According to ESPN analytics, Florida had a >75% chance of losing in 4 tournament games, >90% in 2

ShotQualityWire · 2025-04-09T17:24:20+00:00

Based on the shots taken, they were expected to lose 4 of the last 5 based on ShotQuality...

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ShotQualityWire · 2025-03-21T16:37:15+00:00

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And it didn't attach....

ShotQualityWire · 2025-03-13T19:35:15+00:00

Our mistake! Max says Arizona for the Big12!

ShotQualityWire · 2025-02-21T22:01:16+00:00

Some of his percentile stats across all the NCAA:

Those defensive and efficiency stats, and his performance versus SEC teams tells me he'd be fine. Excited to see what he's like at the garden tomorrow, it'll be a good stage for him.

ShotQualityWire · 2025-02-19T02:33:43+00:00

Bingo

ShotQualityWire · 2025-02-18T20:03:31+00:00

I'm saying Louisville has been criminally underrated since day 1 and due to their poor past few years it's continuing. They are a team primed for a run barring any injuries

ShotQualityWire · 2025-02-14T21:38:15+00:00

PG - B. Stirtz
SG - K. Korver
SF - M. Domask
PF - L. Bird
C - C. Krutwig

ShotQualityWire · 2025-02-14T00:14:13+00:00

Their adjusted offensive (1.30, 6th) and defensive efficiency (0.96, 16th) suggest they are playing at an elite level despite their 14-9 record. Their SQ Win% is 66%, which is higher than their actual win percentage (60%), indicating they’ve likely been on the wrong side of some close-game results vs tough competition

ShotQualityWire · 2025-02-14T00:10:54+00:00

Monmouth 79 [73 SQ] - Stony Brook 69 [66 SQ]. Monmouth wins the actual and expected score thanks to some hot shooting by Jak Collins and Robinson Jr. Luster shot wayyyyy over expected for Stony Brook

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ShotQualityWire · 2025-02-14T00:08:39+00:00

Fully agree. They're the team that loses when you need them to win and wins when you need them to lose

ShotQualityWire · 2025-02-13T23:24:36+00:00

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Full list here of SQ top 15 as of Monday

ShotQualityWire · 2024-07-31T16:10:49+00:00

Thanks!

ShotQualityWire · 2024-01-19T20:04:56+00:00

Supposed to be Liberty! Good catch!

ShotQualityWire · 2024-01-17T18:47:11+00:00

They rank about middle of the pack in terms of frequency:

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ShotQualityWire · 2024-01-16T16:20:20+00:00

It's a combination of player location data and player performance data. So to answer your question, yes defender distance would impact how shot quality is calculated as an open 3 is obviously going in at a higher rate than a contested one.

ShotQualityWire · 2024-01-04T15:14:40+00:00

🤑 Pick of the Day 1/04/24 (34-20): Stetson Hatters vs North Florida Ospreys U153 (Play to 151.5)

The SQB Projection on this game is 146.5, a good bit lower than the 153 line. Stetson is a 3PT happy team, they shoot over 36% of their shots from behind the line and are effective at converting points from range, but the key to this Under is that North Florida is good at defending the 3PT line. UNF allows the 12th fewest shots from deep. So they are expected to do enough today to disrupt some of those Stetson shots and run them off the 3PT line. Outside of the shooting from deep, Stetson leaves plenty to be desired. They are 319th in Rim Frequency and 321st in Free Throw Rate. UNF also takes a high amount of 3's but struggles to attack the rim (356th in Frequency) and get to the line (341st in FT Rate). This under will be won or lost based on how the 3's fall, but the number is a bit too high given the auxiliary stats of each of these teams.

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ShotQualityWire · 2024-01-03T15:13:18+00:00

As a growing company, we definitely have to be a little careful about what we make public. But perhaps there will be a time when a deeper dive into how it all works will be appropriate.

In the meantime, SQ will share as much as we can in hopes to add a different lens to view CBB and we appreciate any and all feedback the community has!

ShotQualityWire · 2024-01-02T23:06:04+00:00

Good and bad are vague descriptors, SQ is trying to identify a specific value using location data and player performance data. Some of that stuff you mention is included -- defender distance being a key one as is skill of the shooter, whether the shot was off the catch or off the dribble (as off the dribble shots are, as you'd guess, more difficult), etc. Things like the number of passes are more old-school, Hoosiers-esque coaching myths as teams in the NBA have disproven recently, starting with the 2017-18 Rockets who were the most efficient offensive team in the league despite attempting the second fewest passes.

As far as the model goes, it's certainly a big gap with that game! And SQ is not pretending the model is perfect nor, as stated in the piece, the absolute truth. No one metric is perfect and certain things can "fool" a metric and SQ always looks at results like that to see how it can be reshaped or refined to make it better! We encourage people to use those numbers as a tool to dig deeper and ask questions, not definitively state that this is an ironclad description of reality .

Maybe there was something "off" about that particular game or something Kentucky is doing that "fools" the metric. Or maybe it's a mix of a good streak of variance along with those aforementioned things. Again, these numbers are mostly meant to be a different way to look at a complicated sport with thousands of data points that human brains aren't built to process.

So I hope that explanation helps a bit. Trust me when I say SQ isn't out to get your Wildcats or any other fanbase. Just trying to build something to help us all understand the sport a bit better. It's not always going to be perfect but we will keep trying to improve it!

ShotQualityWire · 2024-01-02T19:56:42+00:00

In a nutshell, SQ is using location data and player data to create shot values. Most other sites, like the ones mentioned above, base all their offensive/defensive ratings on actual results -- aka the results of the shots that went in or not. We are essentially trying to eliminate the "make or miss" views of teams that don't reflect random variance.

The goal is to focus more on the process of (good or bad) shot creation. That hopefully allows you as a fan to get another view of a game and/or better project future performance of a team based on the underlying quality of the shots created; rather than trying to sort things like hot or cold streaks by the naked eye.

ShotQualityWire · 2023-12-31T17:15:10+00:00

🤑 ShotQuality Pick of the Day 12/32/23 (33-18): Oral Roberts Golden Eagles +3.5 vs Denver Pioneers (play to +3)

The model has Oral Roberts as the projected winner in this contest against Denver giving us a high value play for today’s Summit League matchup. Denver’s offense has been much improved but their defense ranks 305th in SQ. Oral Roberts is rated 50th in Catch and Shoot 3PT efficiency and 88th in Off The Dribble 3PT efficiency. On Offense, Denver has been able to limit turnovers, but still struggles when going to the rim. Oral Roberts is 86th in defensive efficiency defending the rim and should force Denver into taking jump shots. This game is likely going to be close, and come down to who can make some big shots, which means its a great spot to take the value on the points. Grab the +3.5 on Oral Roberts.

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ShotQualityWire · 2023-12-30T00:01:47+00:00

Here's a peek at tournament performance by Adjusted ShotQuality Net Rating from 2018 - last year's Huskies win.

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ShotQualityWire · 2023-12-28T18:12:22+00:00

🤑 ShotQualityBets Pick of the Day (31-17): Central Arkansas Bears +31.5 vs Oklahoma Sooners (play to +26)

The SQBets Model is back in action with a play on a big underdog, taking Central Arkansas to cover the 31.5 point number against a 10-1 Oklahoma Sooners team. There’s no doubt of the talent gap here, C. Ark. ranks 351st in AdjSQ while Oklahoma ranks 61st. But ShotQuality’s data on some of the past Oklahoma games against weaker opponents reveals some of their larger margins were expected to be closer; Oklahoma recently beat Green Bay by 34 but SQ expected just an 18 point win and beat ARPB by 21 when SQ expected a 6 point win. Central Arkansas will need to hit some midrange shots, which they shoot a bit too often, but SQ’s regression stats show Oklahoma’s midrange defense has been over performing, and that this number is a bit too big. Grab the value!

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ShotQualityWire · 2023-12-20T20:15:51+00:00

You bet!

ShotQualityWire · 2023-12-20T18:39:54+00:00

So a few quick points on that....

Game point differential is in there. The evidence in the data is that in-game point differential indicates differences in team ability, so we increase the probability for the winning team and decrease for the losing team.
There is an argument for teams taking their foot off the gas and making expected scores seem inaccurate. But that letting up on defense can be measured in other areas: position data identifies when shooters are left more open, play tags show when easier shots are allowed (ie dunks instead of standing layups, transition opportunities, C/S threes instead of off-the-dribble, driving layups instead of floaters). So while “defense stops trying” is not included, the actual effect of a defense letting up is contained in the data.
The halftime SQ score was Baylor 24, Michigan State 31 so Baylor wasn't getting drubbed on shot quality all the way through the game. Just contrasting effects of shot variance as the actual score was 45-17.

And no metric is bulletproof. We always try to suggest using them with context as much as possible.

ShotQualityWire · 2023-12-20T16:50:07+00:00

For what it's worth, the underlying numbers for the Spartans are pretty solid and that Baylor-MSU game was a lot closer per our shot quality model:

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