Predicting playing time is very critical and also very difficult — lots of ways to do it and not a ton of consensus on the best way. Using similar methods to @tonyelhabr.bsky.social’s meta metrics post, year-to-year correlation (“stability”) is very low relative to other metrics
Data: fangraphs
An interesting iteration on something I also wrote about a while ago tonyelhabr.rbind.io/posts/xg-rat...
per requested, an update to my gamestate xGD plots ahead of this weekend's MLS matches
reasons for changes from the prior version:
1. more data
2. the 8th and 9th place teams in the West have changed
San Diego’s xGD trend against playoff opponents seem to be pretty close to their overall trend.
In contrast, Inter Miami’s stands out as one of the team’s whose numbers drop off the most against stronger opponents.
I have to do some estimations for extra time from match event logs, which leaves some leeway for error. This is why I favor the fbref's listed xGD/90 figure when showing the overall xGD per team.
Your math is probably not bad. It's supposed to be Leading xGD * Leading Duration % + Trailing xGD..., which does come out to -0.03. To "square" things with fbref, I end up using what they list as total xGD/90 on the right-hand side, which was +0.03 at time of scraping. (Looks like it's +0.01 now.)
lmk if this is along the lines of what you're looking for. happy to iterate
absolutely! i’ll post a follow up when i can
MLS playoffs are coming soon, so here's my cheat sheet to digest how every team has performed this season
@cata-bush.bsky.social is actually insane for this… this seems really easy to use, has great aesthetics, and includes just about every kind of plot you can imagine
i believe the author’s name is Tyson Ni. (not sure if he is on bsky.)
i was made aware of these rankings from a @rwohan.bsky.social article
it was offseason for everyone, and now i'm rusty with my viz 😅
Which Big 5 teams improved the most over the summer in the eyes of odds-makers? And which teams fell out of favor?
(Ratings from PitchRank)
That's it, folks, #VizBuzz has come to an end and we've crowned @tonyelhabr.bsky.social champion 👑👑👑
The finals was FILLED with ties, which give me life, including a tie for second between @johnbedwards.io and the mighty @qntkhvn.bsky.social
It's the end of VizBuzz, but it lives in our ❤️
#databs
i feel like this points to a selection bias that pre-shot xG may not capture. players who are good with their weak foot are more likely to take more shots with it and balance out goals with misses, and those who are poor with their weak foot will only take shots with it in very favorable positions
An annual must read. Happy to see my own writing linked here.
memes in 2014 vs. 2024
xGD is live from Opta here (including the ongoing matches)
Comet / dumbbell plot showing the counterfactual standings for the English Premier League in the 2024/25 season if the result of every 1-score match was flipped, through 2024-11-29. Tottenham is at the top, Ipswich is at the bottom.
Fun hypothetical: How would the EPL table look right now if the result of every 1-score match was flipped?
Biggest risers:
1. Wolves: +9 pts, 17th -> 5th
2. Crystal Palace: +9 pts, 19th -> 8th
Biggest fallers:
1. Brighton: -12 pts, 2nd -> 18th
2. Aston Villa: -9 pts, 8th -> 19th
#rstats
Game state xGD table of truth for the MLS 2024 playoff teams
❤️ the references to public studies on finishing
the great bsky migration may be enough to get me to blog again
An 8x12 heatmap showing the average possession value (PV) of historically incomplete passes from the center spot (annotated in blue) to all areas of the pitch. The relative frequency of unsuccessful passes from the center spot to each other cell is shown as a percentage. The exact PV value associated with an incomplete pass ending at the hover point can be viewed above the pitch. Black cells represent areas to which unsuccessful passes from the center spot have never been made.
An 8x12 heatmap (gif) showing the average possession value (PV) of historically incomplete pass from the hover spot to all areas on the pitch. The relative frequency of successful passes from the center spot to each other cell is shown as a percentage. The heatmap updates as the user moves their mouse over cells on the heatmap.
Finally got around to trying out Observable JS in depth. It's really nifty! I used it to make some sick ⚽ plots in this post: tonyelhabr.rbind.io/posts/ball-p...
#rstats
where is my GitHub achievement badge 😈
i'm about to have a bad time, aren't i
