If you follow men’s college basketball, you’ve surely heard by now that the NCAA is finally moving back the three-point line. That won’t take effect in D2 and D3 until 2020, but D1 will use the new line this season. The move is from 20’ 9” to 22’ 1 ¾” (or 6.75 meters–the distance for FIBA and other international competition).
There has been some debate and discussion as to whether this move will matter a whole lot. Since it’s summer, and college basketball news is slow, it seems like a good time to dig into the impact this might have on the game. The NCAA used the FIBA line for the last two NITs, so we have 62 games-worth of a sample to look at. I wouldn’t be surprised if the move creates a domino effect and we see lots of subtle changes–creating more spacing could open up the lanes for drives and give us a boost in two-point percentage, for example–but if I looked at all of them we’d be here until the start of the Maui. For now let’s just focus on the two biggies: three-point percentage (3P%) and three-point rate (3PR, the percentage of a team’s field goal attempts that come from behind the arc).
It’s important to note that a million factors affect both of these numbers in any given game or set of games. How many threes are taken and made is partially a function of the defense, the venue, the score of the game, what the players ate for breakfast…you get the idea. Since I’m just one guy, I just looked at these two numbers for all offenses in both the regular season (including conference tournaments) and in the NIT.
Let’s start with the basics.
We can clearly see that teams made less threes in the NIT the last two seasons. Teams took less threes in 2018 as well, but last year the three-point rate remained about the same in the NIT.
But what if the teams that made the NIT in one or both of these years were worse than average teams at shooting threes? What if they were better? Let’s do it again with the regular season stats from just the 32 NIT teams.
The gap between regular season shooting and NIT shooting is actually even bigger when we look at just the regular season stats from the 32 NIT teams. Of course, shooting in the NIT in general is not necessarily going to be the same as regular-season shooting. It is well-documented that percentages go up throughout the year as players shake off the rust, so the NIT percentages probably would be higher with no change in arc distance. Let’s include 2017 (the last NIT with the same three-point line as the regular season) as a control.
Granted, when dealing with 31-game tournaments the sample is small enough to include a lot of noise. But it looks nice, and isn’t that the only thing that really matters? We can see that 3P% and 3PR both went up in the 2017 NIT, and on the whole this increase is fairly typical if we include previous years. So when we look at 2018 and 2019 compared to 2017, we are really seeing a decrease in two quantities that would have been expected to go up. All the caveats considered, it seems like we can expect a slight dip in three-point rate and three-point percentage when the season kicks off in November.
The last time the three-point line moved back was the 08-09 season, and we saw the same thing then. Three-point shooting did go down for a while, but eventually it stabilized to a pre-08 level as players improved and coaches adjusted the offenses they ran. Three-point shooting varies so much from game to game that the effects might not be discernible by what announcers like to call the “eye test,” but it will be fascinating to watch how both offenses and defenses adapt to the added distance and spacing. There may be some growing pains early on, but I foresee teams figuring it out in the next couple of seasons.
The coaching battle in basketball is often called a chess match. Each coach has to think several moves ahead. One aspect of the game, however, is much more like poker. Normally everyone can hear the plays a coach calls out, but during a timeout a coach can hold all the cards to his chest–no one knows what he is telling his team. Well, other than the millions of us watching those “in the huddle” segments on TV. But hey, at least the other team can’t hear!
Maybe I’m the only one, but I’ve always wondered who wins possessions after timeouts (ATOs)–the offense or the defense. After all, the offense gets a chance to draw up a play without the other team knowing it, but the opposition can come out in another defense that may confuse the offense or render the play ineffective. While there are exceptions in teams like Syracuse that use the same defense every possession no matter what, oftentimes teams switch from man to zone or vice versa after a timeout.
So who wins? The simple way to answer that question is to look at ATO efficiency and overall efficiency and see which one is higher. Overall efficiency is higher by a pretty decent margin, but that misses a crucial piece of information. ATOs are all half-court possessions, and half-court possessions are already less efficient than transition possessions, whether they follow a timeout or not. So we really need to compare ATOs to other half-court possessions. Non-after-timeout-half-court possessions is a mouthful, so I’ll call them NATOs (not to be confused with the North Atlantic Treaty Organization).
I scraped the last eight years-worth of data from Synergy Sports to look further into that question. Synergy has NCAA men’s data going back over a decade, but prior to 2011-12 it is fairly obvious that it’s incomplete, and with 340+ teams each year, eight seasons should be enough of a sample.
Using this data, the points per possession (PPP) is still slightly lower for ATOs than it is for NATOs, but not by much: about 0.847 to 0.875. This is close enough that it begs for some significance testing, but comparing the two sample means does yield a super tiny p-value. In other words, it’s safe to say that ATOs really are slightly less efficient for offenses.
One interesting note from the chart is that while most teams play less efficient offense after timeouts, the range is much wider for ATO efficiency than it is for NATO efficiency. The best few teams at ATOs are better than the best teams in NATOs. This may just be due to the smaller sample of ATOs leading to more variance (depending on the team, somewhere between 13% and 24 % of possessions came after timeouts). Or it may be because coaches have more control over ATOs, causing us to see a bigger difference between the best and worst coaches.
Whatever the case, there seems to be evidence that timeouts favor the defense. The one caveat to reading too much into that finding is that timeouts are more likely to occur late in the game with a team needing a bucket. When teams have less time and more pressure they are probably going to be less efficient anyway, so some of this difference might not be caused by the timeout itself (this is where having play-by-play data would be useful, but I am just a humble peasant in this realm). My hunch is that there are enough media timeouts in the sample (eight per game) that the difference is still relatively meaningful though. The next time you see a coach use a timeout with around 30 seconds left in the half, know that it isn’t necessarily giving his team an advantage.
Evaluating coaches
While it does appear that defenses win ATOs on the whole, the data isn’t clear enough to bash coaches that use timeouts in late-half situations. As we saw above, some teams actually have improved their offense by using timeouts. I wanted to find out if certain coaches were good (or bad) at this.
Overall Leaders
First, I decided to just look at the best overall teams (and coaches) in terms of ATO PPP on both sides of the ball. Being good after timeouts doesn’t necessarily mean a team is actually good at using timeouts–it might just mean it is a good team in all situations. Texas Tech and Virginia, for example, were 4th and 16th respectively in ATO defense last season, but they were the top two overall defenses by most measures, so if anything they weren’t quite as good after timeouts. Nonetheless, I got curious, so I did it. Just for funsies.
This isn’t adjusted for strength of opponent, so add even more grains of salt to it. That being said, I’m not surprised to see Rick Byrd, Mark Few and Bob McKillop-led teams take three of the top four spots. Coach K and Greg McDermott make the list as well. Notably just missing the cut at 11th is Michigan, who had well-known offensive mind John Beilein at the helm for each of the eight-years in the sample. Rick and John, we wish you well in your future endeavors.
Hailing from Big Ten country, I couldn’t help but note the lowest power conference team on the list. Checking in at number 352 out of 353 programs is Rutgers, with 0.715 ATO PPP. (I do believe Rutgers is on the rise, whatever that means, and if they even so much as got to NIT-bubble territory I would hop all over that bandwagon. But I digress.)
Mick Cronin tops the defensive leader board, which bodes well for UCLA, while Mark Few is the only coach to show up on both sides. That guy needs a title. On the surface, seeing Syracuse on the list goes against my theory that switching defenses after a timeout is useful. It doesn’t fully disprove it, however–timeouts allow the Orange to set that zone even more than a regular half-court possession would, and no defense relies more heavily on being set than the famed 2-3.
Timeout Improvement Leaders
Now that you’ve indulged my little side-analysis, let’s get to the juicy stuff–which programs have actually improved the most after timeouts? Hopefully this will tell us something about which coaches use timeouts most effectively.
Out of our sample of exactly 2800 teams, 900 of them (less than one in three) were more efficient on offense following a timeout, while 1859 played better defense after a timeout. In other words, about one-third of teams improved their offense after timeouts and about two-thirds improved their defense. There were 589 teams that got better at both ends of the floor after a timeout. Here are the top ten in both offensive improvement, defensive improvement and overall improvement (just adding the two together).
I’m not sure if there’s something in the water in Denton, Texas, but North Texas has been incredible after timeouts, to the tune of a top-three ranking on both sides of the ball. The weird part is, they have had three different coaches during that time span, so we can’t even give credit to one guy! Johnny Jones coached them in 2012 before heading to LSU. Tony Benford gets the majority of the credit, as he coached from 13-17 (before, ironically, also heading to LSU to be an assistant under Jones’ replacement, Will Wade). Grant McCasland has coached there for the past two years, and has kept the ATO tradition alive. When it comes to the use-it-or-lose-it timeouts at the end of the first half, maybe their C-USA opponents should just opt to lose it.
This chart gives us some context as to how good North Teas has been–they are that little bar on the far right. They weren’t just first in ATO improvement, they were first by a mile. And if any of you are wondering, the one on the far left is Nevada. For some reason the Wolf Pack have gotten much worse on both sides of the ball after timeouts, and they, like North Texas, are an outlier.
The Lions of North Alabama also deserves some special recognition, as technically they would have been third on offense and first on defense on the lists above. Since 2018-19 was their first year in division one, I didn’t include them (small sample size and all). But coach Tony Pujol is on my 19-20 ATO watchlist.
There isn’t any significant relationship between offensive ATOs and defensive ATOs, whether we look at overall PPP or PPP improvement over NATOs, so it doesn’t look as though timeout coaching is a skill that carries over from one side of the ball to the other. Perhaps someone in the Mean Green program has figured it out though. If anyone knows of any timeout whisperers over there, let me know about them in the comments.
