Blog

Learn About Our Meetup

5000+ Members

MEETUPS

LEARN, CONNECT, SHARE

Join our meetup, learn, connect, share, and get to know your Toronto AI community. 

JOB POSTINGS

INDEED POSTINGS

Browse through the latest deep learning, ai, machine learning postings from Indeed for the GTA.

CONTACT

CONNECT WITH US

Are you looking to sponsor space, be a speaker, or volunteer, feel free to give us a shout.

[D] Is there a way to introduce a rating system for an isolated game tournament that could produce better results than simple overall winning percentage?

Hello /r/machinelearning! Long time Redditor, first time posting here. I hope this isn’t too weird of a question.

I’m not looking for someone to do this or anything, this is a conceptual question. It is: Is there a way to introduce a rating system for an isolated game tournament that could produce better results than simple overall winning percentage?

This idea came about because my dad’s friends host a Euchre tournament every year. For those that don’t know, Euchre is a 2v2 card game, and you play to 10 points.

Now this is a friendly tournament among friends, so it’s not like a cut-throat competition. There are players there with a variety of skill levels.

Currently, you just sort of play all day with who is available, and you try not to play with the same person as your teammate all day (that way the two best players don’t just team up all day and have a great record together). You only keep track of wins and losses. At the end of the day, the top 4 winning percentages go into a bracket for the title. We probably play around 20-30 games throughout the day, so the sample size is low.

There are numerous weaknesses to this method. I used to work for a Game Dev studio and really love game design, so it’s a natural hobby of mine to assess the potential exploitation methods. Again, it’s a friendly competition, so I don’t believe anyone is actively practicing any of the below, but there absolutely are some ways that this method falls very flat.

1. There is an active incentive to stop when you’re ahead.

Towards the end of the day, if your winning percentage is high, you basically don’t want to play games anymore. Winning will hardly move your winning percentage up, but a loss can tank your great percentage by a lot.

2. You can absolutely game the system with partner selection and opponent avoidance

If you play games with the people who you know are the best, and play against players who are known to be poor, you can dramatically affect your winning percentage. If a very poor player asks you to be their partner, especially if you go up against a strong team, you’re at a huge disadvantage. If you keep playing these games, your odds aren’t 50/50 to win, but probably more like 75/25 in worst or best case scenarios. This kind of manipulation is incentivized by the rule structure, and a player playing with truly random partners/opponents would be at a disadvantage.

3. Scoring is completely ignored

Losing by 10 or losing by 1 doesn’t matter at all. This is a huge mechanical weakness in my view. If you get behind in a game, there’s a good incentive to try to play aggressively to catch up, but if you come back from being down 7-0 and lose 10-9, you get no points whatsoever for making a great comeback. In the interest of time, it would likely be worth more to just lose the game quickly and start a new one.


There are more criticisms at play here, but these are the main ones I could think of.

How could you go about designing a system that might help mitigate some of these factors? I thought about instituting an ELO-style (or glicko) rating system, but I’m not sure how it would play out given an internal tournament. We also wouldn’t want legacy rankings because lower-ranked players would never make it far enough up the leaderboard to enter the finals.

Is there any sort of system that constantly re-evaluates all the permutations of games that have happened and spits out a ranking of sorts? Something that could actually take into account having a close loss when you have a weak teammate and strong opponents? It would basically take the entire tournament results as a whole, figure out all the rankings and decide on a top 4 players for the tournament.

Is this a concept that exists, or how could current practices apply here?

submitted by /u/HeIsMyPossum
[link] [comments]

Next Meetup

 

Days
:
Hours
:
Minutes
:
Seconds

 

Plug yourself into AI and don't miss a beat

 


Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.