On Alpha Game

Choosing a team

Since Shepherd released the whitepaper me and my team started working on some models. I would like to share some findings to possibly help you guys on choosing your Pack.

For today’s study, we will only work with Points Contribution and player’s share that comes from DPC (Daily Points Contribution). To have a working model, we need to reduce inferences and guesses the most we can. The amount of $WOOL that each player will elect to stake is too much of a guess to start taking conclusions. On the other hand, we can learn a lot by studying the effect DPC has on each player.

So as a starting point we divided each set of players (A5, A6, A7, sheep) equally into the 14 teams (Example: 11992 sheep/14 = 857 on avg for each team). You may be thinking that is no way in the world that such a scenario would happen, you are right. That is what would be the equilibrium. Studying from the equilibrium, we can learn a lot on how the numbers shift once we deviate from that.

So by dividing the players into the Packs and multiplying by their respective DPC (490 for A7, 360 A6, 250 A5, 50 sheep) we get to:

For better viewing experience, all spreadsheet numbers of this study will be rounded up to int.

First discovery. Sheep matters a lot. On an average distributed team, 51.8% of DPC would come from sheep. We love you sheep ❤️

That average team would make 82683 Total Daily Points for their Pack. Now, what we wanted to see is how each player Final Prize is affected once teams start to deviate from equilibrium distribution. Not necessarily, the winning team is going to be the one that generates the most amount of $WOOL to each player. Because players may have to share the fixed prize with too many team members.

Let’s dive in.

A quick reminder of how much $WOOL each place on the leaderboard will receive at the end:

For this next part, we are going to work with 3 teams only for our model. This makes the spreadsheet cleaner and helps us start to understand the model better. For further deeper researches I’ll be always looking for a more complex model for all teams but might choose to share them in a simpler form.

For this toy game, assume there are 3 Packs competing for the top 3 prize spots. So:

Ok, let’s see how much each player would end up winning if there was a 20% deviation from average numbers. Scenario nº1:

Models will have 2nd placed team with average numbers, and 1st and 3rd team with equal deviation (for this first table 20%).

So we can see that in our toy game if 1st player win by a 20% margin for each set, every player of that team is winning more $WOOL at the end.

Scenario nº2 (25% Deviation):

Surprise! Every player from the last place team is winning the same amount of $WOOL as the 1st place team. So in this scenario, it would be better for you to choose the losing team than the average placing team.

Scenario nº3 (30% Deviation):

If players start to concentrate too much on the winning team, opportunities start to emerge. Too much unequal distribution leads to the teams with fewer players to have a bigger piece of the pie, and since we have a fixed prize, and not a proportional one, that will always be true, no matter how big of a difference it gets after 25% deviation. It can get to the point that last place of our toy game wins 50% more wool finishing last than the first place pretty quickly.

So on that note. If your goal is maximum accumulation of $WOOL as possible, you should be careful picking a team that will probably have too many players fighting for that pie. Unless you want to win for the glory for your Alpha, that’s fine too, but I tend to assume most here are still worried about making it in life. I’m here to help if you want the best for you.

Conclusions

• Sheep matters. We need you here, sheep.
• Don’t go out blindly picking the most famous/richer team and expect to win the most. It’s a very complicated game.

Stay tuned for more.

Wolf #1026