Sport Informatics and Analytics/Pattern Recognition

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Practice session.


The conceptualisation and operationalisation of pattern recognition are foundations of sport informatics and analytics. This theme (Theme 2 of the course):

  • Discusses systematic observation of performance.
  • Introduces supervised learning approaches to data analysis.
  • Explores the connections between performance trends and athlete actions.

We present three datasets for you to analyse in this theme: bicycle hire 2013 CitiBike| data; an Australian Rules Football GPS data set (from the 2014 season); and |physical measurements and blood measurements from athletes at the Australian Institute of Sport (2018). Elsewhere, there is a growing network of data sharing. Michael Timbs (2019) for example, shared his AFL Brownlow data. R for Data Science curated data from the FIFA Women's World Cup in France. Keith Lyons (2019) gathered data from the official FIFA record of the tournament. Mark Padgham (2019) created the CRAN package bikedata for downloading and aggregating data from public bicycle hire, or bike share, systems. James Curley (2016) developed the engsoccerdata package that "is mainly a repository for complete soccer datasets, along with some built-in functions for analyzing parts of the data". Mart Jürisoo (2019 [1] has compiled an International football results from 1872 to 2019 dataset that has 40,838 results of international football matches.

In addition to this introduction to the theme, these topics are part of this theme:

Video signpost

In this video, Melissa Breen discusses the impact of pattern recognition data on her performance as an elite athlete. Melissa was the University of Canberra's first athlete in residence in 2014.


The resources to support this theme include:

Theme activities

Artificial intelligence

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In May 2019, the OECD Principles on Artificial Intelligence[2] defined an Artificial Intelligence system as:

a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy.

The authors note the Institute of Electrical and Electronics Engineers’ Global Initiative on Ethics of Autonomous and Intelligence Systems takes the view that the term Artificial Intelligence is too vague and uses instead autonomous and intelligent systems.[3]

Stuart Russell and Peter Norvig (2016)[4] note that the main unifying theme of their textbook is an intelligent agent. They define artificial intelligence as "the study of agents that receive percepts from the environment and perform actions."

Tannya Jalal 2019) [5] distinguishes three definitions of artificial intelligence.

Artificial intelligence

A broad area of computer science that makes machines seem like they have human intelligence.

Artificial narrow intelligence

Pulls information from a specific data-set.

Artificial general intelligence

Refers to machines that exhibit human intelligence.

Tannya also includes a reference to artificial super intelligence. She cites Nick Bostrom's observation that “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest”.

Reading about pattern recognition, machine learning, and artificial neural networks

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Pattern recognition

Take some time to explore the range of resources for this theme. You might like to start with a summary of five papers on pattern recognition. To get a feel for where this work is going, have a look at a 2017 paper written by Nazanin Mehrasa and her colleagues[6] on learning person trajectory representations for team activity analysis and a 2018 paper by Manuel Stein and his colleagues[7] about combining video and movement data. You might also find the discussions of ghosting in association football (2017)[8] and basketball (2018)[9] of interest. For a portfolio of research in pattern recognition, see Luke Bornn and colleagues' (2019)[10] eleven papers submitted to the Sloan Sports Analytics Conference 2014-2019. Patrick Lucey (2019)[11] discussed interactive sport analytics in order "to find play similarity using multi-agent trajectory data, as well as predicting fine-grain plays".

Computer Science

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Philosophy of Computer Science

William Rapaport (2019)[12] has provided a comprehensive discussion of computer science in his book the Philosophy of Computer Science. William suggests that Computer Science tries to answer five central questions:

  • What can be computed and how?
  • What can be computed efficiently, and how?
  • What can be computed practically, and how?
  • What can be computed physically, and how?
  • What can be computed ethically, and how?

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Machine learning

As you explore the pattern recognition theme, you will find references to machine learning and deep learning. These references include Jørgen Veisdal's (2018)[13] account of the first artificial intelligence workshop at Dartmouth. As an example of how approaches to machine learning have developed over the last sixty years, you might like to compare eight papers. The first is by Allen Newell, John Shaw and Herbert Simon (1958)[14] on addressing the problems of designing computerised chess-playing. The second by Arthur Samuel written in 1959[15], Some Studies in Machine Learning Using the Game of Checkers. Four papers were written by David Silver and his colleagues, the first in January 2016[16] Mastering the game of Go with deep neural networks and tree search, the second in October 2017[17], Mastering the game of Go without human knowledge, the third in December 2017[18], Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, and the fourth in December 2018[19], A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. David Foster (2018)[20] discussed the AlphaGo, AlphaGo Zero and AlphaZero machine learning approaches taken by David Silver and his colleagues and provided his guide to help build your own AlphaZero AI with Python and Keras. A eigth paper, written by Nazanin Mehrasa and her colleagues (2018)[21], discussed a generic deep learning model for team activity analysis. J Hallman (2020)[22] discussed how DeepMind restored the beauty to chess and included the observation:

The most exciting thing was that AlphaZero played tactically. It sacrificed pawns and pieces. It moved bishops and queens to the corner of the board. It risked its king in ways no human player would ever consider.

For him "beauty tends to result when a player violates a principle of good play and wins anyway".[23]

Jesus Rodriguez (2019)[24] provided background detail to machine learning and the card game of poker. He reported on the development of Pluribus and a paper that discussed the use of machine learning "in six-player no-limit Texas hold’em poker".[25]

For an overview of this period of machine learning see Andrey Kurenkov (2016)[26] and James Somers (2018)[27]. Emily Cust and her colleagues (2018) have provided a systematic review of machine and deep learning for sport-specific movement recognition.[28] Aman Agarwal (2018)[29] shared his detailed reading of David Silver and colleagues' 2016 paper. Steven Strogatz (2018)[30] extended the discussion of David Silver and his colleagues' work and contemplated a move from AlphaZero to AlphaInfinity. Michael Garbade (2018)[31] sought to clear confusion about the use of the terms AI, machine learning and deep learning.

If these readings have inspired you, Lauri Hartikka (2017)[32] offered a step-by-step guide to building a simple chess AI. Mark Farragher (2019)[33] discussed an artificial intelligence resolution of the Coastal Runners game in which he discussed the reward funtion of artificial intelligence.

You might also find the discussions about predicting the outcomes of Bundesliga football games of interest too.[34]

For an introduction to developing a machine learning model see Victor Roman's (2018)[35] post.

Marc Deisenroth, Aldo Faisal and Cheng Soon Ong (2019)[36] shared, in an open resource, their introduction to mathematics for machine learning.

Omayma Said (2019)[37] shared and introduction to the why and how of machine learning.

Christoph Molnar (2019)[38] provided a guide for making black box models explainable.

Jesus Roderiguez (2019)[39] described the development of a Google Research Football project that used a reinforcement learning environment in which agents learned to play football in a Gameplay environment. A paper by Karol Kurach and colleagues (2019)[40], Google Research Football: A Novel Reinforcement Learning Environment accompanies Jesus's introduction. They note:

Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner.

Further discussion of Google's work in this space can be found in an Emerging Technology update from arXiv (2019).[41]

Mat Herold and his colleagues (2019)[42] provided a review of machine learning in men’s professional football. They provide "a critical appraisal of the application of machine learning in football related to attacking play, discussing current challenges and future directions".

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Hands-On machine Learning with R

Bradley Boehmke & Brandon Greenwell (2019)[43] have written a guide to machine learning with R, Hands-On Machine Learning with R. They observe "Machine learning (ML) continues to grow in importance for many organizations across nearly all domains".[44] Their book focuses on supervised learners (construct predictive models), and unsupervised learners (descriptive models). Bradley and Brandon note that in supervised learning "a predictive model is used for tasks that involve the prediction of a given output (or target) using other variables (or features) in the data set". Unsupervised learning, they suggest, "includes a set of statistical tools to better understand and describe your data, but performs the analysis without a target variable". They propose that "in essence, unsupervised learning is concerned with identifying groups in a data set".[45]

In their book, Chapters 4-14 "focus on common supervised learners ranging from simpler linear regression models to the more complicated gradient boosting machines and deep neural networks". Chapters 15-16 "delve into more advanced approaches to maximize effectiveness, efficiency, and interpretation of machine learning models". "The latter part of the book focuses on unsupervised techniques aimed at reducing the dimensions of data for more effective data representation (Chapters 17-19) and identifying common groups among observations with clustering techniques (Chapters 20-22)".[46]

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Artificial neural networks

Anders Krogh (2008) notes:

Artificial neural networks are inspired by the early models of sensory processing by the brain. An artificial neural network can be created by simulating a network of model neurons in a computer. By applying algorithms that mimic the processes of real neurons, we can make the network ‘learn’ to solve many types of problems.[47]

Brian Ripley (1996)[48] provided a general introduction to pattern recognition with neural networks. Carlos Gershenson (2003)[49] shared his introduction to artificial neural networks for beginners. Branislav Holländer (2018)[50] discussed natural and artificial neural networks. Jay Alammar (2016[51], 2018[52]) has provided an introduction to basic neural networks and the mathematics involved.

In sport, Jürgen Perl has been a leading advocate of the use of artificial neural networks. A 2004 paper[53] introduced his neural network approach to movement pattern analysis. Subsequently, he and his colleague, Stefan Endler, have explored a variety of applications of artificial neural networks in sport contexts. See, for example, their discussions of endurance sports[54][55] and Stefan and his colleagues' report of research into simulated anaerobic threshold compared with lactate-based thresholds[56]. Other examples of Jürgen's work include game creativity[57] and tactical pattern recognition[58][59][60].

Donald Barron and his colleagues (2018)[61] used an artificial neural network to identify key performance indicators that influenced outfield players' league standings in association football. Their analysis used data collected for 966 players.

Data science, machine learning, artificial intelligence and intelligence augmentation

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What is in a name?

David Donoho (2017)[62] noted "there is a solid case for some entity called Data Science to be created, which would be a true science: facing essential questions of a lasting nature and using scientifically rigorous techniques to attack those questions". His paper identified six divisions of Greater Data Science (GDS):

  • Data exploration and preparation
  • Data representation and transformation
  • Computing with data
  • Data modelling
  • Data visualisation and presentation
  • Science about data science

David concludes his article with this observation:

GDS proposes that Data Science is the science of learning from data; it studies the methods involved in the analysis and processing of data and proposes technology to improve methods in an evidence-based manner. The scope and impact of this science will expand enormously in coming decades as scientific data and data about science itself become ubiquitously available.

You might find it interesting to look at the commentary on David's article.

Hanif Samad (2019)[63] looked carefully at the process of finding employment as a data scientist. He included reference to the Conway Venn Diagram[64]. His research led him to look at the profiles of 869 data scientists. His findings included: most data scientists have postgraduate degrees; Computer Science and Engineering, but also Business Analytics dominate fields of study; Currently employed data scientists tend to be in mid-career positions; most data scientist positions are new; half of data scientist roles come from non-technology companies. Hanif concluded "the background of data scientists is incredibly diverse" but noted that "a postgraduate degree is a far better indicator of your prospects as a data science hire". [65]

Roger Peng (2018)[66] discussed the role of theory in data analysis. He identified five tentpoles of data science in a subsequent post (2019)[67]:

  • the application of design thinking to data problems;
  • the creation and management of workflows for transforming and processing data;
  • the negotiation of human relationships to identify context, allocate resources, and characterize audiences for data analysis products;
  • the application of statistical methods to quantify evidence;
  • the transformation of data analytic information into coherent narratives and stories.

David Robinson (2018)[68] sought to distinguish the essential characteristics of data science, machine learning, and artificial intelligence (AI). He used a descriptivist 'rule of three' to propose:

  • Data science produces insights
  • Machine learning produces predictions
  • AI produces actions(Original emphases)

David pointed out that this is not a sufficient qualification but his attempt as "a useful way to distinguish the three types of work, and to avoid sounding silly when you’re talking about it".[69] You might find Gil Press's (2013)[70] discussion of data science of interest in this context. See also, Francesco Corea's (2018)[71] classification of AI technologies.

We believe there is a further clarification to be made in the context of intelligence augmentation (IA) as distinct from artificial intelligence (AI). The epistemological foundations of augmentation can be found in work by Vannevar Bush (1945)[72] and Douglas Engelbart (1962)[73].

Peter Skagestad (1993)[74] observed:

the pioneers of the personal-computer revolution did not theorize about the essence of the computer, but focused rather on the essence of human thinking, and then sought ways to adapt computers to the goal of improving human thinking.[75]

More recently, Melanie Cook (2017) proposed that IA is:

The idea that a computer system supplements and supports human thinking, analysis, and planning, leaving the intentionality of a human actor at the heart of the human-computer interaction. Focusing on the interaction of humans and computers, rather than on computers alone.[76]

You might also find Cassie Kozyrkov's (2018a[77], 2018b[78] ) discussions of machine learning of interest too as well as her discussions of data science (2018c[79], 2018d[80], 2018e[81], 2018f[82]).

Tirthajyoti Sarkar (2018)[83] combined insights from William of Ockham, Thomas Bayes and Claude Shannon to construct a definition of machine learning.

John Rollins (2015)[84] outlined a foundational methodology for data scientists. This has ten stages.

Karen Hao (2019)[85] shared a review of 16,625 papers that referred to artificial intelligence in arXiv.

Varuna De Silva (2018)[86] provided an example of the use of artificial intelligence in an association football club. Marcus Woo (2018)[87] discussed the use of artificial intelligence in NBA basketball.

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As you engage with the theory and practice of sport informatics and analytics, you will notice a lot of technical language. If you have an opportunity to read the authors listed above, you might start to get a feel for this language and have a sense of how you might use the terms you discover. We hope this is a good point in the course for you to reflect on how you will describe your own work and conceptualise the work of others.

In the process of reflection you might like to consider the approaches taken by Peter Sweeney (2018a[88], 2018b[89]) and Zachary Lipton and Jacob Steihardt (2018)[90]. Peter explores philosophical issues in the consideration of artificial intelligence. Zachary and Jacob discuss patterns in machine learning scholarship. Terence Shin (2020)[91] summarises machine learning approaches.

Examples from sport contexts

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American football

NFL data

Have a look at Alex Castrounis' discussion of supervised learning and unsupervised learning with NFL data. How might you use the learning approaches Alex discusses in relation to the Chicago Bears in your sport contexts? See also, Iman Behravan and colleagues' (2019)[92] use of an automatic particle swarm optimisation-clustering algorithm to identify players' roles.

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Association football

Player recognition

Nicolas Bortolotti (2017)[93] has discussed how he has used TensorFlow (an open source machine learning framework) with an ObjectDetection model to analyse a segment of a football game to identify a player. Nicolas described how he:

  • Trained a model.
  • Used the model during a live broadcast of a football game.
  • Considered how such an approach might contribute to conversations about game tactics.

He shared a video of his player recognition model.

Analysis of spatio-temporal data

Michael Horton's PhD thesis (2018)[94] investigated algorithmic approaches to mining sports trajectory data. The thesis reported Michael's research into the automatic classifying of passes made during association football games. The data used for his analysis comprised four games played by Arsenal Football Club in the English Premier League season in 2008. The data contained trajectories for all players that participated in each half of each of the four game, and an event log for each game. The trajectories were sampled at 10 Hz and had a resolution of 10 cm.

Shots, goals and predicting team play

Debangan Dey and Andrew Pita (2018)[95] investigated: where on the field do most shots come from?; what patterns of play give rise to the most effective shots?; and can we develop team level summary measures that are predictive of team performance? Their investigations used data collected by StatsBomb during the2018 FIFA World Cup.

Machine Learning

In 2018[96], the journal Machine Learning published a guest editorial on machine learning for football. As part of the special issue, the editors posed the 2017 Soccer Prediction Challenge that revolved around predicting the outcomes of football matches.

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Australian rules football

Analysing GPS data

An Australian Rules football team shared with us a whole game GPS data set from a game played in the 2014 season.

The data can be found at this location.

The team that provided the data won the game and scored the same number of points each of the four quarters of the game.

The scores by quarter in the game were:

  • 33 v 22 (Q1)
  • 33 v 12 (Q2)
  • 33 v 26 (Q3)
  • 33 v 37 (Q4)


  1. Do the data available help map player effort in relation this scoring pattern?
  2. What inferences can you draw from these data?

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Modelling player movement

There is a substantial literature reporting pattern recognition approaches in basketball. Kirk Goldsberry[97] has explored the use of visual and spatial analytics to investigate shooting abilities. Other research conducted by Kirk and his colleagues includes: score prediction[98]; shot selection[99]; and defending[100][101][102].

Steven Wu and Luke Bornn[103] provide a detailed account of their use of secondary data to analyse attacking play in professional basketball. The example they use is a data set from a 2013 game between the Miami Heat and the Brooklyn Nets.

Positive Residual has used the Shiny application to share insights into basketball performance. See, for example, NBA team rolling charts and NBA team play type.

Derek Corcoran, and Nicholas Watanabe[104] share their use of the R package Spatialball to analyse and visualise spatial data in the NBA. The package enables the user to explore player, team and league patterns of performance.

Evangelos Papalexakis and Konstantinos Pelechrinis (2018)[105] proposed "a framework based on tensor decomposition for obtaining a set of prototype spatio-temporal patterns based on the core spatio-temporal information and contextual meta-data" to provide contextual information about performance patterns.

Long Sha and his colleagues (2018)[106] shared an intelligent human-computer interface that used trajectory data to enhance the retrieval of basketball team and player performance.

Wade Hobbs and his colleagues (2018)[107] measured spatial scoring effectiveness in women's basketball in the 2016 Olympic Games. The aim of this study was to quantify how effectively teams move the ball across the basketball court and to identify the most commonly occurring sequences of ball movement in international women’s basketball.

March madness

In 2014, Kaggle announced a March Machine Learning Mania[108] competition to coincide with the NCAA Division 1 Men's Basketball Tournament hosted in Arlington, Texas. There has been a Kaggle competition each year since then with the most recent taking place in the 2018 season. [109] There was a Kaggle competition for the NCAA Division 1 Women's Basketball Tournament in 2018.[110] Sam Firke (2018)[111] provided a guide to analysing basketball performance at the championships. You might find it interesting to look at the resources he has shared in his GitHub account as a tutorial introduction.

Michael Lopez and Gregory Matthews (2014)[112] shared their reflections on their success in winning the inaugural Kaggle competition in 2014. You might consider their reflection on their model for your own work in this area of prediction, namely:

While one of our two submissions finished first in the Kaggle contest, we estimate that this winning entry had no more than about a 12% chance of doing so, even under the most optimistic of game probability scenarios.

In 2017[113], Google partnered with the NCAA to migrate eighty years of historical and play-by-play data including basketball championships. These data were used in the 2018 NCAA basketball championships to provide real-time data analysis in the two semi-finals of the tournament.[114][115]

Cooperative behaviours

Motokazu Hojo and his colleagues (2018)[116] proposed an automatic recognition system for strategic cooperative plays, which are the minimal, basic, and diverse plays in a ball game. They aimed to shed light on light on inconspicuous players who play important roles in basketball. Data were collected from a Japanese university team.

Analytics at scale

Eric Schmidt and Allen Jarvis (2018)[117] have provided a detailed insight into Google Cloud's involvement in the analysis of NCAA basketball data. We recommend that you read their account of the architecture required to deliver:

  • A flexible and scalable data processing workflow to support collaborative data analysis.
  • New analytic explorations through collaboratively developed queries and visualizations.
  • Real-time predictive insights and analysis related to the games, modeled around NCAA men’s and women’s basketball.

There are two other articles to add to your reading list about analytics at scale. One is by Tariq Shaukat (2017) [118], the other is by Courtney Blacker (2018)[119].

What do these three articles suggest to you about the skills you might need as you work to provide insights from archived data?[120]

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Bicycle journeys

Analysing bicycle journey data

There is growing research interest in the analysis of open data about bicycle journeys. You might find Jake Vandeplas's 2014 paper[121] a good place to start. He writes "this post is as much about how to work with data as it is about what we learn from the data" (original emphases). Two examples of how to work with data to create visualisations and to learn from openly available data are: Todd Schneider's[122] tale of twenty-two million Citi Bike Rides in New York and Luis Carli's[123] analysis of Boston bike sharing data. Mark Padgham (2017)[124] reported the availability of an rOpenSci package, bikedata, that provides access to data from all cities which openly publish bicycle share data. Christoph Molnar (2018)[125] used data from Capital-Bikeshare in Washington to support discussion of machine learning. Florian Teschner (2018a[126], 2018b[127], 2018c[128]) used New York Citi Bike data to discuss embeddings for categorical variables.

In 2018, Mark Padgham published the CRAN package bikedata "an R package for downloading and aggregating data from public bicycle hire, or bike share, systems"[129]. In 2019, Martin Frigaard and Peter Spangler[130] described their analysis of data released by the City of Chicago.

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Fast bowling detection

Joseph McGrath and his colleagues (2018)[131]reported their use of an inertial measurement unit to provide data from fast bowling actions of 17 elite fast bowlers. Their paper shared their machine learning approach to the data collected. You might find their account of interest as you consider how you might approach a machine learning task using smartphone technology.

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Cross-country running

Race strategies

Steve Lane[132] has analysed the pacing strategies of athletes in USA championship collegiate cross-country races and suggests:

Basic statistical analysis suggests a very strong relationship between pacing and finishing time: relatively even pacing predicts faster times.

You might like to read Steve's paper as an introduction to the literature on the use of analytics to understand pacing in sport. Chris Abbiss and Paul Larsen[133] provided a comprehensive review of the pacing literature up to 2008. Mark Waldron and Jamie Highton[134] extended the discussion of the literature with their 2014 paper that explored pacing in high-intensity intermittent team sport. For and example of a sport specific discussion, you might like to read Andrew Edwards and his colleagues[135] of pacing in rowing.

Jürgen Perl has explored how we might model performance in training and competition. He developed a Performance Potential meta-model, PerPot, that "simulates the interaction between load and performance in adaptive physiological processes like training in sport by means of antagonistic dynamics"[136]. His research provides a comprehensive insight into how neural networks can be used in sport settings.

Iztok Fister and his colleagues (2018)[137] discussed the use of pacing strategies in half marathon races and shared their use of a differential evolution algorithm to inform their post hoc analysis of performance.

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Cross-country skiing

Analysing cross country skiing data

Finn Marsland[138] has combined his experiences as a national coach for cross country skiing with a research interest in pattern recognition. With colleagues at the Australian Institute of Sport and the University of Canberra, he has produced three research papers to share his work[139][140][141]. The three papers illustrate how Finn's work developed from a preliminary investigation[142] that considered "the potential of micro-sensors for use in the identification of the main movement patterns used in cross-country skiing" to the use of sensors in on-snow training environments[143] to their use in competition events[144]. The range of Finn's investigations provide an excellent case study in how a coach can develop his understanding of performance through considered use of pattern recognition technology.

You might find Trine Seeberg and her colleagues' (2017)[145] discussion of a multi-sensor system for automatic analysis of classical cross-country skiing techniques of interest and Jihyeok Jang and colleagues' (2018)[146] investigation of a deep-learning model for classifying cross-country skiing techniques.

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Ice hockey

Emmanuel Perry[147] observes "Hockey is inherently random, but it isn’t roulette. With the right data and a little handiwork (a good computer doesn’t hurt either) though, you can make a decent go of it...". He explores a variety of approaches to analyse game outcome in ice hockey. His discussion provides a detailed insight into the range of tools an analyst might use to investigate performance patterns. These include:

  • bagged logistic regression
  • gradient-boosted trees
  • neural networks
  • bagged naive Bayes model
  • a random forest using fuzzy logic

Emmanuel combines eleven sub-models into his prediction model for performance. He discusses each of these in detail and outlines the validation process he used to test his model. He notes that this process is essential but "by far the least enjoyable part of building a statistical model".

We recommend Emmanuel's analysis of ice hockey performance to you as an example of an ensemble of sub-models that is discussed explicitly to guide you as a reader. His model raises an important question about the generalisability of a sport specific approach.

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Movement pattern recognition

Kylie Steel[148] provides an introduction to movement pattern recognition. She notes that it is a field of study that has attracted research interest for over a century. If you would like to explore discussions about human movement characteristics and the attention we pay to movement after reading Kylie's summary, you might find the 1996 paper by Eva Bonda and her colleagues[149] of interest. For a 2017 example of identifying movement patterns in sport we suggest you look at Panna Felsen and Patrick Lucey's[150] investigation of shooting styles in basketball. For an indication of how this work in movement recognition is progressing, you might like to have a look at Hoang Le and colleagues'[151] discussion of coordinated multi-agent imitation learning.

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Physical activity monitoring

Nick Strayer (2018)[152] shared an analysis of the recordings of "30 subjects performing basic activities and postural transitions while carrying a waist-mounted smartphone with embedded inertial sensors". He used Keras to train a convolutional neural network to classify physical activity. Data came from the Smartphone-Based Recognition of Human Activities and Postural Transitions Data Set.

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Estimation of lactate threshold

Urtats Etxegarai and his colleagues (2018)[153] reported the use of a machine learning system that modelled the lactate evolution using recurrent neural networks. Their account provided details of the approach they used to develop a system that predicted with accuracy lactate thresholds and performance.

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Speed skating

Analysing speed skating data

Arno Knobbe and his colleagues[154] discuss their approach to analysing speed skating data. You can find the paper at this location. Note the process they share in the paper as they move from the records kept by a coach over a fifteen-year period to their analysis in order to "extract actionable and interpretable patterns that can provide input to future improvements in training".

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Surf forecasts

Surfing has been included in the 2020 Olympic Games in Tokyo and will take place at Tsurigasaki Beach[155]. It is likely the surfers and the organisers will pay particular attention to surf forecasts developed by Walter Munk[156]. Walter worked with Harald Sverdrup to develop a methodology to forecast the relationships betweem wind, sea and swell. In 1947 they produced a report for the United States of America's Hydrographic office[157] that established the framework for surf forecasts that was extended by Charles Bretschneider[158]. In the 1950s, Walter worked with John Tukey to examine power spectra in wave behaviour[159][160]. Walter was still active in oceanograpgic research on his 100th birthday on 19 October 2017[161].

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Within-match forecasting

Stephanie Kovalchik and Machar Reid (2018)[162] discussed a methodology to provide dynamic updates to within-match forecasting of wins in tennis. They combine a pre-match calibration method with a Bayes updating rule to report on data from the 2017 tennis season.

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Predicting performance

Marian Hoffmann and his colleagues (2017)[163] report their use of two computational approaches to predict Olympic distance triathlon race times of two German male elite triathletes. Their first computational method (a statistical approach) after race time normalisation was: "exploratory factor analysis, as a mathematical preselection method, followed by multiple linear regression and dominance paired comparison"[164]. The second used an expertise-based nonlinear approach that included an artificial neural network.

Marian and his colleagues analysed data from eleven male elite triathletes and in order to undertake the two computational approaches, they note:

Normalization was necessary to obtain comparable individual race times independent of the various triathlon races in which the subjects participated. These normalized race times were fundamental to all following analyses, since they accounted for the slightly different competition calendars of each elite triathlete.[165]

Marian and his colleagues used a reference factor calculated as "the mean value of overall race times of the Top 10 athletes in World Triathlon Series races between 2009 and 2012".[166]

We recommend you read this discussion of triathlon performance prediction. You might find the authors' consideration of the limitations of their study of particular interest.[167]

Data discussions

In our discussions of pattern recognition we are mindful that we need to reflect on the forms data take and how we name files.[168] Hadley Wickham[169] notes the importance of data cleaning and preparation. Tamrapami Dasu and Theodire Johnson, in their introduction to data cleaning, observe:

Most data mining and analysis techniques assume that the data have been joined into a single table and cleaned, and that the analyst already knows what she or he is looking for. Unfortunately, the data set is usually dirty, composed of many tables, and has unknown properties. Before any results can be produced, the data must be cleaned and explored,[170]

We recommend that, as an introduction to data cleaning and preparation, you look at Hadley Wickham's[171] approach to data tidying. You might also consider looking at an R package, tidyr, that provides tools to help tidy messy data. For a 2017 discussion of the tidyverse approach, see Zev Ross, Hadley Wickham and David Robinson's[172] discussion of decluttering R workflow.

Your reading and reflections might lead you consider your own role as a data scientist. Chris Dowsett (2016) notes "it takes people to use data in order for it to have any value"[173]. He explores how we might develop data science as a platform. This approach offers "the opportunity to bring together a great User Experience with holistic insights on-demand"[174]. Aidan Condron (2016) provides an example of a data science as a platform project that sought "to establish a technological infrastructure supporting data archivists and ... researchers in managing and analysing both familiar and new and novel forms of data"[175].

Data science challenges

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Addressing fallacies about a data scientist's role

We suggest you have a look at Shane Brennan's (2017) post The Ten Fallacies of Data Science[176]. In it, Sean lists these ten fallacies for a newly qualified data scientist to consider:

  • The data exist
  • The data are accessible
  • The data are consistent
  • The data are relevant
  • The data are intuitively understandable
  • The data can be processed
  • Analyses can be easily re-executed
  • We do not need encryption
  • Analytics outputs are easily shared and understood
  • The answer you are looking for is there in the first place

Do any of Shane's ten points resonate with your experience?

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An example of a data science process

We suggest you have a look at Vick Szuflita's (2018) post Pitch Recommendation: a look into the data science process[177] for an example of a data science process. Vicky uses data from baseball to consider a process that has the following steps:

  • Identify your problem and goal
  • Gather and clean your data
  • Get to know your data
  • Picking your model
  • How do I know if my model is good?
  • Improving your model
  • Make your model useable

Does Vicky's example help clarify the ways we might approach the collection and analysis of data?

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Explainable artificial intelligence

Prajwal Paudyal (2019)[178] has discussed the need for explanation in artificial intelligence (XAI).

He observes "it is AI that is transparent enough so that the explanations that are needed are part of the design process". He suggests explanations are desirable in these circumstances:

  • help to understand the data better.
  • understand the model better.

Prajwal's post invites us to think about interpretability early, early on. This includes requirements analysis; design; implementation; testing; maintenance.

Does Prajwal's discussion of XAI resonate with any of your experiences?

ePortfolio questions

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Questions about this theme

As you work your way through this theme and compile your ePortfolio, you might like to consider these six questions.

Q 7. What is systematic about ‘systematic’ observation?

Q 8. Do we need to concern ourselves about the reliability and validity of data?

Q 9. Why is it important to de-identify performance data?

Q10. What did you discover in the shared dataset?

Q11. What have you learned about supervised learning approaches?

Q12. What are your thoughts about how we relate patterns of performance to moments of performance within games?


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