What Do You Like Least About Miniatures Wargaming?

One question in WSS' 2023 Great Wargaming Survey asked, 

What do you like least about miniatures wargaming?

Well, this is a loaded question if I ever saw one!  In this case, the question was open-ended meaning respondents could enter whatever was on their mind.  Some did! 

Given that the responses were free text with about a 2100 character limit, responses could be quite long and detailed.  Out of the total number of survey responses, 7,278 entries had text in this field.  A sample snippet of these responses is illustrated below:

As seen from the text snippet, anything goes.

How can we bring order and meaning out of these unstructured texts?  For that, we turn to text analytics including cluster analysis and principal component analysis (PCA).  Without going into details of the machine learning techniques, simply understand that it is a data reduction method that summarizes large quantities of text data into fewer clusters and principal components.  The results are then wrangled to present (hopefully) interpretable graphics.

To begin, each text response is parsed and tokenized to create words.  For example, the highlighted line, below, can be parsed into the following word tokens.  That is, 

"people" "who" "are" "too" "pedanttic" "about" "details".

Preprocessing requires a number of text transformations (including correcting spelling errors) to get the tokens into a standard and more usable form.  In addition, synonyms are used to group similar tokens.  After tokenization and preprocessing, 4,918 unique tokens (words) resulted from over 36 million token combinations.  The result is a large dataset with useful inferences likely obscured by such a large sample space.

To begin data reduction, records having near-zero variance are removed.  After that reduction, only a manageable number of tokens are retained.  Some ambiguous tokens are, likewise, removed.  After the data reduction is complete, only eight tokens remain.  These remaining tokens are:

• rules_lawyer
• painting
• people
• time
• cost
• competition
• find
• miniatures

Already, I reckon these eight tokens tell an interesting story.

Using cluster analysis, these eight tokens are grouped to produce the dendrogram shown below:

Interesting!

Looking at a two-cluster solution,

the resulting clusters are "rules_lawyer" and everything else.  Rules lawyers seem to find themselves high on the "Do not like" list and separate and distinct from all other tokens in a two-cluster solution.  Into the rules_lawyer category, I dumped discussions of rules, themselves, including complexity, ambiguity, arguments, etc..  A four-cluster solution looks like,

with the four clusters as, (1) rules_lawyer, (2) painting, (3) people, and (4) time/cost/competition/find/miniatures.  After rules lawyers, respondents tend to dislike painting followed by people(!) and then the remaining effort constraints.  The "find" and "miniatures" tokens require some explanation.  Into the "find" grouping were lumped discussions on finding opponents, rules, figures, places and time to play, and money to fund the hobby.  Into the "miniatures" grouping were included discussions on painting, storing, assembling, price and quality.

Turning to PCA (and without going into details), the PCA Variable plot below illustrates the loadings of each of the eight variables in 2D space.  Color and length of each vector denotes its contribution to the PCA analysis.

One of the useful traits of PCA analysis when displayed as a graphic is that inferences are easier to understand.  While only two of the dimensional plots are illustrated here, both of these dimensions lend themselves to interpretation.

Looking at Dim1, the graphic suggests that the dislikes are bifurcated into two groups.  These two groups are "People" related attributes and "Effort" related attributes.

Similarly, looking at Dim2, the graphic suggests that the dislikes are again bifurcated into two groups.  Those two groups are "Cost" and then everything else.

First, I find the results amazing in that a 36 million cell tokenized matrix can be distilled down to meaningful inferences.  Second, I find the results surprising in that rules lawyers and gamers, themselves, rise out of the murk to irritate many.  I suppose that I should not be that surprised.  I once had a wargaming friend (I still consider him a friend!) who confidently stated that the hobby would be great if it were not for the people!

Do these Top 8 wargaming dislikes reflect your own hobby dislikes?  Does a rules lawyer stand alone at the top of your wargaming dislike ranking?  If not, what do you like least about the miniature wargaming hobby?

The survey also asks a parallel question only in this question, respondents are asked what they like most about miniatures wargaming.  I may look into that topic another time.