Information and the Paradox of Value
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The 10star Summer Intern Program is designed to introduce undergraduate and graduate students to how we apply data science and quantitative modelling within the sports betting industry.
Through this program, interns will have the opportunity to work on real projects, collaborate with experienced professionals, and gain insights into the workings of a constantly evolving and fast paced industry.
Timeframe
- June - September 2025
Location
- Hybrid working, with regular onsite sessions at our offices in Camden, London
Eligibility
- Strong academic performance in a Mathematical discipline
- Experience using a coding language such as R, Python, C#, VBA
- Ability to work independently and in a team environment
- Interested in sports and thinking about them in an analytical way
Application Process
- Interested candidates should submit their resume and a cover letter outlining their interest in the internship program to careers@10star.com with the subject line "Summer Intern Program Application"
- Closing date for applications 30 April 2025
- Shortlisted candidates will be contacted for interviews
Benefits
- Professional Development
Work closely with experienced professionals and participate in learning sessions tailored to enhance your skills - Networking Opportunities
Connect with professionals across various departments and build relationships that could benefit your career in the future - Hands-on Experience
Engage in meaningful projects and tasks that contribute to the company's goals - Remuneration
Competitive salary offered for the duration of the internship - Flexibility
Work around your term dates, study requirements and holidays
The world of sports betting revolves around information and confidence. The more information you possess then the more confidence you can have in your price – either backing or laying – and the more money you are willing to put behind it.
Looking at the current industry, this correlation of improved knowledge breeding greater confidence stands true. If we assume that the level of information sportsbooks possess is reflected in their pricing, then the decline in the theoretical margin of “win-draw-win” (or 1X2) prices on a Premier League football match is a good example of how knowledge has increased by virtue of the larger amounts of information that are now available.
Matches that were priced up with around 11% margin in the sportsbook’s favour twenty years ago are now priced up by the same books to c.105% and on the exchanges they are highly liquid at less than 101%. More visibility of information means sportsbooks have greater confidence in the odds that they offer, which means that the “protection” they need to build into their prices through margin, or vig, correspondingly decreases.
However, whilst the prevalence of tighter pricing is true for core markets like Matchwinner, the average vig levels applied by sportsbooks for markets overall have increased. Primarily, this is due to the increased popularity of high-margin Same Game Parlays (SGPs). Since their inception, SGPs have been priced very defensively by both operators and their supply chain because they don’t have confidence in the correlations between selections contained within the same bet.
In fact, on closer inspection, the lack of confidence that sportsbooks have in their odds – especially those that they buy in from the existing supply chain – is pervasive in the industry. This results in all sportsbooks that use B2B services restricting the size of a bet that a customer can have – when you don’t have the information on how your price has been constructed then you don’t have the confidence to lay it.
The dominant B2B suppliers of pricing to sportsbooks do not come from a sports betting background and do not have any previous experience of laying their own prices. This undermines industry confidence in a fundamental way. Despite providing trading services, these suppliers do not possess the technology, skills, or capability to understand what prices should be laid and how they should change these prices according to exposure.
So why are these suppliers of pricing and trading services so readily used by operators? The primary reason is that the betting odds they produce by scraping and averaging the market are then supplied as low-priced content to promote the sale of expensive official “game state” data (goals in football, points in tennis etc). This is a form of data – or information – that has facilitated the growth of in-play betting and, subsequently, the perception of healthy industry growth overall.
But herein lies the great myth of the modern industry. If you look back to the time when football matches were priced up to 111% – which were also the early days of in-play betting in regulated jurisdictions – gambling expenditure from then to now has only increased by roughly inflationary levels. Relative to the wider economy, there has been no growth in the betting industry at all and the expansion of in-play turnover has been matched by a corresponding decline on pre-match wagers. For sportsbooks as a whole, all that has grown is their cost of operations. The irony is that a primary driver of this cost increase is the expensive official information sources being used to ease the sale of sub-optimal odds for operators, and which are causing them to leak profits.
It is how the industry uses the forms of data available to it that has created a paradox in the value of this information. In much the same way that diamonds are more expensive than water (but water holds more value for life), so the game-state data which hasn’t generated value overall is perceived more highly than other forms of information that sportsbooks already possess but don’t use.
Consider, for example, the bet flow information collected by sportsbooks generated from customers placing their bets. All operators are sitting on a rich seam of information that, once mined and refined, can be used to enhance prices, empower real-time trading decisions and improve confidence in their trading strategy.
However, extracting this valuable information from raw data requires tooling built on skills and attributes which none of the current supply chain possesses. Conversely, at 10star, we have used our deep trading knowledge and expertise in customer data analysis to create a first-of-a-kind suite of powerful algorithms called Neural. Delivered as a set of SaaS tools, Neural incorporates machine-based learning that allows operators to mine their proprietary bet data and surface outputs that increase confidence in all aspects of their trading operation – whether insourced or outsourced – and drive value creation within their business.
New information generated in areas such as customer sharpness, price efficacy, social responsibility and even how your existing suppliers’ trading practices are leaving you exposed in the market are served as intuitive data points that enable an objective trading strategy for operators and supply a lifeline to an industry that is slowly dying of thirst.
For all enquiries contact info@10star.com