Luke Yaklich and the Sample Size of 3

Luke Yaklich is breaking statistics.

If you don’t know the name, he’s the associate head coach under Shaka Smart at Texas. And he, too, is smart. But his journey has been anything but conventional. Seven years ago, he was a high school social studies teacher. He never played college basketball. The long version is here, but the short version is that people in his situation almost never rise to this high of a position this quickly.

Yaklich is entering his seventh year in the college coaching ranks, and his first for the Longhorns. He spent four years at Illinois State, which is where my pro-Yak bias was formed (#BackTheBirds). Before the 2016-17 season, his final one at ISU, he was promoted to associate head coach. He then spent the past two years on the Michigan sidelines under John Beilein, but Beilein’s departure to the Cavs spelled the end for Yaklich’s Wolverine tenure as well.

So here we are, six years after Yaklich traded his social studies classroom for a coach’s office, and he is breaking statistics.

To understand how Luke Yaklich is breaking statistics, the first thing you need to know is that the dude is defensive dynamite. Like, why-isn’t-he-a-head-coach-yet level of defensive brilliance. Lockdown Luke has transformed decency into dominance, mediocrity into mastery. His teams have been stingier than Ebeneezer Scrooge.

To illustrate the point, let’s look at the three seasons since Yaklich first became an associate head coach. Using data from the illustrious Bart Torvik, let’s compare the preseason projections in adjusted defensive rating for each of those teams to their final ratings.

For the new folks, this is measured in points allowed per 100 possessions, so lower is better. And those end-of-season bars are a lot lower.

That first year, Yaklich helped Dan Muller take a team that hasn’t been to the tournament in this millennium to the brink of it, finishing with a No. 1 seed in the NIT. The next season he played an integral role in transforming Beilein’s historically offensive-minded Big Blue into a defensive stalwart. They finished third in the country in Torvik’s defensive rating and made a run all the way to the national title game. Last year they fell short in the Sweet 16 to Texas Tech, which was also the only team to finish with a better defensive rating than the Wolverines. Those two top-three seasons in defensive rating for Michigan came after Belein had never even cracked the top 30 in his first decade there.

The Yaklich system is complex, yet maddeningly simple: don’t give up easy shots. Limiting fouls is important, the defensive glass is important, but the most important rule in a Yaklich defense is to contest everything. Hands up. All the time. His defenses don’t over-help, and they don’t need to. They are disciplined on closeouts and they take away threes without giving up easy twos. It’s why those last three Yaklich defenses have performed so well in effective field goal percentage against.

Now that’s all well and good, but how is he breaking statistics? Well, I kinda gave it away in the title, but let’s talk about sample size. Sample size is one of the most misunderstood things in all aspects of human existence, but particularly in sports. I’m fairly convinced that if the general populous understood sample size, that half of all sports talk shows would go out of business. How are you supposed to talk about how the surprise NFL team that started 3-0 is gonna win the Super Bowl if everyone understands how little we can really glean from just three games? How are you supposed to talk about the Cy Young in April if everyone understands that April baseball stats are one step short of useless?

Contrary to popular belief, small sample sizes usually mean basically nothing. And yes, small is a subjective word, but three is a small number by almost any measure. Yet, Luke Yaklich has produced statistical significance out of a sample size of three. In those three seasons since first earning the associate head coach title, his teams have outperformed Torvik’s preseason expectations in defensive rating at a statistically significant level. In other words, that first bar graph actually means something. His defenses have been so much better than predicted that only three seasons-worth of data is needed to yield significance.

The non-statheads can skip this paragraph, but the significance comes from a two-sample t-test with the null hypothesis that the mean of the preseason ratings is equal to the mean of the final ratings. The powers that be have chosen p-value of .05 as the arbitrary cutoff below which I have permission to use the phrase “statistical significance” in a blog post. (Well, I guess it’s my blog, so I can do whatever I want, but I’ll follow the rules this time.) The p-value for this test was .03, so my permission was granted and we can reject the null hypothesis. The preseason mean and end-of-season mean are indeed, not equal.

So where does that leave us for this season? Well, Torvik’s system projects Texas at 25th overall, with the 16th-best defensive rating (93.7). A top-25 ranking is already a more optimistic outlook than what is being given to the Horns elsewhere–Joe Lunardi has them as a No. 9 seed, for example. But are any of them taking into account the Yak Effect? My gut says no.

If we factor in the Yak Effect, it may not be unreasonable to expect a top-10 defense and something in the neighborhood of a No. 4 seed in the NCAA Tournament come March. It’s early, a lot can change, and I’ve been pretty wrong about plenty of things before, but as the Yak train makes its next stop in Austin, I’m not deboarding anytime soon. While an eight-figure buyout and an NIT title were enough to keep Smart around for one more season, the temperature on his seat is rising fast. With Yaklich in the fold, however, Shaka may be on the verge of a job-saving campaign.

FBI allegations notwithstanding, Bill Self is the class of the Big 12, and Chris Beard is catching up fast. But there’s a third coaching cognoscente in the conference this year, and he’s the one that’s breaking statistics.

Packing it in: the Chad Holderfield Question

Chad Holderfield doesn’t know who I am, and we will probably never meet, but his tweet was the influence for this blog post. Well, Jordan Sperber deserves some credit too. Chad’s tweet was in response to Sperber, who, by the way, is a fantastic follow for any basketball fan.

Sometimes the best ideas come organically while scrolling through social media. Thanks, Jordan and Chad!

Sperber’s original post was about teams that basically disregard the three-point line on defense but are still able to prevent their opponent from scoring. In other words, lockdown defenses that don’t really guard the arc. You can (and should) read the whole piece here. The 18-19 Virginia Tech team was an extreme case of this, and was the focus of his post.

Chad then wondered if these teams are better at defensive rebounding. Presumably these are teams that pack it in and protect the paint, so that would make intuitive sense. Packing it in means staying between your man and the hoop, which should leave you in good rebounding position. Of course, as Sperber pointed out in our thread, zone-exclusive teams like Syracuse are also likely to fit the mold, and the Orange certainly have trouble boxing out.

That being said, man defense is much more common than zone defense, so I still expect teams that give up threes to rebound better on the whole. That probably means we’re heading towards one of those blog posts where I pretty much find out exactly what we thought was true is, in fact, true, and no one learns anything new. (I must’ve been sick the day we learned about hooking the reader in Englsh class.) Nonetheless, curiosity prevails, and besides, I can’t let Chad down!

For starters, let’s define which specific teams we’re talking about here. I let Chad pick the arbitrary cutoffs, so if you don’t like them you can direct all hate mail his way.

We are going to define the lockdown defenses as those that ranked in the top 25 in KenPom’s adjusted defensive efficiency for that season, and teams that don’t guard the arc as teams in the top 150 in highest opponent three-point rate. There have been somewhere between 7 and 12 of those teams every year in KenPom’s database (2002-19) for a total of 160.

Of course, top-25 defenses are going to be better at defensive rebounding than average teams whether they prevent threes or not, so let’s start by comparing the elite defenses that guarded the arc to the elite defenses that didn’t. Because I hate long and awkward-sounding names and I’m too lazy to come up with a better solution than made-up acronyms, I’ll call the arc-guarders AGs and the non-arc-guarders NAGs. Here are the results:

The NAGs did rebound better than the AGs, but the gap wasn’t quite as big as I thought it would be. In fact, in 7 of the 18 seasons the AGs actually rebounded better. Over half of the Syracuse teams in this timeframe are included, but that still only accounts for 10 of the 160 NAGs. The overall average for NAGs was 69.95%, and for AGs it was 69.42%. That is close enough that it’s time for some good ol’ significance testing. A two-sample t-test gives us a p-value of .00007, so the difference is real. If you are new to this blog or to statistics, that means that our sample is big enough that the small difference in rebounding percentages matters quite a bit.

Now that we’ve confirmed that elite defenses that still give up threes really do gain a rebounding edge, let’s forget the arbitrary groupings and look at this relationship among all defenses. The r² value for the correlation between opponent three-point rate and defensive rebounding percentage is just over 0.25, which means over a quarter of a team’s defensive rebounding percentage can be explained by how often it gives up three-point looks. With 6,157 team-seasons in the KenPom database, 0.25 is extremely meaningful. Here is a plot of all 6,157 teams.

The blue line is the trendline, showing the increase in defensive rebounding percentage as teams allow more threes. Those logos are just for kicks and giggles–all 18 of Jim Boeheim’s Syracuse teams are below the trendline, and all of Tony Bennett’s teams are above it. Boeheim and Bennett are probably the most well-known zone and packline coaches, respectively. If I had either a Sperberesque attention to detail or a bunch of interns to do my bidding, I’d go through all of these teams and separate them into bins based on defensive scheme–the zones, the packlines, the denials, etc. Instead I’ll just leave that idea dangling to keep you up at night.

There’s another Bennett I want to spotlight before I wrap up. Randy Bennett of Saint Mary’s stands out as a coach who doesn’t like to choose. He was probably that kid who took one of every color sucker at the bank, because he couldn’t choose just one. While his colleagues are choosing whether to give their opponents open looks from deep or second-chance opportunities, he is taking away both. Here’s the chart with Randy Bennett’s data points on it:

There you have it. Taking away threes does seem to come at a rebounding cost, although it’s certainly possible to be good at both. That tends to be the beauty of basketball. There are endless different styles of play, and they all involve giving up something to improve something else, but it’s always possible to be good at both with the right combination of skill, effort and coaching. While most teams that run their opponents off the three-point line will have to live with the offensive rebounds, there will always be Randy Bennetts that find ways to get the best of both worlds.

Follow me on Twitter at @cwetzel31 for more hoops content.

Back, back, back it up!

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.

Women Ball Too

Well, it’s that time of year again. If you follow basketball, you know what I’m talking about. The college season ended over two months ago. The NBA Finals are now complete. Basketball fans everywhere know what that means.

It’s the “no basketball until October” time of year.

Fortunately, it doesn’t have to be “no basketball until October” time of year. There is a cure for Albert’s depression, an answer to Urameshi’s withdrawal. Just ask Miles Bridges:

Miles knows the truth–women ball too. If you are a hoops junkie like I am, you don’t have to wait until October. The WNBA season is one month in, and story lines are building. And the best part? The finals will go into the first week of October, just in time for the NBA to come back! So go find a TV, make yourself a sandwich (because no, it’s not their job to make one for you) and catch all the action!

My main focus on this blog will always be NCAA D1 men’s basketball analytics. I’ll be writing about analytics on the women’s side as well, however, it just won’t be here. The website I began working with last month, Her Hoop Stats (HHS), has given me a platform, so anything I write about women’s hoops will be over there. You can find my stuff here and their entire collection of stories and articles here.

In my intro I mentioned that I don’t really have anything unique to offer the men’s college basketball analytics community, and that anything I do has probably already been done by someone better. This isn’t the case in women’s basketball. HHS came to exist precisely because there isn’t enough access to advanced stats and data on the women’s side. A few years ago Sue Bird wrote a piece in the Player’s Tribune calling for this to change, and HHS is doing something about it. (Quick aside–The Player’s Tribune is excellent reading for any fan of any sport, so go check it out.)

Since starting out as a website providing advanced stats for NCAA D1 women’s basketball, HHS has grown significantly. Not only has coverage been expanded to D2, D3 and the WNBA, but it has also come to include the previously mentioned articles, a social media presence, a podcast and more. Here are all the places you can find HHS content:

So yeah, it’s late June, and there’s no high-profile men’s basketball until October. But for true ballers, hoops never stops. Just ask Jordan Bell.

Studying the Effects of Timeouts

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.

Do Teams Create Their Own Luck?

You’ve probably heard an announcer say “sometimes it’s better to be lucky than good” when someone does something crazy like banking in a three at the shot-clock buzzer. Of course, there are also times, more of them actually, where it is better to be good than lucky. But what about teams that are both? Or neither? Can being good make a team lucky? Is it possible to manufacture your own luck?

The What

Anyone familiar with Ken Pomeroy’s website has probably noticed that each team has a luck rating. And anyone not familiar with his website probably has no interest in my blog. For those who have noticed this rating but are unsure what exactly it measures, I was going to type up this obnoxiously long explanation (teaching is my day job–sometimes I can’t turn it off). Then I decided to just let other people do that for me. Dean Oliver invented it, so naturally his explanation is included here, but for something less math-heavy check out this New York Times article.

For those too captivated by my riveting writing to click away, the TLDR version is that close games are basically tossups. Teams should win around 50% of them over time. Teams that win a bunch of close games are considered lucky and vice versa. Teams that win blowouts have better margins of victory than teams that win close games, so luck is just the difference between what a team’s winning percentage “should” be based on its margin and what it actually is.

The Why

After following KenPom for a while, I started to notice that there are a few teams that seem to be either really lucky or really unlucky every year. Let’s look at Kansas. Over the (admittedly arbitrarily selected) last five years, they have ranked 26th, 23rd, 32nd, 75th and 33rd in luck. That’s out of well over 300 teams. The only year they were outside of the top 35 was 2016, when they finished third overall in KenPom’s ratings. In other words, they were supposed to have an extremely good record and their record was still better than it should’ve been.

There’s a reason the Jayhawks won a record 14 straight conference titles in the Big 12. They win lots of close games. Either they are truly a perennially lucky team, or Bill Self is a final-minute wizard. My guess is a little of both, but I wanted to know if teams have any control over their “luck.” I wanted to know if maybe Kansas does something of their own accord to win all these close games–if maybe, just maybe, Self knows something about closing out games.

The How

Here’s where that math stuff comes in. In theory, if KenPom’s luck value is literal luck, then it won’t be caused by anything. While we can’t actually test causation very effectively with this data, we can test correlation. And before you yell at me that correlation doesn’t imply causation, let’s look at which stats correlate with luck and see if we can reasonably hypothesize any causations.

I analyzed the relationship between luck and 70 other stats using KenPom data from 2002 to 2019. If I have enough fun with this, maybe I’ll look at correlations with other data from other sources at some point, but 70 KenPom stats seems like a good starting point. Anyway, 39 of the 70 stats were available for that whole period, while 24 were available since ’07, one since ‘08 and the other six starting in ‘10. This gave us a sample of 6,157 team-seasons for most of the stats, and at least 3,492 for all of them.

I tested the significance of each correlation coefficient using a one-tailed t-test, and found 31 of them to be significant at a p=.01 level. In layman’s terms, 31 of the stats were related strongly enough with luck that we can be pretty sure it’s not just random chance and that there really is some sort of relationship. If 31 out of 70 sounds like a lot, it’s probably because a bunch of the stats are pretty much the same thing. I counted raw tempo and adjusted tempo as two different stats, for example, and there are seven separate categories for height alone (five of them made the list of 31). If you’re judging me for forcing it, you’re not wrong. What’s a good study if not for a little misleading number somewhere in there?

Listing 31 stats seems like a kind of boring exercise that wouldn’t really be worth the effort or space, so I’ll just break down the highlights. I see three categories here–stats that are probably explained by luck, stats that probably explain luck, and stats for which I really have no clue.

Stats Probably Explained by Luck

These are for the most part stats that are affected by the whole foul game at the end of tight games. If you are lucky and winning a lot of close games, then you are probably getting fouled at the end of them, and vice versa. This leads to changes in offensive and defensive free throw rates obviously, but also in a few other areas.

Given that the vast majority of teams score under 1.2 points per possession, any trip to the line from a player shooting 60% or better on free throws is more efficient than other possessions, so offensive efficiency was significant. Interestingly, it lost its significance once adjusted for venue and strength of schedule, but that might just be noise. Additionally, a possession resulting in a quick foul off of the inbound usually takes less than five seconds, making the tempo look faster. Both raw and adjusted tempo were significant, as was offensive possession length. These correlations appear to be a result of luck (winning close games) causing the changes in the other stats, so they aren’t necessarily evidence that teams control their own luck. But…

Stats Probably Explaining Luck

Technically, teams have a tiny bit of control over their opponents’ free throw percentages, pretty much based on who they foul (if you foul Ethan Happ a lot, your opponents will miss more free throws.) While Arizona State may beg to differ, however, teams generally control whether their own free throws go in. If your opponent shoots 3-for-20 from the stripe, you were almost certainly lucky, and if they make all 20 the reverse could be said. Opponent’s FT% is one of the 31, and that seems fairly intuitive.

Some other stats on the list may or may not truly explain luck, but it’s not possible for luck to explain them either, so I am putting them in this category. Random chance is a possibility, but it’s more fun for me to give into confirmation bias and use these stats as evidence that teams have slight control over luck. The most notable ones are height (both minutes-weighted and non-minutes weighted, as well as SG, PF and C height) and experience (as well as minutes continuity, which is measuring something similar). Height is actually negatively correlated, meaning shorter teams are luckier.

It is pretty safe to say that getting lucky does not cause a team’s players to either shrink or become older, so either it’s random chance or the causation goes the other way. Experience makes some sense, considering that luck is pretty closely related to performance in end-game situations. Height, on the other hand, well, if you have any theories I welcome them in the comments.

Stats I Have No Clue About

There are a few stats that correlate with luck whose causations could go either way. As with height, I invite you to share your theories in the comments. Having better two-point percentages and effective field goal percentages as well as getting less shots blocked were all things that related to luck. Another one was two-foul participation, both raw and adjusted percentage as well as total two-foul time on the court. This means teams that play guys with two fouls more often in the first half tend to be slightly luckier. Since all three of those stats were significant, this one doesn’t seem like a fluke, but I’m not sure why first-half events would have any relationship with luck. Then again, I haven’t racked my brain yet trying to figure it out, although something tells me that’s coming the next time I’m super bored.

Final Thoughts

This whole study started with conversations with my brother about how hitting free throws in the last minute of undecided games is big, and therefore may have something to do with luck. He’s the kind of guy that would tell you to get better at free throws if you shot 90%, so he was hoping that we would find team FT% to have a significant correlation with luck. Unfortunately, we had no such luck (eye-rolling is acceptable here). We did, however, find that there were several other predictors of luck, and although none of them had a very strong relationship at all, our sample was big enough that they did still mean something. Not that we didn’t already know this, but it may be officially okay to say that Bill Self’s late-game coaching prowess makes the Jayhawks lucky. It does appear that some teams might be able to create their own luck. That is, if luck is still what you want to call it. Next time you hear that it’s better to be lucky than good, however, know that they might not be mutually exclusive.

Welcome to the Matchup Nightmare

This blog isn’t for you.

Don’t get me wrong, you might like it. If you’re half as into college basketball analytics as I am, you might even love it. But it’s not for you.

It’s for me.

You see, I’m an introvert. Not just like the type that’s a little on the quiet side. No. We’re talking, like, I took that online Myers-Briggs test and I scored 99% introvert and 1% extrovert, then I took it again a week later because I forgot to save my results and I got 100/0. That level. I work online from home. I pretty much don’t hang out with people, ever. You get the idea.

I also can’t get enough college basketball. I grew up in Central Illinois and was in seventh grade the year the Illini made it to the title game. That season got me hooked, and I’ve been a junkie ever since. Mix that with my affinity for numbers (my aforementioned online job is as a math teacher and my degree is in math) and it’s almost automatic–stats and analytics are one step short of an obsession.

So what does a guy do when he has way too much to say about a topic and no one to say it to? He starts a blog. Which is why this blog is for me. I need an outlet to talk about college basketball analytics. Whether or not anyone listens is mostly irrelevant. The very first analytical formula I will share with you looks something like this:

No friends + lots of opinions + big enough head to think the opinions matter = blog.

The story of the name

Out of that equation, Matchup Nightmare was born. If you’re wondering–or if you’re not–the name doesn’t really have any significance to me. In my experience with sports blogs, the best names are just a colloquial phrase from the sport. The type of things that announcers like to say. I like the name Matchup Nightmare because in my view it’s one of the most overused phrases in basketball (I’m looking at you, Seth Greenberg). It’s like calling someone a unicorn. It wasn’t that long ago that calling someone a matchup nightmare or a unicorn meant something. LeBron was a matchup nightmare. Dirk was a unicorn. Now those phrases pretty much mean “player who isn’t terrible at scoring.”

Why do I like how overused it is? Well, I actually hate it in real life, but I love that it’s free advertising for my blog. Every time you watch a game you will probably hear the color commentator unknowingly reference the name of my blog. I’m just kind of assuming that the free publicity will lead to me becoming big time and eventual world domination.

What to expect from me

All joking aside, I’m not big time, and I’m probably never going to be. If you’re looking for true analytics savants, go check out the work of Ken Pomeroy, John Gasaway, Bart Torvik or Dean Oliver. If there’s one thing I know, it’s that in order to get people to read your stuff it has to be unique. It has to provide them with something they can’t find anywhere else. Those guys and others have done so much extensive work that realistically there isn’t much I can give you that you couldn’t already get from them.

That’s not even to mention my data constraints. I have more data than the average college basketball fan, but there is a limit to my programming skills and my wallet size, both of which inhibit the data I can get. That relationship looks something like what is shown below. (What better way to share my first graph with you than by using arbitrary and meaningless units on both axes?)

Most notably this puts a cap on play by play data. Maybe someday. For now, I’ll make do with what’s available from the free/cheap sources that don’t need to be coded.

The one advantage I do have on the famous guys, however, is that I’m accessible. There was a time when Ken Pomeroy was still unknown enough that he responded to my email. Now his name is virtually synonymous with NCAA men’s analytics, and good luck getting a reply from him. I often think of studies I would love to ask him to do, but again, I don’t exactly have him on speed dial.

If you ever have those curious moments like I do, feel free to ask me! Chances are I won’t be able to do the same level of cool analysis as the big guns, but at least you know I’ll respond!

Other places to find me

On this blog I will be mostly sticking to men’s college basketball, but if you are interested in either NCAAW or the WNBA (and you should be) then go check out the website I recently started volunteering for: Her Hoop Stats. My involvement there is in almost as early of stages as this blog is, so I’ll dedicate a separate post to it later on. For now, you can follow me on Twitter or Instagram. I don’t post a bunch of interesting stuff there yet, but maybe I will if I get really motivated.

I think the biggest takeaway is that I am not writing this for you, I don’t have access to tons of fun data, I don’t have great programming skills and I don’t post cool things on social media. Have I convinced you not to follow me yet? If not, then welcome to the Matchup Nightmare!