Problem Chosen
C
2023
MCM/ICM
Summary Sheet
Team Control Number
2301192
Crack the Wordle Puzzle:
Word Attribute Analysis Approaches
Summary
In the past 600 days, a ve-letter puzzle game called ”Wordle” has been launching a blast of
upsurge on Twitter. Wordle players’ scores reports are crucial for managers as they provide valu-
able information for evaluating game diculty, predicting player numbers and making timely
adjustments. To better analyze the reports and provide game improvement suggestions, we con-
duct in-depth and close studies on this topic from multiple perspectives and levels.
Firstly, to explain the changes in the number of Wordle reports and make predictions, we use
an analogy between playing Wordle and the spread of infectious diseases. We compare playing
Wordle with infection, players with infected individuals, individuals who have not played Wor-
dle for a long time with susceptible individuals, individuals who have become tired of the game
with recovered individuals, sharing on Twitter with transmission, and quitting the game with
recovery. Based on these assumptions, we use the SIRS model to t the curve and explain the
overall trend. We also use the Prophet model to insert breakpoints to explain data oscillations
and provide a prediction interval for future data. Model evaluation results show that our model
has high interpretability and accuracy.
Next, we extract various word attributes from a word database containing a large amount of
corpus information and use multiple linear regression to investigate whether there is a relation-
ship between word attributes and Hard-Mode scores. We then test the signicance of the model
based on the F-statistic. The result shows no signicant correlation between these two factors.
Besides, we construct a BP neural network model based on the previously extracted word
attributes to predict the distribution of the number of word guesses. The evaluation results show
that the model has high prediction accuracy and eciency, laying a solid foundation for next
step analysis.
Furthermore, we use K-means++ clustering algorithm to divide words into three categories:
easy, medium, and hard. We analyze the relationship between word attributes and diculty
to classify solution words by diculty. We nd that the diculty of a word is closely related
to the number of dierent letters in the word, the sum of letter frequencies, and the breadth of
usage of the word in dierent elds, but there is no signicant evidence of a correlation between
diculty and word frequency. Combined with the previous BP neural network model, we can
accurately classify words.
In addition, we nd that common words such as ”mummy” and ”watch” have a higher guess-
ing diculty, and there is also a certain correlation between the rst letter of a word and its
guessing diculty.
Finally, we present predictive data and improvement suggestions to the editors of The New
York Times to assist in improving Wordle and boosting the appealing feature of the game.
Keywords: Prophet; SIRS; Multiple Linear Regression; BP Neural Network; K-Means++
剩余24页未读，继续阅读
资源评论
阿拉伯梳子
- 粉丝: 2431
- 资源: 5734
最新资源
- 343366978633126base.apk
- map_mode_escape_1.28.13.12700.pak
- androidx.multidex.MultiDexApplication.apk.1
- 丑子金装美化32(1).zip
- 基于Visual Basic .Net及Python技术的学校需求解决方案设计源码
- 基于Java语言的Web开发学习Demo设计源码
- 基于Java的医院排队叫号系统界面原型设计源码
- 基于Java语言的Himalaya仿喜马拉雅设计源码
- 基于PHP+HTML+CSS+JavaScript的智能电车管家设计源码
- 基于Emscripten编译的纯H5直播流播放器jessibuca设计源码
资源上传下载、课程学习等过程中有任何疑问或建议，欢迎提出宝贵意见哦~我们会及时处理！
点击此处反馈
