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the board game shelf analysis 2022: average additions

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Wouter Groeneveld 2022-07-29 16:27:57 +02:00
parent 7529f52df6
commit 4209dcb44f
2 changed files with 17 additions and 3 deletions
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4
content/post/2022/07/the-2022-board-game-shelf-analysis.md
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@ -76,12 +76,14 @@ All right, so what does that teach us?
3. What's up with all those red triangles floating on top of the rest? The heavier a game, the **less likely it sees the table**! I know many people judge the staying power of a board game by the amount of times it is actually played, but I disagree. My wife dislikes heavy hitters such as Fields of Arle while I _love_ them. Since we mostly play together, the simpler games also see a fair bit of playtime. That could also mean that... 3. What's up with all those red triangles floating on top of the rest? The heavier a game, the **less likely it sees the table**! I know many people judge the staying power of a board game by the amount of times it is actually played, but I disagree. My wife dislikes heavy hitters such as Fields of Arle while I _love_ them. Since we mostly play together, the simpler games also see a fair bit of playtime. That could also mean that...
4. Since I also tend to rate heavier games higher (same correlation, **0.63**), I urgently need to meet up more with friends to play the heavier games in the `-` range! _"We should make this a recurring thing."_ For how long have we been saying that now? 4. Since I also tend to rate heavier games higher (same correlation, **0.63**), I urgently need to meet up more with friends to play the heavier games in the `-` range! _"We should make this a recurring thing."_ For how long have we been saying that now?
5. Judging from the second plot, there are too many games I don't like still in this list, and don't even see many play time. Again, this is a bit unfair since (1) my wife likes nostalgic simple games like _Dead End Drive_ or _Ramses_, even though we don't play them often. This is my personal score, not hers, and we obviously collectively own everything. 5. Judging from the second plot, there are too many games I don't like still in this list, and don't even see many play time. Again, this is a bit unfair since (1) my wife likes nostalgic simple games like _Dead End Drive_ or _Ramses_, even though we don't play them often. This is my personal score, not hers, and we obviously collectively own everything.
6. Averages: BGG score `6.94/10`, own score `2.89/4` (`7.23/10`), weight `2.09/5`. Perhaps that means I could be a tad more critical---or it just means we indeed made the right decisions keeping the games we love and getting rid of the ones we don't like.
7. Average plays: `1.69/3`. Not something to be particularly proud of...
Okay, I know it, the graph shows it, the photograph shows it... We'll have to talk about the elephant in the room: the overload of Euro _Uwe Rosenberg_ games that are heavy and never see play. There are still games in the closet that need to be replayed and then perhaps let go of: _Le Havre_ (too similar to _Agricola_ and _Arle_), _Nightfall_ (_Dominion_ with direct interaction and weird chaining that my play group doesn't like), _Carson City_ (yet another worker placement game that's better with 3, but I love the theme), some only mildly entertaining smaller card games we're not yet sure of, etc. Okay, I know it, the graph shows it, the photograph shows it... We'll have to talk about the elephant in the room: the overload of Euro _Uwe Rosenberg_ games that are heavy and never see play. There are still games in the closet that need to be replayed and then perhaps let go of: _Le Havre_ (too similar to _Agricola_ and _Arle_), _Nightfall_ (_Dominion_ with direct interaction and weird chaining that my play group doesn't like), _Carson City_ (yet another worker placement game that's better with 3, but I love the theme), some only mildly entertaining smaller card games we're not yet sure of, etc.
I tried including a "optimal number of players" property in there, but it would be a bit too much. A quick count says the following about the collection: I tried including a "optimal number of players" property in there, but it would be a bit too much. A quick count says the following about the collection:
1. 26 out of 45 games (`58%`) play great with two players. We try to keep an eye on that. 1. 26 out of 45 games (`58%`) play great with two players---14 of those (`31%`) even exclusively. We try to keep an eye on that.
2. 15 games (`33%`) are card-based. I love card games. Excluding Magic, of course. 2. 15 games (`33%`) are card-based. I love card games. Excluding Magic, of course.
3. Only `24%` of our games see regular play (`++`). `20%` now and then (`+`), and `56%` almost never (`-`). Ouch. This again isn't a very accurate statistic: we bought _The Quest for El Dorado_ last week and so far we think it's awesome but it's obviously only seen play three or so times. It's a fairly telling estimation though. 3. Only `24%` of our games see regular play (`++`). `20%` now and then (`+`), and `56%` almost never (`-`). Ouch. This again isn't a very accurate statistic: we bought _The Quest for El Dorado_ last week and so far we think it's awesome but it's obviously only seen play three or so times. It's a fairly telling estimation though.

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extras/bgg.py
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@ -46,8 +46,9 @@ print(" \t -- OVERALL")
print(np.corrcoef(list(map(lambda v: v['score']['BGG'], data)), list(map(lambda v: v['score']['W'], data)))) print(np.corrcoef(list(map(lambda v: v['score']['BGG'], data)), list(map(lambda v: v['score']['W'], data))))
print("\n\n") print("\n\n")
print("Correlation between weight and plays?") #print("Correlation between weight and plays?")
print(np.corrcoef(list(map(lambda v: v['score']['W'], data)), list(map(lambda v: v['weight'], data)))) #print(np.corrcoef(list(map(lambda v: v['score']['W'], data)), list(map(lambda v: v['weight'], data))))
def play(p): def play(p):
if p == 1: if p == 1:
@ -59,5 +60,16 @@ def play(p):
for e in data: for e in data:
print("| [" + e['name'] + "](" + e['link'] + ") | " + str(e['score']['BGG']) + " (" + str(e['score']['W']) + ") | " + str(e['weight']) + " | " + play(e['plays']) + " |") print("| [" + e['name'] + "](" + e['link'] + ") | " + str(e['score']['BGG']) + " (" + str(e['score']['W']) + ") | " + str(e['weight']) + " | " + play(e['plays']) + " |")
print("Averages:")
print("\t BGG")
print(str(sum(list(map(lambda v: v['score']['BGG'], data))) / len(data)))
print("\t Own")
print(str(sum(list(map(lambda v: v['score']['W'], data))) / len(data)))
print("\t Weight")
print(str(sum(list(map(lambda v: v['weight'], data))) / len(data)))
print("\t plays")
print(str(sum(list(map(lambda v: v['plays'], data))) / len(data)))
plt.show() plt.show()