1) they always paid out USD values, but transfered equivalent Bitcoin.
2) they had problems with overfitting leaderboards based on validation sets, now they pay out based on live results resolved over 4 weeks. That was solved several months ago before the crypto currency went live or really had anything to do with it.
3) Payouts are now in both USD and NMR for the competition (based on live data).
So, they did have some problems at first, but that was solved by paying out on live results rather than a leaderboard based on validation data. That solution didn't have to do with the cryptocoin.
The purpose of the cryptocoin is that data scientists can earn more USD (in addition to the well functioning direct competition) by staking NMR. The NMR and conversion rate they require tells the fund how confident they are in their model. This additional confidence information, outlined in the paper you link, gives the fund a better idea of how to weight individual competition submissions for their meta-model that determines asset allocation.
It is an ETH coin. Read more carefully. The whole benefit of ETH over BTC is that you can build coins inside of ETH.
In this case it is basically just a colored coin, the point of which is for the people who hold them to be able make stakes on their long term performance (hence plain ETH would not be useful).
2) they had problems with overfitting leaderboards based on validation sets, now they pay out based on live results resolved over 4 weeks. That was solved several months ago before the crypto currency went live or really had anything to do with it.
3) Payouts are now in both USD and NMR for the competition (based on live data).
So, they did have some problems at first, but that was solved by paying out on live results rather than a leaderboard based on validation data. That solution didn't have to do with the cryptocoin.
The purpose of the cryptocoin is that data scientists can earn more USD (in addition to the well functioning direct competition) by staking NMR. The NMR and conversion rate they require tells the fund how confident they are in their model. This additional confidence information, outlined in the paper you link, gives the fund a better idea of how to weight individual competition submissions for their meta-model that determines asset allocation.