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I'm not sure the methodology as a whole makes any sense, even with the prediction overfit aside. The Y axis is the mean score of the team, but that score is no
by Galanwe 4y ago
I'm not sure the methodology as a whole makes any sense, even with the prediction overfit aside.
The Y axis is the mean score of the team, but that score is not independent from the opposing team.
It means that it does not refute the hypothesis that sparse top talents in a team happen at low level games, where the top talents make a huge difference.
When reaching higher level games by acquiring new top talents, the playing field is more leveled, and the scores will even out.
To remove that effect, you would need to select games where a team score does not depend on the opposing team score. Or add a new dimension to the regression which is the opposing teams top talent %.
TLDR: if you put Michael Jordan in an amateur team, he will overperform and crank up the team score. If you then add 5 other pro players to the team, you'll enter the NBA. Your team will perform better, even though your number of points may decrease, as other teams are closer to your elite teams level.
- dvt 4y ago> The Y axis is the mean score of the team, but that score is not independent from the opposing team. The paper is garbage, but this is not true. All Y-axes are how they define "team performance" -- in the case of the FIFA study, it's qualification rankings. In the case of the NBA/MLB study it's end-of-season team win percentages.
- Galanwe 4y agoIndeed, my bad. The argument still holds though. Overall team disparity should be much higher at low level than high level. A team composed of "Michael Jordan + random players" would crush any amateur league with 100% win rate. The same team with "Michael Jordan + Magic Johnson + random players" would probably get 75% win rate in an NBA match. So the metric "win rate" alone, or any other metric non independent of the opposing teams level, is naturally useless in this study.