Regionals: who are the most likely upsets (per my Run Strength model), and who is the most likely surprising upset.
My model compares all 308 teams against all the other teams, all at once, using the score differentials of ALL the games, taking into account how good each opponent is, to create a statistically optimal estimate of how good each team is at scoring runs while stopping the opponent from scoring runs. The output is what I call Run Strength, and each team gets a number. But the number is meaningless by itself, you have to compare the Run Strength of two teams to see how close they are. Also, Run Strength is expressed in terms of Runs per Game, since those are the numbers you see on the scoreboard, and everyone knows what a run means as opposed to just showing a percentage. As for comparing my top performers against what the NCAA’s top 32, I’ve linked my top 50 at the bottom, but 31 of the NCAA’s top 32 are in my top 38. So it’s not a perfect match-up, but it’s pretty close considering I’m not doing any manual tweaking, and just printing out what the math says.
Also, Run Strength is a negative number, and I did that partially to keep people from thinking each individual number meant something all by itself. If UCLA (Run Strength -1.02, which is only one run away from the top of the range) plays Oregon (Run Strength -3.10) a large number of times, we expect on average that UCLA will win by about 2 runs. That’s what Run Strength means, and the underlying math is set up to explicitly maximize the likelihood that’s the correct answer.
The league-wide standard deviation of the unmodeled performance is about 4.6 Runs per Game, so if you know how to use that information, feel free.
Anyway, I’m going through the 16 regionals here and posting the Run Strength difference between the top teams, and then at the bottom saying which ones seem pretty close and most likely to hold an upset. Note: I’m not considering the double-elimination aspect of this at all right now. Just know that the more games played, the better the Run Strength should describe what actually happens.
1 Alabama: SELA is about 4 runs weaker than the Tide, so not a likely upset.
2 Texas: I actually have Baylor as slightly stronger than Wisconsin (47 and 50 in my rankings), But Texas is stronger by about 6 runs. This regional is VERY unlikely to upset. This is the LEAST LIKELY upset mathematically, but I have faith that Mike White will find a way to suck so badly he can overcome the odds and leave Texas watching the Supers from the couch.
3 Oklahoma: I have Kansas and Michigan as nearly equal (34 and 35 in my rankings), both around 5.5 runs behind OU. Not a likely upset.
4 Nebraska: I have Louisville and Grand Canyon as nearly equal, at 28th and 29th in my rankings, both about 4.75 runs behind Jordy and the Huskers. Mathematically this is an unlikely upset, but Nebraska is trending up, and so I personally think this is the most certain Regional there is. I would bet my car on it, if winning meant I kept my car and got a nice sandwich out of the deal.
5 Arkansas: only 3.5 runs better than Washington. This is getting into the territory where an Arkansas single-game loss to Wash is around a 1-in-5 territory.
6 Florida: I have Georgia Tech as 0.8 runs better than Texas State, and only 3 runs behind Florida. If Florida gets upset, I think it will be by the unseeded Rambling Wrecks. But not particularly likely.
7 Tennessee: My model has Tennessee tied for 13th with Virginia Tech, and trending down across the season, which suggests that maybe the other rankings are done by humans who really like a good fastball, and think it excuses a lack of hitting. Anyway, I have Indiana as 1.6 runs better than Virginia, so that’s who I would watch here. And Indiana is only 1.35 runs behind Tennessee. So this is STRONG unintended-upset territory in my book. Mind you, Indiana is my model’s top pick that the NCAA didn’t seed, I have them at #21. And as already mentioned, my model doesn’t see Tennessee as all that strong. So I see an upset here as relatively likely. This is by far the place with the strongest disagreement between my model and what the NCAA / ESPN thinks is likely.
8 UCLA: I have South Carolina as 3.2 runs behind UCLA, so not a particularly strong chance of upset here
9 Florida State ‘University’: UCF is about 2.8 runs behind FSU, so not upset territory, but not as far as some others.
10 Georgia: Clemson is 3.65 runs behind, not a likely upset.
11 Texas Tech: my model has the Red Raiders as 4th overall just behind Texas, so I’m estimating them to be stronger than the NCAA thinks they are. They’re 3.75 runs better than Ole Miss, so I think not a likely upset, not nearly as likely as would be implied by their 11th seed.
12 Duke: I have Arizona as only 0.4 runs behind Duke. Per my model, this is basically a toss-up.
13 OK State: Stanford is only 0.5 runs behind the Cowgirls. Again, this is the edge of toss-up territory. Go Cardinal!
14 Oregon: Miss St is about 0.75 runs behind Oregon. Approaching toss-up territory. Quack.
15 TAMU: ASU is about 1.4 runs behind TAMU. So not really toss-up territory, but TAMU did manage to miraculously get a good seat on the couch for the Supers last year, so who knows?
16 LSU: My model puts LSU and VA Tech at 12th and 13th, with only 0.35 runs separating them. I guess it’s good that my model shows the most likely regional upset as the NCAA’s 16th host team, it means my model probably isn’t stupid.
So, the most likely upsets are the ones you’d expect, the 12th through 16th seeds. But TAMU doesn’t really belong there I don’t think, maybe that’s the committee punishing them for last year’s embarrassment? The most likely surprise-upset is Indiana over Tennessee, and I think the committee messed up by seeding Virginia instead of Indiana there. So my model mostly agrees with the NCAA seeding, except the Tennessee regional, where I think they’ve unknowingly set up an upset that’s more likely than they were intending. Also, I’ve watched exactly zero innings of Tennessee or Indiana softball this season, these predictions are based solely on game outcomes and linear algebra.
But all that said, my model does predict each individual regional’s most likely victor is the host. But it does not account for home field advantage at all.
My Top 50, along with how they line up with the NCAA’s seeding: https://old.reddit.com/r/CollegeSoftball/comments/1t8we0y/final_strengths_and_rankings_all_308_teams/ol4dww1/
I will be watching Nebraska to win impressively, Tennessee to squeak by possibly dropping a game, Texas to find a way to be embarrassed by Mike White, and the other real contenders to use this round as a warmup for the Supers.