1 Methodology

We explore the relationship between addresses Degeneracy score and their participation in Levana’s meteor shower event. Participation is measured in two different ways: the first is the action of placing a bid and the second is the total amount bid during the event.

We also explore associations between participation and other variables.

We visualize the data using bar charts and plot charts. We determine associations between variables using Pearson’s correlation coefficient.

2 Gathering the data

Flipside Crypto’s API allows us to pull query results from the Velocity platform.

The following function is used to import our data:

PullVelocityData <- function(endpoint.url){
  raw.results <- readLines(endpoint.url)
  to.return <- data.table(jsonlite::fromJSON(raw.results))
  setnames(to.return, tolower(names(to.return)))
  return(to.return)
}

We prepared a query that includes each address that submitted a bid for the meteor shower event. Also included is the total amount bid by each address during the event. We import the query results from Velocity.

levana.data <- PullVelocityData("https://api.flipsidecrypto.com/api/v2/queries/53ebd8c1-84c2-4c54-947c-026fef6f3e47/data/latest")

Link to query

Our second data set is the LUNAtic score of each address on Terra. Also included are all the variables which are used to calculate the Lunatic score. The data set was downloaded from Flipside’s LUNAtic Rankings

levana.data2 <- read.csv("Lunatic Scores.csv")

By combining both data sets we are able to know:

  1. the lunatic score and associated variables of each address;
  2. whether or not an address has placed a bid on the Meteor Shower event; and
  3. The total amount bid by each address (if applicable).
levana.data5 <- levana.data2 %>% mutate(placed_a_bid = ifelse(levana.data2$address %in% levana.data$address, 1, 0))
levana.data3 <- levana.data %>% left_join(levana.data5)

3 Number of Addresses that Obtained a Meteor.

The minimum bid price for obtaining a meteor was 8 UST. Let’s see if any addresses placed a bid under this amount.

min(levana.data$total_ust_bid)
## [1] 8

All addresses placed a sufficiently large bid to obtain a meteor.

count(levana.data)
##       n
## 1: 6340

6340 Addresses obtained a meteor.

4 Correlations

4.1 Correlation Between Degeneracy Score and Placing a Bid

ggplot(levana.data5, aes(Degeneracy, placed_a_bid)) + geom_col() + stat_cor(method = "pearson", color = 'red', size = 3)

There is a negligible correlation (R = 0.2) between addresses that placed a bid and their degeneracy score. The chart does show that almost 3,000 addresses with the maximum degeneracy score did place bids. On the other hand there are also more than 1,000 addresses with a degeneracy score of 0 that also placed bids.

4.2 Correlation between Total Amount Bid (in UST) and Degenaracy Score

ggplot(levana.data3, aes(Degeneracy, total_ust_bid)) + geom_col() + stat_cor(method = "pearson", color = 'red', size = 6)

There is no correlation (R= 0.05) between the degeneracy score of addresses and their total amount bid. It may seem peculiar that there is no correlation. Our chart shows that addresses with the highest degeneracy placed bids with the highest combined value. How can this be?

There may be more addresses with a degeneracy score of 6. If that is the case than it would be normal for their combined total bids to be the largest.

We count the number of addresses that placed a bid and have a degeneracy score of 6.

sum(levana.data3$Degeneracy == "6")
## [1] 2906

Indeed approximately 45% of our data set is comprised of addresses with a degeneracy score of 6. This gives us confidence in our result showing no correlation between an address’ degeneracy score and its total amount bid.

4.3 Correlation Between Lunatic Score and Placing a Bid.

Let’s see if we can find a stronger correlation in the data. This time instead of the degeneracy score we will be using addresses’ Lunatic score.

ggplot(levana.data5, aes(Total.Score, placed_a_bid)) + geom_col() + stat_cor(method = "pearson", color ='red')

There a weak correlation between addresses placing a bid and their Lunatic score. Nevertheless the correlation (R=0.24) is higher than the correlation with the degeneracy score (R=0.20)

4.4 Correlation between Total Amount Bid (in UST) and Lunatic Score

ggplot(levana.data3, aes(Total.Score, total_ust_bid)) + geom_col() + stat_cor(method = "pearson", color = 'red', size = 6, aes(label = ..r.label..))

There is no correlation between the addresses Lunatic score and their total amount bid. Again we must be careful when interpreting the graph. There could be more addresses with a high Lunatic score. When combining the total amount bid of these addresses, we get a higher total amount bid per lunatic score. This does not mean that individual addresses with a high lunatic score have larger total amounts bid.

5 Ploting the Data

Let’s see if we can learn anything else using plots.

ggplot(levana.data3, aes(Total.Score, total_ust_bid)) + geom_count() + ggtitle("Density Plot, Lunatic Score VS Total Amount Bid") + stat_cor(method = "pearson", color = 'red', size = 5,  aes(label = ..r.label..))

In the plot above, larger size points indicate more bids. The minimum bid amount to obtain a meteor was 8 UST. Regardless of their Lunatic Score, most addresses bid a total of 8 UST.

6 Searching for Correlations with Other Variables

6.1 Correlations with Placing a Bid

So far the strongest correlation that we have found is R=0.24. This is the association between addresses that placed a bid and their Lunatic score. Let’s see if there are other variables which correlate with placing a bid.

levana.data6 <- subset(levana.data5, select = -c(X, address))
B <-cor(levana.data6)
B[22:22,1:22, drop=FALSE]
##              Total.Score  Activity  Airdrops Cash.Out.vs.HODL Degeneracy
## placed_a_bid   0.2381447 0.2063434 0.1635658        0.1137034  0.2044355
##              Governance days_since_last_txn n_airdrop_and_gov_stakes
## placed_a_bid  0.2164922           0.1339915                0.1906713
##              n_airdrops_claimed n_contracts n_dex_trades n_governance_votes
## placed_a_bid          0.1577938   0.2198036    0.1453002          0.1456647
##              n_lp_deposits n_projects_staked n_protocols_claimed n_tokens_used
## placed_a_bid     0.1948329         0.1857592           0.1623477     0.1718669
##              n_transactions net_from_shuttle_cex prop_drops_kept
## placed_a_bid     0.09920481          -0.07307513       0.1142837
##              prop_luna_staked repeat_protocol_claims placed_a_bid
## placed_a_bid       0.09575854              0.1555822            1

The strongest association with placing a bid is an address’ Lunatic score. It should be noted that there are some variables that are more predictive than the Degeneracy score. These are:

  • Activity (recently completing transactions);
  • Governance (staking in a governance pool); and
  • Number of contracts (transactions with multiple contracts).

6.2 Correlations with Total Amount Bid

levana.data4 <- subset(levana.data3, select = -c(X, address))
M <-cor(levana.data4)
 head(M,1)
##               total_ust_bid Total.Score   Activity   Airdrops Cash.Out.vs.HODL
## total_ust_bid             1  0.05938314 0.05509721 0.05589612      0.007549904
##               Degeneracy Governance days_since_last_txn
## total_ust_bid 0.05370952 0.06546458          0.03678745
##               n_airdrop_and_gov_stakes n_airdrops_claimed n_contracts
## total_ust_bid               0.05184653         0.05543865  0.04571633
##               n_dex_trades n_governance_votes n_lp_deposits n_projects_staked
## total_ust_bid   0.03735994         0.05632067    0.05327695        0.05581243
##               n_protocols_claimed n_tokens_used n_transactions
## total_ust_bid          0.04487415    0.03487556     0.05035754
##               net_from_shuttle_cex prop_drops_kept prop_luna_staked
## total_ust_bid          -0.05393553      0.02290329      0.003634655
##               repeat_protocol_claims placed_a_bid
## total_ust_bid              0.0602361           NA

None of the variables included in addresses Lunatic score are correlated with the total amount bid.

7 Conclusion

What have we learned?

  1. The minimum bid price for obtaining a meteor was 8 UST. The majority of bids placed were 8 UST;
  2. 6340 addresses obtained a meteor;
  3. There is a weak association between addresses that placed bids and their degeneracy score (R=0.2);
  4. When looking at addresses that placed a bid, the variables ‘Activity’, ‘Governance’ and ‘number of contracts’ also have correlations that are sightly stronger than Degeneracy score;
  5. The strongest association with placing a bid is an address’ Lunatic score (R=0.24). Nevertheless the correlation is weak;
  6. There is no association between addresses’ total amount bid and their degeneracy score (R=0.054);
  7. No other underlying variables used to calculate the Lunatic score correlate with the total amount bid.

8 Limitations

The main point of this analysis was to identify the predictive value of certain variables such as Degenary. We tried to predict 1) whether or not an address placed a bid on the Meteor Shower event, and 2) the total amount bid. The Lunatic rank (and underlying variables including Degeneracy) were pulled on November 15, 2021. The Meteor Shower event was held on November 8, 2021. Our analysis would have more predictive power if it had pulled the Lunatic ranks from November 7, 2021 (one day before the auction event).

special thanks to: