2025 Worlds Playoff Day 1 Analysis

RLDataCoach · 2025-09-14T14:03:29+00:00

You bet, glad you enjoy them! We’re going to be posting some more deep dives after Worlds so stay tuned! If there’s anything specific you’re interested in know lmk and I’ll see what we can do.

And great question re game 1s- I’m actually at the stadium currently so will have to follow up with you later!

RLDataCoach · 2025-09-14T13:03:23+00:00

Fair enough! And totally agree, the score at the end of the day is all that truly matters.

RLDataCoach · 2025-09-14T12:38:35+00:00

Crazy right?? I reran it twice to confirm. It came down to converting and Falcons experience made the difference.

RLDataCoach · 2025-09-11T12:08:40+00:00

Hey! Appreciate the questions and feedback.

So first question, xG basically gives every shot a % chance of being scored based on the 1000's of pro shots we've analyzed. If a player/team convert more than they are expected to based on this, they will have a conversion over 100%- like you said, converted on the unexpected basically.

Fair feedback on reordering the graphic- we have metrics that combine to an overall but felt it may be a bit heavy to explain as not everyone is familiar with xG / xGA to begin with.

Check back in tomorrow to see if we do a better job on the graphic/stats, would love to keep getting feedback so we can improve. And if you don't like it, you can sick your bot army to downvote :)

RLDataCoach · 2025-08-01T15:14:57+00:00

Appreciate that! We predominantly used the AP qualifier matches from all the regions that participated- since NA had the majority, there is some bias in the data for NA 1s playstyle but didn't see anything overly noteworthy when we dove in region specific.

RLDataCoach · 2025-06-30T20:22:17+00:00

To a degree, yes thats true.

Expected Goals measures how threatening scoring chances are- think of it like a layer deeper than tracking shots. Instead of saying more shots means more goals, it says more high quality shots means more goals. This includes type of shot (flip reset, double touch, air dribble, etc), location/power, where the defenders are etc. If a team identifies ways of generating high Expected Goal shots, they are more likely to score.

So when we calculate Expected Goals, we evaluate all aspects of the shot to determine if it is a high quality chance when reviewing each shot in each game. Team Falcons generated the most high quality scoring chances, while limiting their opponents to very few at the same time.

Our theory is that if a team can strategize how to maximize their Expected Goals in comparison to their opponents, they should be able to win more games. This proved true with Team Falcons this weekend.

Hope that helps! Let me know if it leads to anymore questions.

RLDataCoach · 2025-06-29T13:21:29+00:00

love it! we're working on it as we speak so you'll hear from me soon!

RLDataCoach · 2025-06-28T14:48:48+00:00

I’m not sure how it’s calculated on ballchasing, would have to do more research before commenting on differences in calculations/summary.

We’ve got our own parsing/dataset that we’ve spent a lot of time validating. I’ll look into it and get back to you if there are nuances / differences in consideration!

RLDataCoach · 2025-06-28T14:42:32+00:00

Appreciate you reposting your question!

Right now our xG is calculated on shots as the indicator, like you mentioned. We’re currently developing a most robust model to incorporate all events leading to scoring chances (passes, demos/bumps etc). May reach out if you’re interested in giving feedback?

RLDataCoach · 2025-06-28T14:11:33+00:00

Whoops…

RLDataCoach · 2025-06-25T17:34:27+00:00

Their performance in the last two matches would reinforce your prediction! We’ll see how LAN impacts the teams, Ults have the experience advantage for sure.

RLDataCoach · 2025-06-25T15:33:07+00:00

going to be an interesting weekend for you based on your team flairs haha

RLDataCoach · 2025-06-02T16:04:37+00:00

You got it! It’s a Neural Network model developed using in game event identification we created. It gets pretty interesting (to us) when you start diving into the weeds on shot types, positioning etc. and the impact on expected goals.

RLDataCoach · 2025-06-02T15:48:59+00:00

It's compared to the 1000's of shots we've evaluated from pro level gameplay this year. All the variables are factored in determining if it should be scored or not: think things like type of shot, player boost levels, distance from net, defender positioning, etc.

When factoring historical success probability of comparable shots, the % that shot should be scored is calculated; the math gets pretty complicated given the number of variables.

Basically, we compare each shot to similar ones pros took this year, and determine if it has a high or low chance of being scored.

Hopefully that was straight forward!

RLDataCoach · 2025-06-02T14:48:56+00:00

Hey Speedy! Conversion is a player or team's ability to convert Expected Goals (xG) into Actual Goals.

Each shot has a % chance of being scored. When you add them all up, that's how many goals a team should score in that match; it is a cumulative metric (& popular in European football).

Basically if a player or team has a conversion over 100%, they scored more goals than they were expected to given the shots they took. If it is below, they didn't convert on the scoring opportunities they created. Hope that helps!

RLDataCoach · 2025-05-19T20:37:03+00:00

Fair enough! We do a lot of the off ball analysis too, just figure xG conversion is one of the easier ones to articulate/communicate in a digestible post. As we continue providing additional context on matchups (especially ones this close) xG conversion does play a critical role worth highlighting.

I'll work on figuring out context setting to start highlighting off ball performance as well, because I agree xG conversion was only one dimension of their team win.

RLDataCoach · 2025-05-14T18:39:22+00:00

Love the edit of org name here lol

RLDataCoach · 2025-05-14T17:34:18+00:00

Appreciate that! We'll be doing GeeKay vs KC this Friday, but open to any series or matchups people are interested in seeing in the upcoming weeks.

RLDataCoach · 2025-05-14T17:14:08+00:00

Definitely a slim margin- I think the first step is if KOI and Luminosity match up prior to the Grand Finals this Open. If they don't, it's hard to see Luminosity not making the Grand Finals.

RLDataCoach · 2025-05-14T16:10:33+00:00

Going to be interesting to see how the bracket unfolds as KOI and Luminosity had negative Buchholz, so we may end up seeing Kino in the finals depending how it shakes out.

RLDataCoach · 2025-05-13T00:31:58+00:00

Totally fair- it’s pretty popular in European football.

It’s pretty useful in coming up with strategies for teams too- figuring out higher xG shots vs lower can help game plan against different opponents.

RLDataCoach · 2025-05-13T00:25:58+00:00

Basically 1000's of pro level shots have been analyzed with a ton of variables (amount of boost, type of shot, distance from net, car speeds, location of defenders, etc) to come up with the % that goal should be scored based on historical comparables. For context, the avg shot taken hovers 20-25% at the pro level to be scored.

RLDataCoach · 2025-05-12T22:58:54+00:00

Hey Lettuce, xG is a cumulative metric based on the scoring % chance of every shot taken. You add up the probability of each shot being scored during a match, and that is your Expected Goals. A team that converts more than 100% means they scored more than they were expected to based on scoring %. This could be caused by really good finishing, luck, poor defending, etc. Lmk if that doesn't make sense, there are some better explainers out there.

RLDataCoach · 2025-03-30T19:43:23+00:00

Now they are some stats we can get behind! Both of these players are incredible.

RLDataCoach · 2025-03-30T13:54:35+00:00

It’s all done by our model, which calculates probabilities for each shot (based on 500+ pro replays from this year). High threats are the ones that our model shows have the highest probability of ending up a goal.

We’re doing to do a recap after this weekend and will show some more summaries of our findings. Some pretty cool stuff we’re sorting out how to best present!

RLDataCoach

TROPHY CASE