Does the team’s actions really have an impact on the price of the coin?

Do the team's actions truly affect the coin's price?

“Teamwork” really makes the token price rise more in a bull market? Does it resist falling more in a bear market? This article will tell you the answer using 10 years of historical data.

Original author: LUCIDA & FALCON

When we hold cryptocurrency assets, “teamwork” is the confidence that the token price will take off in a bull market and the bottom line of “continued holding” in a bear market.

But does “teamwork” really make the token price rise more in a bull market? Does it resist falling more in a bear market?

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This article will tell you the answer using 10 years of historical data.

The four bull and bear cycles in the Crypto market

Bitcoin was born in its genesis block in 2009, and its token price has shown multiple cycles of bull and bear markets in the following 14 years, with the emergence of “ICO era,” “public chain explosion,” “DeFi Summer,” “NFT wave,” and other industry narratives.

For convenience of analysis, this article defines 2015.07-2018.01 as the first bull market, 2018.01-2020.03 as the first bear market, 2020.03-2021.05 as the second bull market, and 2021.05-present as the second bear market.

Due to the distance from the first “ICO” bull market from 2015.7 to 2018.1, there is too little data available to obtain rigorous results. Therefore, this article focuses on analyzing the last three cycles.

What factors can reflect “teamwork”? We have identified six factors!

Most projects in the industry are based on blockchain technology, and the code is open source on GitHub (GitHub is a platform for code hosting and sharing).

Therefore, **Falcon uses the six factors on GitHub as quantitative standards to measure “teamwork,” specifically including: Star, Fork, Commit, Issues, Pull requests, Watchers.** The following are the specific meanings and types of the six factors.

The GitHub data for all projects in this article can also be found on Falcon’s platform, visit the link:https://falcon.lucida.fund/ch/asset_tracker/73/github?uid=

Effective Sample Size and Glossary

The team has compiled the token price trends and corresponding project GitHub six-factor data for three market cycles, after handling outliers, there are 81, 330, and 596 valid token samples for the three market cycles respectively.

The following text will provide explanations for the terms appearing in the charts:

First bear market (2018.1-2020.3) GitHub data has a certain anti-decline effect on prices, but its effectiveness is limited, perhaps due to a small sample size.

Let’s start with the first bear market:

Descriptive statistics of GitHub data six factors and price changes:

**The token data during the first bear market is relatively dispersed, consistent with the characteristics of the early stages of the crypto market’s rise.** The standard deviation of the 7 statistical measures during this period significantly deviates from the average, indicating significant differences in prices and GitHub data among different tokens. Developed tokens during this stage, such as Bitcoin and ETH, had extremely high attention in their GitHub factors, but many emerging tokens had low attention and developer contributions on GitHub.

The statistical situation of tokens whose price decline is less than the average decline value (bold black) and their corresponding GitHub data six factors:

**The gray cells represent tokens that behave opposite to market trends. We consider these tokens to have special characteristics that require comprehensive analysis in conjunction with market conditions.** There is only one token, binance-exchange, in this interval. Observing its GitHub data six factors, the star and fork values are in the top 10 of the statistical measures, but commit, issues, pull_requests, and watchers are extremely low. This is mainly because bnb, the token, only had the attribute of “platform coin” before 2019 and had no “public chain” attribute, so its code was not open source. However, in the second half of 2018, the market focused on the platform coin sector, and bnb had a high price increase during that period, showing resilience. For this token, only the star and fork factors in GitHub data have a certain correlation with price.

Among the tokens with price declines less than the average, 40% of them have GitHub factors in the top 10 of the statistical measures, while the rest have generally low GitHub situations. Preliminary inference suggests that during this period, GitHub factors have a certain positive effect on reducing price declines, but this effect is not particularly significant.

Second bull market (2020.3-2021.5) More active projects on GitHub had higher price increases during the bull market.

Descriptive statistics of GitHub data six factors and price changes:

**The data of tokens in the second bull market are relatively concentrated, indicating an increase in maturity and prosperity in the crypto market.** The standard deviation of these seven statistical measurements is close to the average, suggesting a more concentrated distribution of sample data compared to the statistics from 2018-2020. Based on market analysis, this can be attributed to the maturity of the token market in 2020, where tokens that emerged in 2018 have seen significant development in this period, and their corresponding GitHub data has also experienced a notable increase. Furthermore, as the market continues to develop, the number of tokens issued in this period has significantly increased, leading to a further increase in the concentration of data distribution as more samples become available for reference.

The statistical details of the token prices that exceed the average price increase (highlighted in bold) and their corresponding GitHub data for the six factors:

Out of the 330 data tokens, 11 exceeded the average price increase, and among them, 5 also surpassed the average GitHub data for the six factors, accounting for approximately 45%.

Preliminary analysis suggests a certain level of correlation between the increase in GitHub data and price surge, and the specific extent of this correlation will be discussed in the third part of the article.

Projects that experience price drops instead of surges in a bull market tend to have very inactive development on GitHub

Exceptional cases of token prices during the bull market (a decrease in price):

Among the 330 valid samples in this cycle, 28 tokens witnessed a price decrease against the trend, indicating their weakness. Additionally, the corresponding GitHub data for these tokens were 90% lower than the average and generally approached the minimum value.

In the second bear market (from May 2021 to present), more active GitHub projects contributed to resisting price drops, but their impact remains limited

Descriptive statistics for the six factors of GitHub data and price fluctuations:

Sorting by the “star” factor, data for the top 20 tokens and the other six statistical measurements (tokens exceeding the average value are highlighted in bold):

**As the crypto market continues to evolve, the data for tokens in the second bear market becomes more dispersed, indicating a further differentiation within the industry.** The standard deviation of these seven statistical measurements shows a significant difference from the average, suggesting a more scattered distribution of token data in the second bear market phase. In 2021, the token market is still in a vigorous development stage, with more people entering the market. Initially, people focus on tokens that have performed well and are more mature. The GitHub activity for such tokens reaches tens of thousands of views. However, for emerging tokens during this period, they require time to gain popularity among the general public, and naturally, their attention and development level are relatively lower.

According to the statistical analysis of the top 20 tokens ranked by Star data, there is a certain similarity in the statistical patterns of tokens whose GitHub data six-factor ranking exceeds the average value. This suggests that there is a high correlation between the six factors. It is also observed that tokens with high GitHub data six-factor rankings are generally more mature and were mostly issued between 2015 and 2018, such as bitcoin, ETH, and dogecoin.

Regarding abnormal token prices in bear markets:

Among the 596 token data, 28 are considered abnormal. Among them, 6 tokens have one or more GitHub data factors that exceed the average value, accounting for 28%. According to the table, it can be inferred that the increase in GitHub data contributes to resistance against market downturns to some extent, but its impact is not particularly significant. These tokens’ price advantage mainly depends on factors from other categories.

How can we quantify the correlation between GitHub factors and prices? What coefficient should we use to evaluate?

In the previous text, we found that GitHub data plays a different role in bull and bear markets through simple statistical analysis.

So how can we quantify the correlation between GitHub factors and prices?

The Q-Q plot uses sample quantiles as the x-axis and corresponding quantiles calculated according to the normal distribution as the y-axis, representing the sample as a scatter plot in a Cartesian coordinate system. If the dataset follows a normal distribution, the sample points will form a straight line around the first quadrant diagonal. Pearson correlation coefficient analysis is more appropriate for datasets that follow a normal distribution, while Spearman correlation coefficient analysis is more suitable for datasets that do not follow a normal distribution.

The Q-Q plot results for the six factors in three intervals are shown below.

According to the table, the sample points of the six factors in three intervals (Star, Fork, Commit, Issues, Pull_requests, Watchers) do not form a distribution around the first interval diagonal, indicating that they do not follow a normal distribution. The analysis of the correlation between the six factors and token prices will be based on the results of the Spearman coefficient.

The first bear market cycle (2018.1-2020.3): Due to sample size limitations, the correlation between GitHub factors and token prices is limited.

The correlation table between the six factors and the price increase of the token:

According to the table, the five factors of GitHub data have a positive effect on the resistance of token prices in bear markets. It can be seen that the correlation coefficients between star, fork, issues, pull_requests, watchers, and the price are all around 0.260, and all show significance at the 0.05 level, indicating that these five factors are positively correlated with token prices.

**In this interval, there is no significant relationship between the commit factor and the price increase of the token.** The correlation coefficient between commit and the price increase is -0.032, close to 0, and the P-value is 0.776>0.05, indicating that commit is not correlated with the price.

The correlation results of star, fork, issues, pull_requests, watchers, and price are consistent with our previous judgment, that is, they have a certain positive effect. We already know that this correlation will not be too high, but a correlation of 0.260 is meaningful for our subsequent research on token price trends and the construction of related factor strategies. The results of commit are slightly different from the previous text, and we tentatively conclude that it is due to the limited sample data. In the second and third intervals, we collected more token data and will further investigate the correlation between commit and price.

The 2nd bull market (2020.3-2021.5): The more active GitHub is, the higher the coin price rises

Correlation table between six factors and price increase:

In the second bull market, as the effective sample increased from 81 to 330, the correlation between star, fork, commit, issues, pull_requests, watchers, and price significantly increased, with a correlation around 0.322, significantly higher than the mean correlation of 0.260 in the first interval, and significant at the 0.01 level. Among them, the factors star, commit, and watchers have a correlation with price as high as 0.350. All six factors in this interval are positively correlated with price, which also verifies our speculation that commit and price are negatively correlated in the first interval, indicating that the sample data is not enough and is affected by individual extreme values.

The 2nd bear market (2021.5-present): GitHub factors have timeliness! They are still significantly correlated with the coin price in the bear market, but not necessarily resistant to decline

Correlation table between six factors and price increase:

For the third interval, the number of valid samples increased to 597, **compared with the first interval, the correlation between the six factors (star, fork, commit, issues, pull_requests, watchers) and price increased**, with a mean correlation of 0.216 at the 0.01 significance level, slightly higher than the 0.205 in the first bear market, but significantly weaker than the correlation of 0.322 in the second interval.

We believe that all six factors of GitHub data are positively correlated with the increase in coin price, but they have a certain timeliness!

3. In a bull market, the more active a project is on GitHub, the higher its growth rate; in a bear market, the more active a project is on GitHub, the more resistant it is to falling.

4. The correlation between GitHub and the price of a cryptocurrency is significantly higher in a bull market than in a bear market.

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