dpleuler

MLSE (Maple Leaf Sports & Entertainment Ltd)

Toronto, ON, Canada | Remote | Contract Role (3-12 Months)

We're looking for a Computer Vision Engineer to help our Sport Performance Lab improve our various in-house Pose Estimation models. Open to consulting groups.

Reach out to me at Devin dot Pleuler at MLSE dot com with inquiries.

Legally, there isn't much. Most of those sites are powered by Opta data, which is exclusively for-purchase.

Statsbomb is a little different. They've started collecting their own data and are offering some free data from various Womens leagues.

Defensive metrics are very difficult for a couple reasons.

The first problem is the data. The soccer viewing public is largely familiar with event-level data, typically provided by (my previous employer) Opta. They've done a great job normalizing soccer statistics on the cultural level, but the information they've collected at scale isn't that useful for creating good defensive metrics.

Other companies have sensed an opportunity here and have started providing more detailed data around things such as defensive pressure. Suddenly, you can contextualize each offensive event with the level of defensive pressure applied to it. I think this will be a game changer, but we're in early days there.

Other companies provide player-tracking solutions that give you real-time position of all players on the field. This is great because you have a "complete" picture of the game, but it requires a lot of work to build more sophisticated spatial/geometric models.

There's also the "Howard Effect", coined largely as a basketball term, but it's similar to the Maldini example you provided. Some defenders are so good that they don't have to be "active" defenders. That's something which is really difficult to adequately control for.

There isn't a ton of great public resources out there. For the most part, anyone who's writing smart analysis in the public space gets hired away to a club. It's exactly what happened in other sports.

But, I would read "The Numbers Game: Why Everything You Know About Soccer Is Wrong" and read the backlogs of the StatsBomb blog. That will get you up to speed pretty quickly.

I stumbled into it largely by coincidence and luck. As you could imagine, there aren't many people that study computer science who were also relatively high level soccer players. I'm very fortunate to occupy the very narrow intersection of that obscure venn diagram.

I stumbled across some high resolution soccer data during college and began writing a blog that became popular in soccer analytics circles. The company that produced the data that I was scraping eventually hired me to their data science team. I spent a few years with them before I was recruited to my current team.

I have never worked in gambling but there are a few people in my sort of role that have that sort of background. It requires a pretty similar set of skills. I am not sure that I could beat the market by a large enough margin to make it worthwhile. It's quite efficient. But for the most part, I'm more interested in understanding the underlying mechanics of tactics than I am interested in predicting the result of games.

I'm the other analyst mentioned in the article, but my position is probably quite similar.

I work with a team's coaching staff and management to help them make more data-driven decisions. This ranges from topics around opposition analysis to player recruitment.

Funny to stumble across this on Hacker News.

I'm the analyst mentioned in the article. Happy to answer any questions about our weird little industry.

I visited Acrosanti this past summer. They have a few rooms available on AirBnB which I fully suggest checking out.

They might be the first NBA team to do it, but I know of at least one Major League Soccer team that does this.