at the dollar amount other players have signed for, this may be the best value signing of the offseason so far.
Natasha Mack is a better/more important player than what simple box score numbers would indicate.
d'oh! will fix, thanks for that. as a fan of both Austin and Edwards, I still have last season's logjam in the frontcourt in the back of my mind 😅
appreciate you reading!!
wnba offseason grades are up after the draft and the initial wave of free agency!
some grades are tricky. the storm did really well in the draft but endured a massive exodus of talent. sun had a good draft, too, but the vision is long-term.
www.si.com/wnba/wnba-of...
i couldn't come close to beating @thefalkon.bsky.social this year but still finished with a respectable 🥈 in the kaggle march madness competition! 144th out of 3,483 teams this year with no gambling involved
predictions of integrity? juuuuust missed out on the top eight, but I'm very happy with the score my models acheived. (No gambling or hand-modifying of predictions)
congrats to everyone else!
"The Sky are known as a cheap franchise because Alter is a cheap owner. This season will be the first in which players don’t share locker room space with anyone who can afford the $49 monthly membership at Sachs Recreation Center"
great column by Julia that gets into the rift in Chicago:
The Angel Reese trade exposes the Sky's central flaw: they mismanage their most valuable assets—their players.
More on today's deal and Sky's history of failing to retain and build around their stars:
www.si.com/wnba/angel-r...
We think it's a good list! It came together fast, and there's plenty of room for debate on the order of names and the names themselves.
(Lauren Whittaker, Kennedy Blair, Kyla Oldacre, Breya Cunningham, Serah Williams, Sydney Shaw, Chance Gray among those just missing the cut.)
At SI, we ranked the top 68 players who will be competing in the women's NCAA tournament. These are the names to know in March.
(many thanks to @emmabaccellieri.bsky.social, @blakesilverman.bsky.social, Tim Capurso and Kristen Nelson for their help with this project!)
www.si.com/college-bask...
yes! Veronica Burton v. Courtney Williams last night, but that's the only one.
Kelsey Mitchell v. Rachel Banham and Arike Ogunbowale v. Brittney Sykes (both last night) are the only two non-finals games where a player has had 1 possession total.
Did Veronica Burton beat Courtney Williams that bad?
Burton's win was the fastest Unrivaled 1-on-1 tournament victory for any non-finals game.
With the Unrivaled 1-on-1 Tournament starting today, I broke down the best storylines, the top scorers heading in, what's changed since last year and more for @sportsillustrated.bsky.social:
(there's a table ranking the top 10 players by points per possession, too!)
www.si.com/wnba/everyth...
A+ ad placement on ESPN’s trade tracker
some definitions for the stats:
true shot: any FG attempt + free throws stemming from shooting fouls. it's a middle ground between eFG% and TS%
usage percentage: (true shots + turnovers)/(player's offensive possessions). how often a player was used when on the floor.
Scatter plot showing usage percentage on the x-axis and points per true shot on the y-axis. True shots are defined as a field goal attempt or a free throw attempt resulting from a shooting foul. Usage is calculated as a player's true shots plus turnovers, divided by their number of offensive possessions. Chelsea Gray and Paige Bueckers are the highest efficiency players as of February 3rd.
Who are the highest usage and most efficient players in Unrivaled?
Paige Bueckers is coming close to taking Chelsea Gray's efficiency crown.
2015 Warriors Elevator Screen, with tracking data!
(This was the last of Brandon Rush's 14 third quarter points in a win against the Kings)
Table showing the top three-player lineups in Unrivaled, sorted by net rating. The Breeze's Cameron Brink, Paige Bueckers and Kate Martin and the Mist's Allisha Gray, Veronica Burton and Breanna Stewart rank as the top two lineups.
The full table (a bit long!) of all lineups with at least 100 possessions is here!
ICYMI: Assessing which team has the best three-player lineup in Unrivaled should be an easy task. The league doesn't publish lineup stats, though, so I went ahead and pulled the data.
I ranked each team's best lineup, sorted by net rating:
www.si.com/ranking-top-...
Which Unrivaled team has the best three-player lineup? Lineup stats aren't available ... until now!
For @sportsillustrated.bsky.social, I parsed play-by-play and possession data, and ranked each team's best lineup by net rating.
Full table at the bottom of the story!
www.si.com/ranking-top-...
Spun through the best games to watch for the women's college basketball week 11 slate, which will kick off in full force tomorrow night when TCU goes on the road to face a gritty West Virginia team:
www.si.com/college-bask...
thanks again to @sumersports.bsky.social and the Shrine Bowl for running this competition and making the data available! it was a really great opportunity to get hands-on experience with tracking data.
important caveats:
- small sample size (2024 alone, many players only have 3-4 reps. 10 seconds, or as few as ~2 seconds for stall/bull score, isn't enough for a comprehensive evaluation and is subject to noise.)
- no film (makes ground truth validation for contact onset tough)
... and for bull score:
Here are the top six reps by stall score. I found these handy for assessing whether the metrics were measuring what they were intended to measure!
Secondary metrics:
Stall Score (did you stop the rusher’s progress?) and Bull Score (given engagement, did you drive the blocker back?). Both focus on what happens after contact onset.
Top 3 leaderboard for both, along with media quotes from 2024 East-West Shrine Bowl Practice:
Primary metric: DL Depth Allowed at Frame 25
Median time to throw was ~2.68 seconds in 2025; ESPN Analytics pass rush win rate is anchored at 2.5 seconds. Use DL depth @ frame 25 as a proxy for how much ground an OL gave
Top 5 leaderboard below, small sample size caveats apply:
spaghetti plot showing the mean, median, 10%ile and 90%ile curves for closing velocity relative to the identified contact-onset frame. closing velocity collapses near the contact-onset frame.
Feature engineering and "contact onset":
1-on-1 reps have phases (get off, set, engagement, etc.) and properly quantifying a lineman’s performance can be tricky.
So, approximate frame when closing ends and engagement (likely) begins. It's not ground truth, but it's a start.
Small amt. of manual inputs --> Outer algorithm that scans through drill period and triggers when a candidate rep is detected --> Local algorithm to extract OL-DL pair, rep start and rep end --> Continue loop --> Tidy dataframe of 133 reps
9x9 patchwork grid of a random frame from 81 different OL-DL 1v1 reps
For the @sumersports.bsky.social x Shrine Bowl Analytics Competition, I built an automated pipeline to detect OL vs. DL reps from player tracking data, engineer contact-onset features and create metrics for evaluating interior line play.
Details below!
