In the world of sports betting/analytics, you have baseball and basketball at the forefront, and then American football, soccer, and hockey (roughly in that order).

Off the top of my head, there are several reasons why the latter three sports have all lagged behind:

-Lack of data

It wasn't until the last 4-5 years that widely available, affordable, and accurate data for soccer matches was available. Companies like Opta have accomplished this by outsourcing the watching of games and the manual tagging of events, which was made possible by the advent of cheap cloud computing.

It should be self-evident why tracking the position and actions of 22 players is more complicated than something like baseball, where for the most part you are looking at one pitcher vs. one batter, much of which can be automated with computer vision that tracks pitch position, speed, and spin.

-Complexity

It's no accident that baseball was the first sport to be revolutionized by analytics. Most of the time, it's a static game, with a clearly defined action set. I.e. do I swing at the pitch or not. Do I throw a fastball or not. Do I attempt to steal a base or not.

In games like American football, soccer, and hockey, you have anywhere from 12-22 players on the field at a time. Tracking what the players without the ball or the puck are doing is a difficult task technically, as is quantifying their impact. Concepts like expected goals and expected goals added are recent ones.

-Sample size

Typical elite soccer leagues see each team play each other twice. In England and Spain, this means you have 38 games per season.

Baseball has a 162 game season and playoff games, basketball has an 82 game season and playoff games, etc. Coupled with the fact that quality data has been only collected for a few years, and you get other problems.

In basketball and baseball, the effects of aging on player performance and statistics is fairly well understood now. We can generally calculate the 5-year market value of a player etc. In the other sports I mentioned, we don't yet have that kind of time series data to be able to make those judgements.

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Specific to the World Cup, there are other reasons why you may find it hard to predict results.

-Team chemistry and style

Even though the World Cup is the most high-profile soccer event in the world, most players are spending 1-3 months a year with their national teams. Their "day jobs" with their clubs teams take up most of their playing time and attention.

As anyone who has played the game Football Manager will know, managing a national team is a tough job. You have no say over how the players are practicing when they're away from you, and no control over the physical condition in which they arrive at the World Cup. This year, there was barely a month between the end of the regular European seasons and the start of the World Cup.

In that month's time, you have to get at least 11 players who have not played with each other, to learn your style of play. Do you want to play a pressing style? Are you attempting a slow buildup, or trying long balls? Etc. etc.

-Home field advantage

In baseball and basketball, most modern statistical models account for home field advantage. Having 60,000 Russian fans chanting and heckling likely played a role in the team's ability to upset Spain, particularly during penalty kicks.

This goes back to the sample issue. How many times before have Spain played Russia IN Russia in front of a large crowd? Probably never.

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All this is to say, cut Goldman some slack. There are a number of non-nefarious reasons why you may expect a soccer model to produce some spectacular miscues.