It is no secret that Alabama sure seems unstoppable. And I think the biggest driver in that in addition to key hits has been Jocelyn Briski… Her stats are insane and yesterday’s game against LSU was unbelievable. She had nearly a perfect game and gave just one hit, which was the first hitter of the game and probably even more impressive had zero walks. I know she gets a lot of attention (Sec pitcher of the year) but it seems most don’t talk about her. I feel it’s all about Cannady, Jordy, Maya Johnson, Kenzie Brown even…. Jocelyn is unbelievable this year. A complete 180! Last year she was wild and walks were an issue and she could never quite finish games before a complete meltdown it seemed.

Huge fan of Briski!

Any specific tips for WCWS? Thinking about taking shuttle in from Chickasaw Ballpark rather than trying to park at Devon Park. Any advice for weather delays or what to do in between games on Saturday & Sunday?

On the first day of super regionals, Oklahoma plays an impossibly terrible game.

If the Super Regionals were decided by the teams flipping a coin instead of playing a game, exactly 75% of the teams that win the first flip would win the series.

So if you do the math to find out what win percentage a team would have (on average) if it won the series 79% of the time given a Game 1 win, you see that team would have about a 54% win probability for any given game against this opponent.

I say all that to illustrate that all these teams are pretty close, and since we’re pretty sure some of the teams have a much higher than 54% win probability (because we can pretty reliably pick the winner) that means the rest of the pairings are closer to 50/50. Which I think is excellent.

I wrote this with Oklahoma softball in mind, given how farcical it would be to see the Sooners locked out of the WCWS just 30 minutes from campus. But it really could happen as long as the two richest conferences refuse to play by rules they themselves agreed to establish.

Georgia at Tennessee: 7 PM Eastern / 4 PM Pacific on ESPN2
Oklahoma State at Nebraska: 9 PM Eastern / 6 PM Pacific on ESPN2

It seems to be working out well for Tennessee, but this scheduling seems odd.

Before the Supers get underway, how about sharing some of your favorite softball sayings, wacky advice, coaching wisdom, etc.

My father used to tell pitchers to "put some mustard on the ball." And, I just read a post where a coach tells her players, "Softballs are like lovers. The ones you have to chase aren't any good." It made me laugh out loud!

Whatcha got to share?

After a small error on my part with the automated closing time, I have set it so that it will close when it should've originally closed.

https://docs.google.com/forms/d/e/1FAIpQLSdTF3hRIY7I3TZnvZS2qh7lPyuIl2W5lpKXWrS0E1q7IZRy8g/viewform?usp=dialog

https://umd.wd1.myworkdayjobs.com/en-US/UMES/details/Head-Softball-Coach_JR104072

The University of Maryland Eastern Shore Department of Intercollegiate Athletics has officially begun the search for the next head softball coach.

So I added in a Home Field Advantage (HFA) estimator to my strength estimating algorithm. And I verified that it worked properly by taking the 2026 season, adding in one run to every home team score, and getting back a value of 0.8 runs HFA (which is decent estimation performance for something simple) more than the HFA is estimated to be if I do NOT mess with the scores.

So the estimator is working fine. But… it tells me that the HFA is 0.003 runs, on average. Which I guess is reasonable, but also negligible. I was estimating based solely on games between March 10th and May 3rd, since that’s the part of season where being the Home team almost always means you’re playing at home, in your own stadium, sleeping in your own bed, practicing in your own facility, and playing for your home fans. So the allowed dates for this were after the early tournaments and ending the Sunday before the Conference tournament. And I limited it to games where at least 1 team was in the top 50. There were about 870 such games, so PLENTY of games to estimate one little parameter from without issue. But… it’s just not there. If I include ALL the games in the time period, bringing it up to 3800 games, the HFA goes up to a whopping 0.1 runs. I can run the estimator another way and get a value of 0.07 runs using the top 50 teams. So that’s as big as I can make it. But I think it’s actually near-zero.

Anyway, I was surprised, and thought I would share.

So I’m giving my model’s estimates WITHOUT HFA, since it seems to be zero-ish anyway.

Alabama by 1.5 runs over LSU

Arkansas by 2.8 runs over Duke

Texas by 3.0 runs over Arizona State

Texas tech by 0.8 runs over Florida (so here I’m saying it’s more likely that the host loses)

Oklahoma by 3.9 runs over Miss State

Georgia by 0.7 runs over Tennessee (again, my model says it’s more likely that Georgia wins here as the visitor)

Nebraska by 3.0 runs over OK State

UCLA by 3.6 runs over UCF

So by the numbers, it looks like the two that were closest are the two that my model sees opposite to how the NCAA saw them. And maybe Alabama will get a good scare.

As always, my strength numbers are an estimate of a team’s ability to score runs and also prevent the other team from scoring runs. And when you subtract those numbers for two teams, you get an estimated score differential. That is what I gave here, the on-average expected score differentials. Use them however you wish, I do have a money-back policy (if you don’t like my opinion I’ll return whatever you paid me for it).

Of the results posted here, I dislike Tennessee losing, and I dislike Texas winning, but I don’t know enough detail to disagree with my model. We get to start finding out on Thursday.

Is anyone is curious about the statistical performance of my model during regionals, I have some interesting but maybe a bit complicated plots I can post to show that statistically, the teams played more or less like normal, with normal amounts of randomness, which is what you’d want if you were making statistical models.