Goldman Sachs model to predict World Cup game results didn’t come close

There are two probabilities in question: The first, of course, is the probability of victory. The second is the probability that the first probability is correct.

Consider: If someone offered to give you $2 every time a fair coin toss came up heads, or take $0.50 every time it came up tails, you'd be foolish not to take that bet a million times as you can because you know that the coin has exactly a 50% chance of coming up heads.

However, if it was an unfair coin, you'd want to know the degree to which it was unfair, and you'd have to measure it. How much do you trust those measurements? You might say that you're 90% sure that the coin has a 40-60% chance of coming up heads, or give a probability of 2% that a $1.04 to $0.96 wager would be profitable while a $1.03 to $0.97 wager would be unprofitable.

Hillary had a 95% chance to win the election. But on top of the fact that 1 in 20 times she'd lose that election if that really was the probability, the 95% number was uncertain because the measurements were difficult to pin down - maybe she'd have lost 1 in 40 times, or maybe she'd have lost 1 in 5 times. All we know now is that she lost, and that many of the assumptions and measurements the pollsters had to make concerning factors like voter turnout, nationalism, corruption, foreign interference, debate results, and fundraising turned out to be inaccurate.

With unfair coin measurements, you can get very accurate numbers with just a handful tests. When predicting election results or World Cup games, you're much less likely to make an accurate estimate. The confidence is an estimate of how likely that estimate is to be accurate.

Soccer is a sport with a big random component. This is probably why it is so exciting. An average team can beat a better team.

The reason is easy to see. The game can be decided by one, two or three key plays. Compare that to basket ball. To win a game you have to consistently score more and defend better. Rarely the game is decided by one or two plays. That only happens when the game is already very tight.

The Volcker Rule shutdown approximately 0 amount of proprietary trading on wall street. Any articles you can point me to were merely media stunts by their respective firms.

The rule was too complex and onerous to be implemtable. Case in point, it's already being rolled back... Certainly because of the current administration we're in. But more because it was just a poorly written and thought out idea to start with.

Financial modeling is about risk adjust return. Because GS knows they can not determine with certainty the outcome of a given investment, they diversify and hedge. Most of all, GS is a market maker, the equivalent of a bookie. To say that GS’s models “didn’t come close” is to ignore all the ways in which such a grading scheme is different than GS’s actual business model. If their WC prediction efforts acted as anything more than a fun spirited PR project, it was likely that GS wanted to somehow keep its employees engaged and adding business value during the WC which they otherwise would have been certainly watched all month.

The problem is that the 2018 World Cup is not a repeatable event. Neither are most open-market trades (presumably the point of this whole PR stunt being to show that their quants are good at making smart bets in the markets) but they're a LOT closer.

Soccer is a pretty data-poor environment, or at least was historically. Before movement trackers, there was very little data to play with. With movement tracking data slowly building up, I suspect that soccer analytics will soon have their "Moneyball" moment the way baseball did.

The reason baseball got there sooner is that, even without advanced player movement tracking, baseball is a data rich environment. There are ~2500 MLB games played per year in the the 30-team era, and we have at least box scores going back to the late 19th century for most professional games, and pitch-by-pitch data going back to the eighties. In addition, a lot of the most important data is cleaner in nature (pitcher-batter match-ups) and also abundant (compare ~200 pitches in a baseball game to ~15 shots on goal in a soccer game, to take a guess at the order of magnitude).

Computing power can help squeeze more information from the soccer data we collect going forward, but there is a century or more of player tracking data that we can just never ever have, since it wasn't being collected. We know Babe Ruth's batting line but we will never have the soccer equivalent of UZR for Pele. I don't know if there is a retrosheet-equivalent effort for soccer to collect stats from old film, but that would be one way to partially bridge the gap.