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Behind all the odds at an online betting site is information. Odds are a representation of probability, and the gauging of that probability comes from research and analysis. Information is easier to put together on major competitions like La Liga, tennis Grand Slams and major PGA Golf events.
That’s because statistics on star players and teams are easy to find with so many sources covering them. However, if you look at the list of bookies by Legalbet.uk, an expert platform that reviews legal bookmakers, you’ll see that in the UK alone, there are over 100 trusted betting sites. All of them should offer a wide variety of betting markets from different leagues, competitions, and sports around the world, many of which don’t have a high volume of information available.
Gathering data on less popular soccer matches from obscure leagues that aren’t in the spotlight is, therefore, a lot more difficult. So how do bookmakers set accurate betting odds when there is a relative lack of information to go off of?
What people see in professional sports is only the tip of a very large iceberg that’s supporting it. Think of European football, where the big leagues like La Liga, Premier League, Bundesliga and Serie A dominate all the headlines and broadcasting coverage. Then consider how many other divisions of football there are across the continent beyond those.
A good example of this is the UEFA Europa Conference League, where bettors are likely to see a host of teams from domestic leagues from places like Romania, Bulgaria and Northern Ireland that they are completely unfamiliar with.
But those teams still have to be researched and analysed for the purpose of setting odds, even when there is a lack of relative information about them.
The information collected about lesser-known sports and events comes from big data mining efforts covering historical information, team performance, player performance, and trends. Analytics specialists, like Opta and Sportradar, break down sports into extremely niche statistics that bookmakers can use.
Bookmakers do have their own teams of traders and sometimes for niche matches, the analysts have to manually dig a little deeper with whatever information can be scraped from the web to assess potential outcomes.
Official league and tournament websites are also highly valuable for sportsbooks. Official organisations typically have a huge volume of specialised statistics because the English Premier League, for example, is only focused on the English Premier League.
Bookmakers, usually through API integration to get those stats from official bodies, can use the multiple data points to integrate into the odds calculations.
Collecting information about every team and every player from every sport is just too much for one operator, so bookmakers also rely on another type of third-party specialist - odds and sportsbook providers.
A bookmaker can call upon several of these, each with expertise in different areas. One could provide targeted data from South American football leagues, another from lower-ranked tennis events, and another that has information about up-and-coming boxers who are working their way up the ladder to title fights.
Many of these platforms like Kambi and Gammastack provide odds which are integrated into a bookmaker’s sportsbook through an API (Application Programming Interface). These curated odds have to be accurate, of course, so there is a lot of trust in third-party companies delivering accuracy.
But the company will do that to build trust and business, so the partnership works and along with the odds, more integrated data like player statistics and quick data retrieval for updated live prices are also provided.
AI trading has been heavily integrated into online sports betting. AI and machine learning can set automated pricing from the data that has been analysed and put an emphasis on accurate margins that help bookmakers with risk management.
But it is also used for other purposes, such as tracking betting trends on obscure matches to see what type of action is going onto it. This on-the-fly analysis of public sentiment can then be stacked up against what limited data is available to adjust the odds if need be.
This is important because the odds found on less well-known events are typically higher risk for bookmakers. The lack of information about players competing in a first-round match on the ATP’s Challenger Tour compared to a semi-final at the US Open, for example, is vast. Even with information, bookmakers counter this by putting higher margins on markets that will get less public support.
Bookmaker odds are created out of vast banks of information that are rapidly processed and condensed into probability. To balance the popular competitions with niche events that only a handful of people will likely look at, comes from a balance of technology and manual expertise.
