Summary: I describe how the works using concepts you’re already familiar with. TrueSkill is used on to rank and match players and it serves as a great way to understand how statistical machine learning is actually applied today. I’ve also created an where I implemented TrueSkill three different times in increasing complexity and capability. In addition, I’ve created a that works out equations that I gloss over here. Feel free to jump to sections that look interesting and ignore ones that seem boring. Don’t worry if this post seems a bit long, there are lots of pictures. It seemed easy enough:
I wanted to create a database to track the skill levels of my coworkers in and. I already knew that I wasn’t very good at foosball and would bring down better players. I was curious if an algorithm could do a better job at creating well-balanced matches. I also wanted to see if I was improving at chess. I knew I needed to have an easy way to collect results from everyone and then use an algorithm that would keep getting better with.
I was looking for a way to compress all that data and distill it down to some simple knowledge of how skilled people are. Based on some that I had heard about, this seemed like a good fit for “. ”Machine learning is a hot area in Computer Science— but it’s intimidating. Like most subjects, there’s to be an expert in the field. I didn’t need to go very deep I just needed to understand enough to solve my problem.
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I found a link to describing the TrueSkill algorithm and I read it several times, but it didn’t make sense. It was only 8 pages long, but it seemed beyond my capability to understand. I felt dumb. Even so, I was too stubborn to give up. Jamie Zawinski:
“Not knowing something doesn’t mean you’re dumb— it just means you don’t know it. ”I learned that the problem isn’t the difficulty of the ideas themselves, but rather that the ideas make too big of a jump from that. This is sad because underneath the apparent complexity lies some beautiful concepts. In hindsight, the algorithm seems relatively simple, but it took me several months to arrive at that conclusion. My hope is that I can short-circuit the haphazard and slow process I went through and take you directly to the beauty of understanding what’s inside the gem that is the TrueSkill algorithm.
It might make sense to just tally the total number of wins and losses, but this wouldn’t be fair to people that played a lot (or a little). Slightly better is to record the percent of games that you win. However, this wouldn’t be fair to people that or players who got decimated but maybe learned a thing or two. The goal of most games is to win, but if you win too much, then you’re probably not challenging yourself. Ideally, if all players won about half of their games, we’d say things are balanced.
In this ideal scenario, everyone would have a near 55% win ratio, making it impossible to compare using that metric. Finding universal units of skill is too hard, so we’ll just give up and not use any units.