The Meta Cricket League Collection uses a dynamic rarity scoring system to rank the different NFTs. Here, weightage is given to not only the image properties, but also the game properties of every NFT.

How did we calculate?

Comparing values of image properties and game properties is similar to comparing apples and oranges. Therefore, to arrive at a level playing field for both properties, we needed to normalize the two first. For this, we compressed the entire raw score for each property to a range of 0 to 1.

To normalize the game score, we had to do an additional step of calculations. This is because the Meta Cricket Bowler Players have scores for only 2 or 3 traits, hence we only had to take into account the traits with nonzero scores, as seen below:

Once we had the Normalized Image Properties (NIR) and Normalized Game Stats (NGS), we multiplied them into weighted factors (W1 and W2), before adding the two, as seen below.

With this, we reach the final score for each NFT. As an NFT levels up in-game, the score will update to reflect the new changes.

For Meta Cricket League Players, we used a 50% weighted factor for Normalized Image Properties (NIR) and a 50% weighted factor Normalized Game Stats (NGS). This is because the MCL Players have upgradable game stats.

For Meta Cricket League Bats, we used a 75% weighted factor for Normalized Image Properties (NIR) and a 25% weighted factor Normalized Game Stats (NGS). This is because the MCL Bats have non-upgradable game stats.

What does this mean?

A dynamic rarity scoring system allows users to enhance the rarity of their NFTs by reflecting the time and effort invested by them to progress in the game.

This one-of-a-kind system ensures that each time you level up any of your MCL NFT in-game, you also increase its rarity score, overall ranking, and marketplace value.

Users can also use this dynamic scoring pattern to find the rarest and most impactful NFTs, one that will benefit them both in the game and on the marketplace.