But my question is why would anyone ever feel the need to distinguish the better players from the winners. That seems counter-intuitive and insulting, IMO.
Score should reflect the better player (in principle, I think “winner” should be the better platyer, and winner is based off score). A “game” isn’t an event, it is a series of events. If it is unlikely for even good teams against bad teams to score more than a goal or two, then there is a problem there IMO.
Of course there couldn’t be a 100% guaranteed system… even in games without any chance at all like chess players can still have bad days. But then it is clear in such a case that they really were the worse team.
I am suspicious of any behavior where competency doesn’t guarantee championship (however those terms are applied for the behavior in question).
I am an American. I don’t like soccer as much as some other sports played in the U.S., and when I mention the reasons why this is so to non-American soccer fans, they usually respond with a statement to the effect that “you just don’t understand the game”.
I agree with the original poster’s point, and think that soccer could be improved by decreasing the difficulty of scoring. The goal area is currently 8 yds. by 8 feet (7.32 m x 2.44 m). I would start increasing this. How about 2.9 m high ( 9ft 6 in: most athletic men would be able to touch the cross bar) and 9 m wide.
We keep twiddling with this till the occurance of a 0-0 tie after 90 min of play is 1%.
If this sounds blasphemous to you, what is so sacred about 7.32m by 2.44? Why not play with a goal 71cm x 71cm (1 cm more than the diameter of the ball) Would complaining about the low scores in this version of soccer be just an “American desire for constant action” or a realization that there is not an unlimited amount of time to see who has more skill in kicking a ball at a target?
(Continuing on with the If-I-Ran-FIFA tirade: Increase net size, move pentaly shot location back (22 m?) to compensate for larger net, add more referees to be closer to fouls, add instant replay, move the area where offsides can not occur from the midfield line, to another line that is closer to the opponent’s goal)
Maybe with these modifications Christian Vieri’s wide-open shot in the Korea-Italy game would have gone in and the world would be spared a lot of Italian whining.
Here’s another point in favor of the OP: Look at the numbers we’ve used in our simplified models. We single out “chance to score on a scoring drive” as the main statistic and then compare numbers like 10% vs 20%. Or 1% vs 2%. It seems unlikely that teams at this level of competition would have skill levels that vary so much.
I’d expect the teams’ “chance to score” to be closer - maybe we should compare 18% vs 20%. That would create more opportunity for the noise to drown out the signal.
And one point against the OP: As others have already said, it’s an oversimplification to single out that one factor in a game. The better team will initiate more scoring drives, get further with each drive, and maintain control of the ball longer than the weaker team.
Here’s the bottom line, in my own humble opinion. The low number of scoring drives is a bigger problem than the low scores. Each goal results from a combination of skill and chance. Fewer scoring drives makes it more likely that chance will decide the game.
I’m neither a soccer fan nor a statistician, so this may seem quite ignorant, but it seems like an important point is being missed: that the number of scoring drives is not predetermined, but rather determined by the abilities of the players.
Scoring goals and defending against goals are important aspects of gameplay, but attempting to maximize one’s own opportunities to score while minimizing the opportunities of the opponent is significant as well.
Take the coin analogy and extend it–imagine that the number of times each coin is tossed is determined by rolling a die; and imagine that one of the coins is associated with a die that comes up as six 20% of the time, while the other coin’s die only comes up as a six 10% of the time.
I’m sure the probability stuff gets more tricky now (and I realize that for this to work I’d have to define the probabilities for each other number on the dice as well), but it seems like this would be a more useful analogy than the simple coin-tossing one.
Let me know if I’m wrong (or, since I’m sure I’m wrong about something here, let me know how wrong I am about what).
Soccer, and to a certain extent hockey, are sports where the most scoring opportunities don’t frequently correspond with the most goals scored.
Many teams, such as the US team, don’t try to push forward often for scoring opportunities, but rather wait to make counterattacks. This usually is a result of a team thinking that it has a good defense, but only an adequate offense. You try to frustrate the team that attacks more and hope that it sends too many players forward and then gets caught outnumbered on a counterattack.
So, one could make the case that the US team was not as skilled as the Mexican team, but it was more disciplined and made better use of its infrequent scoring opportunities.
In hockey, this happens although in a different way. A team like Carolina would hope to get an early score and then play a style that would make it nearly impossible for the other team to catch up. Unless you were a team that was much more skilled, such as Detroit.
As I said previously, and the last 4 or 5 posts have illustrated, coming up with an accurate mathematical model of the individual soccer game is near impossible.
A paper was published on this subject back in 2010.
Secondary source: Can a formula predict the outcome of a soccer match?
Quote: The analysis also has interesting effects on how we tend to view soccer matches, according to the researchers. For example, the media will often comment that a team that won or lost played particularly good or bad in that match. In contrast, the results here suggest that a team’s fitness level doesn’t change very much from match to match. Yet media reports (and fans) may have a strong tendency to judge a team’s fitness level based too much on the overall score, while ignoring the random effects that may have actually led to the overall score.
In addition to predicting the outcomes of soccer matches, the analysis could serve as a framework to classify different types of sports in terms of degree of competitiveness. For example, in sports with many points such as basketball, random effects are probably less pronounced, so that the stronger team has a better chance of winning than in sports with low-scoring games.
Read more at: Can a formula predict the outcome of a soccer match? Emphasis added. The researchers started with a Poisson process and evaluated that model’s accuracy. Such a model breaks down with very small score differentials. But it’s pretty good otherwise.