2019 Tennessee high school football preseason projections

Hello, and welcome to the new hosting site for my Tennessee High School Football Preseason Projections! Returning for my third season, The System™ produced excellent results in 2018 with a smaller, East Tennessee-only sample size in the regular season: a 279-54 (83.8%) likelihood of correctly calling the outcome of a game, well above the expected mark of ~77%. In the Tennessee state playoffs, which included all teams from Tennessee, not just those in my personal region, the ratings correctly predicted 177 of 221 games (80.1%), slightly above that same expected mark. On the whole, The System™ went 456-98 (82.3%) and correctly predicted the winner in 24 of 27 state semifinal/championship games. It was a good season; now comes the challenge of somehow topping that.

Even if that doesn’t happen and these projections regress to a mean of 77-78%, this still means they’re correctly projecting nearly four out of every five games played weekly in Tennessee. Considering Cal Preps/Max Preps’ ratings alone correctly projected 84% of winners over the last four regular season weeks + playoff action in 2018, the rate of correct projections could reasonably stay stable or even slightly rise. (Quick nerd note: anything above 75% correct is considered good; 80% or higher and you’re doing very well. 85%+ and I get hired by STATS LLC or something.)

Anyway, that’s too much precursory writing. If you clicked on this, you came for what the headline promises: statewide Tennessee high school football projections. For the first time since I’ve been doing this, all teams in the state of Tennessee are receiving full projections, not just East Tennessee. This covers 342 teams in region play in the state of Tennessee. While this does exclude a few non-region programs, this still gathers every program that can potentially play in the TSSAA playoffs this November/December.

In the below sheet, you’ll get the following to start with: a full sheet of projected records, win totals, and region records for all 342 teams, plus individualized team sheets. The team sheets feature schedule data and projections for each game. Throughout the season, these will be updated weekly and will still be available for viewing. Below are the official 2019 Preseason Win Projections for Tennessee High School Football, sorted by Class and Region:

Here’s the big, giant sheet, which is linked here as a finalized published version. At that link, you can access all 342 individual team sheets, with full game-by-game projections and season-long win totals. These will be updated weekly, even while I’m on a honeymoon in early October. (Don’t tell my fiance about that part.) While you can access season-long win projections on Massey Ratings (and, to some extent, Cal Preps), this is the first Tennessee-exclusive high school football analytics sheet that has existed, as far as I know.

After Week 1, all preseason projections will be frozen and available for viewing throughout the season on the 2019 Preseason Win Totals tab. They won’t change, but they’ll still be used on the 2019 Running Win Totals tab, which will be made available after Week 1. On that tab, you can keep track of how your team is overperforming or underperforming their preseason projections. It’s very nerdy, but also very useful to see (hypothetically) who’s coaching a team up well beyond their expectations.

As a reminder, here are the main features of the projection system:

  • Team ratings from diverse statistical sources. This year’s sources are Cal Preps/Max Preps, Massey Ratings, and Sonny Moore’s power ratings. All three use different methods to achieve their final rating, and all three are worthy, useful tools. The current plan is that, after this season, we should be closer to achieving a Stats By Will rating as well. To start the season, the ratings are weighted towards the more reactive Cal Preps system, as Massey and Sonny Moore are more retrodictive and use last year’s numbers heavily to start a season.
  • A small boost for home field advantage. Per a recent study, the average home team win rate in high school football is around 57%, which converts to almost exactly 2.5 points. Because there’s no objective way to measure home field advantage across high schools otherwise, this is the best we’ve got. So: if you’re at home, your projected margin is aided by 2.5 points.
  • A normal distribution system for win likelihood, similar to Bill Connelly’s S&P+. The official equation, if you use Excel, is NORMDIST((relevant cell), 0, 17, True). 17 is an unusually conservative deviation number, but because of the elevated level of variance in high school football, it’s necessary. That’s why even the best teams in the metrics don’t have a 99% chance of going undefeated.

Added features for 2019:

  • Project-A-Game. Nothing new to loyal followers, but this is my Single Game Projection Tool, now available for full season usage. You can project any game your heart desires at any location your heart desires.
  • Game Score Adjustments. This is a tool that takes a game’s Power Score (basically, the combined ratings of the two teams) and adjusts it for how close or how not-close a game is expected to be. If a game is projected within ~2.5 points – the margin of home field advantage – it can get as large as a 30-point boost. If a game is projected to be a 30+ point destruction…well, it can receive a 30-point demerit. This is also a useful way of getting 1A, 2A, etc. games higher in the weekly rankings, as those teams won’t rank as highly in statewide rating systems.

This should cover everything, at least prior to the week-by-week preview posts. If you have any questions, comments, corrections, etc., please email statsbywill@gmail.com.

On the next pages, you can find the following:

Building a Better Basketball Offense, Part 5: Cuts

Cuts, by and large, are the easiest way to score points in basketball. By their Synergy terms, they’re downhill actions that can come in a variety of ways: backdoor, off screens, curls, flares, basket cuts, flashes, etc. To borrow a phrase from several different coaches, there’s a million different ways to run a cut. However, there’s also a few select ways that should work best for you and your team.

This past season, the Cuts play type on Synergy was the most efficient play type on average. It’s been the most efficient play type since Synergy has existed. And yet: it’s the fourth-most used play type in college basketball. Why don’t more teams run cuts? Is this simply Synergy designating a “cut” as a different action at times? Are teams not as influenced by the Golden State Warriors (by far the highest user of cuts in the NBA) as we thought? If Cuts only represent around 8.4% of college basketball possessions, are they really that important?

There’s no one answer, obviously, but we can attempt to provide a few different ideas. First off, it’s impossible and silly to run the same play type for a full game. You’ve got to be diverse, to be creative, and to be unpredictable. The best offenses in college basketball have to have at least two of these three items: 1. Great shooters; 2. A great, unique system; 3. A coach unafraid of switching from a game plan. (Most commonly, they have all three.) The highest-usage cut rate over the 14 seasons in the Synergy database is Grove City’s 20.8% use in 2017-18. 20% seems to be a realistic limit; even Golden State only uses them 11% of the time. (In the Notes section of this piece on the last page, you’ll see some brief work on Grove City’s cuts.)

So: why are Cuts so important if most teams won’t run them more than 8-9% of the time? Because plays ending in cuts aren’t the only ones that count. The vast majority of basketball offenses use off-ball cuts, screens, motions, and more just to set up a potential shot. If a player gets a pass off of a cut and doesn’t shoot it, that won’t go down in the database. Chances are that these teams are using cuts by the technical term more often than the average 8.4%; it’s my duty to show you which ones are the best ones, theoretically.

In this series, you’ll see three teams that run a variety of unique looks offensively, all of which heavily involve cuts. Bellarmine went from going 18 seasons without a Division II NCAA Tournament bid to winning 275 games this decade on the back of Scott Davenport’s backdoor-heavy offense. On the other hand, Notre Dame’s women’s program has made 26 straight NCAA Tournaments and seven of the last nine Final Fours on the back of a routinely great offense. In between, Aaron Johnston’s hard work for South Dakota State’s women’s program has taken them from a Division II power to their first-ever Sweet Sixteen appearance in Division I this past season.

All three programs are impressive in their own way, with each finding a unique, creative way to win games on the back of their cuts. In terms of great college offenses to mine ideas from, this might be one of the better collections you’ll find.

To skip ahead to the section of your choice, please click one of the following below:

Building a Better Basketball Offense, Part 4: ATOs and OOB Plays

It’s been said for as long as I’ve watched basketball that the plays a coach has the most control over come out of timeouts. It makes sense: that’s the only time of the game where you can draw up a play on the fly or tell the entire huddle at once what to run. Sure, coaches can call sets from the sideline, but they don’t get to draw up the set while the shot clock bleeds away.

In terms of in-game control, this is indeed where the coach has the most influence. Of course, that’s only part of the equation: a coach is made better by his out-of-game control more than anything else. Quality practices, a smart system, informed recruiting, and creativity/innovation help a coach stand out more than anything they can do in a game. That said, being able to draw up a good set for a quick two or three points out of a timeout or an out-of-bounds situation can be the final piece in a coach’s arsenal.

As with the rest of this series, ATOs and OOB plays are meant to be part of your better basketball offense, not the entirety of it. Rare is it that a team is great at both, but not at least good at the rest of their offense. Per Synergy, of the top 15 ATO offenses this year, just one ranked below the 85th-percentile in overall offensive efficiency nationally, and 13 of the 15 were in the 91st-percentile or higher. (Holy Family University in Philadelphia either has the greatest ATO coach or the worst non-ATO coach in the nation, with a 36th-percentile offense.) Generally, the plays are going to work better than most others if you have the players to execute them.

However, this doesn’t discount the necessity of the aforementioned creativity and innovation. If you’re only running a couple ATO sets and haven’t changed things up in a while, an opposing coaching staff can snuff it out pretty quickly. Continuous tweaks and new ideas can allow you to spring a player for a wide-open three or an easy cut off of a screen to the rim. Considering I am not a coach and know next-to-nothing about what makes ATOs work, I figured I should discuss this with experts.

The three teams in this portion of the series are either very well-known for their ATO prowess or should be. Any coach or fan of the game reading this knows that the Belmont Bruins have possessed insanely good ATO sets for as long as Rick Byrd coached there. You know Jim Crutchfield from his work at West Liberty; now you’ll get to see what he’s doing at Nova Southeastern. Lastly, Scott Heady isn’t a household name, but the Marian Knights had the fourth-best offense in all of college basketball this year and his ATO/OOB sets were a big part of it. Exploration is good, just like innovating is.

To skip ahead to the section of your choice, please click one of the following below:

Building a Better Basketball Offense, Part 3: Threes & Perimeter Actions

Think back to when you watched basketball games a decade ago. Sure, the players were different, and the jerseys and shorts were quite a bit baggier. Not every game came in HD, and for March Madness, you couldn’t watch four games at once; it was whatever CBS determined was of interest to you at the time. Most importantly, the style of play was a lot different: three-pointers represented 33.1% of all shots, compared to 38.5% now. Plus, offensive rebounds mattered a lot more – the average team’s OREB% was 34.5%, while last season’s average was the lowest in modern history at 28.4%. Teams are more perimeter-oriented than they’ve ever been before, and it’s required both systematic and philosophical adjustments from coaches nationwide.

Of course, if you’ve been paying attention, you’ve seen this rise happen in real time, not all at once. After the NCAA introduced the three-point line in 1986 at 19 feet, 9 inches, the rate of three-pointers attempted to overall shots rose for 22 consecutive seasons. The NCAA moved it back a foot in 2008-09, and for the next two seasons, it seemed like it had shorted out the rise: a drop from 34.5% of all shots in 2007-08 to 33.1% the next season, followed by 32.6% in 2009-10. And then, it’s exploded: a 6.1% rise in the course of nine seasons, with teams attempting more threes than ever before.

What’s the deal? Why are teams so perimeter-oriented now? And, yes, what happened to the offensive rebound? We’ll answer all of those in this piece, but we need to unlock the keys to the three-pointer’s value first. Here’s why the three-pointer is good:

  • It counts for more than a two-pointer. Duh.
  • More importantly: it’s a more efficient shot than a mid-range attempt. It doesn’t take a genius to figure out the Houston Rockets’ impact on basketball throughout America. Daryl Morey’s group was the first team to truly prioritize shot selection above all other metrics, taking exclusively three-pointers or shots at the rim because, simply enough, they were the most valuable shots. 82games.com was one of the first sites to publish this, stating that close shots and threes correlated with more regular season wins. I’d also credit them with popularizing the corner three, the most beloved three-point shot of all, because it was worth 13.5 more points per 100 possessions at the time. Mid-range attempts, well…not so good.
  • It’s the great equalizer. Any coach worth their weight in America will tell you that you can’t rely entirely on threes to win games. Most coaches, in fact, would add that threes still aren’t the first shot they want; that’s a rim attempt. But a hot night from three can open up new possibilities. That’s why eFG% is a much better stat than FG%. For example, let’s say Team A makes 28 of their 56 attempts, but Team B makes 25 of 56. (For ease of time, assume they make the same number of free throws.) In 1985 and prior, Team A wins by six points. However, Team B made 12 of their 25 three-point attempts, while Team A went 5 of 19 from three. Because of the three-pointer, Team B wins by a point in a game where they made fewer shots.
  • Lastly: it can open up the rest of the floor. Each coach I talked to told me just how important spacing was for their offense. No spacing, no shots; no shots, no spacing. If you’re able to hit threes, you’re able to force defenders out of the paint, which gives you easier buckets inside. It’s not a coincidence that, for this piece, all three teams profiled attempted threes, layups, or dunks for at least 80% of their shots. They had excellent shot selection, and it allowed them to open up the entire floor.

However, not all threes are equal. Catch-and-shoot threes, on average, are worth 24.1 points more per 100 possessions than pull-up/off-the-dribble threes. Open threes, per Synergy, are +15.6 per 100 possessions versus guarded ones. Here’s the ultimate question: how do you maximize your number of open catch-and-shoot threes in a game where everyone knows that’s what you’re looking for? That’s for these excellent teams, and their smart coaches, to answer.

To skip ahead to the section of your choice, please click one of the following below:

Building a Better Basketball Offense, Part 2: Ball Screens

Ball screen usage (alternately, pick plays, pick-and-roll, pick-and-pop, etc.) has exploded over the last decade. It’s not a perfect number, but we can at least get a solid estimate via Synergy Sports. Per their measurement of ball screen possessions that include passes, the 2008-09 college basketball season saw an average team usage of about 9.8%. In just ten years, this has more than doubled to 24.8%. That’s a crazy jump!

Surely, if teams are using it this much more, this must be because it’s also exploded in efficiency, correct? Well…not really. From that same data set via Synergy, the average team’s PPP has only increased by 0.008 over the last decade. That’s not even a one point jump for every 100 possessions, which is surprising considering the ball screen’s widespread usage. Have we reached ball screen oversaturation? Is this the peak of the pick-and-roll?

Considering it’s the easiest set to run in basketball, we probably have some growth still to go. The rate of growth has slowed in the last half-decade – from 18.9% in 2013-14 to 24.8% last year, with just a 0.2% jump from 2017-18 to this season – but it’s still going up. As teams see the benefit of running these simpler sets and see its frequent usage by recent champions, coaches will likely continue to make ball screens an offensive focus.

Plus, the rates haven’t yet caught up to the most important league of all: the NBA. Their P&R usage has risen from an 18.1% P&R usage a decade ago to 31.1% in 2013-14 and 32% this season. Their curve has flattened, as you can tell. It’s actually dropped from its peak in 2015-16 of 34.8% of all possessions. If the NBA’s reached peak P&R, we can assume college will do so soon enough. However, that time likely isn’t now, and you can continue to tweak and differentiate what you do from others for better results.

The three teams you’ll read about take three very different approaches to ball screens. Northwest Missouri completely eschews the most popular ball screen of all, the continuity. Oregon and Kelly Graves use the wings, and a secondary passer, to their advantage. At Saint John’s in Minnesota, Pat McKenzie runs a tight motion offense with tons of spacing. All this being said, these three teams combined for a 94-10 record this past season, all of them having wildly efficient ball-screen offenses. What they’re doing is working, and it’s worth investigating why it works.

You can skip to any desired section by clicking below:

Building a Better Basketball Offense, Part 1: Transition

“I will never, ever play the game the other way. We’re not going to play like everyone else, just because every other coach is doing it. I’d throw up.” – Tim Cluess, Iona head coach

Imagine a world where, at your profession, you control the pace of your work. Sure, maybe you do so right now, but it isn’t true for most of us. The vast majority of what you do is at a plodding pace, one where you’re forced to get creative to hit a goal in an efficient manner. Occasionally, though, an opportunity comes about: a way to get things done that offers a fast pace, higher efficiency, and, generally, a more fun outcome. If everyone else is working at a slower, more deliberate pace 85% of the time and only doing the fun, quick stuff for the other 15%, you’d like to increase the amount of time you spend on the fun and easy stuff, right? Congrats: on part one of a seven-part series, you’ve solved college basketball.

It would be nice if it was that easy. Like most sports, points come at a premium in college basketball. Unsurprisingly, it’s much easier to score in transition than in half-court. The average transition offense in D-1 puts up 1.11 points per shot; average half-court, 0.994 PPS. That’s a difference of eight points in an average D-1 game. Why don’t more teams play fast, then? Because:

    • It’s just about impossible to play an entire game in transition.
    • Unless you have a really, really deep team, you’re going to have to slow the game down somewhat to keep your players from falling over.

However, playing faster and scoring more points is starting to equal better efficiency. The ten fastest teams in D-1 in 2018-19 averaged 1.066 PPP; the ten slowest, 1.03. This has greatly shifted from the 35-second shot clock era. In 2014-15, the last year of the 35-second shot clock, only four of the ten fastest offenses in America ranked in the top 100 in efficiency; five of the ten slowest did, with none ranking below #245.

While it’s impossible to build the perfect offense, even for transition, we can at least look at what ties the best offenses together to form an easier path forward. This series isn’t about building the perfect basketball offense. It’s about making your offense better. This edition includes interviews with Whitman College head coach Eric Bridgeland and West Virginia Tech head coach Bob Williams. If you’d like to skip ahead to a certain team’s section, you may do so here:

The most efficient offenses in men’s college basketball, 2018-19

I posted this on Twitter a couple weeks ago, but since it’s time to build out my new site, I figured I’d share it here as well. Below are the most efficient offenses in all of college basketball for the 2018-19 season:

Team PPP TO% OREB% ShotVol FTA/FGA
NW Missouri 1.269 0.120 0.267 1.147 0.305
West Liberty 1.260 0.150 0.374 1.224 0.310
NE Wesleyan 1.246 0.163 0.312 1.148 0.288
Marian 1.245 0.129 0.303 1.174 0.262
St. John’s (MN) 1.230 0.172 0.347 1.175 0.297
Barton 1.226 0.155 0.374 1.219 0.278
Gonzaga 1.226 0.149 0.315 1.166 0.353
Notre Dame OH 1.216 0.156 0.351 1.195 0.326
Valdosta St. 1.215 0.151 0.342 1.192 0.314
Charleston (WV) 1.214 0.158 0.335 1.177 0.400
WV Tech 1.209 0.166 0.357 1.191 0.324
Bellarmine 1.209 0.173 0.281 1.108 0.373
Colorado Mines 1.206 0.155 0.339 1.184 0.391
Whitman 1.201 0.157 0.354 1.197 0.403
Southwestern (KS) 1.201 0.172 0.337 1.165 0.295
Nova SE 1.196 0.147 0.349 1.202 0.301
Emory 1.195 0.154 0.368 1.214 0.282
Morningside 1.189 0.147 0.276 1.129 0.277
Hofstra 1.188 0.138 0.268 1.130 0.377
Northern St. 1.187 0.158 0.306 1.148 0.342

Synergy uses a different formula to calculate possessions, declaring offensive rebounds as the start of a new possession. I am using the more standard formula:

FGA + (0.475 x FTA) – OREBs + TOs = Possessions

Then, you take your points scored in a season and divide it by your number of possessions. If you scored 2,235 points in a 2,000-possession season, your PPP is 1.117.

Here’s the statistical commonalities I see:

  • Each team was below the national average in TO%. For convenience purposes, I’m using the Division 1 averages here. The D-1 TO% average, per KenPom, was 18.5%. Every single team listed beat that, and it played a huge part in maximizing their possessions.
  • 16 of the 20 were above the national average in OREB%. Unsurprisingly, getting extra possessions helps you score more points.
  • Most importantly: every team beat the national Shot Volume average of 109.9, and only one team ended up lower than 112.9. For further research on Shot Volume, I strongly recommend this piece by John Gasaway, which I’ve used as the basis of this data for years. He has a new metric called SVI that’s a little more difficult to calculate, but upon request, I can do that, too.
  • However, the teams actually ranked slightly below the national average for their ratio of FT attempts to FG attempts. Considering that all but two teams in this list took at least 35.4% of their attempts from three, I’ll chalk it up to more perimeter-oriented offenses.

Update, April 19, 2019: Here’s each team’s shot chart, their best play types, and team shooting splits, and tempo. All numbers listed below are from Synergy Sports, with tempo calculated with the equation listed above. The colors you see below are based on this Synergy grading scale:

Efficiency

Onward.

20. Northern State Wolves (Aberdeen, SD)

Northern St

  • Points Per Possession: 1.187
  • Best Play Types (90th-percentile or above, at least 5% usage): Spot-Up (1.184 PPP on 597 possessions, 100%), Cuts (1.427 PPP on 206 possessions, 100%)
  • Percentage of Shots Attempted: 40.6% Rim (layups, dunks, tips), 23.3% Non-Rim Twos (all other shots), 36.1% 3PA
  • Shots Made by Category: 62.6% Rim, 40.5% Non-Rim Twos, 41.6% 3PT
  • Tempo: 66.95 possessions per game (would rank 280th in D-1 among 353 teams)

19. Hofstra Pride (Hempstead, NY)

Hofstra

  • Points Per Possession: 1.188
  • Best Play Types: Spot-Up (1.084 PPP/669 possessions, 97%), P&R Ball Handler (0.929 PPP/562 possessions, 98%), Cuts (1.398 PPP/171 possessions, 100%)
  • Percentage of Shots Attempted: 32.9% Rim, 28.3% Non-Rim Twos, 38.8% 3PA
  • Shots Made by Category: 65.7% Rim, 41.7% Non-Rim Twos, 38.5% 3PT
  • Tempo: 68 possessions (232nd in D-1)

18. Morningside Mustangs (Sioux City, IA)

Morningside

  • Points Per Possession: 1.189
  • Best Play Types: Post-Up (1.097 PPP/392 possessions, 99%), Transition (1.159 PPP/333 possessions, 95%), P&R Ball Handler (1 PPP/226 possessions, 98%)
  • Percentage of Shots Attempted: 45.9% Rim, 16.2% Non-Rim Twos, 36.9% 3PA
  • Shots Made by Category: 64.9% Rim, 47.1% Non-Rim Twos, 37.7% 3PT
  • Tempo: 71.89 possessions (would rank 51st of 353 in D-1)

17. Emory Eagles (Atlanta, GA)

Emory

  • Points Per Possession: 1.195
  • Best Play Types: Transition (1.112 PPP/643 possessions, 91%), Cuts (1.274 PPP/175 possessions, 94%)
  • Percentage of Shots Attempted: 40.7% Rim, 19.4% Non-Rim Twos, 39.9% 3PA
  • Shots Made by Category: 62% Rim, 42% Non-Rim Twos, 36% 3PT
  • Tempo: 77.73 possessions (would rank #3 of 353 in D-1)

16. Nova Southeastern Sharks (Davie, FL)

Nova SE

  • Points Per Possession: 1.196
  • Best Play Types: Spot-Up (1.111 PPP/760 possessions, 96%)
  • Percentage of Shots Attempted: 44.7% Rim, 19.1% Non-Rim Twos, 36.2% 3PA
  • Shots Made by Category: 57.5% Rim, 41% Non-Rim Twos, 40.5% 3PT
  • Tempo: 80.73 possessions (would rank #1 of 353 in D-1)

15. Southwestern Moundbuilders (Winfield, KS)

SWKS

  • Points Per Possession: 1.201
  • Best Play Types: P&R Ball Handler (0.983 PPP/302 possessions, 97%), Cuts (1.348 PPP/184 possessions, 98%)
  • Percentage of Shots Attempted: 32.4% Rim, 21.3% Non-Rim Twos, 46.3% 3PA
  • Shots Made by Category: 65.4% Rim, 47.1% Non-Rim Twos, 39.4% 3PT
  • Tempo: 74.18 possessions (would rank #13 of 353 in D-1)

14. Whitman Blues (Walla Walla, WA)

Whitman

  • Points Per Possession: 1.201
  • Best Play Types: Transition (1.122 PPP/892 possessions, 93%), Spot-Up (1.073 PPP/686 possessions, 96%), P&R Ball Handler (0.955 PPP/198 possessions, 98%)
  • Percentage of Shots Attempted: 42.7% Rim, 17.7% Non-Rim Twos, 39.6% 3PA
  • Shots Made by Category: 60.3% Rim, 41.7% Non-Rim Twos, 39.6% 3PT
  • Tempo: 85.95 possessions (would be #1 of 353 in D-1, would be most since 2006-07 VMI)

13. Colorado Mines Orediggers (Golden, CO)

Mines

  • Points Per Possession: 1.206
  • Best Play Types: P&R Ball Handler (0.948 PPP/267 possessions, 95%)
  • Percentage of Shots Attempted: 33.7% Rim, 32.4% Non-Rim Twos, 33.9% 3PA
  • Shots Made by Category: 62.6% Rim, 45.6% Non-Rim Twos, 39.4% 3PT
  • Tempo: 68.4 possessions (would rank #211 of 353 in D-1)

12. Bellarmine Knights (Louisville, KY)

Bellarmine1

  • Points Per Possession: 1.209
  • Best Play Types: Spot-Up (1.114 PPP/722 possessions, 98%), Cuts (1.368 PPP/386 possessions, 98%), Transition (1.215 PPP/381 possessions, 99%), Post-Up (1.075 PPP/308 possessions, 97%)
  • Percentage of Shots Attempted: 43.1% Rim, 20.3% Non-Rim Twos, 36.6% 3PA
  • Shots Made by Category: 71.2% Rim, 49% Non-Rim Twos, 37% 3PT
  • Tempo: 66.37 possessions (would rank #300 of 353 in D-1)

11. West Virginia Tech Golden Bears (Beckley, WV)

WVU Tech

  • Points Per Possession: 1.209
  • Best Play Types: Spot-Up (1.127 PPP/647 possessions, 97%), P&R Ball Handler (0.922 PPP/293 possessions, 91%)
  • Percentage of Shots Attempted: 40.3% Rim, 18.9% Non-Rim Twos, 40.8% 3PA
  • Shots Made by Category: 60.1% Rim, 37% Non-Rim Twos, 41.9% 3PT
  • Tempo: 78.43 possessions (would rank #3 of 353 in D-1)

10. Charleston Golden Eagles (Charleston, WV)

Charleston

  • Points Per Possession: 1.214
  • Best Play Types: Transition (1.211 PPP/342 possessions, 98%), Post-Up (1.224 PPP/161 possessions, 100%)
  • Percentage of Shots Attempted: 49.3% Rim, 17.2% Non-Rim Twos, 33.5% 3PA
  • Shots Made by Category: 65.7% Rim, 38.9% Non-Rim Twos, 36.4% 3PT
  • Tempo: 71.25 possessions (would rank #72 of 353 in D-1)

9. Valdosta State Blazers (Valdosta, GA)

VSU

  • Points Per Possession: 1.215
  • Best Play Types: Spot-Up (1.085 PPP/492 possessions, 94%), Cuts (1.296 PPP/206 possessions, 92%), Post-Up (1.03 PPP/199 possessions, 94%)
  • Percentage of Shots Attempted: 39.9% Rim, 19.8% Non-Rim Twos, 40.3% 3PA
  • Shots Made by Category: 62.7% Rim, 39.7% Non-Rim Twos, 39.4% 3PT
  • Tempo: 73.86 possessions (would rank #18 of 353 in D-1)

8. Notre Dame Falcons (South Euclid, OH)

NDOH

  • Points Per Possession: 1.216
  • Best Play Types: Post-Up (1.004 PPP/258 possessions, 92%), Cuts (1.302 PPP/255 possessions, 93%), P&R Ball Handler (1.009 PPP/227 possessions, 100%)
  • Percentage of Shots Attempted: 36.6% Rim, 20.9% Non-Rim Twos, 42.5% 3PA
  • Shots Made by Category: 66.9% Rim, 39.1% Non-Rim Twos, 36% 3PT
  • Tempo: 72.23 possessions (would rank #45 of 353 in D-1)

T-6. Gonzaga Bulldogs (Spokane, WA)

Gonzaga

  • Points Per Possession: 1.226
  • Best Play Types: Transition (1.159 PPP/659 possessions, 94%), Spot-Up (1.074 PPP/651 possessions, 96%), Post-Up (1.073 PPP/286 possessions, 99%)
  • Percentage of Shots Attempted: 42.9% Rim, 21.8% Non-Rim Twos, 36.3% 3PA
  • Shots Made by Category: 69.7% Rim, 45.2% Non-Rim Twos, 36.3% 3PT
  • Tempo: 71.4 possessions (ranked #66 in D-1)

T-6. Barton Bulldogs (Wilson, NC)

Barton

  • Points Per Possession: 1.226
  • Best Play Types: Transition (1.244 PPP/488 possessions)
  • Percentage of Shots Attempted: 41.8% Rim, 24.6% Non-Rim Twos, 33.6% 3PA
  • Shots Made by Category: 61% Rim, 43.6% Non-Rim Twos, 41% 3PT
  • Tempo: 73.41 possessions (would rank #22 of 353 in D-1)

5. St. John’s Johnnies (Collegeville, MN)

SJUMN

  • Points Per Possession: 1.23
  • Best Play Types: Spot-Up (1.072 PPP/544 possessions, 96%), P&R Ball Handler (1.003 PPP/316 possessions, 98%), Cuts (1.372 PPP/266 possessions, 99%)
  • Percentage of Shots Attempted: 45.5% Rim, 19.2% Non-Rim Twos, 35.3% 3PA
  • Shots Made by Category: 66.3% Rim, 47.1% Non-Rim Twos, 40.6% 3PT
  • Tempo: 66.51 possessions (would rank #295 of 353 in D-1)

4. Marian Knights (Indianapolis, IN)

Marian

  • Points Per Possession: 1.245
  • Best Play Types: Spot-Up (1.112 PPP/457 possessions, 96%), Transition (1.218 PPP/440 possessions, 99%), P&R Ball Handler (1.1 PPP/420 possessions, 100%)
  • Percentage of Shots Attempted: 33% Rim, 31.6% Non-Rim Twos, 35.4% 3PA
  • Shots Made by Category: 63.6% Rim, 46.9% Non-Rim Twos, 42.5% 3PT
  • Tempo: 71.05 possessions (would rank #80 of 353 in D-1)

3. Nebraska Wesleyan Prairie Wolves (Omaha, NE)

NWU

  • Points Per Possession: 1.246
  • Best Play Types: Transition (1.171 PPP/598 possessions, 98%), Spot-Up (1.074 PPP/557 possessions, 97%), Cuts (1.422 PPP/268 possessions, 99%)
  • Percentage of Shots Attempted: 41.7% Rim, 14.7% Non-Rim Twos, 43.6% 3PA
  • Shots Made by Category: 69.8% Rim, 44.6% Non-Rim Twos, 40.6% 3PT
  • Tempo: 72.43 possessions (would rank #44 of 353 in D-1)

2. West Liberty Hilltoppers (Wheeling, WV)

WLU

  • Points Per Possession: 1.26
  • Best Play Types: Spot-Up (1.111 PPP/845 possessions, 97%), Hand-Off (1.075 PPP/214 possessions, 93%), Off Screen (1.12 PPP/183 possessions, 93%)
  • Percentage of Shots Attempted: 43.3% Rim, 17.5% Non-Rim Twos, 39.2% 3PA
  • Shots Made by Category: 61.4% Rim, 37.2% Non-Rim Twos, 42.5% 3PT
  • Tempo: 80.02 possessions (would rank #1 of 353 in D-1)

1. Northwest Missouri State Bearcats (Maryville, MO)

NWMO

  • Points Per Possession: 1.269
  • Best Play Types: Spot-Up (1.131 PPP/870 possessions, 99%), P&R Ball Handler (1.021 PPP/426 possessions, 100%), P&R Roll Man (1.22 PPP/268 possessions, 10%)
  • Percentage of Shots Attempted: 43.1% Rim, 11.4% Non-Rim Twos, 45.5% 3PA
  • Shots Made by Category: 62.5% Rim, 45.3% Rim, 40.9% 3PT
  • Tempo: 64.92 possessions (would rank #336 of 353 in D-1)

Later this offseason, you’ll see video investigations of most of these offenses in a series to be revealed soon. Any and all questions may be directed to statsbywill@gmail.com OR on Twitter @statsbywill.