User:Butternutbamboo
| |
| About | |
|---|---|
| Known as | butternut / butter |
| In Freedonia | |
| First joined | 5 September 2020 |
| First building | Blue Hill mayor's house |
| Support level | **** Supporter IV |
| Kit level | **** Gold |
| View profile and statistics | |
Hi, welcome to my user page! Feel free to leave a message on my talk page :)
I'm named after the butternut squash and the lucky bamboo plant (dracaena sanderiana). At the time, butternut squash was my favorite vegetable and lucky bamboo was my favorite plant.
I can usually be found AFKing to collect items from farms. If I'm not responding, feel free to /mail me!
Projects
| Name | Type | Started | Note |
|---|---|---|---|
| Blue Hill | Category:Towns | 2020-09-20 | |
| Primrose | Category:Cities | 2023-08-05 | |
| Item farms | Category:Howto | 2023-09-15 | Directory of public farms and guides on how to build your own |
| MCO Case Book | Category:Rules | 2023-10-12 | Review of scenarios of uncertain legality |
| Freedonia Public Library Network | Category:Organisations | 2023-11-28 |
I like to build things at a small / realistic scale, i.e. one story = 3 blocks. It doesn't leave a lot of room for detail, but I find that it makes builds more immersive to walk through :)
I love doing interior decorating of builds also!
Player homes
| # | Location | Coordinates / Map link | Contribution |
|---|---|---|---|
| 1 | Blue Hill | 4352, -14622 | Built, along with the surrounding town |
| 2 | Shackcity | 15693, -9695 | Built |
| 3 | Computia | -18569, -2851 | Furnished (built by Headmate)
|
| 4 | Sandy Shades | 14455, -2843 | Built |
| 5 | Georgetown | -2404, -2997 | Empty |
| 6 | Newport Tower apartment 29 (floor 11) | -10688, 18905 | Furnished (built by snakyman r9q Darkerfly
|
| 7 | Obsidian Town Butternut Ranch | 8199, 15697 | Built |
| 8 | Spawn | 25, 274 | Built |
There is a convenient mailbox in front of my spawn house if you want to drop me any items!
Interesting links / places
- Yannapurna National Park, or more generally, builds by
YugRekooh: Super interesting builds that play with scale and gravity. - The very tall chunk by
Berselius33 in the Mosaic Mansion is super interesting to traverse and explore! It really feels like an adventure to start at ground level and work your way up through all the different areas and builds within - I haven't experienced anything similar on the server. You'll need a kit with at least /thruor a lot of ender pearls. - A cute + peaceful little fountain area built by
RainBd in the North Borderlands, at 4658, -18137.
Research
Impact of early gifts for new players
Background: The impact of giving gifts such as iron or diamond tools or armor to new players has been debated, with some arguing that early assistance can help ease players into the server while others argue that doing so disincentivizes further play. However, knowledge has been limited to anecdotal evidence and no formal analysis has been carried out to research the impacts of early assistance.
Research questions:
- How do different gifts given to new players affect their relationship with the server and its community?
- Do different gifts have different impacts?
Hypothesis: In general, early gifts will not have an impact on player survival or citizenship, as these will mostly be driven by external factors upstream of character and personality, or by mentorship from more experienced players.
Study: Do early gifts affect survival or activity?
Data: Chat logs were obtained for one year covering September 1st, 2022 to September 1st, 2023.
Timestamps of advancements were extracted from these chat logs; advancements were detected by searching for advancement messages such as "[player] has made the advancement Cover Me With Diamonds" chatted by McObot. Specifically, the following three advancements were searched for:
- Suit Up (iron armor)
- Isn't It Iron Pick (iron pickaxe)
- Cover Me With Diamonds (diamond armor)
Among users with at least one of these advancements, timestamps of first and last seen and time played as of December 6, 2023 were obtained from the server's Web Scripts.
Additionally, the following cutoff points were used to create indicator variables:
- Early gift: If a player received an advancement within 45 minutes of joining (polled from ingame chat)
- Left server: If a player was last seen more than 30 days ago
Methods: A conceptual framework is shown below. The causal path of interest is shown in green and is biased by confounding paths in red through who is online, reflecting who can provide gifts or help in general, and personal characteristics. It is hypothesized that how friendly a new user is may affect the propensity of other players to give gifts or help, though this requires further study.
The following methods are used to investigate the relationship of interest:
- Crude analysis: In the crude, unadjusted analysis, a robust Poisson model was fit to estimate probability of having left the server due to having received an early gift.
- Model:
glm(left_server ~ early_gift, data = merged, family = quasipoisson(link = "log"))
- Model:
- Cluster analysis (Canceled): In the cluster analysis, players were matched to each other by clustering based on similarity of online player lists at the time of joining the server. The cluster IDs were then included as dummy variables in the same robust Poisson model.
Results: A total of 775 player joins were detected over the study period. The table below shows the characteristics of these players relevant to the study. The accompanying figure also shows the relationship between time-to-first-gear vs. overall time played among the different gear types.
| Variable | Median (IQR) or Count (%) |
|---|---|
| Banned | 4 (0.52%) |
| Received an early gift | 135 (17.42%) |
| Left the server | 456 (58.84%) |
| Time played (hours) | 0.34 (1.20) |
| Time since last seen (days) | 444 (45) |
| First gear = iron armor | 52 (6.71%) |
| First gear = iron pick | 40 (5.16%) |
| First gear = diamond armor | 43 (5.55%) |
| First gear = none | 640 (82.58%) |
| Time-to-first-gear, if any (hours) | 0.5 (2.4) |
Results from the regression modeling are shown below. A significant relationship between early gift and left server over the time period could not be found, either across the whole sample or within strata of gift type.
| Coefficient | Whole-sample (n = 124) | Iron armor (n = 48) | Iron pickaxe (n = 37) | Diamond armor (n = 39) |
|---|---|---|---|---|
| Intercept | 0.96 (0.91, 1.00) | 0.88 (0.72, 1.01) | 1.00 (1.00, 1.00) | 1.00 (1.00, 1.00) |
| Early gift | 0.98 (0.90, 1.04); p = 0.61 | 1.07 (0.86, 1.26); p = 0.47 | 0.95 (0.85, 1.03); p = 0.34 | 0.95 (0.85, 1.03); p = 0.32 |
Analyses did not proceed to the cluster analysis due to very low power likely precluding meaningful results.
Discussion:
The crude analysis confirms our hypothesis that receiving an early gift does not affect a player's propensity to leave the server. However, the sample used for the analysis was very small: n = 124 in the whole-sample model, where the variables for both early gift and left server were not missing (i.e. join, first gear, and last seen timings). Further research is needed to provide more evidence for this.
The following are potential areas that this study could be improved upon:
- An opportunity to close the second biasing path through personal characteristics may be available by using sentiment analyses or mixed methods to quantify and adjust for personal characteristics.
- Early gifts have been identified using a simple cutoff value which is prone to misclassification. Future work can review the chat logs before each advancement to determine manually if it was an early gift or not.
- Left server has been identified using a simple cutoff of 30 days. It's possible that people could rejoin the server again after this time.
Factors affecting propensity to donate
Background: WIP
Research questions: WIP
Hypothesis: WIP
Study: identifying predictors of server donations via gradient boosted trees
Data: WIP
Methods: Several features were engineered from the sample data, shown below:
- Year joined
- Time played and kits
- Velocity of time played
- Season, using periodic terms for day-of-the-year
- Number and overall sentiment of chat messages sent and times mentioned in chat
- Wiki activity (presence of user page; number of edits)
- Achievements earned (iron pick, diamond armor, etc.)
These features were then used to train an XGBoost machine learner with hyperparameters optimized via Bayesian optimization, using 10-fold cross validation. Shapley values were used to assess importance of individual features for the overall model fit and the impact that each feature had on model predictions.
Results: WIP
Discussion: WIP


