Gambling Transaction Analytics


My current work explores the vast amounts of transaction data made available by the use of cryptocurrencies for gambling. By gathering this transaction data, more sophisticated analytical techniques can be developed than have been historically possible. This work encompasses topics from algorithm design and data management in computer science through to behavioural tracking research in the field of gambling studies. By better understanding how players gamble, we can help identify those at risk of problematic spending, and develop tools to help intervene in other domains. Studying which analytical techniques to apply, and how to apply them, is how I spend the majority of my time.

Shadowing Kester Scrope during the CEOx1 Day 2016

I'm currently looking for collaborators to help decipher several large transaction based data sets I've collected, and am available for research placements of 2-6 months. If either of these are of interest, please get in touch! If you have reached this site from a post about the York Combined Transaction Set, please click here to go to the OSF repository.

Short CV available here.


Full Paper: Ethereum Crypto-Gaming: Form, Prevalence, and Loot Box Similarities

October 24th 2019

ACM CHI PLAY (Barcelona, Spain)

Written Evidence Submission: Decentralised Gambling Overview

October 9th 2019

House of Lords Select Committee (London, UK)

Full Paper: Unconventional Exchange: Methods for Statistical Analysis of Virtual Goods

August 22nd 2019

IEEE Conference on Games (London, UK)

Speaker: Decentralised Gambling: Data Abundance and Technical Concerns

July 12th 2019

SSA Current Advances in Gambling Research (London, UK)

Extended Abstract: High Performance Exploration of Emergent Financial Phenomena in Spatial Segregation Models

September 23rd-28th 2018

CSS Conference on Complex Systems (Thessaloniki, Greece)

Speaker: Analyzing Emergent Financial Phenomena

June 6th 2018

UoH CSRG Symposium (Hull, UK)

Speaker: High Performance Collaboration and Investigating Complexity

May 31st 2018

UoH SECS Postgraduate Seminar Series (Hull, UK)



Primary author roles on several group academic articles, managing conflicting research ideas between co-authors. Also CEOx1 day winner 2016, selected for leadership qualities at undergraduate level.


C++ and Python expertise to the framework and module development levels respectively. Experience with multiple programming paradigms, including parallelized execution and polymorphic model design.


From big data sized high performance computer outputs to live cryptocurrency market data feeds. Knowledge of techniques from econometrics to statistical mechanics and machine learning.


Demonstrated a range of modules, from data mining to advanced programming to up to 140 students per lab. Speaker at seminars on code documentation and best practices.


Multiple formal presentations to postgraduate and research audiences including heavily technical topics and mathematics. Presenting to a conference audience in September 2018


Worked in international teams at research intern and postgraduate level to deliver posters, tech notes, and other publications. Several live collaborations including gambling analysis.


If you want to know more about any of my work, or discuss potential consultancy or research placements, please get in touch:

Reading List

Reading is as much a part of my work as data gathering and analysis. Find a full reading list below along with an executive summary for each. This section is designed to give a flavour of the expertise developed throughout my postgraduate study, and give some insight into the path my career has taken as a researcher so far.

Introduction to Multiagent Systems

Michael Wooldridge

Multiagent systems sit on the opposite end of the agent based software engineering spectrum than complex systems simulations. This book introduces their key architectural components, including conflict resolution, goal based design, and the technical design of inter-agent reasoning and communication.

Introduction to the Modeling and Analysis of Complex Systems

Hiroki Sayama

One of the most insightful on this list, this work covers complex systems in a general sense and their many applications. Analytical techniques from a range of disciplines are also introduced for a general audience, and examples of important real world systems are given to complete an already thorough introduction.

Complexity A Guided Tour

Melanie Mitchell

Broad in its scope, this book covers a range of related topics with specific attention to biological examples and their computational counterparts. References many important historical works and describes them in an easily comprehensible way. Recommend fully as a high level overview of this well developed discipline.

Introduction to Thermodynamics and Statistical Mechanics

Keith Stowe

The most far-out of the works on this list, this textbook covers a huge collection of analytical methods used by physicists for particle systems and fluids. Many of the methods discussed can also be applied to complex systems of different types, this book is therefore somewhat out of place here but still extremely useful for my studies.

Modeling Complex Systems

Nino Boccara

Expands previous introductions into a specific piece on the implementation of complex systems models. Discusses all of the dominant techniques for modelling and representing these systems, including differential equations, spatial models, and networks.

Think Complexity

Allen B. Downey

Less technical than others on this list, this work describes high level concepts including self-organised criticality and fractals, and how they relate to the overarching theme of complexity in the natural and simulated world. Concludes with several case studies upon which concepts discussed in the book can be mapped.


Bruce E. Hansen

The first extension outside of complexity and more in the interest of economic systems, this book introduces the subject of econometrics for a postgraduate audience. It's a work in progress but is full of useful references and explanations of important concepts in the field.

Introductory Econometrics: A Modern Approach

Jeffrey M. Wooldridge

Perhaps studied too late in this list, this introductory textbook covers most of the topics in the previous work for a less advanced audience. Again full of useful references and examples of the techniques discussed being applied to real data sets.

Python for Data Analysis

William McKinney

The author of the data wrangling framework Pandas covers a range of important low-level topics in this essential work. Python is emerging as the tool to use for data analysis, this book introduces it well and is core reading for any computer science researcher, also a useful insight into taking your own work further and building your own libraries.

Introduction to Spatial Econometrics

James LeSage & Robert Kelly Pace

Back to the econometrics side of my studies, this work introduces concepts specific to economic systems with spatial components. Fascinating read especially following both econometrics texts above and discussion of complex spatial systems in early studies. Strongly recommend for the specific audience it addresses.

The Analysis of Time Series: An Introduction

Chris Chatfield

The first venture into the filed of time series, this book concisely introduces a range of techniques, some of which have roots in previously covered topics in econometrics. Essential reference for any type of temporal analysis, and a very useful and well written book overall.

New Approaches to Macroeconomic Modeling

Masanao Aoki

Now moving towards more sophisticated mathematical methods of economic systems analysis, this work introduces and quickly advances more recent analytical techniques. At times very tough for non-specialist readers, but overall useful for understanding how different approaches to the same problem yield different analytical methods.

Spatial Econometrics: Methods and Models

Luc Anselin

As seminal work in the field of spatial econometrics this work covers a lot of ground building on important historical work in general econometrics. Again somewhat tough to move through at times, this work is definitely more targetted for the advanced reader making it a long-run effort to complete.

The Computational Beauty of Nature

Gary William Flake

My personal second favourite book on this list, Flake covers a range of fascinating topics in an engaging and informative way. Thoroughly recommend for anyone interested in the computational aspects of the world around us, and how bigger questions around intelligence and interacting systems might be answered.

Deep Learning with Python

Francois Chollet

With traditional and spatial econometrics comprehensively studied, this was a delve into the realm of modern data analytics and evolution-inspired methods. This book covers everything the modern researcher needs to know about neural networks and their implementation, taking a similar approach to the Python book above.

On Growth and Form

D'Arcy Wentworth Thompson

The oldest book on this list, this classic work describes the mechanical and emergent processes which occur to give biological organisms their form. Fascinating from start to finish and full of wonderful examples, this work is a treat following discussion of complex systems and their applications in nature described above.

Advances in Financial Machine Learning

Marcos López de Prado

Back to the neural network side of analytical methods, this work highlights their use specifically in relation to financial systems. Full of practical advice this book ranks very highly on this list and should be read by anyone using machine learning for any type of data analysis. Also interesting following econometric reading for comparison.

Introduction to Statistical Methods for Financial Models

Thomas A. Severini

To solidify the earlier study of thermodynamics and statistical mechanics this book covers a range of analytical methods specific to financial systems. As with the time series work above, this is a well thought out and concise introduction to this exciting and ever advancing field.

The Statistical Mechanics of Financial Markets

Johannes Voit

Again revisiting statistical mechanics, this market-specific work focusses on the application of techniques to similar systems. With lots of real world examples this work is a great complement to the others on this list, coming back full circle to the physics inspired methods for analysing large systems.


Paul Krugman & Robin Wells

This classic work by Nobel laureate Paul Krugman and american economist Robin Wells covers the foundations of the discipline. Revisited in my own studies to grasp the fundamentals that govern virtual systems, this is a must-study for anyone interested in rules that govern the complex financial systems in which we exist.

Virtual Economies Design and Analysis

Vili Lehdonvirta and Edward Castronova

Directing focus specifically towards virtual economies now, this work by Lehdonvirta and Castronova introduces those concept in the previous book through the lens of digital entertainment and online services. This work is most closely aligned with my current research so is an important piece of the economic/complexity puzzle.

Writing Science

Joshua Schimel

This meta-level book on presenting academic work using compelling narratives is my favourite book on this list and the most recently studied. It covers the entire writing toolbox for authors publishing scientific work, and should be mandatory reading for all researchers worldwide. As expected it is exceptionally well written and very hard to put down.