Tim

Check now - should be good! I accidentally had the GitHub actions finishing in December, oops. Let me know if any issues!
Send me whatever you make or analyse too!

Oh weird. I’ll have a look tonight. I get an error email when it doesn’t scrape correctly and haven’t received any. I’ll check it out!

Don’t need to see my model to see Curran’s very high impact! 4-10 from his 4 overs

Wonder if my T20 win probability models work live at the game?
#BBL15

Will miss Alyssa Healy! A genuine superstar of the game.
Will be very glad to hear her insights in the commentary box!

You bet! Developed & maintained by myself :)
There's also the cricketdata package which reads in data from cricsheet.org, but cricsheet.org only seems to update once a week or so as opposed to daily, which is why I developed cricreadR.
github.com/albtree/cric...

github.com/robjhyndman/...

Scatter plot Batting statistics. Non Boundary Strike Rate X axis with higher to the right. Boundary % on Y axis with higher up. Meg Lanning middle top. Wareham and Wyatt-Hodge middle top right

Batting Statistics - Non Boundary Strike Rate x Boundary %
Our 3 impact leaders in Lanning/Wareham/Wyatt-Hodge all featuring prominently top right again.
Also Georgia Wareham sighting number 3 - truly making a mark on both sides of the game

3/3

#WBBL

Scatter plot. X axis dots bowled with higher amount to the right. Y Axis boundary % against with lower amount up. Sophie Ecclestone, AJ Wellington, and Darcie Brown top right.

Bowling Statistics - Dots Bowled % x Boundary % against
The Strikers trio of Wellington, Brown, and Ecclestone is so damn efficient. Good luck scoring runs for those 12 overs a match!
Georgia Wareham sighting number 2

2/3

#WBBL

Total impact per game chart. X axis = balls faced or bowled per game. Y axis = total impact per game. Meg Lanning top right, following by Georgia Wareham and Danni Wyatt-Hodge

Almost halfway through the regular season for the Women's Big Bash League #WBBL - time for some charts from the #cricreadR package.
Very much love following this league!

Total impact per game by total balls faced or bowled - Meg Lanning is such a beast
Georgia Wareham sighting number 1
1/3

Changelog

#nflverse news: we've just published nflreadr v1.5.0 to CRAN in time for the new season 🎉

It comes with a few 🚨breaking changes🚨 to core functions, of note:

- load_players()
- load_player/team_stats()
- load_depth_charts()
- load_participation()

Full changelog here, details in thread 🧵

Keep in mind that the sample for these are a lot lower than for MLB where teams play 162 games per season, so there is a bit of a team component here given teams have home grounds i.e. if a team is good year on year then their home ground will likely rate higher

Wanted to compare how different cricket grounds produce different scores. Utilised a similar methodology as Baseball - see link - however used strike rate.

Give the Hundred is on currently, makes most sense to present the Women's and Men's 100 Park Factors
www.mlb.com/glossary/adv...

White Ball Analytics A website that uses statistics to explore cricket and other sport; one day matches, T20, IPL. Uses analytical tools such as Runs Added and Wins Added to understand the impact of individual players on ...

Nothing really forward facing for WP models either. My pkg cricreadr has one but has similar issues as others - eg the unpredictability in the last few overs (and first few). All similar - balls/wickets remaining + target. There are some good articles on this website - www.whiteballanalytics.com

batting This app was built in Streamlit! Check it out and visit https://streamlit.io for more awesome community apps. 🎈

@danny.page nothing like PFF exists where with a (affordable) subscription you can scroll advanced stats. Cricinfo is pretty much the only place that exists.

t20bat.streamlit.app by Himanish probably comes closest to PFF - can breakdown player stats - it’s all just cricinfo manipulated data 1/2

Screenshot of a successful GitHub action being pushed to cricreadR

Due to Cricinfo API changes cricreadr previously hadn’t ingested any new matches since October 2024. I’ve now fixed this! 🎉🥳

Data is now being updated again on a daily basis for the *main* T20 competitions being played:

github.com/albtree/cric...

Thanks, I was wondering!

Variable name comparison of v1 and v2 nflverse players data.

Rare breaking change in the #nflverse!

`nflreadr::load_players()` now loads v2 players data.

During the process of the rewrite I decided to rename some variables for consistency and removed some irrelevant variables. Please see the below table for a detailed comparison of old and new data.

That function naming is gold

My first post on my new site is now available!

Suppose every baguette molecule is a data point, the whole loaf obviously has a negative correlation and each slice obviously has a positive correlation. Should think of simpson's paradox as a kind of selection bias. Every time you cut a data set you cut along some sort of shape.

Wild

Wow this looks horrendous

Was taking a look at the college RTMs done by @talbtree.bsky.social

There are 3 players drafted in the top 10 of the NFL draft with a 99+ Overall Score...

Science abstract: POLICING High-frequency location data show that race affects citations and fines for speeding Pradhi Aggarwal1, Alec Brandon2*, Ariel Goldszmidt3, Justin Holz4, John A. List3, Ian Muir5, Gregory Sun6, Thomas Yu7 Prior research on racial profiling has found that in encounters with law enforcement, minorities are punished more severely than white civilians. Less is known about the causes of these encounters and their implications for our understanding of racial profiling. Using high-frequency location data of rideshare drivers in Florida (N = 222,838 individuals), we estimate the effect of driver race on citations and fines for speeding using 19.3 million location pings. Compared with a white driver traveling the same speed, we find that racial or ethnic minority drivers are 24 to 33% more likely to be cited for speeding and pay 23 to 34% more money in fines. We find no evidence that accident and reoffense rates explain these estimates, which suggests that an animus against minorities underlies our results.

missed this last month: data science informing psych, sociology, polisci

in Lyft data (222K drivers), Black or minority drivers get speeding tickets 1/3 more often **when they are known to be going the same speed**

beautiful work, it's racial profiling, case closed

www.science.org/doi/10.1126/...

I used my monthly bluesky open for good then 😂

The music is very soothing!

These look great! Will follow along tomorrow

“Analyzing Baseball Data with R” book club ⚾️

Dive into baseball analytics with us! Perfect for beginner (to advanced) R coders who want to explore sabermetrics. #rstats

Join us by sharing your availability here: dslc.io/bookclubber?... (If it asks for a URL for the slack it’s dsclio.slack.com)

😭

Scatter plot for top 20 bowlers by balls bowled in the 2025 Women's Indian Premier League cricket competition. X axis is dot balls bowled %. Y axis is boundary % against. Both axes are reversed so best remains top right. Shabnim Ismail and Shikha Pandey are the top 2 bowlers in the top right.

Scatter plot for top 20 batters by balls facedin the 2025 Women's Indian Premier League cricket competition. X axis is non boundary strike rate. Y axis is boundary %. Annabel Sutherland and Sarah Bryce are middle right leading the way in non boundary strike rate over 5. Richa Ghosh is top middle leading the way in boundary % at about 40%

My cricket R package (github.com/albtree/cric...) has been out of action since October after an API change. Have finally started working on it again. Popped out some Women's Premier League charts from some old code.