Adjusted Board Game Geek ratings (2025 update)

Introduction

BoardGameGeek (BGG) serves as an important beacon for board game enthusiasts—much like IMDb for films—offering a comprehensive database of user-driven reviews and ratings on thousands of tabletop games. However, the very nature of community-driven ratings means that BGG's overall ratings and rankings tend to reflect the collective preferences of its active userbase rather than tabletop gamers as a whole. The platform's community disproportionately represents more invested and "hardcore" tabletop gamers, which can skew ratings toward heavier, more complex games that appeal to this core demographic.

Since individual preferences for board games can vary significantly from BGG community consensus, it can be useful to adjust for some of these community biases to obtain a more personalised view of the top games. This blog post presents interactive analysis and visualisations of the patterns that exist in the BGG dataset, with sliders to adjust your preferences for board game weight (complexity), publication year, and popularity to discover games that better match your personal taste.

Complexity-preference adjustment

BGG users tend to favour big heavy games, and we see a roughly linear correlation between game weight (complexity) and game rating. This can be corrected for with a simple linear regression of game rating on game "weight" rating. Move the slider or push the buttons below to see what the base BGG values look like, and how the adjustment factor affects the complexity-rating relationship. Set it to a value that matches your complexity preference. You can even apply a negative-correction or an over-correction if you have a particularly strong affinity or aversion to heavy games.

Publication-year-preference correction

Board games have generally been getting better with time as the industry has been maturing and growing. This can also be seen in the base BGG game ratings data, where the average ratings for games have generally been increasing with time, following a (slow) exponential increase with time. While I think that it's useful for the BGG data to reflect this gradual improvement of games over time, it's also useful to be able to adjust for it to find some good classics, or conversely to ramp up the preference for new games even more. The publication year preference is corrected for with an exponential regression on game age. Move the slider or push the buttons below to see what the base BGG values look like, and how the adjustment factor affects the complexity-rating relationship. Set it to a value that matches your preference. As above, you can also apply a negative-correction or an over-correction if you have a particularly strong affinity or aversion to newer games.

Bayesian rating

The third adjustment factor isn't about adjusting for community preference, but rather about navigating a trade-off for games with a small number of ratings. Any community-driven rating system faces the problem that there can be entities (games in our case) that receive a singe rating with perfect score, or more generally, a small sample of high rating scores that don't accurately reflect the game's true standing relative to its peers. Any good ranking system needs to handle this problem to prevent the top of the list being dominated by games with a low sample size in ratings. The most common method to handle this is to assume that all games start off with a certain number of average-rating votes, and all user-submitted ratings are then added over this initial or "prior" rating for the game. By doing this, all games are assumed to be average, until there's enough evidence (rating votes) to suggest otherwise. Ratings that include this sort of adjustment are typically called Bayesian ratings because they follow the Bayesian inference statistical principles. This is a very useful tool, but the strength of this Bayesian prior can hinder the visibility of underground hits and cult classics since they will generally not have as many ratings, so are indistinguishable from the spurious results (games whose ratings are disproportionately high through random chance, sampling bias, or any other means). The slider below allows you adjust the strength of the prior (how many average rating votes games start off with). A low value allows more underground hits to show up on the top games at the cost of also surfacing more games with unrepresentative or manipulated ratings; a higher value tends to mainly keep "popular" good games at the top of the list.

Overall adjusted ratings

The table below shows the list of games with all 3 adjustments above applied. It is sorted by the final adjusted rating by default, but can be sorted on other fields by clicking on the corresponding column headers.

The inputs below allow additional filters to be applied to the results in the table.