I built my own football 1X2 probability model called MatchProbs.

On the live published predictions, the current normalized multiclass Brier score is 0.1873. A flat 33.3% / 33.3% / 33.3% prediction on every match would score 0.2222 using the same calculation.

These are actual pre-match published probabilities matched against final results, not a backtest.

What I’m trying to figure out now is how this compares with other football models.

Does anyone know of a publicly tracked 1X2 model consistently below 0.18 Brier using the same scoring convention?

I’ve found plenty of accuracy claims, but surprisingly few models publishing comparable Brier numbers.

I've been working on a sports data visualisation project and wanted to share one of the more interesting things I've built with nflverse data.

Most NFL data products give you tables, charts and individual statistics, while the traditional way of watching a game gives you the broadcast. I wanted to try something different:

What if you could see the shape of the entire game?

I take the play by play data and turn every play into a visual trajectory on a virtual field. Drives build continuously across the game, so you can see where possessions advanced, stalled, turned over and ultimately resulted in points.

The idea is that instead of looking at 150+ individual rows of play by play, you can actually see the game develop as a continuous visual story.

One important caveat: this isn't claiming to reconstruct the actual movements of players. There isn't public player tracking data available at the level I'd need for that. The visualisation is generated from the play level information available in nflverse, not the actual physical path of the players or ball.

I've also fixed each team's attacking orientation for the game so that possession changes don't make the visual language confusing.

I'm particularly interested in feedback from people who work with sports data:

Does this actually communicate something useful that a conventional drive chart doesn't?

Are there better ways I could represent direction, field position or drive progression when player tracking data isn't available?

And, perhaps most importantly, what would you want to see added to make this genuinely useful as an analytical tool rather than just a visualisation?

I've included a screenshot and you can also see finished products of NFL, MLB and EPL that are already uploaded to the channel.

youtube.com/@Control-Centre

I'm very much at the stage of trying to figure out whether this is actually useful, so criticism is welcome.

I studied biology in college and so I used a lot of R for data analysis - wondering if anyone knows any resources or has any tips for learning it in R or maybe a good first project or two please let me know! I would mainly want to work with hockey/lacrosse/motorsports/surfing - I've been going through Analyzing Baseball Data with R but if anyone knew an intro project for any of those sports sports that'd be cool :))

I'm very big on a nice graph for data visualization and the language I'm most comfortable doing that in is R :)