How much mid-range is too much mid-range?

Hello out there. I hope you’re trying to enjoy the dog days of summer. Every day is exactly the same; an 88-to-93 degree high, a 69-to-73 degree low. Sometimes it rains. Sometimes it doesn’t. Much like basketball, something either goes down or it stays out. This is perhaps the peak time of boredom, something we rarely get anymore with our collective addiction to social media and online life. You can zone out for minutes, even hours and realize that nothing around you has changed all that much. In its own way, it is quite nice.

More than any other time this could possibly be written, mid-August in the middle of Sludge Weather seems like the ideal time to continue the Mid-Range Discourse.

AFTER THE JUMP: The Discourse begins anew

Continue reading “How much mid-range is too much mid-range?”

What matters most in winning college basketball’s closest games?

Sports, in general, lend themselves to classic cliches. The team that continuously wins coin-flip fixtures wants it more. They get the 50/50 plays. Clearly, they have more heart, or perhaps they’re just the more experienced team. Sometimes, we talk about how you can’t let a team like them hang around and how these teams, or players, or coaches, or heck, fans are simply winners. They get it done when it counts.

All of the above are various cliches I’ve heard surrounding close, tightly-contested games. Also, all of the above are cliches I’ve heard across every single sport I watch. The same teams with experience or heart or devil magic seem to exist in all sports, from football to basketball to hockey to European football to curling. They’re everywhere, pervasive at all times, unable to be hidden from. Announcers and sportswriters love cliches like these because they’re narrative-friendly and for the most part, you can’t really disprove them.

How is one supposed to disprove an individual or team having the larger amount of heart, exactly? Do we get postgame MRIs detailing heart girth? Do we get live blood pressure readings in the final moments of a high-leverage situation? Along with that, I’ve never understood how I can say a team didn’t want it more. I mean, I can’t get in their heads or read their inner thoughts. I don’t know if one player is thinking about wanting to take the last shot or throw the final pitch while another is thinking about Arby’s.

Basketball, particularly of the college variety, could be the best testing grounds for all sorts of ideas and philosophies. Are there certain statistical elements that lend themselves to teams winning more close games? Are these elements different in any way from those that decide every other basketball game? Can we actually prove or disprove some of the less airy cliches surrounding basketball’s closeness? I spent a month’s time this offseason diving deep into these questions and more. Whether or not it proves to be of real use, we’ll see.

NEXT PAGE: What defines a close game? What are some of the common stats-unfriendly tropes that can be proven or disproven?

Statcast Goofin’, Vol. 2: The weird and weirdly fun Miami Marlins

Even the idea of writing the title of this piece felt a little off. The fun Miami Marlins? The same franchise that sold everything it could the second they had any type of success? The franchise that, prior to 2020, had made the playoffs twice ever and won the World Series both times? The Marlins, a team that’s never won their division and finished above .500 for the fifth time ever last season (out of 28 tries) at 31-29? This is all before we get to how they routinely played in empty stadiums in a pre-COVID time and that they’ve ranked in the bottom five in MLB attendance every year from 2001 to now except for 2012, the first year of their new ballpark.

Yes, I’ve elected to write about the fun Miami Marlins. Even if you get past all those qualifiers, you’ll have to stick with me here past the surface level of fun, because the Marlins meet very few of the traditional requirements I’d look for. As of May 20, Miami ranks 24th in runs scored per game, 26th in Weighted On-Base Average (wOBA), and 27th in strikeout rate. Statcast offers these Marlins as weak hitters, too: their average exit velocity ranks 24th (one spot below the Orioles), they have fewer hard-hit balls (385) than all but five other teams, and dead last in MLB in expected Weighted On-Base Average.

When everything shakes out after small sample sizes become larger, the Marlins are most likely going to end up being one of the five worst offenses in the league. This is all before we pay attention to how they rank eighth-best in runs allowed per game and fourth-best in average exit velocity allowed. If you attend a Marlins game this year, the odds you’re going to see a low-scoring game are higher than almost any other team in baseball. If you attend a Marlins-Mets game, you should probably stay at home instead.

So now that you have all of that in mind, please take me seriously when I say the Marlins – 19-23, currently fourth in the NL East, with playoff odds on Fangraphs of 0.8% – are one of the most aesthetically enjoyable watches you can have in baseball this season. Do they hit like the Astros, White Sox, or Dodgers? No, but they have one budding star and a wide collection of weird also-rans that keep them entertaining. To go with that, they’ve got three of the game’s best young pitchers, all of whom may already be top 50ish pitchers in the league, all of whom are 25 or younger. Let’s explore these weirdos who have great uniforms and reside in one of the strangest stadiums in America.

Also, they just unveiled these utterly amazing new uniforms.

Smooth Jazz is the young star Miami deserves

Of the cities I’ve personally been to, Miami is a strange one. It’s like this weird clash of party culture combined with gorgeous architecture; nothing about what you see in Miami makes all that much sense, but it dances together. It’s a fascinatingly unique place, which is why Jazz Chisholm, Jr. is the perfect future star for this franchise.

Chisholm is barely 23 years old and barely broke into last year’s Marlins roster mostly thanks to the variety of maladies that befell the Marlins earlier that season. Chisholm posted a mortifying slash line of .161/.242/.321, which is, uh, bad. He did post a pair of home runs, but he very much seemed not ready for the whole thing; in 62 plate appearances, he struck out 19 times and reached base just 15 times. It wasn’t a great introduction.

Fast forward to today, and suddenly, Chisholm has developed into one of the game’s most exciting young stars. This is, of course, aided by the fact that he seems like a grand personality. Chisholm dyed his hair blue prior to the season, regularly gives good quotes, and, well, seems like a perfect fit for a franchise (and league) in dire need of a marketable player.

It also helps that in his true rookie year, Jazz has turned into the best hitter on the Marlins roster, posting a slash line of .301/.372/.554. His weighted Runs Created (wRC+) is 157, good enough for 18th-best in baseball and one slot below a guy you may have heard of. Enough about the fancy stats, though: I think you probably want to see the dingers.

The above video represents one of Chisholm’s two homers against 100+ MPH pitches this season, the only two home runs against 100+ MPH pitches by anyone in baseball. (It’s also the only home run surrendered by Jacob deGrom on an 0-2 count in his illustrious career.) In fact, it was the first home run against a 100+ MPH pitch since September 9, 2019 (Miguel Rojas). To do this once in a career is pretty impressive, especially considering Statcast reports no player from 2008 to 2020 managed to hit multiple home runs off of 100+ MPH pitches. So, yeah, imagine the pleasant surprise when Chisholm did it again barely a month later.

Look at where that pitch is; it’s way, way out of the zone. Chisholm doesn’t care, and his bat catches up to a 100.5 MPH pitch several inches out of the zone with relative ease.

What Chisholm is doing is rather unique, especially for a rookie who wasn’t all that much of a can’t-miss prospect. Chisholm signed as an international free agent with the Diamondbacks in 2015 and ended up with the Marlins in the 2019 Zac Gallen trade. Entering 2021, Fangraphs had Chisholm as a low-end top 50 prospect and the Marlins’ fourth-best; their most likely outcome showed him as a fine everyday starter. He’s been more than fine, I’d say. The below graph is a simple, meaningless one from Statcast showing those who are both hitting the ball well and are fast on the basepaths:

(The dot to the immediate left on Acuna is Mike Trout, for the record.) Is Chisholm anywhere as good as Ohtani, Buxton, Acuna, or Trout? Uh, probably not, at least not yet. But as a general list of Guys I Look Forward to Watching Every Day, Chisholm has rapidly ascended to the very top of this list. This combination of power and speed really isn’t that common, and as of the time of writing, Chisholm and Fernando Tatis, Jr. (another player at the top of such a list) rank almost equally. Baseball needs more players as fun, as likable, and as talented as Jazz Chisholm.

The Marlins also have some other players of interest – Jesus Aguilar and Miguel Rojas are both very good hitters worth your time – but everything here is put in the shadow of Chisholm, a player eight years younger than both Aguilar and Rojas and with a far brighter future than either.

A three-headed monster that deserves serious investment

If you’re old enough to remember the 2003 Marlins, that team had an intoxicating collection of young pitching talent on their roster. Some names you’d remember right off the bat (Josh Beckett); others might take a few minutes or hours to picture (Dontrelle Willis, Brad Penny). Either way, those Marlins rode their way to stardom off the back of three excellent 25-or-younger pitchers, the three of whom combined to allow just five earned runs in 32.1 innings of work in their World Series victory over the Yankees.

These Marlins, of course, are very likely to not achieve the same level of stardom. Two of their three semi-stars are already 25, with the third (Trevor Rogers, 23) being two years older than the youngest of the 2003 group. Still, Miami has somehow stumbled their way into a fabulous collection of pitching talent that, hopefully, they won’t sell away this time.

The best of the three, at least via his work this season, is Rogers. The 23-year-old ranks 15th in the majors in Wins Above Replacement (1.5, one spot below Max Scherzer), holds a 1.74 ERA (a problematic stat, obviously) that ranks fifth-best, and is striking out 11.32 batters per nine innings of work. Rogers’ go-to is a four-seam fastball, a pitch he throws 61% of the time that’s accumulated 43 strikeouts in just nine games of work:

Interestingly, Rogers appears to have made this pitch a bit better from 2020 to 2021, when it was actually a below-average MLB pitch. While 2020 provides the smallest sample size of any MLB season in modern history, it did seem notable that hitters weren’t having much of an issue catching up to Rogers’ fastest pitch. Opposing hitters posted a .444 wOBA (very bad!) on his four-seamer. However, the Statcast data showed a player who may have simply been unlucky. Per their expected Batting Average and expected wOBA data, Rogers’ best pitch should’ve been posting a .195 opposing BA and a .314 wOBA, not the actual .314 and .444 numbers it got.

Fast-forward to May 2021, and he’s experiencing more positive regression to the mean than almost anyone in baseball. This year, batters are hitting his four-seamer at just a .205 rate, and he’s added a full 1.4 MPH on average to this pitch. That’s important, because it makes his best secondary pitch, a changeup with elite vertical movement (+5.7 inches of movement versus the average, fifth-highest out of 63 qualifying pitchers) significantly more effective.

In both seasons, Rogers has held a remarkably stable Whiff% of around 34-35% on this pitch, but the key this year is that fewer opponents are able to hit it well. The Hard-Hit Percentage on his changeup is down around 4% from last year, and opposing batters aren’t having much luck getting a productive at-bat out of it. Hitters are 9-for-50 (.180) against this pitch, with only two extra-base hits.

Lastly, Rogers’ most improved pitch is a slider that he tries to push down and away from right-handed hitters and down and in on left-handers. (Interestingly, he uses his slider much more frequently against lefties than he does his changeup. We only have 117 pitches of data here, but that’s just under the 124 times he threw the slider last year. Compare these numbers and you’ll see how much better it’s been:

  • 2020: 30.9% Whiff%; .404 wOBA; .593 Slugging Percentage (.410 expected)
  • 2021: 45% Whiff%; .241 wOBA; .214 Slugging Percentage (.291 expected)

This isn’t typically Rogers’ at-bat-ending pitch, but it’s one he uses to get a lot of weakly-hit balls. Only 27.3% of contact against Rogers’ slider is that of the 95+ MPH exit velocity variety, and it rarely produces anything other than a weak groundout or line drive.

Rogers is making things look easy right now. Even if his numbers regress somewhat – both Fangraphs and Statcast see some less-sunny numbers coming his way – he’s still on track to be one of the 30 or so best starting pitchers in baseball. Lest we forget, this is a 23-year-old in his first full season doing that. The potential is very much there, and he’s a fairly obvious pick for the Marlins’ most likely All-Star in two months.

The #2 pitcher here is the oldest of the trio by a few months. Sandy Alcantara came to Miami by way of a trade with St. Louis in 2017, and in this, his third full season as a starter but only his second as a 162-game feature player, he’s blossoming in real time. The ERA of 3.63 may not jump off the page, and neither will the less-flashy strikeout numbers, but Alcantara has progressed from a likely reliever to the second cornerstone of one of baseball’s best rotations. In lieu of strikeouts, Alcantara has produced an array of off-speed material that makes his 96-98 MPH four-seam fastball a tough one to catch up to.

Alcantara has always had elite arm strength – FanGraphs themselves highlighted it in their 2019 scouting report – but the off-speed stuff took a while to develop. In 2017, Alcantara’s first partial year in the majors, all four of his pitches where seen as below league-average, the best of which was a changeup he used around 13% of the time. Even as recently as last season, Alcantara rarely relied on this pitch, and it was generally seen by Statcast as roughly a league-average off-speed option.

In 2021, Alcantara has somehow turned his most forgettable pitch offering into his very best. It’s produced a 37% Whiff%, a .127 opponent batting average, and a lot of gross swings:

The easiest explanation for this sudden shift is a relatively simple one: improved command. In 2020, Alcantara’s 68 changeups were scattered all over the place, and it appears he genuinely did not know where it would end up:

After a full offseason of work, Alcantara has nearly quadrupled his usage of the pitch this season, clearly in part because he’s improved his command of it:

Largely gone are the attempts to make this a down-and-in pitch for left-handers. Too frequently, Alcantara’s pre-2021 changeups ended up just being inside and not sinking, which led to five homers against the pitch in 2019 and 2020 combined and a lot of easy hits. Now, Alcantara just makes this pitch go down and stay down, and hitters haven’t adjusted to it yet. His control has improved immensely, which is allowing him to make a lot of guys look silly. Most importantly, they just can’t make good contact: no starting pitcher in baseball has a lower Barrel% (how often a batter hits it in the “sweet spot”) than Alcantara as of today at just 2.6% of all contact swings.

Lastly, Pablo Lopez doesn’t strike out that many people (8.4 per nine innings, the lowest of the three), partially because he has a league-average fastball at 94 MPH and very little spin or movement on the pitch. Instead, like Alcantara, Lopez has made a pretty dramatic shift to his changeup and to off-speed pitches in general. Just 31.8% of Lopez’s pitches are four-seam fastballs, and instead, he’s made a shifty change his main pitch of choice:

However, Lopez’s four-seamer is giving him a lot of productive outs. Statcast produces something called Run Value, which is a measurement of how many runs your pitch is keeping off the board per 100 pitches against the average pitcher. (Or something like that.) Lopez’s four-seamer is one of the 50 best pitches in baseball right now, mostly because he leaves very few balls over the middle of the plate. Like Alcantara, his command is up and the improvement is real:

The fun thing about these Marlins is that, after profiling their four best players, there’s still some more unique and intriguing pieces beneath the surface. Yimi Garcia (a reliever) is running wild with a 1.53 ERA and rarely walks anyone. Dylan Floro regularly produces some of the lowest exit velocities in baseball and offers a sinker/four-seamer combo that’s proving extremely difficult to put in play.

Along with that, the minor leagues have a pair of fascinating pieces that I’d really like to see in the majors at some point this year. Jesus Sanchez is a 23-year-old outfielder that got a few plate appearances last year, and while it seemed like his momentum towards the majors had largely died out, he’s been demolishing AAA pitchers so far this season. In just 13 games of play, Sanchez has knocked seven homers and is hitting .509.

Alongside Jesus is another Sanchez: Sixto. He’s 22 and likely would’ve made the roster to start the season had he not had a shoulder injury. Sanchez got seven starts last year (plus one in the playoffs) and showed why he’s such a tantalizing prospect. Sanchez’s fastball (used 23.8% of the time last year) averages 98 MPH, has an unusually high amount of horizontal movement, and resulted in a swing-and-miss nearly 30% of the time.

However, the real star of the show was his changeup. (Something about the Marlins and their changeups, I tell you.) Sanchez’s change merely rated out as one of the most dangerous pitches in the game; in terms of pitches used in at least 50 plate appearances, only two pitches in all of baseball – Tyler Clippard and Devin Williams’ changeups – were deemed superior on a RV/100 rate. It doesn’t get a ton of whiffs, but it’s proven extremely difficult to make good contact with.

Collectively, these Marlins probably won’t make a ton of memories for the average baseball fan. They aren’t expected to hang around the playoff race for long, and truth be told, they’re more likely to end up with 90+ losses than they are to factor into a wild card race at all. And yet: you really need to watch this team, particularly if any of Rogers/Alcantara/Lopez are starting and especially if one or more of the Sanchezs are called up soon. Even if none of their three best pitchers are on the mound, Jazz Chisholm has become a must-watch hitter on every at-bat.

Yes, the Marlins are weird, but they’re a particularly pleasing type of weird that makes for a good viewing experience. They’ve got a load of fascinating prospects, some quality young talent, and multiple future All-Stars. Watch them before everyone else catches on in two years.

How “Show Me My Opponent” gets made

Like any normal-brained person, for most of the last 15 years, I’ve had a real obsession with the television show How It’s Made. On the off-chance you’ve never seen it, it’s a nominally Canadian TV show that got lots of run on the Discovery Channel in the late 2000s/early 2010s and now resides on the Science Channel. I don’t know that I could properly explain why I love this show so deeply, beyond stating that something about the start-to-finish process of watching a product become A Product has always been and will always be oddly compelling.

The idea behind this post is a somewhat self-indulgent version of How It’s Made. A few different people have asked in the past how I do what I do with regards to previewing 30+ Tennessee basketball games every season. This last season completed my third straight year previewing every Tennessee basketball game, meaning I’ve written about the last 96 Volunteer basketball fixtures in great detail. Every preview in the 2020-21 season was at least 2,000+ words and all but two were 2,500+, meaning that at least twice a week every week, I’m writing anywhere from 5,000-7,000 words about the basketball program at the university I attended.

Doing this repeatedly for free is, of course, a form of insanity. It has also afforded me opportunities I never would’ve received otherwise: seeing and hearing my stats referenced on television, forming new friendships in sports media, growing my “platform” and “brand”, etc. If nothing else, I think this could be mildly useful for younger (than me) writers, like those in college or high school, who would like to write on things they’re passionate about one day.

By popular request, here is a rough timeline of how the Show Me My Opponent series on this website gets made. To give the most accurate representation of how this works, I’ve picked a game at random from the middle of the SEC conference season – February 10, 2021, a Wednesday, when Tennessee played Georgia.

NEXT PAGE: How the, uh, sausage? gets made

Statcast Goofin’, Vol. 1: Let’s find some regression candidates

I’ve resolved to make this the year I write about something other than basketball. The last two offseasons, I’ve done long interview series across all levels of college hoops; this year, I want to try something different. I’ve always liked baseball to some extent, with my love for the sport waning from the time I started college in 2011 until I realized how much I actually did love it when it’s good in 2020. With six months to go until the next college basketball game, it makes sense for me to pivot to writing in some form about baseball.

Why not give it a shot? Baseball is the sport that’s had the greatest advancements in statistics, not only with Bill James’ work in the late 1970s/early 1980s and the Fangraphs posts you may think of. It’s an easier stats-heavy sport to get invested in than nearly anything else.

For my first crack at it in a very long time, I thought I’d apply a similar principle I’ve used in my basketball writing to this MLB season thus far: positive and negative regression. I write frequently about the idea of a player being due or not due for something based on the quality of their offensive/defensive efforts; the same absolutely applies to baseball, which is a sport with higher variance and a healthy amount of luck involved. If you let yourself get too attached game-to-game, you’ll go mad; if you appreciate the madness and embrace it, you can laugh and laugh when small sample sizes go haywire.

Below, I’ve singled out some teams, batters, and pitchers who could be in line for positive/negative regression based on MLB’s amazing depth of tools, including Statcast/Baseball Savant. Whether this proves to be useful or “true” is still to be seen – these are very small sample sizes, after all – but I’d rather start with a rough-draft post than never start at all. Hopefully, it’ll give you something to watch for, if nothing else.


One of the biggest stories of the first month of the season was the suddenly-moribund New York Yankees offense. After a few years of owning a roster full of mashers, it was widely expected that New York would have the best offense in MLB this season. Why wouldn’t they? If your roster contains Giancarlo Stanton, Aaron Judge, Gleyber Torres, and D.J. LeMahieu, it should be the best in baseball. Even in a shortened season last year, the Yankees were on a 162-game pace of 254 homers and 851 runs scored, which would have been downgrades from their amazing 2019 (306 HRs, 943 runs) but were both great numbers. And yet: April looked like the Yankees would somehow continue this downward turn.

Through one month of play, the Yankees were hitting just .224, scoring barely 3.9 runs a game, and were on pace for 231 homers, a fine total that would’ve ranked barely above the league average in 2019. (There’s discussion to be had on how much the deadened ball MLB has introduced has hurt the Yankees in particular, but I’d prefer to leave that to smarter writers.) Prior to starting a weekend series against Detroit on April 30, New York was 11-14 and in a world of trouble, at least if you pay attention to the New York press. The Yankees struggles on offense, strangely enough, could be explained in one simple fact: they were very unlucky.

No team had a worse batting average on balls in play with an expected batting average of .300 or higher than the Yankees, per Statcast. They were hitting just .542 on these; they were expected to hit .623, which was much closer to league average. Along with that, only three teams were less lucky on balls with an exit velocity of 95 MPH or higher (.473 BA, .553 xBA). Regression would come soon enough; it was just a matter of how quickly it arrived. Luckily for the Yankees, there are few things that can provide the positive boost you need than playing the Detroit Tigers.

The Yankees swept that series and, as of this writing, are on a five-game win streak. They trail the division-leading Red Sox by a game-and-a-half, just a week or so after being in last place in the division. Baseball’s a weird sport, and team-wide cold streaks can exist. It did for the league’s probable best offense, and it wouldn’t surprise me to see them return to stardom pretty quickly.

A team with lower expectations and a lower high-end that’s still due for something better offensively: the Cleveland baseball team. Lake Erie’s faves are in a three-way tie for the AL Central lead, which is great and all, but their offense has been quite unbearable to watch. They’re hitting .209 (third-worst in the league) with a 90 OPS+ (22nd), and while batting average is a pretty tired metric at this point, it’s still notable that over half of Cleveland’s main lineup is below .200 on the season. It’s not like anyone expects Cesar Hernandez to be this amazing hitter, but he’s a .277 career hitter with remarkable consistency over the last eight seasons. He’s hitting .187 at the time this sentence is being typed.

In the metric mentioned above with the Yankees – expected BA on a 95+ MPH exit velocity – Cleveland was .120 below their expected value in April. It’s really hard for that to stay the same for an entire season. In particular, Hernandez is hitting an unbelievable .235 on balls with an exit velocity of 95+ MPH; his expectation based on the speed and launch angle of these balls in play is .498. Four of Cleveland’s generally-everyday starters have a batting average .163 worse than their expected batting average on these swings. (Jose Ramirez is also due for some serious positive regression.) In a time where the Central looks a little topsy-turvy thanks to Minnesota’s horrid start, Cleveland could be in position to capitalize on some positive regression soon enough.

Because of the nature of the deadened ball, a lot of teams are hitting slightly below what they’re expected to through a month-plus of play. In terms of negative regression, most of it looks to come on the pitching side, particularly from the Washington Nationals and the Seattle Mariners. Consider it the flipside of the offensive issues for Cleveland and New York. Both Washington and Seattle allow an above-average amount of hard-hit balls, and per Statcast’s xwOBA metric, Washington should be the team most victimized by hard-hit balls so far this season. Instead, only San Diego has allowed a lower batting average on these pounded pitches.

Considering the Nats were expected to have one of the better pitching staffs in baseball this year, perhaps it’s not a huge surprise they’re doing well in this department, but you still should see some sort of regression coming. The same should doubly go for the Mariners, who were expected to have a bottom-six staff in MLB but have instead allowed fewer runs than the Rays, Braves, and Athletics per game this year. Again, this is because Seattle’s failed to be victimized on hard-hit balls this year. They’re allowing the third-lowest batting average on these swings right now, which would be fine if their expected value wasn’t closer to league average. The hardest hit ball they’ve given up this season was a Shohei Ohtani line drive that became a 342-foot out:

The Mariners being in wild card position is very funny, however, and I hope it lasts.


Unsurprisingly, if you read the previous section, you’ll be expecting to see Cleveland’s Cesar Hernandez in the positive regression category. If you look at Hernandez’s Statcast profile:

You see a guy who’s doing a lot of things well. He doesn’t swing and miss often; he draws a lot of walks; he’s got a very good expected batting average. So color me skeptical that a guy who simply appears to be a consistently good offensive player is going to hit .187 forever. Through a month-plus of play, Hernandez actually has his highest average exit velocity (89.4%), hard-hit percentage (39.5%), and xwOBA (.376) in the Statcast era. I don’t play fantasy baseball, but if it’s up your alley, it could be a good time to load up on Hernandez.

Likewise, Houston’s Kyle Tucker is overdue for some good batted-ball luck. Tucker’s metrics aren’t as all-around solid as Hernandez, but he still should have a better hitting profile through 30ish games of play than he’s posted. Tucker ranks in the 79th percentile in average exit velocity, the 73rd percentile in hard-hit percentage, and the 81st percentile in whiff percentage. He’s far from a high-average hitter, sitting at .230 for his career, but he appears to be unusually unlucky this year. Tucker’s expected batting average is .279 with an xwOBA of .359; he’s at .183 and .255 on both, respectively. Tucker hits more fly balls than the average hitter, so I can see where his profile could be boom-or-bust, but there still should be something positive coming Tucker’s way.

On the negative side of regression-land: the wonderful Yermin Mercedes (White Sox) and Jared Walsh (Los Angeles Angels). Mercedes has been an absolute joy so far, posting a hilarious .386/.426/.614 split that’s made him one of the best hitters in baseball so far. Mercedes actually has a very high expected batting average (.311) and, if he keeps a similar contact profile, should still be one of the better hitters in baseball this year. However: .311 isn’t .386, and his hard-hit percentage is essentially league average. Still, this is worth enjoying as long as he’s able to ride it.

Walsh is probably a less-likely breakout candidate but a pretty fascinating one in his own right. A 27-year-old, Walsh garnered 63 games worth of action across the 2019 and 2020 seasons after a scalding-hot AAA run in 2019 (.325/.423/.686, 161 wRC+, 36 HRs). His first season was awful (.203/.276/.329, -0.2 WAR); his second was great (.293/.324/.646). Year three would’ve fairly been expected to be something in-between. Instead, Walsh has turned into the second-best hitter on a team that employs Mike Trout, Shohei Ohtani, Anthony Rendon, and Justin Upton.

Walsh has a low hard-hit percentage and a low average exit velocity, which normally spells trouble for any batter and is why his current average is nearly 60 points higher than his expected average. Still, even if Walsh falls to merely being an Upton-level contributor, he’s going to be an important piece to an Angels team that desperately needs a playoff bid.


At least in the city where I reside (Knoxville, TN), no pitcher has had a more upsetting start to the season than Atlanta’s Max Fried. After a fabulous 2019-20 run that resulted in Fried becoming a consensus top-20 pitcher in the league, he’s been a disaster in 2021. Fried’s ERA sits at an awful 8.44, he’s allowed more runs in four starts than he did in 2020’s eleven, and most surprisingly, his slider has gone from one of the best in the league to an eminently-hittable pitch.

My case for positive regression in Fried is both statistical and hopeful; I would like for a great young player to get back to being great. Fried’s expected ERA is still 5.72, which is way above what he or anyone else would want it to be, and he’s allowing a shockingly high amount of hard-hit balls – 40.7% of balls in play, in fact. Fried’s slider has lost a full four inches of break compared to last year:

Which makes a huge difference in terms of where it lands on the bat. Here’s his slider this year, from the same Truist Park camera angle:

If he’s able to find his slider again, he should be able to recover this season’s trajectory somewhat. If he doesn’t…well, pretend you didn’t read this. Luckily, Fried just had a pretty solid outing against Washington, so perhaps he’s on the mend.

Another guy I’m hoping to see positive things from soon is Logan Webb (Giants) He’s been middling so far, with his Statcast profile suggesting better fortunes ahead. Webb’s profile is particularly intriguing: his average exit velocity is just 85.8 MPH (league average 88.3%), he induces a ton of ground balls (60.4%; league average 45.3%), and his sinker has gained three inches of drop compared to last season. All of this should add up to a pretty good starting pitcher. It’s added up to a 5.34 ERA, a full two runs above his expected value based on his performance thus far.

I’m curious to see what happens to Webb, as it’s not like he’s had an amazing run of play in his career to this point (5.36 career ERA, .334 xwOBA, .272 xBA). However, he’s a guy who deserves better results than he’s gotten.

The best men’s college basketball offenses of 2020-21

This is a simple post. It’s the most efficient men’s college basketball offenses of the 2020-21 season, a continuation of a project I’ve done in years prior

First up, the Synergy Sports section. This one is pretty simple: it’s the 20 best offenses of the season, as determined by a minimum number of possessions (1100 or more). Normally, I don’t really have to filter out many teams, but there was a huge variety in how many games teams were able to play this season thanks to COVID-19. Two of the teams in the top 20 here only played 13 games, while one played 35. We’ve never had that much of a disparity in games played, and hopefully, we’ll never have it again.

Something unusual also happened: there was a four-way tie for 19th, which means this list is 22 teams long instead of 20. I’ve included the extra two, because they shouldn’t be excluded arbitrarily.

The difference between this section and the next is a simple one. Synergy includes offensive rebounds as separate possessions; most other places out there count them as part of the same possession. I’ve included both calculations.

Honorable Mentions: Fairmont State (1.015 PPP), Virginia (1.017), Bellarmine (1.018).

T-19. William Penn University Statesmen (Oskaloosa, IA)

  • Points Per Possession: 1.019
  • Best Play Types (90th-percentile or higher): P&R Ball Handler (93rd-percentile)
  • Percentage of Shots Attempted: 49.7% Rim, 11.6% Non-Rim Twos, 38.7% Threes
  • Shots Made by Category: 68.3% Rim, 45.5% Non-Rim Twos, 32.2% 3PT
  • Tempo: 84.29 possessions (would rank 1st of 347 teams in D-1)

T-19. St. Edward’s Hilltoppers (Austin, TX)

  • Points Per Possession: 1.019
  • Best Play Types (90th-percentile or higher): Isolation (95th), Spot-Up (92nd)
  • Percentage of Shots Attempted: 31.3% Rim, 20.4% Non-Rim Twos, 48.3% Threes
  • Shots Made by Category: 65.4% Rim, 42.3% Non-Rim Twos, 36.9% 3PT
  • Tempo: 72.76 possessions (37th of 347)

T-19. Marietta Pioneers (Marietta, OH)

  • Points Per Possession: 1.019
  • Best Play Types (90th-percentile or higher): Spot-Up (97th), Off-Screen (93rd)
  • Percentage of Shots Attempted: 38.4% Rim, 25.9% Non-Rim Twos, 35.7% Threes
  • Shots Made by Category: 60.9% Rim, 40.7% Non-Rim Twos, 39.5% 3PT
  • Tempo: 74.98 possessions (8th of 347)

T-19. Iowa Hawkeyes (Iowa City, IA)

  • Points Per Possession: 1.019
  • Best Play Types (90th-percentile or higher): Spot-Up (97th), Post-Up (97th)
  • Percentage of Shots Attempted: 35.9% Rim, 24.3% Non-Rim Twos, 39.8% Threes
  • Shots Made by Category: 62% Rim, 38.8% Non-Rim Twos, 38.6% 3PT
  • Tempo: 70.8 possessions (98th of 347)

T-17. Marian Knights (Indianapolis, IN)

  • Points Per Possession: 1.021
  • Best Play Types (90th-percentile or higher): Transition (100th), Post-Up (92nd)
  • Percentage of Shots Attempted: 40% Rim, 26.5% Non-Rim Twos, 33.5% Threes
  • Shots Made by Category: 61.3% Rim, 43.4% Non-Rim Twos, 35.6% 3PT
  • Tempo: 68.9 possessions (183rd of 347)

T-17. Hillsdale Chargers (Hillsdale, MI)

  • Points Per Possession: 1.021
  • Best Play Types (90th-percentile or higher): P&R Ball Handler (98th), Post-Up (97th), Cut (95th)
  • Percentage of Shots Attempted: 44.2% Rim, 20.7% Non-Rim Twos, 35.1% Threes
  • Shots Made by Category: 66.7% Rim, 39.1% Non-Rim Twos, 35.6% 3PT
  • Tempo: 67.8 possessions (233rd of 347)

16. Colgate Raiders (Hamilton, NY)

  • Points Per Possession: 1.033
  • Best Play Types (90th-percentile or higher): Spot-Up (100th), Transition (97th), Cut (90th)
  • Percentage of Shots Attempted: 47.2% Rim, 16.5% Non-Rim Twos, 36.4% Threes
  • Shots Made by Category: 61.3% Rim, 34.5% Non-Rim Twos, 40.5% 3PT
  • Tempo: 72.6 possessions (44th of 347)

T-14. Weber State Wildcats (Ogden, UT)

  • Points Per Possession: 1.035
  • Best Play Types (90th-percentile or higher): Spot-Up (100th), Cut (97th)
  • Percentage of Shots Attempted: 36% Rim, 25.8% Non-Rim Twos, 38.2% Threes
  • Shots Made by Category: 67.3% Rim, 44% Non-Rim Twos, 38.9% 3PT
  • Tempo: 71.5 possessions (78th of 347)

T-14. Dubuque Spartans (Dubuque, IA)

  • Points Per Possession: 1.035
  • Best Play Types (90th-percentile or higher): Spot-Up (97th), Transition (95th), P&R Ball Handler (92nd)
  • Percentage of Shots Attempted: 33.6% Rim, 30.6% Non-Rim Twos, 35.8% Threes
  • Shots Made by Category: 55.2% Rim, 45.8% Non-Rim Twos, 44.6% 3PT
  • Tempo: 74.23 possessions (18th of 347)

13. West Texas A&M Buffaloes (Canyon, TX)

  • Points Per Possession: 1.036
  • Best Play Types (90th-percentile or higher): Transition (94th)
  • Percentage of Shots Attempted: 33.7% Rim, 21.9% Non-Rim Twos, 44.4% Threes
  • Shots Made by Category: 65.2% Rim, 41.3% Non-Rim Twos, 36.6% 3PT
  • Tempo: 75.21 possessions (8th of 347)

12. West Liberty Hilltoppers (West Liberty, WV)

  • Points Per Possession: 1.038
  • Best Play Types (90th-percentile or higher): none; highest Off-Screen (89th)
  • Percentage of Shots Attempted: 45.3% Rim, 14.5% Non-Rim Twos, 40.2% Threes
  • Shots Made by Category: 60.8% Rim, 46% Non-Rim Twos, 36% 3PT
  • Tempo: 82.44 possessions (1st of 347)

11. Liberty Flames (Lynchburg, VA)

  • Points Per Possession: 1.042
  • Best Play Types (90th-percentile or higher): Spot-Up (99th), Transition (96th), P&R Ball Handler (92nd)
  • Percentage of Shots Attempted: 39.2% Rim, 13.3% Non-Rim Twos, 47.4% Threes
  • Shots Made by Category: 61.7% Rim, 45.3% Non-Rim Twos, 39% 3PT
  • Tempo: 64.7 possessions (334th of 347)

10. Charleston Golden Eagles (Charleston, WV)

  • Points Per Possession: 1.048
  • Best Play Types (90th-percentile or higher): Cut (100th), Spot-Up (98th)
  • Percentage of Shots Attempted: 43% Rim, 15.8% Non-Rim Twos, 41.2% Threes
  • Shots Made by Category: 69% Rim, 40.5% Non-Rim Twos, 37.4% 3PT
  • Tempo: 70.7 possessions (102nd of 347)

T-8. Westmont Warriors (Santa Barbara, CA)

  • Points Per Possession: 1.052
  • Best Play Types (90th-percentile or higher): Spot-Up (100th), Transition (98th)
  • Percentage of Shots Attempted: 41.2% Rim, 16.6% Non-Rim Twos, 42.2% Threes
  • Shots Made by Category: 64.4% Rim, 47.6% Non-Rim Twos, 38.4% 3PT
  • Tempo: 78.71 possessions (2nd of 347)

T-8. Huntington University Foresters (Huntington, IN)

  • Points Per Possession: 1.052
  • Best Play Types (90th-percentile or higher): P&R Ball Handler (9th), Transition (97th), Spot-Up (94th), Post-Up (93rd)
  • Percentage of Shots Attempted: 37.9% Rim, 17.8% Non-Rim Twos, 44.3% Threes
  • Shots Made by Category: 63.1% Rim, 52% Non-Rim Twos, 37.1% 3PT
  • Tempo: 74.16 possessions (18th of 347)

7. Dallas Baptist Patriots (Dallas, TX)

  • Points Per Possession: 1.07
  • Best Play Types (90th-percentile or higher): Spot-Up (100th), Post-Up (98th), Transition (91st), P&R Ball Handler (90th)
  • Percentage of Shots Attempted: 39.2% Rim, 16.1% Non-Rim Twos, 44.7% Threes
  • Shots Made by Category: 66.4% Rim, 50.5% Non-Rim Twos, 39.9% 3PT
  • Tempo: 73.84 possessions (22nd of 347)

6. Northwestern College Red Raiders (Orange City, IA)

  • Points Per Possession: 1.071
  • Best Play Types (90th-percentile or higher): Spot-Up (99th), Transition (99th), Post-Up (97th), Isolation (95th)
  • Percentage of Shots Attempted: 40.9% Rim, 21% Non-Rim Twos, 38.1% Threes
  • Shots Made by Category: 69% Rim, 45.3% Non-Rim Twos, 39.2% 3PT
  • Tempo: 72.77 possessions (36th of 347)

5. Lincoln Memorial Railsplitters (Harrogate, TN)

  • Points Per Possession: 1.075
  • Best Play Types (90th-percentile or higher): Spot-Up (97th), Transition (95th), Cut (91st), Hand-Off (91st)
  • Percentage of Shots Attempted: 45.8% Rim, 8.8% Non-Rim Twos, 45.4% Threes
  • Shots Made by Category: 67% Rim, 35.9% Non-Rim Twos, 40.9% 3PT
  • Tempo: 77.35 possessions (2nd of 347)

4. Indiana Wesleyan Wildcats (Marion, IN)

  • Points Per Possession: 1.084
  • Best Play Types (90th-percentile or higher): Cut (98th), Post-Up (98th), Transition (91st)
  • Percentage of Shots Attempted: 48.1% Rim, 19.2% Non-Rim Twos, 32.7% Threes
  • Shots Made by Category: 68% Rim, 43.5% Non-Rim Twos, 37.9% 3PT
  • Tempo: 78.75 possessions (2nd of 347)

3. Gonzaga Bulldogs (Spokane, WA)

  • Points Per Possession: 1.085
  • Best Play Types (90th-percentile or higher): Post-Up (100th), Cut (99th), Transition (97th), P&R Ball Handler (97th), P&R Roll Man (97th)
  • Percentage of Shots Attempted: 48.2% Rim, 18.5% Non-Rim Twos, 33.2% Threes
  • Shots Made by Category: 72.6% Rim, 41.5% Non-Rim Twos, 36.8% 3PT
  • Tempo: 74.3 possessions (14th of 347)

2. Lubbock Christian Chaps (Lubbock, TX)

  • Points Per Possession: 1.114
  • Best Play Types (90th-percentile or higher): Everything except P&R Ball Handler and P&R Roll Man were in the 92nd-percentile or higher.
  • Percentage of Shots Attempted: 37.2% Rim, 21.4% Non-Rim Twos, 41.4% Threes
  • Shots Made by Category: 65.9% Rim, 46.8% Non-Rim Twos, 43% 3PT
  • Tempo: 66.7 possessions (#285 of 347)

1. Northwest Missouri State Bearcats (Maryville, MO)

  • Points Per Possession: 1.12
  • Best Play Types (90th-percentile or higher): Literally every single play type that isn’t putbacks.
  • Percentage of Shots Attempted: 42.1% Rim, 11.5% Non-Rim Twos, 46.4% Threes
  • Shots Made by Category: 67.6% Rim, 41.9% Non-Rim Twos, 42.2% 3PT
  • Tempo: 65.7 possessions (#317 of 347)

NEXT PAGE: Top 20 via traditional possession calculations

The best women’s college basketball offenses of 2020-21

This is a very simple post. It’s a list of the most efficient women’s college basketball offenses this season, and it’s a list I’ve made in years prior. This year, I cut the list from 25 down to 20 for one simple reason: COVID-19 and a lower number of games than normal.

There will be two calculations included here. The first, and the one I note in tweets, is from Synergy Sports, which accumulates stats from every single college basketball program in America. Their points per possession numbers will look smaller than most for one specific reason: Synergy notes offensive rebounds as separate possessions. Most others (i.e. KenPom, StatBroadcast, etc.) do not.

First up, Synergy. This one is pretty simple: it’s the 20 best offenses of the season, as determined by a minimum number of possessions (1100 or more). Normally, I don’t really have to filter out many teams, but there was a huge variety in how many games teams were able to play this season thanks to COVID-19. Hopefully, this is the only season we’ll ever have to filter out teams again.

20. Minnesota-Duluth Bulldogs (Duluth, MN)

  • Points Per Possession: 0.93
  • Best Play Types (90th-percentile or higher): P&R Ball Handler (99th-percentile), Spot-Up (97th), Post-Up (93rd)
  • Percentage of Shots Attempted: 39.5% Rim (0-4 feet from the rim), 28% Non-Rim Twos, 32.5% Threes
  • Shots Made by Category: 55.9% Rim, 38.2% Non-Rim Twos, 36% 3PT
  • Tempo: 66.03 possessions

19. Taylor University Trojans (Upland, IN)

  • Points Per Possession: 0.932
  • Best Play Types (90th-percentile or higher): Spot-Up (100th), Post-Up (94th), P&R Ball Handler (92nd)
  • Percentage of Shots Attempted: 31.5% Rim, 14.8% Non-Rim Twos, 53.7% Threes
  • Shots Made by Category: 61% Rim, 36.7% Non-Rim Twos, 37.1% 3PT
  • Tempo: 72.39 possessions

18. Colorado State Rams (Fort Collins, CO)

  • Points Per Possession: 0.933
  • Best Play Types (90th-percentile or higher): Transition (99th), P&R Ball Handler (96th)
  • Percentage of Shots Attempted: 31.5% Rim, 35.5% Non-Rim Twos, 33% Threes
  • Shots Made by Category: 59.3% Rim, 37.7% Non-Rim Twos, 37.7% 3PT
  • Tempo: 73.83 possessions

17. Central Michigan Chippewas (Mount Pleasant, MI)

  • Points Per Possession: 0.934
  • Best Play Types (90th-percentile or higher): Transition (100th), P&R Ball Handler (99th), Spot-Up (92nd)
  • Percentage of Shots Attempted: 33.2% Rim, 21.1% Non-Rim Twos, 45.7% Threes
  • Shots Made by Category: 62.4% Rim, 39.2% Non-Rim Twos, 35.1% 3PT
  • Tempo: 72.55 possessions

16. Rutgers Scarlet Knights (New Brunswick, NJ)

  • Points Per Possession: 0.936
  • Best Play Types (90th-percentile or higher): Transition (99th), P&R Ball Handler (99th)
  • Percentage of Shots Attempted: 37.2% Rim, 30.7% Non-Rim Twos, 32.1% Threes
  • Shots Made by Category: 63.4% Rim, 37.2% Non-Rim Twos, 36.1% 3PT
  • Tempo: 68.27 possessions

T-14. Stanford Cardinal (Palo Alto, CA)

  • Points Per Possession: 0.937
  • Best Play Types (90th-percentile or higher): Transition (97th), P&R Ball Handler (97th), Cut (94th), Spot-Up (91st)
  • Percentage of Shots Attempted: 40.3% Rim, 23.7% Non-Rim Twos, 36% Threes
  • Shots Made by Category: 57.6% Rim, 39.2% Non-Rim Twos, 37.6% 3PT
  • Tempo: 69.81 possessions

T-14. New Mexico Lobos (Albuquerque, NM)

  • Points Per Possession: 0.937
  • Best Play Types (90th-percentile or higher): Off-Screen (99th), Cut (93rd), Spot-Up (90th)
  • Percentage of Shots Attempted: 33.5% Rim, 20.8% Non-Rim Twos, 45.7% Threes
  • Shots Made by Category: 61.5% Rim, 42.5% Non-Rim Twos, 32.9% 3PT
  • Tempo: 76.06 possessions

13. Louisville Cardinals (Louisville, KY)

  • Points Per Possession: 0.939
  • Best Play Types (90th-percentile or higher): Transition (97th), P&R Ball Handler (97th)
  • Percentage of Shots Attempted: 35.9% Rim, 30.4% Non-Rim Twos, 33.7% Threes
  • Shots Made by Category: 61.6% Rim, 39.6% Non-Rim Twos, 34.7% 3PT
  • Tempo: 70.16 possessions

12. Westminster College Lady Griffins (Salt Lake City, UT)

  • Points Per Possession: 0.944
  • Best Play Types (90th-percentile or higher): Post-Up (99th), Cut (99th), Spot-Up (95th), P&R Ball Handler (92nd)
  • Percentage of Shots Attempted: 41.3% Rim, 26.3% Non-Rim Twos, 32.4% Threes
  • Shots Made by Category: 58.8% Rim, 43% Non-Rim Twos, 36.4% 3PT
  • Tempo: 66.31 possessions

11. North Carolina State Wolfpack (Raleigh, NC)

  • Points Per Possession: 0.946
  • Best Play Types (90th-percentile or higher): P&R Ball Handler (98th), Spot-Up (96th), Transition (94th)
  • Percentage of Shots Attempted: 39.8% Rim, 28.3% Non-Rim Twos, 31.9% Threes
  • Shots Made by Category: 61.4% Rim, 39.2% Non-Rim Twos, 36.3% 3PT
  • Tempo: 71.61 possessions

10. Sterling College Warriors (Sterling, KS)

  • Points Per Possession: 0.948
  • Best Play Types (90th-percentile or higher): Transition (98th), P&R Ball Handler (97th)
  • Percentage of Shots Attempted: 40.6% Rim, 35% Non-Rim Twos, 24.4% Threes
  • Shots Made by Category: 57.5% Rim, 43.2% Non-Rim Twos, 39.2% 3PT
  • Tempo: 78.23 possessions

9. Drury Panthers (Springfield, MO)

  • Points Per Possession: 0.956
  • Best Play Types (90th-percentile or higher): Transition (100th), Hand-Off (94th)
  • Percentage of Shots Attempted: 36.8% Rim, 35.6% Non-Rim Twos, 27.6% Threes
  • Shots Made by Category: 63.4% Rim, 41.7% Non-Rim Twos, 34.7% 3PT
  • Tempo: 74.97 possessions

8. Lubbock Christian Chaps (Lubbock, TX)

  • Points Per Possession: 0.963
  • Best Play Types (90th-percentile or higher): Spot-Up (100th), Transition (97th), Post-Up (95th)
  • Percentage of Shots Attempted: 41.9% Rim, 18.3% Non-Rim Twos, 39.8% Threes
  • Shots Made by Category: 58.2% Rim, 40.7% Non-Rim Twos, 36.7% 3PT
  • Tempo: 70.13 possessions

7. Bryan College Lions (Dayton, TN)

  • Points Per Possession: 0.964
  • Best Play Types (90th-percentile or higher): Transition (100th), Spot-Up (96th), P&R Ball Handler (94th), Cut (92nd)
  • Percentage of Shots Attempted: 46.7% Rim, 13.1% Non-Rim Twos, 40.2% Threes
  • Shots Made by Category: 56.9% Rim, 39.2% Non-Rim Twos, 37.2% 3PT
  • Tempo: 79.71 possessions

6. Arkansas Razorbacks (Fayetteville, AR)

  • Points Per Possession: 0.975
  • Best Play Types (90th-percentile or higher): Spot-Up (100th), Hand-Off (96th), P&R Ball Handler (94th), Transition (91st)
  • Percentage of Shots Attempted: 34.7% Rim, 26.5% Non-Rim Twos, 38.8% Threes
  • Shots Made by Category: 57.5% Rim, 33.8% Non-Rim Twos, 38.4% 3PT
  • Tempo: 76.64 possessions

5. Connecticut Huskies (Storrs, CT)

  • Points Per Possession: 0.986
  • Best Play Types (90th-percentile or higher): Transition (98th), Cut (97th), Post-Up (95th), P&R Ball Handler (95th), Spot-Up (90th)
  • Percentage of Shots Attempted: 46.3% Rim, 22% Non-Rim Twos, 31.7% Threes
  • Shots Made by Category: 67.3% Rim, 51.7% Non-Rim Twos, 35.4% 3PT
  • Tempo: 71.4 possessions

4. Florida Gulf Coast Eagles (Fort Myers, FL)

  • Points Per Possession: 0.988
  • Best Play Types (90th-percentile or higher): P&R Ball Handler (100th), Off-Screen (97th), Cut (96th), Spot-Up (94th)
  • Percentage of Shots Attempted: 37.3% Rim, 7.8% Non-Rim Twos, 54.9% Threes
  • Shots Made by Category: 63.6% Rim, 49.3% Non-Rim Twos, 32.9% 3PT
  • Tempo: 75.56 possessions

3. Cedarville Yellow Jackets (Cedarville, OH)

  • Points Per Possession: 0.993
  • Best Play Types (90th-percentile or higher): Spot-Up (100th), P&R Ball Handler (93rd), Hand-Off (93rd), Transition (90th)
  • Percentage of Shots Attempted: 36.4% Rim, 20.8% Non-Rim Twos, 42.8% Threes
  • Shots Made by Category: 57.4% Rim, 36.7% Non-Rim Twos, 38.8% 3PT
  • Tempo: 75.7 possessions

2. Maryland Terrapins (College Park, MD)

  • Points Per Possession: 1.023
  • Best Play Types (90th-percentile or higher): Transition (99th), Spot-Up (98th), P&R Ball Handler (97th), Cut (97th)
  • Percentage of Shots Attempted: 41.9% Rim, 28.2% Non-Rim Twos, 29.9% Threes
  • Shots Made by Category: 61.4% Rim, 41.2% Non-Rim Twos, 40.2% 3PT
  • Tempo: 74.25 possessions

1. Iowa Hawkeyes (Iowa City, IA)

  • Points Per Possession: 1.034
  • Best Play Types (90th-percentile or higher): Everything but Isolation, Hand-Off, and P&R Roll Man.
  • Percentage of Shots Attempted: 38.1% Rim, 23.4% Non-Rim Twos, 38.5% Threes
  • Shots Made by Category: 65.5% Rim, 45.5% Non-Rim Twos, 40.3% 3PT
  • Tempo: 75.28 possessions

NEXT PAGE: Top 20 teams by traditional possession calculations

Final Four Preview: (1) Baylor vs. (2) Houston

No long-winded introduction here; this is merely the game I’ve been hoping to see since the Field of 68 was announced. (Though I’m still a little sore over Ohio State blowing it in the first round. I root for you people once and that’s how you repay me?) These are two shot volume machines, with Houston being the very best team in America in terms of generating shots per 100 possessions. Baylor hits a ton of threes; Houston brutalizes you for 40 minutes. It’s the most enjoyable matchup of styles we’ll get until the title game.

When Houston has the ball

No proper Houston preview can start without heading directly to their prime strength (and Baylor’s main team weakness): rebounding. Or, if you prefer, shot volume versus shot efficiency. I started noticing a very specific trend that I decided to call The Houston because no other team does it so frequently and so brutally. To achieve The Houston, you need to rebound 35% or more of your misses and turn it over on 16% or less of your possessions. Houston did it 15 times this season. No other team in America got past eight.

It’s why the Cougars’ struggles in actually hitting shots has been the B-story of sorts. In the NCAA Tournament alone, Houston has posted 63, 62, and 67 points in their last three games, with an eFG% of 44.1%, 44.2%, and 41.1% along the way. They went 9-for-30 on two-pointers against Oregon State and 14-for-37 against Rutgers. By all means, teams that post those numbers generally shouldn’t be anywhere near the Final Four. And yet: here’s the Houston Cougars, who have only posted a sub-1 PPP five times this year and keep getting there because of an absolutely bonkers amount of offensive rebounds.

The Cougars have rebounded 39.8% of their misses, the second-highest rate in college basketball and the highest by any Final Four team not named North Carolina since 2014. This is important, because we should note that offensive rebounding percentage has slowly dwindled over the last 15 years and tied for an all-time low this season at just 28%. Offensive rebounds will always be important, but they don’t hold the same level of importance that they did in, say, 2006. You can’t tell Kelvin Sampson and the Houston Cougars that, though. You certainly can’t tell their opponents this March, either. Houston has attempted 51 more field goals and 13 more free throws than their NCAA Tournament opposition because they are demolishing the glass:

Houston has been held below a 30% OREB% twice all season, the last of which was over two months ago against Temple. It really hasn’t mattered as to who the opposition is, either. Houston has played teams ranked 25th (Boise State), 31st (Memphis, twice), and 38th (Western Kentucky) in defensive rebounding; the Cougars went for 41.7%, 36.6%, 37.9%, and 35.3% OREB%, respectively. They’ve gone 43% or better in three of four NCAA Tournament games.

This is a serious problem for Baylor before we even get to actual attacks/counterattacks strategy. The Bears rank 273rd in defensive rebounding percentage, easily the lowest ranking of the remaining Final Four teams. In the team’s first loss to Kansas in late February, the Bears allowed the Jayhawks to rebound an astounding 48.3% of their misses, which helped Kansas overcome a 3-for-16 day from deep and Baylor winning the turnover battle 14-3.

If that level of poor defensive rebounding shows up, the Bears may be done before the game even starts. Even if their normal levels attend, it’s going to be very tough. Of Baylor’s four NCAA Tournament opponents, only one (Arkansas) ranked above the national average in offensive rebounding. They haven’t really faced a tough test on this front since playing West Virginia in early March, but even Arkansas and Villanova easily beat their season averages in terms of offensive rebounding. Villanova, a team that averaged rebounding 27.8% of their misses this year and is not exactly tall, got back a third of their missed shots. Arkansas: 37.9%.

If Baylor can’t clean this up, the game really could swing Houston’s way to an extent a lot of people may not expect.

Beyond the rebounding battle, there’s two clear areas where Houston has to succeed: finding open shots from deep and avoiding getting themselves into a mid-range chuck-fest. Houston would be a fairly ideal underdog in a different setting for two reasons: they keep the tempo very slow (64.9 possessions per game, 319th of 347 teams) and they jack up lots of threes. 42.5% of all Houston shots are from downtown, and their 34.9% hit rate is a bit above the national average.

The primary shooter is Quentin Grimes, the Kansas transfer who entered late-bloomer status this year and quietly became one of the best players in college basketball. Grimes is shooting 41.2% from deep on 240 attempts, and as evidenced by Houston’s run so far, he’s very unafraid to shoot. Grimes has taken a hilarious 39 three-point attempts in four games, but he’s backing it up by having hit 17 of these so far (43.6%). In fact, Grimes has hit at least four threes in seven straight games and nine of the last ten despite being the primary offensive focus for opponents to gameplan against, which is very impressive.

Grimes has been lethal this year in the Cougars’ rare transition runs: 30-for-59 in the first 10 seconds of the shot clock and 69-for-181 on all other attempts. It’s not natural for Houston to run and gun, but when you have a shooter as good as Grimes, you’re kind of obligated to do it occasionally. Watch for Houston to push the pace off of steals and, every now and then, off of a particularly bad Baylor miss.

Baylor’s defense has been excellent this season, and aside from a blip in February/March due to their three-week COVID pause, they’ve been hard to score on. The most successful team to do so in this Tournament was easily Arkansas, who didn’t shoot particularly well from deep but worked to push the pace off of misses + rare steals. By doing so, it earned the Razorbacks several open layups when Baylor wasn’t settled, as well as forcing some key Bear defenders into foul trouble. Still, this is a Baylor defense that’s excellent at guarding threes and even better at forcing the right people to take them.

Lastly: ball screens. We haven’t seen Houston run a massive amount of these over their last couple of games, as both Syracuse and Oregon State went heavy with zone defense in an attempt to force the Cougars to shoot over the top of them. Houston has a good zone offense, but zones take away Houston’s two most efficient play types: transition ball and the pick-and-roll. Houston’s ball-screen offense ranks in the 80th-percentile, per Synergy, with the ball handler having a ton of success. The main ball handlers this season have been DeJon Jarreau, Marcus Sasser, and Grimes, with Grimes/Sasser being more likely to pull up for threes and Jarreau being more likely to take a mid-range jumper.

Baylor’s goal in this game should be taking away these jumpers from Jarreau and forcing him/Sasser to shoot over the top of them instead. Neither Jarreau (35.2% 3PT%) nor Sasser (32.6%) are quite as automatic from deep as you’d hope, but both are solid rim scorers, and everyone in Houston’s main rotation converts at least 57% of their attempts at the rim. The problem: they don’t get to the rim all that often (25.8% of all attempts). If Baylor can drag Houston’s possessions out and force them to take 25-footers deep in the shot clock, it’s an optimal outcome for Scott Drew and company.

They just have to remember to rebound. Good luck!

NEXT PAGE: When Baylor has the ball

Final Four Preview: (1) Gonzaga vs. (11) UCLA

Amazingly, of all possible games Gonzaga could’ve been involved in to make the national title game, this is the opponent they drew. The team responsible for what was the definitive Gonzaga loss for a generation. The team that went all the way to the title game that year. A program with so much history, so many championships, and so much success…and a program that we are now simultaneously treating as a massive underdog against the team that championed being the underdog.

This is a weird game to preview, but I can’t help but love it. It’s been a weird year. We deserved at least one out-of-nowhere Final Four game, and I’m sure Gonzaga fans are probably happy that they’re the likely beneficiary of such a draw. But: you cannot underestimate this UCLA team. No one has for weeks now, not after they knocked off analytics darling Alabama and sentimental favorite Michigan. (Do you realize how cool Juwan Howard has to be to make Michigan a sentimental favorite?) They’re coming into this one with nothing to lose against the one team that hasn’t experienced a loss to date. I can’t wait to see how it unfolds.

NEXT PAGE: When UCLA has the ball

Game Preview: (12) Oregon State vs. (8) Loyola Chicago

Here comes a Sweet Sixteen fixture that roughly 0.36% of people appear to have projected, per ESPN. It was one thing for Loyola Chicago to be a top 10 KenPom team, to be underrated the entire season, to get an 8 seed they deserved better than…but it was honestly an entire other thing for an Oregon State team that entered the Pac-12 Tournament outside the KenPom top 100 to be here, too.

To put it in perspective, think of it this way: every other 12 seed, two 13 seeds, and a 14 seed all were picked by more people to make the Sweet Sixteen than the Beavers. Who could blame them? The Beavers have gone from an afterthought that hadn’t won an NCAA Tournament game in 39 years to 40 minutes away from their second Elite Eight appearance in the last 55 years. For Loyola, it’s a chance to show everyone that 2018 wasn’t a one-hit wonder. While this isn’t the game anyone saw coming, it’s perhaps the game with the richest possible storylines.

NEXT PAGE: When Oregon State has the ball