Pricing the outsiders: how Flutter set odds on the World Cup's underdogs

Deciding odds for big sporting nations is one thing, but pricing a team with patchy data is the real test for a bookmaker. Here is how Flutter did it for the World Cup.

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For big sporting nations, the odds almost price themselves. Watch Spain and Argentina and you can see every pass, shot and foul, backed by years of data on players who can turn out as many as 60 times a season. With the World Cup final now set to be contested by two of the pre-tournament favorites, that kind of pricing is the easy part. For smaller, lower-profile nations like Cape Verde and Curaçao, sometimes all that's available is two minutes of highlights from a regional friendly.

The 2026 World Cup added 16 extra places to the usual 32, filled by debutants such as Uzbekistan or teams like Haiti that hadn't qualified in half a century. Flutter needed to price every player in every squad to offer the variety of team and player markets that customers now expect.

That meant odds on more than 1,200 players, covering up to 15 inputs each – shots, goals, tackles, cards and set-piece duties – more than 50,000 selections in all. Getting prices right on lesser understood teams depended on the Flutter Edge: the collective experience and expertise of Flutter's trading teams across the world.

48 teams.

1,200+ players.

50,000+ selections.

A call for volunteers

A handful of traders, however expert, could not cover 48 teams in the required detail. So Flutter's trading leadership put out a group-wide open call. Around 80 colleagues volunteered, many choosing teams with a personal connection, others assigned at random – handed out via Flutter's own World Cup draw, complete with a colleague's tongue-in-cheek recreation of Rod Stewart's chaotic Scottish Cup draw. They worked to a shared method: settle a team's expectancies first, then determine each player's share of the stats.

Every team also came with a preview card listing the likely starting 11, substitutes, and set-piece takers. Even then, certainty was thin on the ground. "We always acknowledge that we don't know what we don't know," says Jason Murphy, Principal Football Trader at Flutter UK and Ireland, who led the project. "Even with England."

With no data, price the team sheet

For outsiders, barely any statistics are available, so the first challenge is projecting who will play. Take Cape Verde, researched by Barney Spooner, an experienced FanDuel Trader with little available information on the team. With little footage to study, he went through the qualifying campaign game by game to confirm the first-choice striker, set-piece takers and likely formation, filling the gaps with positional averages drawn from Flutter data.

"The most important thing was projecting who was going to be playing, and who would get the lion's share of the minutes," says Spooner. It worked: nine of his projected 11 started the opening game, and Spain against Cape Verde was one of Flutter's strongest markets of the tournament – Spain had 74% of possession and 27 shots, yet Cape Verde held them to a goal-less draw.

Cape Verde went on to become the first World Cup newcomer to reach the knockout stage since Slovakia in 2010, coming within a whisker of an upset against holders Argentina before losing in extra time. At 1000/1 pre-tournament, a Cape Verde win became the biggest liability in Flutter's book, potentially worth tens of millions worldwide. Almost one-fifth of those bets were Irish, drawn by local hero Roberto 'Pico' Lopes, the Dublin-born League of Ireland player called up via a LinkedIn message he initially ignored because he didn't speak Portuguese.

An ever-changing squad

Some squads made a good price even harder to pin down. Iran's – drawn largely from a domestic league with little international coverage – was reshaped shortly before the tournament, complicated further by the country's ongoing conflict with the USA, a host nation.

Sam Turnbull, the trader who priced them, is half-Iranian and has followed the team for years, yet even he admits, "I wouldn't know many of the players if they passed me in the street." Still, his attention to detail paid off when he noticed defender Ramin Rezaeian playing a more attacking role. This led Flutter to rate his goal and shot chances higher than many others in the market. He went on to score twice.

A similar story played out with Maximiliano Araújo, a left-back at Sporting CP but a more advanced player for Uruguay. Flutter's research flagged the discrepancy early and priced his shots and goals accordingly; he finished with two goals, though Uruguay were eliminated at the group stage.

The framework and the data only get you to a starting price. The final one is set by the soccer trading team, pooling experience and unique skillsets to refine pricing for a game that is stubbornly chaotic. Pricing the marquee players and teams is a trader's bread and butter – whereas pricing the outsiders and unknowns is where the real skill emerges, and it's one Murphy is happy to let the numbers answer. "The price is the final piece," he says. "If you get the price right, the results will look after themselves." Cape Verde's run to the knockout stages proved the point – and long after the two favorites have played out their final, it's the work done on these smaller nations that will keep setting the Flutter Edge apart at the next tournament.