The Three Things I Did To Play More Video Games
I own a lot of video games. Like many of us in my generation, they have been an increasing part of life for nearing four decades at this point. For the most recent half of that time, since the advent of digital distribution, my catalogue — and crucially my backlog of “Games I’ve Purchased That I’ll Get Around To Playing One Fine Day™” — has grown to an almost unimaginable amount.
It’s the culmination of the sheer volume of titles being released, the want to support an industry I care deeply about, the seemingly irresistible trappings of getting a bargain, and FOMO.
I have a reputation in my circle of friends as the person who buys games but seldom plays them. It’s probably a fair assessment, but I suspect I’m not alone.
For me personally, with other competing priorities in my life, these purchases have by and large been a false economy for entertainment purposes. The amount of time needed to play the games that I bought outpaces the time I avail myself to actually play them.
With the pay-monthly models being introduced (as well as the well established free-to-play model), it’s easy to see how attention, and not cost, has come to be a dominant commodity at play.
So as befits my personality, rather than have this remain as some overwhelming, impenetrable, monolithic statement of obligation-inducing paralysis, I took a step back and enlisted the help of a spreadsheet.
Buckle up, friends. Here’s the three things I did to decide what to actually play.
1. Making a list (and checking it twice).
When I talk about a “backlog” — it was actually several backlogs spread across various platforms. So the first thing I did was compile a consolidated listing of everything.
I would ask myself; is the game I’m putting on this list truly something I want to play and experience? Or is the sunk cost fallacy at play here and I had it on a list purely out of a sense of obligation because I bought it on a whim years ago?
If it was the former then I added it in, alongside some accompanying meta-data like the platform and year of release — foresight for knowing I’d get a kick out of doing some analysis on it down the road.
The backlog will update as I finish and acquire new titles. The current listing at time of writing is 348 titles, primarily PC games (via Itch.io and Steam).
Looking at the backlog by year of release shows the steady increase over the last decade. The decline in the last two years most likely being a) the mix of games that happened to be in any bundles I bought, and b) not picking things up in a discount-driven whim, in a conscious effort to enact behaviour change to play more than I buy.
Just a note that PS5 means “anything I’m able to play on a PS5” -not necessarily a made-for-PS5 game etc. I’m not some kind of time travelling, non-games-playing fiend (or am I?).
Now onto prioritising them!
2. Making metrics (for what matters to me).
Metrics drive behaviour. I decided to implement some sort of metrics that were conducive to the goal of what I was trying to achieve, and would allow me to measure and rank this list in some way. In my case, I wanted to be able to play games end to end, making the most efficient use of my time as possible. I.e. games that offer the most bang-for-your-buck per hour of gameplay. That won’t be the best way to prioritise one game over another for everyone, but I thought for me it made sense.
I utilised two sources of information to help measure this:
- The Metacritic (“MC”) score of a game (an aggregation site that scores a title based on the weighted average of critical reviews).
- The “How Long To Beat” (“HLTB”) score of a game. HLTB is a site featuring community of people who register their average time to beat a game, specifying the format, and whether their reported time included optional extra content as well as the main campaign etc.
The idea being that if there were two games with the same MC score, and one had a HLTB score of two hours, and the other twenty hours, then I would prioritise the former over the latter.
I took the extra step to somewhat normalise the scores given with the current method, a four hour critically acclaimed game may get a significantly lower score than a two hour critically slightly-above-average game. To take this into account I assigned MC and HLTB bands to weights as per below:
The weights and bands were determined by me subjectively based on preferences.
The overall score for the ranking then became a function of 20% raw MC to HLTB ratio, and 80% weighted MC to HLTB ratio. I chose an 80/20 split for no particular reason other than having the score calculation slightly take into account the desire to rank games above one another even if they both belonged to the same bands.
Taking pause for a moment, it’s huge-caveat-time with this approach:
- Games, particularly those with a strong audiovisual and/or narrative identity, are considered by many (myself included) to be an art-form. To use numerical metrics based analysis to rank them is inherently imprecise and open to a slew of subjectivity within.
- Metacritic itself is an amalgamation of opinion. Also the HLTB averages probably do not represent me (I love games but I’m not particularly great at playing them!).
- There’s also the added challenge that MC and HLTB data is not always available. I have a proclivity for games often developed by micro-indie studios or single-person developers. There is often not a MC or HLTB for some of these games. In the end I replaced anything missing with the mean average of the rest of the population. That would undoubtedly reward some games and punish others in the ranking.
- I needed to prioritise this listing somehow, and as imperfect as it is, there is a rational logic underlying the analysis. Whilst these metrics and this approach won’t suit everyone, in my opinion it’s a reasonable approach to the problem I was trying to solve which is prioritising a backlog when I have limited time to spend but still wanted to play a broad range of titles.
At the end of it all, I had a prioritised list of games to play.
As an aside, below is a heatmap of average rankings when profiling by year of release and platform. At this point I’m showing averages of a number which itself is an amalgamated blend of subjectivity, so take it with a pinch of salt!
Interestingly, looking at the above heatmap it appears as though the average rankings decline after 2016, and despite nearly 90% of my backlog being PC games, there are no average high average rankings for PC after 2008 (though I’m sure there are a few high ranking titles underneath these numbers).
This doesn’t imply that games are getting progressively worse. It implies that the titles I want to play are not, on average, conforming to the metric that I prioritised here. If I look at the top twenty ranked games on my backlog, sixteen of them were made in 2016 or earlier. Maybe it’s my choice of what I’m adding to the backlog in the first place that’s contrary to what I’m actually after!
3. Making peace with the fact that I will never play everything I want to.
If I made an estimate on how many hours in a typical week I reasonably set aside to play games in the evenings, it would be anywhere from zero to twenty hours. I certainly prioritise other things going on in my life, but for argument’s sake let’s say four hours a week during the week with an additional four at the weekend. Eight hours a week is a more indicative amount of time for me.
The accumulated HLTB of my backlog is currently sitting at 3,293 hours. That’s approximately eight years to finish the backlog at my current pace. Assuming I play nothing else other that’s what on the list. Assuming I don’t go back to anything again. Assuming I take as long as the average person (which as I mentioned above is probably the biggest assumption of all).
That’s when I realised I am going to have to let go of the want and need to finish this backlog of games. Instead, it’s more likely to be useful to me as a barometer of future purchases. Maybe something piques my interest and it would be relatively higher ranking than most of my other backlog titles. Or maybe it would highlight that I have dozens of other games better suited to me, readily available that I can play over that one.
One possibility is to focus on the population where the MC and HLTB simply overlap within the regions I want to prioritise, and chop the remainder from the backlog. This is basically the same as prioritising the top x% of my rankings. A heatmap showing that cross section is below; the green region encompasses almost two thirds of the backlog:
Another avenue to explore is whether to focus on the HLTB by weighting shorter games even more favourably. Looking at the amount of titles and cumulative HLTB of the bands I created, games that are 10 hours in length or under represent nearly 75% of my total backlog but only 42% of the total estimated time requirement.
Conclusion
The irony is not lost on me that I could have spent the time constructing, analysing and writing about this endeavor simply just playing games. But I’d be lying if I said I didn’t derive just as much enjoyment from deciding what games to play as I do actually playing games.
At the end of the day, you can analyze these things until the cows come home, but I wanted to use this analysis to understand what it would take to play every game on this list, to help me avoid the paralysis of choice, and actually help me to play more games. I believe it’s achieved that.
I’ve definitely played through more games as a result of this exercise, and it’s given me pause for thought on which games I look to play in the future, as well as reminding me of the wonderful, original, eclectic mix of games I already have available.
Thanks for reading!
