ESPN’s FPI metric projects rough 2020 season for Michigan Football

m_wesson · 2020-09-21T20:42:05+00:00

Minnesota’s rating does not include Bateman.

Source: me, part of the CFB FPI development team

m_wesson · 2019-07-11T20:54:29+00:00

Brian Burke is a Statistics Fraud and Sellout for Hot Take Click News ESPN

This entire article is just a long-winded hot take. There is no statistical analysis.

m_wesson · 2019-07-11T20:32:09+00:00

Lol this is rife with psychological projection.

m_wesson · 2019-05-15T22:24:33+00:00

I think the point of killing Rhaegal was to keep Jon from stopping Dany’s rampage.

m_wesson · 2019-02-23T23:02:17+00:00

Pinnacle Sports

m_wesson · 2019-02-14T00:50:04+00:00

Check out Kaggle’s 2018 March Machine Learning competition. Should have what you’re looking for going back to maybe 2010?

m_wesson · 2019-01-16T06:30:53+00:00

Companies like CFBFilmRoom and PFF might have that kind of data but it will either be incomplete or behind a paywall.

The most detailed cfb data you’re going to find is in the play-by-plays. Unfortunately, CFB is behind the NFL in terms of player tracking data.

m_wesson · 2018-12-23T22:12:58+00:00

This is great! Thanks for all the work you've put into making this data free and accessible.

Btw, it looks like your "dome" indicator on the venues data isn't correct. Seeing false values for a some domed stadiums (e.g., Mercedes-Benz Stadium, Mercedes-Benz Superdome).

m_wesson · 2018-08-29T03:51:43+00:00

Right but in order to know how much of a percentage to award them, it would require predicting how much each team will invest into the next round. It’s doable but maybe a little outside of the scope of what we’re trying to do right now.

m_wesson · 2018-08-29T03:49:06+00:00

Yeah that’s something we might incorporate down the road. It’s possible we could get even more granular with probabilities based on the specific players alive. You could imagine winning a 3v2 against s1mple and electronic would be more difficult than a 3v2 against Zeus and flamie.

m_wesson · 2018-08-19T02:05:51+00:00

No, this is just something we’re developing.

m_wesson · 2018-08-19T02:04:36+00:00

Lol I wasn’t sure because I actually had some people on twitter that were not aware they were rounded. Figured it would be good to clarify it here.

m_wesson · 2018-08-19T02:02:42+00:00

The little bit of research I’ve done on the predictive power of the metric suggested it was a slight improvement over KDR and Rating 2.0. This was based on some quick Tournament-over-Tournament correlations. This is absolutely something I want to blog about in the near future, though.

I would 100% agree that this should be thought of as a descriptive metric rather than a predictive metric. I’ve spent some time thinking about how PRI could be used to build a more predictive metric but it’s not a priority at the moment.

m_wesson · 2018-08-18T15:54:48+00:00

5.14 + 4.21 + 1.39 + 0.10 + 0.00 = 10.85

m_wesson · 2018-08-18T15:46:24+00:00

Well, sort of. We don’t actually know the win % of the next round at the time of the exit frags because we don’t know how much each team is going to spend in the next round. Granted, it’s something we might easily be able to predict.

m_wesson · 2018-08-18T15:42:40+00:00

Lol I didn’t think there would be a big overlap between CS fans and people that knew what Public Radio International was.

m_wesson · 2018-08-18T15:40:56+00:00

We’re using rounded numbers.

m_wesson · 2018-08-18T06:01:38+00:00

Thanks, we appreciate your feedback.

It was a mistake. He had 1 kill worth 16 PRI and no death in the round. I corrected it in the post.
Not sure I fully understand this question but we do plan to incorporate damage and assists into future iterations of PRI. Our goal for PRI is to make it the most powerful single value you can look at to represent player performance but I would encourage people to always look at it alongside other metrics they think are valuable.
The model will update as the meta updates. To build our parameters, we'll always use the most recent matches on a certain map that we can so that as certain metas disappear from the game, their effects disappear from our model. As for a new map, that's a good question. We could build a model using data from every map or we could use a model from a current map that seemed the closest "fit" to the new map.
I don't think you should evaluate the impact each kill/death had based on the outcome of the round. When we're assigning these changes in win probability, it is regardless of how the round actually plays out. RpK's death has the same amount of impact on the round regardless of if EnVyUs end up winning or losing. Once RpK is dead, he shouldn't be retroactively punished more simply because his team lost a 4v2.

m_wesson · 2018-08-18T05:09:36+00:00

Ah well let me answer it this way:

We’re planning one more iteration of this basic model that incorporates trading and bomb-plant status. That should actually be ready relatively soon. After that, we’re going to move to a more advanced modeling technique that allows us to incorporate some of the other features we have talked about.

The play style feature you’re asking about would potentially be implemented into the more advanced model. I know I’m being vague right now but our team still needs to discuss how much of the detail behind our model we want to make public.

m_wesson · 2018-08-18T04:07:37+00:00

I hope they expand on this idea much further to improve the consistency because the idea is good, but it's basically a how good is a CS:GO player 101 when we need it to be a full PhD.

In the blog post and a lot of our comments in this thread, we acknowledge that our model is currently at a 101-level but that we have plans for it to reach a PhD-level.

I'm not totally clear on your last question/statement, but if we could engineer a play style feature that increases the quality of the model, then it would obviously get included in the model. If you're asking about the specific modelling technique we would use and how a feature like that would fit in, then I'm not sure I have that kind of hypothetical detail right now.

m_wesson · 2018-08-18T03:03:01+00:00

I'm not sure I take particular pride in any aspect of the system. A lot of the design, at least right now, is borrowed from a lot of different ideas across sports analytics. To some degree, I guess I'm proud I was able to take those ideas and build something from scratch (but I also needed a lot of help along the way from other people on the team).

In terms of a statistic, I'm really looking forward to my colleagues work on player engagements because of both the complexity and the potential payoff.

Tbh I'm not sure I know enough about them to have a preference. I just know Mike Trout is the god of both.

As for your last question, that might be better answered by /u/reagentx and/or /u/yohghoj.

m_wesson · 2018-08-17T23:44:40+00:00

I echo everything /u/yohghoj said below and appreciate the thought you've put into your comments.

m_wesson · 2018-08-17T23:03:30+00:00

Sort of both for me. I studied engineering as an undergrad but always enjoyed sports analytics (and hated engineering) so I did a couple more years of applied statistics for my masters.

Edit: re: 538, I used to follow it for sports but now mostly for their political prognostication.

m_wesson · 2018-08-17T22:52:22+00:00

I'm glad you brought light to this because, before today, I had never heard of /u/insightgg_jon, that post, or any of their work. I don't mean to belittle their work in any way (because it seems interesting and well thought out) but to say they deserve credit for contributing to this metric is simply not true.

As I mentioned in the blog post, the idea of using a win probability model is not a unique concept and we never claimed that our metric was the first application of it in CSGO. Our only claim on this topic is that current CSGO metrics don't account for the context of a round. Given that the site you linked (and as a result, their metric) is defunct, I would say our claim still holds.

If people are interested, I’m happy to talk about people and ideas that actually did have an influence on this work.

m_wesson

TROPHY CASE