyle Boddy - sabermetrics

- Patrick Lucey - Chief Scientist at Stats Perform

- David Sumpter - Soccermatics Author

- William Spearman - Liverpool Analytics

- Javier Fernandez - Barcalona Analytics

- Luke Bornn - Sim Fraser University

- Keita Watanabe - Japanese Volley Ball

- Tom Decroos - Soccer data analytics researcher



## Books



- Handbook of Statistical Methods and Analyses in Sports (Chapman & Hall/CRC Handbooks of Modern Statistical Methods) 1st Edition



## Papers



### Player Evaluation



- Actions Speak Louder than Goals: Valuing Player Actions in Soccer (KDD 2019)

**Best Paper**, Applied Data Science Track

- Player Vectors: Characterizing Soccer Players’ Playing Style from Match Event Streams (ECML PKDD 2019)



### Team Evaluation



- Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data

- Spatio-temporal Analysis of Tennis Matches



### Result Prediction



### Player/Ball Tracking



- DeepBall: Deep Neural-Network Ball Detector (VISIGRAPP 2019)

- Towards Real-Time Detection and Tracking of Basketball Players using Deep Neural Networks (NIPS 2017)



### Action / Event Detection



- Predicting soccer highlights from spatio-temporal match event streams (AAAI 2017) [link]

- A Context-Aware Loss Function for Action Spotting in Soccer Videos (CVPR 2020) [[link](https://arxiv.org/pdf/1704.02581.pdf)]



### Future Trajectory



- Predicting Wide Receiver Trajectories in American Football (IEEE WACV 2016)

- Coordinated Multi-Agent Imitation Learning (ICML 2017)

- Neural Relational Inference for Interacting Systems (ICML 2018)

- Long Range Sequence Generation via Multiresolution Adversarial Training (NIPS 2018)

- Where Will They Go? Predicting Fine-Grained Adversarial Multi-Agent Motion using Conditional Variational Autoencoders (ECCV 2018)

- Generating Defensive Plays in Basketball Games (ACM MM 2018)

- Generating Multi-Agent Trajectories using Programmatic Weak Supervision (ICLR 2019)

- Stochastic Prediction of Multi-Agent Interactions from Partial Observations (ICLR 2019)

- Diverse Generation for Multi-agent Sports Games (CVPR 2019)

- DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting (2020)

- VAIN: Attentional Multi-agent Predictive Modeling (NIPS 2020)



### Other



- Winning a Tournament by Any Means Necessary (IJCAI 2018) [[link](https://www.researchgate.net/profile/Sanjukta_Roy2/publication/326205400_Winning_a_Tournament_by_Any_Means_Necessary/links/5c3e7b18458515a4c7294f83/Winning-a-Tournament-by-Any-Means-Necessary.pdf)]



## Software



- Python - High-level programming language. Norm for ML/DL research

- R - Language for statistical computing and graphics

- D3.js - Javascript library (nearly a language of its own) for cool vizualizations

- Tableau - Data analysis software

- Excel - Spreadsheet software from microsoft

- 



## Datasets



**Soccer**



- StatsBomb Open Data [[link](https://github.com/statsbomb/open-data)]

- football.db [[link](https://openfootball.github.io/)]

- FIFA 19 complete player dataset [[link](https://www.kaggle.com/karangadiya/fifa19)]

- Fifa 18 More Complete Player Dataset [[link](https://www.kaggle.com/kevinmh/fifa-18-more-complete-player-dataset)]

- FIFA World Cup [[link](https://www.kaggle.com/abecklas/fifa-world-cup)]

- International football results from 1872 to 2020 [[link](https://www.kaggle.com/martj42/international-football-results-from-1872-to-2017)]

- Wyscout (paid)



**Basketball** 



- NBA shot logs [[link](https://www.kaggle.com/dansbecker/nba-shot-logs)]

- NBA player of the week [[link](https://www.kaggle.com/jacobbaruch/nba-player-of-the-week)]

- Daily Fantasy Basketball - DraftKings NBA [[link](https://www.kaggle.com/alandu20/daily-fantasy-basketball-draftkings)]

- NCAA Basketball [[link](https://www.kaggle.com/ncaa/ncaa-basketball)]



**American Football**



- Detailed NFL Play-by-Play Data 2009-2018 [[link](https://www.kaggle.com/maxhorowitz/nflplaybyplay2009to2016)]

- [NFLsavant.com](http://nflsavant.com) [[link](http://nflsavant.com/about.php)]



**Baseball**



- Lahman’s Baseball Database [[link](http://www.seanlahman.com/baseball-archive/statistics/)]



**Hockey**



- NHL Game Data [[link](https://www.kaggle.com/martinellis/nhl-game-data)]



**Other**



- FiveThirtyEight [[link](https://github.com/fivethirtyeight/data)]

- Sports-1M [[link](https://cs.stanford.edu/people/karpathy/deepvideo/index.html)]

- 120 years of Olympic history: athletes and results [[link](120 years of Olympic history: athletes and results)]



## Tutorials and Talks



- International Workshop on Computer Vision in Sports at CVPR [[2013]](https://vap.aau.dk/cvsports/1st-ieee-internation-workshop-on-computer-vision-in-sports-at-cvpr-2013/) [[2015]](https://vap.aau.dk/cvsports/2nd/) [[2017]](https://vap.aau.dk/cvsports/3rd-ieee-international-workshop-in-computer-vision-in-sports-at-cvpr-2017/) [[2018](https://vap.aau.dk/cvsports/4th-ieee-international-workshop-on-computer-vision-in-sports-at-cvpr-2018/)] [[2019]](https://vap.aau.dk/cvsports/5th-ieee-international-workshop-on-computer-vision-in-sports-at-cvpr-2019/)

- AAAI Workshop on AI in Team Sports [[2020](https://ai-teamsports.weebly.com/schedule.html)]

- MIT Sloan Sports Analytics Conference [[2020](http://www.sloansportsconference.com/2020-conference/2020-research-paper-finalists-posters/)]

SSAC don't seem to archive past conferences so search Google Scholar with `source:MIT Sloan Sports Analytics Conference` like [th](source:MIT Sloan Sports Analytics Conference)[i](https://scholar.google.com/scholar?q=source%3AMIT+Sloan+Sports+Analytics+Conference&hl=en&as_sdt=0%2C5&as_ylo=2011&as_yhi=2012)[s](source:MIT Sloan Sports Analytics Conference) and you should get around a hundred results.

- Workshop on Machine Learning and Data Mining for Sports Analytics [[2019](https://dtai.cs.kuleuven.be/events/MLSA19/submission.php)] [[2018](https://dtai.cs.kuleuven.be/events/MLSA18/)]

- Workshop on Large-Scale Sports Analytics [[2016](https://www.euro-online.org/websites/orinsports/event/kdd-2016-workshop-on-large-scale-sports-analytics/)]



## Resources for students



## Webstites/Blogs/Youtube



## Other



## ※ Notes



Leave out stuff like,



- Robo-Sports: So many papers, especially RoboCup, not sure which are important.

- Coaching/Physiology/Medicine: Fast twitch, ATP, periodization, creatine etc. New methods + lots of data might provide insight but it's too broad.

- Pose estimation/Object Detection: Too general. There are awesome lists specifically for those areas already.

- Low impact research: I can't include everything!